id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1902.09934 | Jiahang Xu | Jiahang Xu, Fangyang Jiao, Yechong Huang, Xinzhe Luo, Qian Xu, Ling
Li, Xueling Liu, Chuantao Zuo, Ping Wu and Xiahai Zhuang | A Fully-Automatic Framework for Parkinson's Disease Diagnosis by
Multi-Modality Images | 16 pages, 6 figures, 4 tables | null | null | null | q-bio.QM cs.LG stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Background: Parkinson's disease (PD) is a prevalent long-term
neurodegenerative disease. Though the diagnostic criteria of PD are relatively
well defined, the current medical imaging diagnostic procedures are
expertise-demanding, and thus call for a higher-integrated AI-based diagnostic
algorithm. Methods: In this paper, we proposed an automatic, end-to-end,
multi-modality diagnosis framework, including segmentation, registration,
feature generation and machine learning, to process the information of the
striatum for the diagnosis of PD. Multiple modalities, including T1- weighted
MRI and 11C-CFT PET, were used in the proposed framework. The reliability of
this framework was then validated on a dataset from the PET center of Huashan
Hospital, as the dataset contains paired T1-MRI and CFT-PET images of 18 Normal
(NL) subjects and 49 PD subjects. Results: We obtained an accuracy of 100% for
the PD/NL classification task, besides, we conducted several comparative
experiments to validate the diagnosis ability of our framework. Conclusion:
Through experiment we illustrate that (1) automatic segmentation has the same
classification effect as the manual segmentation, (2) the multi-modality images
generates a better prediction than single modality images, and (3) volume
feature is shown to be irrelevant to PD diagnosis.
| [
{
"created": "Tue, 26 Feb 2019 13:52:41 GMT",
"version": "v1"
}
] | 2019-02-27 | [
[
"Xu",
"Jiahang",
""
],
[
"Jiao",
"Fangyang",
""
],
[
"Huang",
"Yechong",
""
],
[
"Luo",
"Xinzhe",
""
],
[
"Xu",
"Qian",
""
],
[
"Li",
"Ling",
""
],
[
"Liu",
"Xueling",
""
],
[
"Zuo",
"Chuantao",... | Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the diagnostic criteria of PD are relatively well defined, the current medical imaging diagnostic procedures are expertise-demanding, and thus call for a higher-integrated AI-based diagnostic algorithm. Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD. Multiple modalities, including T1- weighted MRI and 11C-CFT PET, were used in the proposed framework. The reliability of this framework was then validated on a dataset from the PET center of Huashan Hospital, as the dataset contains paired T1-MRI and CFT-PET images of 18 Normal (NL) subjects and 49 PD subjects. Results: We obtained an accuracy of 100% for the PD/NL classification task, besides, we conducted several comparative experiments to validate the diagnosis ability of our framework. Conclusion: Through experiment we illustrate that (1) automatic segmentation has the same classification effect as the manual segmentation, (2) the multi-modality images generates a better prediction than single modality images, and (3) volume feature is shown to be irrelevant to PD diagnosis. |
2311.14434 | Yujiang Wang | Sarah J. Gascoigne, Nathan Evans, Gerard Hall, Csaba Kozma, Mariella
Panagiotopoulou, Gabrielle M. Schroeder, Callum Simpson, Christopher
Thornton, Frances Turner, Heather Woodhouse, Jess Blickwedel, Fahmida
Chowdhury, Beate Diehl, John S. Duncan, Ryan Faulder, Rhys H. Thomas, Kevin
Wilson, Peter N. Taylor, Yujiang Wang | Incomplete resection of the icEEG seizure onset zone is not associated
with post-surgical outcomes | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Delineation of seizure onset regions from EEG is important for effective
surgical workup. However, it is unknown if their complete resection is required
for seizure freedom, or in other words, if post-surgical seizure recurrence is
due to incomplete removal of the seizure onset regions.
Retrospective analysis of icEEG recordings from 63 subjects (735 seizures)
identified seizure onset regions through visual inspection and algorithmic
delineation. We analysed resection of onset regions and correlated this with
post-surgical seizure control.
Most subjects had over half of onset regions resected (70.7% and 60.5% of
subjects for visual and algorithmic methods, respectively). In investigating
spatial extent of onset or resection, and presence of diffuse onsets, we found
no substantial evidence of association with post-surgical seizure control (all
AUC<0.7, p>0.05).
Seizure onset regions tends to be at least partially resected, however a less
complete resection is not associated with worse post-surgical outcome. We
conclude that seizure recurrence after epilepsy surgery is not necessarily a
result of failing to completely resect the seizure onset zone, as defined by
icEEG. Other network mechanisms must be involved, which are not limited to
seizure onset regions alone.
| [
{
"created": "Fri, 24 Nov 2023 12:19:24 GMT",
"version": "v1"
}
] | 2023-11-27 | [
[
"Gascoigne",
"Sarah J.",
""
],
[
"Evans",
"Nathan",
""
],
[
"Hall",
"Gerard",
""
],
[
"Kozma",
"Csaba",
""
],
[
"Panagiotopoulou",
"Mariella",
""
],
[
"Schroeder",
"Gabrielle M.",
""
],
[
"Simpson",
"Callum",
... | Delineation of seizure onset regions from EEG is important for effective surgical workup. However, it is unknown if their complete resection is required for seizure freedom, or in other words, if post-surgical seizure recurrence is due to incomplete removal of the seizure onset regions. Retrospective analysis of icEEG recordings from 63 subjects (735 seizures) identified seizure onset regions through visual inspection and algorithmic delineation. We analysed resection of onset regions and correlated this with post-surgical seizure control. Most subjects had over half of onset regions resected (70.7% and 60.5% of subjects for visual and algorithmic methods, respectively). In investigating spatial extent of onset or resection, and presence of diffuse onsets, we found no substantial evidence of association with post-surgical seizure control (all AUC<0.7, p>0.05). Seizure onset regions tends to be at least partially resected, however a less complete resection is not associated with worse post-surgical outcome. We conclude that seizure recurrence after epilepsy surgery is not necessarily a result of failing to completely resect the seizure onset zone, as defined by icEEG. Other network mechanisms must be involved, which are not limited to seizure onset regions alone. |
2301.05826 | Louxin Zhang | Elahe Khayatian, Gabriel Valiente, Louxin Zhang | The k-Robinson-Foulds Dissimilarity Measures for Comparison of Labeled
Trees | 39 pages, 11 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Understanding the mutational history of tumor cells is a critical endeavor in
unraveling the mechanisms underlying cancer. Since the modeling of tumor cell
evolution employs labeled trees, researchers are motivated to develop different
methods to assess and compare mutation trees and other labeled trees. While the
Robinson-Foulds distance is a widely utilized metric for comparing phylogenetic
trees, its applicability to labeled trees reveals certain limitations. This
paper introduces the $k$-Robinson-Foulds dissimilarity measures, tailored to
address the challenges of labeled tree comparison. The Robinson-Foulds distance
is succinctly expressed as n-RF in the space of labeled trees with n nodes.
Like the Robinson-Foulds distance, the k-Robinson-Foulds is a pseudometric for
multiset-labeled trees and becomes a metric in the space of 1-labeled trees. By
setting k to a small value, the k-Robinson-Foulds dissimilarity can capture
analogous local regions in two labeled trees with different size or different
labels.
| [
{
"created": "Sat, 14 Jan 2023 06:09:07 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Nov 2023 06:48:31 GMT",
"version": "v2"
}
] | 2023-11-17 | [
[
"Khayatian",
"Elahe",
""
],
[
"Valiente",
"Gabriel",
""
],
[
"Zhang",
"Louxin",
""
]
] | Understanding the mutational history of tumor cells is a critical endeavor in unraveling the mechanisms underlying cancer. Since the modeling of tumor cell evolution employs labeled trees, researchers are motivated to develop different methods to assess and compare mutation trees and other labeled trees. While the Robinson-Foulds distance is a widely utilized metric for comparing phylogenetic trees, its applicability to labeled trees reveals certain limitations. This paper introduces the $k$-Robinson-Foulds dissimilarity measures, tailored to address the challenges of labeled tree comparison. The Robinson-Foulds distance is succinctly expressed as n-RF in the space of labeled trees with n nodes. Like the Robinson-Foulds distance, the k-Robinson-Foulds is a pseudometric for multiset-labeled trees and becomes a metric in the space of 1-labeled trees. By setting k to a small value, the k-Robinson-Foulds dissimilarity can capture analogous local regions in two labeled trees with different size or different labels. |
2408.07713 | Geoffrey Stuart | Geoffrey Willam Stuart | Miscalibration of simulations: A comment on Luebbert and Pachter:
'Miscalibration of the honeybee odometer' arXiv:2405.12998v1 | 3 pages, one table, one figure; comment on arXiv:2405.12998v1 | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | In this commentary I review the claim by Luebbert and Pachter
(arXiv:2405.12998v1) that the reported R-Squared value in Srinivasan et al.
(Science, 287(5454):851-853, 2000), describing the relationship between
distance to a food source and mean waggle duration of honeybee dances, was too
high to be consistent with the reported means and standard deviations in the
latter study. There is one serious limitation of the simulations conducted by
Luebbert and Pachter, and two flaws that compromise their findings. The
reported R-squared value of Srinivasan. et al. is within the expected range, as
far as that can be determined given the limitations of the available data.
| [
{
"created": "Wed, 14 Aug 2024 03:45:12 GMT",
"version": "v1"
}
] | 2024-08-16 | [
[
"Stuart",
"Geoffrey Willam",
""
]
] | In this commentary I review the claim by Luebbert and Pachter (arXiv:2405.12998v1) that the reported R-Squared value in Srinivasan et al. (Science, 287(5454):851-853, 2000), describing the relationship between distance to a food source and mean waggle duration of honeybee dances, was too high to be consistent with the reported means and standard deviations in the latter study. There is one serious limitation of the simulations conducted by Luebbert and Pachter, and two flaws that compromise their findings. The reported R-squared value of Srinivasan. et al. is within the expected range, as far as that can be determined given the limitations of the available data. |
0807.4772 | Peter David Drummond | Alexei J. Drummond and Peter D. Drummond | Extinction in a self-regulating population with demographic and
environmental noise | 40 pages, 9 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an explicit unified stochastic model of fluctuations in population
size due to random birth, death, density-dependent competition and
environmental fluctuations. Stochastic dynamics provide insight into small
populations, including processes such as extinction, that cannot be correctly
treated by deterministic methods. We present exact analytical and
simulation-based results for extinction times of our stochastic model and
compare the different effects of environmental stochasticity and intrinsic
demographic stochasticity. We use both the discrete master equation approach
and an exact mapping to a Fokker-Planck equation (the Poisson method) and
stochastic equation, showing they are precisely equivalent. We also calculate
approximate extinction times using a steepest descent method. This model can
readily be extended to accommodate metapopulation structure and genetic
variation in the population and thus represents a step towards a
microscopically explicit synthesis of population dynamics and population
genetics.
| [
{
"created": "Wed, 30 Jul 2008 02:59:28 GMT",
"version": "v1"
},
{
"created": "Thu, 31 Jul 2008 03:34:11 GMT",
"version": "v2"
}
] | 2008-07-31 | [
[
"Drummond",
"Alexei J.",
""
],
[
"Drummond",
"Peter D.",
""
]
] | We present an explicit unified stochastic model of fluctuations in population size due to random birth, death, density-dependent competition and environmental fluctuations. Stochastic dynamics provide insight into small populations, including processes such as extinction, that cannot be correctly treated by deterministic methods. We present exact analytical and simulation-based results for extinction times of our stochastic model and compare the different effects of environmental stochasticity and intrinsic demographic stochasticity. We use both the discrete master equation approach and an exact mapping to a Fokker-Planck equation (the Poisson method) and stochastic equation, showing they are precisely equivalent. We also calculate approximate extinction times using a steepest descent method. This model can readily be extended to accommodate metapopulation structure and genetic variation in the population and thus represents a step towards a microscopically explicit synthesis of population dynamics and population genetics. |
2405.04810 | Kento Nakamura | Kento Nakamura and Tetsuya J. Kobayashi | Gradient sensing limit of a cell when controlling the elongating
direction | 14 pages, 5 figures | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Eukaryotic cells perform chemotaxis by determining the direction of chemical
gradients based on stochastic sensing of concentrations at the cell surface. To
examine the efficiency of this process, previous studies have investigated the
limit of estimation accuracy for gradients. However, these studies assume that
the cell shape and gradient are constant, and do not consider how adaptive
regulation of cell shape affects the estimation limit. Dynamics of cell shape
during gradient sensing is biologically ubiquitous and can influence the
estimation by altering the way the concentration is measured, and cells may
strategically regulate their shape to improve estimation accuracy. To address
this gap, we investigate the estimation limits in dynamic situations where
cells change shape adaptively depending on the sensed signal. We approach this
problem by analyzing the stationary solution of the Bayesian nonlinear
filtering equation. By applying diffusion approximation to the ligand-receptor
binding process and the Laplace method for the posterior expectation under a
high signal-to-noise ratio regime, we obtain an analytical expression for the
estimation limit. This expression indicates that estimation accuracy can be
improved by elongating perpendicular to the estimated direction, which is also
confirmed by numerical simulations. Our analysis provides a basis for
clarifying the interplay between estimation and control in gradient sensing and
sheds light on how cells optimize their shape to enhance chemotactic
efficiency.
| [
{
"created": "Wed, 8 May 2024 04:45:11 GMT",
"version": "v1"
}
] | 2024-05-09 | [
[
"Nakamura",
"Kento",
""
],
[
"Kobayashi",
"Tetsuya J.",
""
]
] | Eukaryotic cells perform chemotaxis by determining the direction of chemical gradients based on stochastic sensing of concentrations at the cell surface. To examine the efficiency of this process, previous studies have investigated the limit of estimation accuracy for gradients. However, these studies assume that the cell shape and gradient are constant, and do not consider how adaptive regulation of cell shape affects the estimation limit. Dynamics of cell shape during gradient sensing is biologically ubiquitous and can influence the estimation by altering the way the concentration is measured, and cells may strategically regulate their shape to improve estimation accuracy. To address this gap, we investigate the estimation limits in dynamic situations where cells change shape adaptively depending on the sensed signal. We approach this problem by analyzing the stationary solution of the Bayesian nonlinear filtering equation. By applying diffusion approximation to the ligand-receptor binding process and the Laplace method for the posterior expectation under a high signal-to-noise ratio regime, we obtain an analytical expression for the estimation limit. This expression indicates that estimation accuracy can be improved by elongating perpendicular to the estimated direction, which is also confirmed by numerical simulations. Our analysis provides a basis for clarifying the interplay between estimation and control in gradient sensing and sheds light on how cells optimize their shape to enhance chemotactic efficiency. |
2209.07527 | Emilio Dorigatti | Emilio Dorigatti, Bernd Bischl, Benjamin Schubert | Improved proteasomal cleavage prediction with positive-unlabeled
learning | Extended Abstract presented at Machine Learning for Health (ML4H)
symposium 2022, November 28th, 2022, New Orleans, United States & Virtual,
http://www.ml4h.cc, 8 pages | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Accurate in silico modeling of the antigen processing pathway is crucial to
enable personalized epitope vaccine design for cancer. An important step of
such pathway is the degradation of the vaccine into smaller peptides by the
proteasome, some of which are going to be presented to T cells by the MHC
complex. While predicting MHC-peptide presentation has received a lot of
attention recently, proteasomal cleavage prediction remains a relatively
unexplored area in light of recent advancesin high-throughput mass
spectrometry-based MHC ligandomics. Moreover, as such experimental techniques
do not allow to identify regions that cannot be cleaved, the latest predictors
generate decoy negative samples and treat them as true negatives when training,
even though some of them could actually be positives. In this work, we thus
present a new predictor trained with an expanded dataset and the solid
theoretical underpinning of positive-unlabeled learning, achieving a new
state-of-the-art in proteasomal cleavage prediction. The improved predictive
capabilities will in turn enable more precise vaccine development improving the
efficacy of epitope-based vaccines. Pretrained models are available on GitHub
| [
{
"created": "Wed, 14 Sep 2022 11:29:15 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Oct 2022 07:42:08 GMT",
"version": "v2"
}
] | 2022-10-31 | [
[
"Dorigatti",
"Emilio",
""
],
[
"Bischl",
"Bernd",
""
],
[
"Schubert",
"Benjamin",
""
]
] | Accurate in silico modeling of the antigen processing pathway is crucial to enable personalized epitope vaccine design for cancer. An important step of such pathway is the degradation of the vaccine into smaller peptides by the proteasome, some of which are going to be presented to T cells by the MHC complex. While predicting MHC-peptide presentation has received a lot of attention recently, proteasomal cleavage prediction remains a relatively unexplored area in light of recent advancesin high-throughput mass spectrometry-based MHC ligandomics. Moreover, as such experimental techniques do not allow to identify regions that cannot be cleaved, the latest predictors generate decoy negative samples and treat them as true negatives when training, even though some of them could actually be positives. In this work, we thus present a new predictor trained with an expanded dataset and the solid theoretical underpinning of positive-unlabeled learning, achieving a new state-of-the-art in proteasomal cleavage prediction. The improved predictive capabilities will in turn enable more precise vaccine development improving the efficacy of epitope-based vaccines. Pretrained models are available on GitHub |
1805.08239 | Joshua Glaser | Joshua I. Glaser, Ari S. Benjamin, Roozbeh Farhoodi, Konrad P. Kording | The Roles of Supervised Machine Learning in Systems Neuroscience | null | null | null | null | q-bio.NC cs.LG stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Over the last several years, the use of machine learning (ML) in neuroscience
has been rapidly increasing. Here, we review ML's contributions, both realized
and potential, across several areas of systems neuroscience. We describe four
primary roles of ML within neuroscience: 1) creating solutions to engineering
problems, 2) identifying predictive variables, 3) setting benchmarks for simple
models of the brain, and 4) serving itself as a model for the brain. The
breadth and ease of its applicability suggests that machine learning should be
in the toolbox of most systems neuroscientists.
| [
{
"created": "Mon, 21 May 2018 18:11:26 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Nov 2018 15:36:21 GMT",
"version": "v2"
}
] | 2018-11-27 | [
[
"Glaser",
"Joshua I.",
""
],
[
"Benjamin",
"Ari S.",
""
],
[
"Farhoodi",
"Roozbeh",
""
],
[
"Kording",
"Konrad P.",
""
]
] | Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML's contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions to engineering problems, 2) identifying predictive variables, 3) setting benchmarks for simple models of the brain, and 4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists. |
1212.1262 | David Saakian | Zara Kirakosyan, David B. Saakian, Chin Kun Hu | Finite genome length corrections for the mean fitness and gene
probabilities in evolution models | 8 pages | JSP (2011), v. 144, p. 198 | 10.1007/s10955-011-0254-3 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using the Hamilton-Jacobi equation approach to study genomes of length $L$,
we obtain 1/L corrections for the steady state population distributions and
mean fitness functions for horizontal gene transfer model, as well as for the
diploid evolution model with general fitness landscapes. Our numerical
solutions confirm the obtained analytic equations. Our method could be applied
to the general case of nonlinear Markov models.
| [
{
"created": "Thu, 6 Dec 2012 09:09:56 GMT",
"version": "v1"
}
] | 2015-06-12 | [
[
"Kirakosyan",
"Zara",
""
],
[
"Saakian",
"David B.",
""
],
[
"Hu",
"Chin Kun",
""
]
] | Using the Hamilton-Jacobi equation approach to study genomes of length $L$, we obtain 1/L corrections for the steady state population distributions and mean fitness functions for horizontal gene transfer model, as well as for the diploid evolution model with general fitness landscapes. Our numerical solutions confirm the obtained analytic equations. Our method could be applied to the general case of nonlinear Markov models. |
2005.09426 | Osmar Pinto Neto | Osmar Pinto Neto, Jose Clark Reis, Ana Carolina Brisola Brizzi,
Gustavo Jose Zambrano, Joabe Marcos de Souza, Wellington Amorim Pedroso,
Rodrigo Cunha de Mello Pedreiro, Bruno de Matos Brizzi, Ellysson Oliveira
Abinader, Deanna M. Kennedy, Renato Amaro Zangaro | Mathematical model of COVID-19 intervention scenarios for Sao Paulo-
Brazil | 24 pages, 4 figures, 1 table | null | 10.1038/s41467-020-20687-y | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | An epidemiological compartmental model was used to simulate social distancing
strategies to contain the COVID-19 pandemic and prevent a second wave in Sao
Paulo, Brazil. Optimization using genetic algorithm was used to determine the
optimal solutions. Our results suggest the best-case strategy for Sao Paulo is
to maintain or increase the current magnitude of social distancing for at least
60 more days and increase the current levels of personal protection behaviors
by a minimum of 10% (e.g., wearing facemasks, proper hand hygiene and avoid
agglomeration). Followed by a long-term oscillatory level of social distancing
with a stepping-down approach every 80 days over a period of two years with
continued protective behavior.
| [
{
"created": "Mon, 18 May 2020 16:23:40 GMT",
"version": "v1"
}
] | 2021-04-28 | [
[
"Neto",
"Osmar Pinto",
""
],
[
"Reis",
"Jose Clark",
""
],
[
"Brizzi",
"Ana Carolina Brisola",
""
],
[
"Zambrano",
"Gustavo Jose",
""
],
[
"de Souza",
"Joabe Marcos",
""
],
[
"Pedroso",
"Wellington Amorim",
""
],
[
... | An epidemiological compartmental model was used to simulate social distancing strategies to contain the COVID-19 pandemic and prevent a second wave in Sao Paulo, Brazil. Optimization using genetic algorithm was used to determine the optimal solutions. Our results suggest the best-case strategy for Sao Paulo is to maintain or increase the current magnitude of social distancing for at least 60 more days and increase the current levels of personal protection behaviors by a minimum of 10% (e.g., wearing facemasks, proper hand hygiene and avoid agglomeration). Followed by a long-term oscillatory level of social distancing with a stepping-down approach every 80 days over a period of two years with continued protective behavior. |
1407.6570 | Gerald Weber | Guilherme Bicalho Saturnino, Caio Padoan de S\'a Godinho, Denise
Fagundes-Lima, Alcides Castro e Silva, Gerald Weber | Detection of construction biases in biological databases: the case of
miRBase | 9 pages, 8 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological databases can be analysed as a complex network which may reveal
some its underlying biological mechanisms. Frequently, such databases are
identified as scale-free networks or as hierarchical networks depending on
connectivity distributions or clustering coefficients. Since these databases do
grow over time, one would expect that their network topology may undergo some
changes. Here, we analysed the historical versions of miRBase, a database of
microRNAs where we performed an alignment of all mature and precursor miRNAs
and calculated a pairwise similarity index. We found that the clustering
coefficient shows important changes during the growth of this database. For two
consecutive versions of the year 2009 we found a strong modification of the
network topology which we were able to associate to a technological change in
miRNA discovery. To evaluate if these changes could have happened by chance, we
performed a set of simulations of the database growth by sampling the final
version of miRBase and creating several alternative histories of miRBase. None
of the simulations were close to the actual historical evolution of this
database, which we understand as a clear indication of a very strong
construction bias.
| [
{
"created": "Thu, 24 Jul 2014 13:25:57 GMT",
"version": "v1"
}
] | 2014-07-25 | [
[
"Saturnino",
"Guilherme Bicalho",
""
],
[
"Godinho",
"Caio Padoan de Sá",
""
],
[
"Fagundes-Lima",
"Denise",
""
],
[
"Silva",
"Alcides Castro e",
""
],
[
"Weber",
"Gerald",
""
]
] | Biological databases can be analysed as a complex network which may reveal some its underlying biological mechanisms. Frequently, such databases are identified as scale-free networks or as hierarchical networks depending on connectivity distributions or clustering coefficients. Since these databases do grow over time, one would expect that their network topology may undergo some changes. Here, we analysed the historical versions of miRBase, a database of microRNAs where we performed an alignment of all mature and precursor miRNAs and calculated a pairwise similarity index. We found that the clustering coefficient shows important changes during the growth of this database. For two consecutive versions of the year 2009 we found a strong modification of the network topology which we were able to associate to a technological change in miRNA discovery. To evaluate if these changes could have happened by chance, we performed a set of simulations of the database growth by sampling the final version of miRBase and creating several alternative histories of miRBase. None of the simulations were close to the actual historical evolution of this database, which we understand as a clear indication of a very strong construction bias. |
2005.07703 | Todd R Lewis PhD | Paul B.C. Grant, Todd R. Lewis, Thomas C. LaDuke and Colin Ryall | Caiman crocodilus (Spectacled caiman). Opportunistic foraging | null | Herpetological Review 39 (2008) 345-346 | 10.6084/m9.figshare.11302706.v2 | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | We document opportunistic foraging behavior by Caiman crocodilus in a
post-inundation forest at Estac\'ion Biolog\'ica Ca\~no Palma, Costa Rica.
| [
{
"created": "Mon, 18 May 2020 13:25:42 GMT",
"version": "v1"
}
] | 2020-05-19 | [
[
"Grant",
"Paul B. C.",
""
],
[
"Lewis",
"Todd R.",
""
],
[
"LaDuke",
"Thomas C.",
""
],
[
"Ryall",
"Colin",
""
]
] | We document opportunistic foraging behavior by Caiman crocodilus in a post-inundation forest at Estac\'ion Biolog\'ica Ca\~no Palma, Costa Rica. |
0704.2964 | Ashok Palaniappan | Ashok Palaniappan | Fourier Analysis of Biological Evolution: Concept of Selection Moment | null | null | null | null | q-bio.BM q-bio.QM | null | Secondary structure elements of many protein families exhibit differential
conservation on their opposing faces. Amphipathic helices and beta-sheets by
definition possess this property, and play crucial functional roles. This type
of evolutionary trajectory of a protein family is usually critical to the
functions of the protein family, as well as in creating functions within
subfamilies. That is, differential conservation maintains properties of a
protein structure related to its orientation, and that are important in
packing, recognition, and catalysis. Here I define and formulate a new concept,
called the selection moment, that detects this evolutionary process in protein
sequences. A treatment of its various applications is detailed.
| [
{
"created": "Mon, 23 Apr 2007 10:06:49 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Palaniappan",
"Ashok",
""
]
] | Secondary structure elements of many protein families exhibit differential conservation on their opposing faces. Amphipathic helices and beta-sheets by definition possess this property, and play crucial functional roles. This type of evolutionary trajectory of a protein family is usually critical to the functions of the protein family, as well as in creating functions within subfamilies. That is, differential conservation maintains properties of a protein structure related to its orientation, and that are important in packing, recognition, and catalysis. Here I define and formulate a new concept, called the selection moment, that detects this evolutionary process in protein sequences. A treatment of its various applications is detailed. |
2302.02919 | Giorgio Gonnella | Giorgio Gonnella | Unambiguosly expressing expectations about the content of prokaryotic
genomes | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In recent years, the sequencing, assembling and annotation of prokaryotic
genomes has become increasingly easy and cheap. Thus it becomes increasingly
feasible and interesting to perform comparative genomics analyses of new
genomes to those of related organisms. Thereby related organisms can be defined
by different criteria, such as taxonomy or phenotype.
Expectations regarding the contents of genomes are often expressed in
scientific articles describing group of organisms. Evaluating such
expectations, when a new genome becomes available, requires analysing the text
snippets which express such expectations, extracting the logical elements of
the text and enabling a formal expression, more suitable for further automated
analyses.
Hereby we present a theoretical framework, alongside practical consideration
for expressing expectations about the content of genomes, with the purpose of
enabling such comparative genomics analyses. The components of the framework
include a system for the definition of groups of organisms, supported by a
Prokaryotic Group Types Ontology, a system for the definition of genomic
contents, supported by a Prokaryotic Genomic Contents Definition Ontology.
Finally we discuss how the combination of these two systems may enable an
unambiguous definition of absolute and relative genome content expectation
rules.
| [
{
"created": "Mon, 6 Feb 2023 16:47:50 GMT",
"version": "v1"
},
{
"created": "Fri, 5 May 2023 11:05:09 GMT",
"version": "v2"
}
] | 2023-05-08 | [
[
"Gonnella",
"Giorgio",
""
]
] | In recent years, the sequencing, assembling and annotation of prokaryotic genomes has become increasingly easy and cheap. Thus it becomes increasingly feasible and interesting to perform comparative genomics analyses of new genomes to those of related organisms. Thereby related organisms can be defined by different criteria, such as taxonomy or phenotype. Expectations regarding the contents of genomes are often expressed in scientific articles describing group of organisms. Evaluating such expectations, when a new genome becomes available, requires analysing the text snippets which express such expectations, extracting the logical elements of the text and enabling a formal expression, more suitable for further automated analyses. Hereby we present a theoretical framework, alongside practical consideration for expressing expectations about the content of genomes, with the purpose of enabling such comparative genomics analyses. The components of the framework include a system for the definition of groups of organisms, supported by a Prokaryotic Group Types Ontology, a system for the definition of genomic contents, supported by a Prokaryotic Genomic Contents Definition Ontology. Finally we discuss how the combination of these two systems may enable an unambiguous definition of absolute and relative genome content expectation rules. |
1207.3137 | Arwen Bradley | Arwen Vanice Bradley (Meister), Ye Henry Li, Bokyung Choi, Wing Hung
Wong | Learning a nonlinear dynamical system model of gene regulation: A
perturbed steady-state approach | Published in at http://dx.doi.org/10.1214/13-AOAS645 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org) | Annals of Applied Statistics 2013, Vol. 7, No. 3, 1311-1333 | 10.1214/13-AOAS645 | IMS-AOAS-AOAS645 | q-bio.MN stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological structure and function depend on complex regulatory interactions
between many genes. A wealth of gene expression data is available from
high-throughput genome-wide measurement technologies, but effective gene
regulatory network inference methods are still needed. Model-based methods
founded on quantitative descriptions of gene regulation are among the most
promising, but many such methods rely on simple, local models or on ad hoc
inference approaches lacking experimental interpretability. We propose an
experimental design and develop an associated statistical method for inferring
a gene network by learning a standard quantitative, interpretable, predictive,
biophysics-based ordinary differential equation model of gene regulation. We
fit the model parameters using gene expression measurements from perturbed
steady-states of the system, like those following overexpression or knockdown
experiments. Although the original model is nonlinear, our design allows us to
transform it into a convex optimization problem by restricting attention to
steady-states and using the lasso for parameter selection. Here, we describe
the model and inference algorithm and apply them to a synthetic six-gene
system, demonstrating that the model is detailed and flexible enough to account
for activation and repression as well as synergistic and self-regulation, and
the algorithm can efficiently and accurately recover the parameters used to
generate the data.
| [
{
"created": "Fri, 13 Jul 2012 03:13:03 GMT",
"version": "v1"
},
{
"created": "Fri, 12 Apr 2013 18:10:00 GMT",
"version": "v2"
},
{
"created": "Thu, 28 Nov 2013 08:14:14 GMT",
"version": "v3"
},
{
"created": "Fri, 25 Mar 2016 17:50:21 GMT",
"version": "v4"
}
] | 2016-03-28 | [
[
"Bradley",
"Arwen Vanice",
"",
"Meister"
],
[
"Li",
"Ye Henry",
""
],
[
"Choi",
"Bokyung",
""
],
[
"Wong",
"Wing Hung",
""
]
] | Biological structure and function depend on complex regulatory interactions between many genes. A wealth of gene expression data is available from high-throughput genome-wide measurement technologies, but effective gene regulatory network inference methods are still needed. Model-based methods founded on quantitative descriptions of gene regulation are among the most promising, but many such methods rely on simple, local models or on ad hoc inference approaches lacking experimental interpretability. We propose an experimental design and develop an associated statistical method for inferring a gene network by learning a standard quantitative, interpretable, predictive, biophysics-based ordinary differential equation model of gene regulation. We fit the model parameters using gene expression measurements from perturbed steady-states of the system, like those following overexpression or knockdown experiments. Although the original model is nonlinear, our design allows us to transform it into a convex optimization problem by restricting attention to steady-states and using the lasso for parameter selection. Here, we describe the model and inference algorithm and apply them to a synthetic six-gene system, demonstrating that the model is detailed and flexible enough to account for activation and repression as well as synergistic and self-regulation, and the algorithm can efficiently and accurately recover the parameters used to generate the data. |
1404.2158 | Naser Mozaffari Mr | Naser Mozaffari, Nahid Dehghan Nayeri, Behrouz Dadkhah | Social well-being of a sample of Iranian nurses: a descriptive-analytic
study | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/3.0/ | Nurses social well-being deserves special attention. Effective well-being
promotion strategies should be executed for promoting their social well-being
particularly in areas of social integration and social acceptance. Moreover,
nurses, particularly female nurses, need strong financial, emotional,
informational, and social support for ensuring their social well-being.
| [
{
"created": "Mon, 31 Mar 2014 09:32:03 GMT",
"version": "v1"
}
] | 2014-04-09 | [
[
"Mozaffari",
"Naser",
""
],
[
"Nayeri",
"Nahid Dehghan",
""
],
[
"Dadkhah",
"Behrouz",
""
]
] | Nurses social well-being deserves special attention. Effective well-being promotion strategies should be executed for promoting their social well-being particularly in areas of social integration and social acceptance. Moreover, nurses, particularly female nurses, need strong financial, emotional, informational, and social support for ensuring their social well-being. |
1904.01655 | Surabhi Datta | Surabhi Datta, Elmer V Bernstam, Kirk Roberts | A frame semantic overview of NLP-based information extraction for
cancer-related EHR notes | 2 figures, 4 tables | null | null | null | q-bio.QM cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: There is a lot of information about cancer in Electronic Health
Record (EHR) notes that can be useful for biomedical research provided natural
language processing (NLP) methods are available to extract and structure this
information. In this paper, we present a scoping review of existing clinical
NLP literature for cancer. Methods: We identified studies describing an NLP
method to extract specific cancer-related information from EHR sources from
PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion
criteria were used in this study. We excluded articles where the extraction
techniques used were too broad to be represented as frames and also where very
low-level extraction methods were used. 79 articles were included in the final
review. We organized this information according to frame semantic principles to
help identify common areas of overlap and potential gaps. Results: Frames were
created from the reviewed articles pertaining to cancer information such as
cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis,
prostate cancer diagnosis and pain in prostate cancer patients. These frames
included both a definition as well as specific frame elements (i.e. extractable
attributes). We found that cancer diagnosis was the most common frame among the
reviewed papers (36 out of 79), with recent work focusing on extracting
information related to treatment and breast cancer diagnosis. Conclusion: The
list of common frames described in this paper identifies important
cancer-related information extracted by existing NLP techniques and serves as a
useful resource for future researchers requiring cancer information extracted
from EHR notes. We also argue, due to the heavy duplication of cancer NLP
systems, that a general purpose resource of annotated cancer frames and
corresponding NLP tools would be valuable.
| [
{
"created": "Tue, 2 Apr 2019 20:27:42 GMT",
"version": "v1"
}
] | 2019-04-04 | [
[
"Datta",
"Surabhi",
""
],
[
"Bernstam",
"Elmer V",
""
],
[
"Roberts",
"Kirk",
""
]
] | Objective: There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer. Methods: We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames and also where very low-level extraction methods were used. 79 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps. Results: Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 79), with recent work focusing on extracting information related to treatment and breast cancer diagnosis. Conclusion: The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable. |
1304.2823 | David Green | David Green, Chris Mason | The Maintenance of Sex: Ronald Fisher meets the Red Queen | 41 pages, 7 figures, final submission | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sex in higher diploids carries a two-fold cost of males that should reduce
its fitness relative to cloning and result in its extinction. Instead, sex is
widespread and it is clonal species that face early obsolescence. One possible
reason is that sex is an adaptation to resist ubiquitous parasites, which
evolve rapidly and potentially antagonistically. We use a heuristic approach to
model mutation-selection in finite populations where a parasitic haploid mounts
a negative frequency-dependent attack on a diploid host. The host evolves
reflexively to reduce parasitic load. Both host and parasite populations
generate novel alleles by mutation and have access to large allele spaces. Sex
outcompetes cloning by two overlapping mechanisms. First, sexual diploids adopt
advantageous homozygous mutations more rapidly than clonal diploids under
conditions of lag load. This rate advantage can offset the lesser fecundity of
sex. Second, a relative advantage to sex emerges under host mutation rates that
are fast enough to retain fitness in a rapidly mutating parasite environment
and increase host polymorphism. Clonal polymorphic populations
disproportionately experience interference with selection at high mutation
rates, both between and within loci. This slows clonal population adaptation to
a changing parasite environment and reduces clonal population fitness relative
to sex. The interference increases markedly with the number of loci under
independent selection. Rates of parasite mutation exist that not only allow sex
to survive despite the two-fold cost of males but which enable sexual and
clonal populations to have equal fitness and co-exist. Since all higher
organisms carry parasitic loads, the model is of general applicability.
| [
{
"created": "Wed, 10 Apr 2013 01:23:23 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Apr 2013 04:53:54 GMT",
"version": "v2"
}
] | 2013-04-24 | [
[
"Green",
"David",
""
],
[
"Mason",
"Chris",
""
]
] | Sex in higher diploids carries a two-fold cost of males that should reduce its fitness relative to cloning and result in its extinction. Instead, sex is widespread and it is clonal species that face early obsolescence. One possible reason is that sex is an adaptation to resist ubiquitous parasites, which evolve rapidly and potentially antagonistically. We use a heuristic approach to model mutation-selection in finite populations where a parasitic haploid mounts a negative frequency-dependent attack on a diploid host. The host evolves reflexively to reduce parasitic load. Both host and parasite populations generate novel alleles by mutation and have access to large allele spaces. Sex outcompetes cloning by two overlapping mechanisms. First, sexual diploids adopt advantageous homozygous mutations more rapidly than clonal diploids under conditions of lag load. This rate advantage can offset the lesser fecundity of sex. Second, a relative advantage to sex emerges under host mutation rates that are fast enough to retain fitness in a rapidly mutating parasite environment and increase host polymorphism. Clonal polymorphic populations disproportionately experience interference with selection at high mutation rates, both between and within loci. This slows clonal population adaptation to a changing parasite environment and reduces clonal population fitness relative to sex. The interference increases markedly with the number of loci under independent selection. Rates of parasite mutation exist that not only allow sex to survive despite the two-fold cost of males but which enable sexual and clonal populations to have equal fitness and co-exist. Since all higher organisms carry parasitic loads, the model is of general applicability. |
2110.06193 | Christopher Overton | Christopher E. Overton, Lorenzo Pellis, Helena B. Stage, Francesca
Scarabel, Joshua Burton, Christophe Fraser, Ian Hall, Thomas A. House, Chris
Jewell, Anel Nurtay, Filippo Pagani, Katrina A. Lythgoe | EpiBeds: Data informed modelling of the COVID-19 hospital burden in
England | null | null | 10.1371/journal.pcbi.1010406 | null | q-bio.PE stat.AP | http://creativecommons.org/licenses/by/4.0/ | The first year of the COVID-19 pandemic put considerable strain on the
national healthcare system in England. In order to predict the effect of the
local epidemic on hospital capacity in England, we used a variety of data
streams to inform the construction and parameterisation of a hospital
progression model, which was coupled to a model of the generalised epidemic. We
named this model EpiBeds. Data from a partially complete patient-pathway
line-list was used to provide initial estimates of the mean duration that
individuals spend in the different hospital compartments. We then fitted
EpiBeds using complete data on hospital occupancy and hospital deaths, enabling
estimation of the proportion of individuals that follow different clinical
pathways, and the reproduction number of the generalised epidemic. The
construction of EpiBeds makes it straightforward to adapt to different patient
pathways and settings beyond England. As part of the UK response to the
pandemic, EpiBeds has provided weekly forecasts to the NHS for hospital bed
occupancy and admissions in England, Wales, Scotland, and Northern Ireland.
| [
{
"created": "Tue, 12 Oct 2021 17:46:51 GMT",
"version": "v1"
}
] | 2022-10-12 | [
[
"Overton",
"Christopher E.",
""
],
[
"Pellis",
"Lorenzo",
""
],
[
"Stage",
"Helena B.",
""
],
[
"Scarabel",
"Francesca",
""
],
[
"Burton",
"Joshua",
""
],
[
"Fraser",
"Christophe",
""
],
[
"Hall",
"Ian",
""... | The first year of the COVID-19 pandemic put considerable strain on the national healthcare system in England. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, which was coupled to a model of the generalised epidemic. We named this model EpiBeds. Data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow different clinical pathways, and the reproduction number of the generalised epidemic. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds has provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland. |
1205.3743 | Sandip Ghosal | Zhen Chen, Sandip Ghosal | Strongly nonlinear waves in capillary electrophoresis | 7 pages, 5 figures, 1 Appendix, 2 videos (supplementary material) | null | 10.1103/PhysRevE.85.051918 | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In capillary electrophoresis, sample ions migrate along a micro-capillary
filled with a background electrolyte under the influence of an applied electric
field. If the sample concentration is sufficiently high, the electrical
conductivity in the sample zone could differ significantly from the
background.Under such conditions, the local migration velocity of sample ions
becomes concentration dependent resulting in a nonlinear wave that exhibits
shock like features. If the nonlinearity is weak, the sample concentration
profile, under certain simplifying assumptions, can be shown to obey Burgers'
equation (S. Ghosal and Z. Chen Bull. Math. Biol. 2010, 72(8), pg. 2047) which
has an exact analytical solution for arbitrary initial condition.In this paper,
we use a numerical method to study the problem in the more general case where
the sample concentration is not small in comparison to the concentration of
background ions. In the case of low concentrations, the numerical results agree
with the weakly nonlinear theory presented earlier, but at high concentrations,
the wave evolves in a way that is qualitatively different.
| [
{
"created": "Wed, 16 May 2012 17:37:02 GMT",
"version": "v1"
}
] | 2015-06-05 | [
[
"Chen",
"Zhen",
""
],
[
"Ghosal",
"Sandip",
""
]
] | In capillary electrophoresis, sample ions migrate along a micro-capillary filled with a background electrolyte under the influence of an applied electric field. If the sample concentration is sufficiently high, the electrical conductivity in the sample zone could differ significantly from the background.Under such conditions, the local migration velocity of sample ions becomes concentration dependent resulting in a nonlinear wave that exhibits shock like features. If the nonlinearity is weak, the sample concentration profile, under certain simplifying assumptions, can be shown to obey Burgers' equation (S. Ghosal and Z. Chen Bull. Math. Biol. 2010, 72(8), pg. 2047) which has an exact analytical solution for arbitrary initial condition.In this paper, we use a numerical method to study the problem in the more general case where the sample concentration is not small in comparison to the concentration of background ions. In the case of low concentrations, the numerical results agree with the weakly nonlinear theory presented earlier, but at high concentrations, the wave evolves in a way that is qualitatively different. |
1903.00372 | Alfonso Represa | Fanny Sandrine Martineau (AMU), Lauriane Fournier, Emmanuelle Buhler
(INMED), Fran\c{c}oise Watrin (IBDM), Francesca Sargolini (LNC), Jean-Bernard
Manent, Bruno Poucet (LNC), Alfonso Represa (INMED) | Spared cognitive and behavioral functions prior to epilepsy onset in a
rat model of 2 subcortical band heteropia | null | Brain Research, Elsevier, 2019, 1711, pp.146-155 | 10.1016/j.brainres.2019.01.030 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 13 Subcortical band heterotopia (SBH), also known as doublecortex syndrome,
is a 14 malformation of cortical development resulting from mutations in the
doublecortin gene 15 (DCX). It is characterized by a lack of migration of
cortical neurons that accumulate in the 16 white matter forming a heterotopic
band. Patients with SBH may present mild to moderate 17 intellectual disability
as well as epilepsy. The SBH condition can be modeled in rats by in 18 utero
knockdown (KD) of Dcx. The affected cells form an SBH reminiscent of that
observed in 19 human patients and the animals develop a chronic epileptic
condition in adulthood. Here, 20 we investigated if the presence of an SBH is
sufficient to induce cognitive impairment in 21
| [
{
"created": "Fri, 1 Mar 2019 15:36:30 GMT",
"version": "v1"
}
] | 2019-03-04 | [
[
"Martineau",
"Fanny Sandrine",
"",
"AMU"
],
[
"Fournier",
"Lauriane",
"",
"INMED"
],
[
"Buhler",
"Emmanuelle",
"",
"INMED"
],
[
"Watrin",
"Françoise",
"",
"IBDM"
],
[
"Sargolini",
"Francesca",
"",
"LNC"
],
[
"M... | 13 Subcortical band heterotopia (SBH), also known as doublecortex syndrome, is a 14 malformation of cortical development resulting from mutations in the doublecortin gene 15 (DCX). It is characterized by a lack of migration of cortical neurons that accumulate in the 16 white matter forming a heterotopic band. Patients with SBH may present mild to moderate 17 intellectual disability as well as epilepsy. The SBH condition can be modeled in rats by in 18 utero knockdown (KD) of Dcx. The affected cells form an SBH reminiscent of that observed in 19 human patients and the animals develop a chronic epileptic condition in adulthood. Here, 20 we investigated if the presence of an SBH is sufficient to induce cognitive impairment in 21 |
2012.01637 | Hermann Riecke | Xize Xu and Hermann Riecke | Paradoxical phase response of gamma rhythms facilitates their
entrainment in heterogeneous networks | 24 pages, 7 Figs, 3 Supp Figs | null | 10.1371/journal.pcbi.1008575 | null | q-bio.NC nlin.AO | http://creativecommons.org/licenses/by/4.0/ | The synchronization of different $\gamma$-rhythms arising in different brain
areas has been implicated in various cognitive functions. Here, we focus on the
effect of the ubiquitous neuronal heterogeneity on the synchronization of PING
(pyramidal-interneuronal network gamma) and ING (interneuronal network gamma)
rhythms. The synchronization properties of rhythms depends on the response of
their collective phase to external input. We therefore determined the
macroscopic phase-response curve for finite-amplitude perturbations (fmPRC),
using numerical simulation of all-to-all coupled networks of integrate-and-fire
(IF) neurons exhibiting either PING or ING rhythms. We show that the intrinsic
neuronal heterogeneity can qualitatively modify the fmPRC. While the
phase-response curve for the individual IF-neurons is strictly positive (type
I), the fmPRC can be biphasic and exhibit both signs (type II). Thus, for PING
rhythms, an external excitation to the excitatory cells can, in fact, delay the
collective oscillation of the network, even though the same excitation would
lead to an advance when applied to uncoupled neurons. This paradoxical delay
arises when the external excitation modifies the internal dynamics of the
network by causing additional spikes of inhibitory neurons, whose delaying
within-network inhibition outweighs the immediate advance caused by the
external excitation. These results explain how intrinsic heterogeneity allows
the PING rhythm to become synchronized with a periodic forcing or another PING
rhythm for a wider range in the mismatch of their frequencies. We demonstrate a
similar mechanism for the synchronization of ING rhythms. Our results identify
a potential function of neuronal heterogeneity in the synchronization of
coupled $\gamma$-rhythms, which may play a role in neural information transfer
via communication through coherence.
| [
{
"created": "Thu, 3 Dec 2020 01:58:35 GMT",
"version": "v1"
}
] | 2021-09-15 | [
[
"Xu",
"Xize",
""
],
[
"Riecke",
"Hermann",
""
]
] | The synchronization of different $\gamma$-rhythms arising in different brain areas has been implicated in various cognitive functions. Here, we focus on the effect of the ubiquitous neuronal heterogeneity on the synchronization of PING (pyramidal-interneuronal network gamma) and ING (interneuronal network gamma) rhythms. The synchronization properties of rhythms depends on the response of their collective phase to external input. We therefore determined the macroscopic phase-response curve for finite-amplitude perturbations (fmPRC), using numerical simulation of all-to-all coupled networks of integrate-and-fire (IF) neurons exhibiting either PING or ING rhythms. We show that the intrinsic neuronal heterogeneity can qualitatively modify the fmPRC. While the phase-response curve for the individual IF-neurons is strictly positive (type I), the fmPRC can be biphasic and exhibit both signs (type II). Thus, for PING rhythms, an external excitation to the excitatory cells can, in fact, delay the collective oscillation of the network, even though the same excitation would lead to an advance when applied to uncoupled neurons. This paradoxical delay arises when the external excitation modifies the internal dynamics of the network by causing additional spikes of inhibitory neurons, whose delaying within-network inhibition outweighs the immediate advance caused by the external excitation. These results explain how intrinsic heterogeneity allows the PING rhythm to become synchronized with a periodic forcing or another PING rhythm for a wider range in the mismatch of their frequencies. We demonstrate a similar mechanism for the synchronization of ING rhythms. Our results identify a potential function of neuronal heterogeneity in the synchronization of coupled $\gamma$-rhythms, which may play a role in neural information transfer via communication through coherence. |
2310.20312 | Gabriel Palma | Idemauro Antonio Rodrigues de Lara, Gabriel Rodrigues Palma, Victor
Jos\'e Bon, Carolina Reigada, Rafael de Andrade Moral | Multi-state models for double transitions associated with parasitism in
biological control | 16 pages | null | null | null | q-bio.PE q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Competition between parasitoids can reduce the success of pest control in
biological programs using two species as bio-control agents or when multiple
species exploit the same host crop. Parasitoid foraging behavior and the
ability to identify already parasitized hosts affect the efficacy of parasitoid
species as bio-agents to regulate pest insects. We evaluated the behavioural
changes of parasitoids according to the quality of hosts ({\it i.e.},
previously parasitised or not), and the characterisation of these transitions
over time via multi-state models. We evaluated the effects of previous
parasitism of the brown stinkbug {\it Euschistus heros} eggs on the parasitism
rate of the species {\it Trissolcus basalis} and {\it Telenomus podisi}. We
successively modelled the choice of eggs (with three possibilities: non
parasitised eggs, eggs previously parasitised by {\it T. podisi}, and eggs
previously parasitised by {\it T. basalis}) and the conditional behaviour given
the choice (walking, drumming, ovipositing or marking the chosen egg). We
consider multi-state models in two successive stages to calculate double
transition probabilities, and the statistical methodology is based on the
maximum likelihood procedure. Using the Cox model and assuming a stationary
process, we verified that the treatment effect was significant for the choice,
indicating that the two parasitoid species have different choice patterns. For
the second stage, i.e. behaviour given the choice, the results also showed the
influence of the species on the conditional behaviour, especially for
previously parasitised eggs. Specifically, {\it T.podisi} avoids intraspecific
competition and makes decisions faster than {\it T. basalis}. In this work, we
emphasise the methodological contribution with multi-state models, especially
in the context of double transitions.
| [
{
"created": "Tue, 31 Oct 2023 09:37:58 GMT",
"version": "v1"
}
] | 2023-11-01 | [
[
"de Lara",
"Idemauro Antonio Rodrigues",
""
],
[
"Palma",
"Gabriel Rodrigues",
""
],
[
"Bon",
"Victor José",
""
],
[
"Reigada",
"Carolina",
""
],
[
"Moral",
"Rafael de Andrade",
""
]
] | Competition between parasitoids can reduce the success of pest control in biological programs using two species as bio-control agents or when multiple species exploit the same host crop. Parasitoid foraging behavior and the ability to identify already parasitized hosts affect the efficacy of parasitoid species as bio-agents to regulate pest insects. We evaluated the behavioural changes of parasitoids according to the quality of hosts ({\it i.e.}, previously parasitised or not), and the characterisation of these transitions over time via multi-state models. We evaluated the effects of previous parasitism of the brown stinkbug {\it Euschistus heros} eggs on the parasitism rate of the species {\it Trissolcus basalis} and {\it Telenomus podisi}. We successively modelled the choice of eggs (with three possibilities: non parasitised eggs, eggs previously parasitised by {\it T. podisi}, and eggs previously parasitised by {\it T. basalis}) and the conditional behaviour given the choice (walking, drumming, ovipositing or marking the chosen egg). We consider multi-state models in two successive stages to calculate double transition probabilities, and the statistical methodology is based on the maximum likelihood procedure. Using the Cox model and assuming a stationary process, we verified that the treatment effect was significant for the choice, indicating that the two parasitoid species have different choice patterns. For the second stage, i.e. behaviour given the choice, the results also showed the influence of the species on the conditional behaviour, especially for previously parasitised eggs. Specifically, {\it T.podisi} avoids intraspecific competition and makes decisions faster than {\it T. basalis}. In this work, we emphasise the methodological contribution with multi-state models, especially in the context of double transitions. |
0912.5381 | Duygu Balcan | Berkin Malkoc, Duygu Balcan, Ayse Erzan | Information content based model for the topological properties of the
gene regulatory network of Escherichia coli | 58 pages, 3 tables, 22 figures. In press, Journal of Theoretical
Biology (2009). | Journal of Theoretical Biology 263 (2010) 281-294 | 10.1016/j.jtbi.2009.11.017 | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene regulatory networks (GRN) are being studied with increasingly precise
quantitative tools and can provide a testing ground for ideas regarding the
emergence and evolution of complex biological networks. We analyze the global
statistical properties of the transcriptional regulatory network of the
prokaryote Escherichia coli, identifying each operon with a node of the
network. We propose a null model for this network using the content-based
approach applied earlier to the eukaryote Saccharomyces cerevisiae. (Balcan et
al., 2007) Random sequences that represent promoter regions and binding
sequences are associated with the nodes. The length distributions of these
sequences are extracted from the relevant databases. The network is constructed
by testing for the occurrence of binding sequences within the promoter regions.
The ensemble of emergent networks yields an exponentially decaying in-degree
distribution and a putative power law dependence for the out-degree
distribution with a flat tail, in agreement with the data. The clustering
coefficient, degree-degree correlation, rich club coefficient and k-core
visualization all agree qualitatively with the empirical network to an extent
not yet achieved by any other computational model, to our knowledge. The
significant statistical differences can point the way to further research into
non-adaptive and adaptive processes in the evolution of the E. coli GRN.
| [
{
"created": "Tue, 29 Dec 2009 22:47:53 GMT",
"version": "v1"
}
] | 2010-12-14 | [
[
"Malkoc",
"Berkin",
""
],
[
"Balcan",
"Duygu",
""
],
[
"Erzan",
"Ayse",
""
]
] | Gene regulatory networks (GRN) are being studied with increasingly precise quantitative tools and can provide a testing ground for ideas regarding the emergence and evolution of complex biological networks. We analyze the global statistical properties of the transcriptional regulatory network of the prokaryote Escherichia coli, identifying each operon with a node of the network. We propose a null model for this network using the content-based approach applied earlier to the eukaryote Saccharomyces cerevisiae. (Balcan et al., 2007) Random sequences that represent promoter regions and binding sequences are associated with the nodes. The length distributions of these sequences are extracted from the relevant databases. The network is constructed by testing for the occurrence of binding sequences within the promoter regions. The ensemble of emergent networks yields an exponentially decaying in-degree distribution and a putative power law dependence for the out-degree distribution with a flat tail, in agreement with the data. The clustering coefficient, degree-degree correlation, rich club coefficient and k-core visualization all agree qualitatively with the empirical network to an extent not yet achieved by any other computational model, to our knowledge. The significant statistical differences can point the way to further research into non-adaptive and adaptive processes in the evolution of the E. coli GRN. |
1705.08396 | Antony Humphries | Daniel Camara De Souza, Morgan Craig, Tyler Cassidy, Jun Li, Fahima
Nekka, Jacques Belair and Antony R Humphries | Transit and lifespan in neutrophil production: implications for drug
intervention | null | Journal of Pharmacokinetics and Pharmacodynamics, 45 (2018), 59-77 | 10.1007/s10928-017-9560-y | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We compare and contrast the transit compartment ordinary differential
equation modelling approach with distributed and discrete delay differential
equation models. We focus on Quartino's extension to the Friberg transit
compartment model of myelosuppression, widely relied upon in the pharmaceutical
sciences to predict the neutrophil response after chemotherapy, and on a QSP
delay differential equation model of granulopoiesis. We extend the Quartino
model by considering a general number of transit compartments and introduce an
extra parameter which allows us to decouple the maturation time from the
production rate of cells, and review the well established linear chain
technique from the delay differential equation (DDE) literature which can be
used to reformulate transit compartment models with constant transit rates as
distributed delay DDEs. We perform a state-dependent time rescaling of the
Quartino model in order to apply the linear chain technique and rewrite the
Quartino model as a distributed delay DDE, which yields a discrete delay DDE
model in a certain parameter limit. We then perform stability and bifurcation
analyses on the models to situate such studies in a mathematical pharmacology
context.
We show that both the original Friberg and the Quartino extension model
incorrectly define the mean maturation time, essentially treating the
proliferative pool as an additional maturation compartment, which can have far
reaching consequences on the development of future models of myelosuppression
in PK/PD.
| [
{
"created": "Tue, 23 May 2017 16:28:02 GMT",
"version": "v1"
}
] | 2021-12-03 | [
[
"De Souza",
"Daniel Camara",
""
],
[
"Craig",
"Morgan",
""
],
[
"Cassidy",
"Tyler",
""
],
[
"Li",
"Jun",
""
],
[
"Nekka",
"Fahima",
""
],
[
"Belair",
"Jacques",
""
],
[
"Humphries",
"Antony R",
""
]
] | We compare and contrast the transit compartment ordinary differential equation modelling approach with distributed and discrete delay differential equation models. We focus on Quartino's extension to the Friberg transit compartment model of myelosuppression, widely relied upon in the pharmaceutical sciences to predict the neutrophil response after chemotherapy, and on a QSP delay differential equation model of granulopoiesis. We extend the Quartino model by considering a general number of transit compartments and introduce an extra parameter which allows us to decouple the maturation time from the production rate of cells, and review the well established linear chain technique from the delay differential equation (DDE) literature which can be used to reformulate transit compartment models with constant transit rates as distributed delay DDEs. We perform a state-dependent time rescaling of the Quartino model in order to apply the linear chain technique and rewrite the Quartino model as a distributed delay DDE, which yields a discrete delay DDE model in a certain parameter limit. We then perform stability and bifurcation analyses on the models to situate such studies in a mathematical pharmacology context. We show that both the original Friberg and the Quartino extension model incorrectly define the mean maturation time, essentially treating the proliferative pool as an additional maturation compartment, which can have far reaching consequences on the development of future models of myelosuppression in PK/PD. |
1707.07959 | Johannes Knebel | Matthias Bauer, Johannes Knebel, Matthias Lechner, Peter Pickl, Erwin
Frey | Ecological feedback in quorum-sensing microbial populations can induce
heterogeneous production of autoinducers | 44 pages, 7 figures, 3 linked videos | eLife 2017;6:e25773 | 10.7554/eLife.25773 | null | q-bio.PE cond-mat.stat-mech nlin.AO physics.bio-ph q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Autoinducers are small signaling molecules that mediate intercellular
communication in microbial populations and trigger coordinated gene expression
via "quorum sensing". Elucidating the mechanisms that control autoinducer
production is, thus, pertinent to understanding collective microbial behavior,
such as virulence and bioluminescence. Recent experiments have shown a
heterogeneous promoter activity of autoinducer synthase genes, suggesting that
some of the isogenic cells in a population might produce autoinducers, whereas
others might not. However, the mechanism underlying this phenotypic
heterogeneity in quorum-sensing microbial populations has remained elusive. In
our theoretical model, cells synthesize and secrete autoinducers into the
environment, up-regulate their production in this self-shaped environment, and
non-producers replicate faster than producers. We show that the coupling
between ecological and population dynamics through quorum sensing can induce
phenotypic heterogeneity in microbial populations, suggesting an alternative
mechanism to stochastic gene expression in bistable gene regulatory circuits.
| [
{
"created": "Tue, 25 Jul 2017 12:49:13 GMT",
"version": "v1"
}
] | 2017-07-26 | [
[
"Bauer",
"Matthias",
""
],
[
"Knebel",
"Johannes",
""
],
[
"Lechner",
"Matthias",
""
],
[
"Pickl",
"Peter",
""
],
[
"Frey",
"Erwin",
""
]
] | Autoinducers are small signaling molecules that mediate intercellular communication in microbial populations and trigger coordinated gene expression via "quorum sensing". Elucidating the mechanisms that control autoinducer production is, thus, pertinent to understanding collective microbial behavior, such as virulence and bioluminescence. Recent experiments have shown a heterogeneous promoter activity of autoinducer synthase genes, suggesting that some of the isogenic cells in a population might produce autoinducers, whereas others might not. However, the mechanism underlying this phenotypic heterogeneity in quorum-sensing microbial populations has remained elusive. In our theoretical model, cells synthesize and secrete autoinducers into the environment, up-regulate their production in this self-shaped environment, and non-producers replicate faster than producers. We show that the coupling between ecological and population dynamics through quorum sensing can induce phenotypic heterogeneity in microbial populations, suggesting an alternative mechanism to stochastic gene expression in bistable gene regulatory circuits. |
2406.19109 | Jochen Kursawe | Jochen Kursawe, Antoine Moneyron, Tobias Galla | Efficient approximations of transcriptional bursting effects on the
dynamics of a gene regulatory network | null | null | null | null | q-bio.MN physics.bio-ph q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | Mathematical models of gene regulatory networks are widely used to study cell
fate changes and transcriptional regulation. When designing such models, it is
important to accurately account for sources of stochasticity. However, doing so
can be computationally expensive and analytically untractable, posing limits on
the extent of our explorations and on parameter inference. Here, we explore
this challenge using the example of a simple auto-negative feedback motif, in
which we incorporate stochastic variation due to transcriptional bursting and
noise from finite copy numbers. We find that transcriptional bursting may
change the qualitative dynamics of the system by inducing oscillations when
they would not otherwise be present, or by magnifying existing oscillations. We
describe multiple levels of approximation for the model in the form of
differential equations, piecewise deterministic processes, and stochastic
differential equations. Importantly, we derive how the classical chemical
Langevin equation can be extended to include a noise term representing
transcriptional bursting. This approximation drastically decreases computation
times and allows us to analytically calculate properties of the dynamics, such
as their power spectrum. We explore when these approximations break down and
provide recommendations for their use. Our analysis illustrates the importance
of accounting for transcriptional bursting when simulating gene regulatory
network dynamics and provides recommendations to do so with computationally
efficient methods.
| [
{
"created": "Thu, 27 Jun 2024 11:40:13 GMT",
"version": "v1"
}
] | 2024-06-28 | [
[
"Kursawe",
"Jochen",
""
],
[
"Moneyron",
"Antoine",
""
],
[
"Galla",
"Tobias",
""
]
] | Mathematical models of gene regulatory networks are widely used to study cell fate changes and transcriptional regulation. When designing such models, it is important to accurately account for sources of stochasticity. However, doing so can be computationally expensive and analytically untractable, posing limits on the extent of our explorations and on parameter inference. Here, we explore this challenge using the example of a simple auto-negative feedback motif, in which we incorporate stochastic variation due to transcriptional bursting and noise from finite copy numbers. We find that transcriptional bursting may change the qualitative dynamics of the system by inducing oscillations when they would not otherwise be present, or by magnifying existing oscillations. We describe multiple levels of approximation for the model in the form of differential equations, piecewise deterministic processes, and stochastic differential equations. Importantly, we derive how the classical chemical Langevin equation can be extended to include a noise term representing transcriptional bursting. This approximation drastically decreases computation times and allows us to analytically calculate properties of the dynamics, such as their power spectrum. We explore when these approximations break down and provide recommendations for their use. Our analysis illustrates the importance of accounting for transcriptional bursting when simulating gene regulatory network dynamics and provides recommendations to do so with computationally efficient methods. |
2306.12045 | Gehua Ma | Gehua Ma, Runhao Jiang, Rui Yan, Huajin Tang | Temporal Conditioning Spiking Latent Variable Models of the Neural
Response to Natural Visual Scenes | Accepted at NeurIPS 2023
(https://openreview.net/forum?id=V4YeOvsQfu). 22 pages, 7 figures, 3 tables | null | null | null | q-bio.NC cs.CV cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Developing computational models of neural response is crucial for
understanding sensory processing and neural computations. Current
state-of-the-art neural network methods use temporal filters to handle temporal
dependencies, resulting in an unrealistic and inflexible processing paradigm.
Meanwhile, these methods target trial-averaged firing rates and fail to capture
important features in spike trains. This work presents the temporal
conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural
response to natural visual stimuli. We use spiking neurons to produce spike
outputs that directly match the recorded trains. This approach helps to avoid
losing information embedded in the original spike trains. We exclude the
temporal dimension from the model parameter space and introduce a temporal
conditioning operation to allow the model to adaptively explore and exploit
temporal dependencies in stimuli sequences in a {\it natural paradigm}. We show
that TeCoS-LVM models can produce more realistic spike activities and
accurately fit spike statistics than powerful alternatives. Additionally,
learned TeCoS-LVM models can generalize well to longer time scales. Overall,
while remaining computationally tractable, our model effectively captures key
features of neural coding systems. It thus provides a useful tool for building
accurate predictive computational accounts for various sensory perception
circuits.
| [
{
"created": "Wed, 21 Jun 2023 06:30:18 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Jul 2023 12:14:10 GMT",
"version": "v2"
},
{
"created": "Mon, 25 Sep 2023 15:05:28 GMT",
"version": "v3"
},
{
"created": "Mon, 23 Oct 2023 10:30:04 GMT",
"version": "v4"
},
{
"c... | 2023-12-21 | [
[
"Ma",
"Gehua",
""
],
[
"Jiang",
"Runhao",
""
],
[
"Yan",
"Rui",
""
],
[
"Tang",
"Huajin",
""
]
] | Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temporal filters to handle temporal dependencies, resulting in an unrealistic and inflexible processing paradigm. Meanwhile, these methods target trial-averaged firing rates and fail to capture important features in spike trains. This work presents the temporal conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural response to natural visual stimuli. We use spiking neurons to produce spike outputs that directly match the recorded trains. This approach helps to avoid losing information embedded in the original spike trains. We exclude the temporal dimension from the model parameter space and introduce a temporal conditioning operation to allow the model to adaptively explore and exploit temporal dependencies in stimuli sequences in a {\it natural paradigm}. We show that TeCoS-LVM models can produce more realistic spike activities and accurately fit spike statistics than powerful alternatives. Additionally, learned TeCoS-LVM models can generalize well to longer time scales. Overall, while remaining computationally tractable, our model effectively captures key features of neural coding systems. It thus provides a useful tool for building accurate predictive computational accounts for various sensory perception circuits. |
1402.3523 | Benjamin Hepp | Benjamin Hepp and Ankit Gupta and Mustafa Khammash | Adaptive Hybrid Simulations for Multiscale Stochastic Reaction Networks | 43 pages, 12 figures, 5 tables | null | 10.1063/1.4905196 | null | q-bio.QM math.PR q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The probability distribution describing the state of a Stochastic Reaction
Network evolves according to the Chemical Master Equation (CME). It is common
to estimated its solution using Monte Carlo methods such as the Stochastic
Simulation Algorithm (SSA). In many cases these simulations can take an
impractical amount of computational time. Therefore many methods have been
developed that approximate the Stochastic Process underlying the Chemical
Master Equation. Prominent strategies are Hybrid Models that regard the firing
of some reaction channels as being continuous and applying the quasi-stationary
assumption to approximate the dynamics of fast subnetworks. However as the
dynamics of a Stochastic Reaction Network changes with time these
approximations might have to be adapted during the simulation. We develop a
method that approximates the solution of a CME by automatically partitioning
the reaction dynamics into discrete/continuous components and applying the
quasi-stationary assumption on identifiable fast subnetworks. Our method does
not require user intervention and it adapts to exploit the changing timescale
separation between reactions and/or changing magnitudes of copy numbers of
constituent species. We demonstrate the efficiency of the proposed method by
considering examples from Systems Biology and showing that very good
approximations to the exact probability distributions can be achieved in
significantly less computational time.
| [
{
"created": "Fri, 14 Feb 2014 16:37:08 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Feb 2014 10:13:33 GMT",
"version": "v2"
},
{
"created": "Mon, 5 Jan 2015 15:51:54 GMT",
"version": "v3"
}
] | 2015-06-18 | [
[
"Hepp",
"Benjamin",
""
],
[
"Gupta",
"Ankit",
""
],
[
"Khammash",
"Mustafa",
""
]
] | The probability distribution describing the state of a Stochastic Reaction Network evolves according to the Chemical Master Equation (CME). It is common to estimated its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases these simulations can take an impractical amount of computational time. Therefore many methods have been developed that approximate the Stochastic Process underlying the Chemical Master Equation. Prominent strategies are Hybrid Models that regard the firing of some reaction channels as being continuous and applying the quasi-stationary assumption to approximate the dynamics of fast subnetworks. However as the dynamics of a Stochastic Reaction Network changes with time these approximations might have to be adapted during the simulation. We develop a method that approximates the solution of a CME by automatically partitioning the reaction dynamics into discrete/continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from Systems Biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. |
2005.10223 | Xinyi Shen | Xinyi Shen (1), Chenkai Cai (1 and 2), and Hui Li (3) ((1) Department
of Civil and Environmental Engineering, University of Connecticut, (2)
College of Hydrology and Water Resources, Hohai University, (3) Department of
Finance, University of Connecticut) | Quantifying socioeconomic activities and weather effects on the global
spread of COVID-19 epidemic | null | null | null | null | q-bio.PE q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The COVID-19 has caused more than three million infections and over two
hundred thousand deaths by April 20201. Limiting socioeconomic activities (SA)
is among the most adopted governmental mitigating efforts to combat the
transmission of the virus, though the degree varies dramatically among
different regimes2. This study aims to quantify the contribution from the SA
and weather conditions to the transmission of COVID-19 at global scale. Ruling
out the unobservable factors including medical facilities and other control
policies (MOC) through region-by-time fixed effects3,4, we show that the
limiting SA has a leading contribution to lower the reproductive number by
18.3%, while weather conditions, including ultraviolet, relative humidity, and
wind explain a smaller amount of variation. Temperature might have a
non-monotonic impact on the transmission. We further show that in developed
countries5 and China, the SA effect is more pronounced whereas the weather
effect is significantly downplayed possibly because people tend to stay indoors
most of the time with a controlled climate. We finally estimate the reduced
reproductive number and the population spared from infections due to
restricting SA at 40,964, 180,336, 174,494, in China, United States, and Europe
respectively. From late January to mid-April, all regions, except for China,
Australia, and south Korea show a steep upward trend of spared infections due
to restricting SA. US and Europe, in particular, show far steeper upward trends
of spared infections in the analyzed timeframe, signaling a greater risk of
reopening the economy too soon.
| [
{
"created": "Wed, 20 May 2020 17:41:39 GMT",
"version": "v1"
}
] | 2020-05-21 | [
[
"Shen",
"Xinyi",
"",
"1 and 2"
],
[
"Cai",
"Chenkai",
"",
"1 and 2"
],
[
"Li",
"Hui",
""
]
] | The COVID-19 has caused more than three million infections and over two hundred thousand deaths by April 20201. Limiting socioeconomic activities (SA) is among the most adopted governmental mitigating efforts to combat the transmission of the virus, though the degree varies dramatically among different regimes2. This study aims to quantify the contribution from the SA and weather conditions to the transmission of COVID-19 at global scale. Ruling out the unobservable factors including medical facilities and other control policies (MOC) through region-by-time fixed effects3,4, we show that the limiting SA has a leading contribution to lower the reproductive number by 18.3%, while weather conditions, including ultraviolet, relative humidity, and wind explain a smaller amount of variation. Temperature might have a non-monotonic impact on the transmission. We further show that in developed countries5 and China, the SA effect is more pronounced whereas the weather effect is significantly downplayed possibly because people tend to stay indoors most of the time with a controlled climate. We finally estimate the reduced reproductive number and the population spared from infections due to restricting SA at 40,964, 180,336, 174,494, in China, United States, and Europe respectively. From late January to mid-April, all regions, except for China, Australia, and south Korea show a steep upward trend of spared infections due to restricting SA. US and Europe, in particular, show far steeper upward trends of spared infections in the analyzed timeframe, signaling a greater risk of reopening the economy too soon. |
2304.06134 | Josef Tkadlec | Josef Tkadlec, Christian Hilbe, Martin A. Nowak | Mutation enhances cooperation in direct reciprocity | 37 pages, 14 figures | null | 10.1073/pnas.2221080120 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Direct reciprocity is a powerful mechanism for evolution of cooperation based
on repeated interactions between the same individuals. But high levels of
cooperation evolve only if the benefit-to-cost ratio exceeds a certain
threshold that depends on memory length. For the best-explored case of
one-round memory, that threshold is two. Here we report that intermediate
mutation rates lead to high levels of cooperation, even if the benefit-to-cost
ratio is only marginally above one, and even if individuals only use a minimum
of past information. This surprising observation is caused by two effects.
First, mutation generates diversity which undermines the evolutionary stability
of defectors. Second, mutation leads to diverse communities of cooperators that
are more resilient than homogeneous ones. This finding is relevant because many
real world opportunities for cooperation have small benefit-to-cost ratios,
which are between one and two, and we describe how direct reciprocity can
attain cooperation in such settings. Our result can be interpreted as showing
that diversity, rather than uniformity, promotes evolution of cooperation.
| [
{
"created": "Wed, 12 Apr 2023 19:37:51 GMT",
"version": "v1"
}
] | 2023-05-31 | [
[
"Tkadlec",
"Josef",
""
],
[
"Hilbe",
"Christian",
""
],
[
"Nowak",
"Martin A.",
""
]
] | Direct reciprocity is a powerful mechanism for evolution of cooperation based on repeated interactions between the same individuals. But high levels of cooperation evolve only if the benefit-to-cost ratio exceeds a certain threshold that depends on memory length. For the best-explored case of one-round memory, that threshold is two. Here we report that intermediate mutation rates lead to high levels of cooperation, even if the benefit-to-cost ratio is only marginally above one, and even if individuals only use a minimum of past information. This surprising observation is caused by two effects. First, mutation generates diversity which undermines the evolutionary stability of defectors. Second, mutation leads to diverse communities of cooperators that are more resilient than homogeneous ones. This finding is relevant because many real world opportunities for cooperation have small benefit-to-cost ratios, which are between one and two, and we describe how direct reciprocity can attain cooperation in such settings. Our result can be interpreted as showing that diversity, rather than uniformity, promotes evolution of cooperation. |
2303.08993 | Gregory Bowman | Vincent A. Voelz, Vijay S. Pande, and Gregory R. Bowman | Folding@home: achievements from over twenty years of citizen science
herald the exascale era | 24 pages, 6 figures | null | 10.1016/j.bpj.2023.03.028 | null | q-bio.BM physics.chem-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Simulations of biomolecules have enormous potential to inform our
understanding of biology but require extremely demanding calculations. For over
twenty years, the Folding@home distributed computing project has pioneered a
massively parallel approach to biomolecular simulation, harnessing the
resources of citizen scientists across the globe. Here, we summarize the
scientific and technical advances this perspective has enabled. As the
project's name implies, the early years of Folding@home focused on driving
advances in our understanding of protein folding by developing statistical
methods for capturing long-timescale processes and facilitating insight into
complex dynamical processes. Success laid a foundation for broadening the scope
of Folding@home to address other functionally relevant conformational changes,
such as receptor signaling, enzyme dynamics, and ligand binding. Continued
algorithmic advances, hardware developments such as GPU-based computing, and
the growing scale of Folding@home have enabled the project to focus on new
areas where massively parallel sampling can be impactful. While previous work
sought to expand toward larger proteins with slower conformational changes, new
work focuses on large-scale comparative studies of different protein sequences
and chemical compounds to better understand biology and inform the development
of small molecule drugs. Progress on these fronts enabled the community to
pivot quickly in response to the COVID-19 pandemic, expanding to become the
world's first exascale computer and deploying this massive resource to provide
insight into the inner workings of the SARS-CoV-2 virus and aid the development
of new antivirals. This success provides a glimpse of what's to come as
exascale supercomputers come online, and Folding@home continues its work.
| [
{
"created": "Wed, 15 Mar 2023 23:49:58 GMT",
"version": "v1"
}
] | 2023-08-09 | [
[
"Voelz",
"Vincent A.",
""
],
[
"Pande",
"Vijay S.",
""
],
[
"Bowman",
"Gregory R.",
""
]
] | Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations. For over twenty years, the Folding@home distributed computing project has pioneered a massively parallel approach to biomolecular simulation, harnessing the resources of citizen scientists across the globe. Here, we summarize the scientific and technical advances this perspective has enabled. As the project's name implies, the early years of Folding@home focused on driving advances in our understanding of protein folding by developing statistical methods for capturing long-timescale processes and facilitating insight into complex dynamical processes. Success laid a foundation for broadening the scope of Folding@home to address other functionally relevant conformational changes, such as receptor signaling, enzyme dynamics, and ligand binding. Continued algorithmic advances, hardware developments such as GPU-based computing, and the growing scale of Folding@home have enabled the project to focus on new areas where massively parallel sampling can be impactful. While previous work sought to expand toward larger proteins with slower conformational changes, new work focuses on large-scale comparative studies of different protein sequences and chemical compounds to better understand biology and inform the development of small molecule drugs. Progress on these fronts enabled the community to pivot quickly in response to the COVID-19 pandemic, expanding to become the world's first exascale computer and deploying this massive resource to provide insight into the inner workings of the SARS-CoV-2 virus and aid the development of new antivirals. This success provides a glimpse of what's to come as exascale supercomputers come online, and Folding@home continues its work. |
1902.03510 | Xiaohui Yang | Xiao-Hui Yang, Li Tian, Yun-Mei Chen, Li-Jun Yang, Shuang Xu, and
Wen-Ming Wu | Inverse Projection Representation and Category Contribution Rate for
Robust Tumor Recognition | 14 pages, 19 figures, 10 tables | IEEE/ACM Transactions on Computational Biology and Bioinformatics,
2018 | 10.1109/TCBB.2018.2886334 | null | q-bio.QM cs.CV cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sparse representation based classification (SRC) methods have achieved
remarkable results. SRC, however, still suffer from requiring enough training
samples, insufficient use of test samples and instability of representation. In
this paper, a stable inverse projection representation based classification
(IPRC) is presented to tackle these problems by effectively using test samples.
An IPR is firstly proposed and its feasibility and stability are analyzed. A
classification criterion named category contribution rate is constructed to
match the IPR and complete classification. Moreover, a statistical measure is
introduced to quantify the stability of representation-based classification
methods. Based on the IPRC technique, a robust tumor recognition framework is
presented by interpreting microarray gene expression data, where a two-stage
hybrid gene selection method is introduced to select informative genes.
Finally, the functional analysis of candidate's pathogenicity-related genes is
given. Extensive experiments on six public tumor microarray gene expression
datasets demonstrate the proposed technique is competitive with
state-of-the-art methods.
| [
{
"created": "Sat, 9 Feb 2019 23:07:22 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Jun 2019 04:07:28 GMT",
"version": "v2"
}
] | 2019-06-28 | [
[
"Yang",
"Xiao-Hui",
""
],
[
"Tian",
"Li",
""
],
[
"Chen",
"Yun-Mei",
""
],
[
"Yang",
"Li-Jun",
""
],
[
"Xu",
"Shuang",
""
],
[
"Wu",
"Wen-Ming",
""
]
] | Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods. |
1810.01159 | Hans Jacob Teglbj{\ae}rg Stephensen | Hans Jacob Teglbj{\ae}rg Stephensen, Sune Darkner, Jon Sporring | A Highly Accurate Model Based Registration Method for FIB-SEM Images of
Neurons | 8 pages, 5 figues. Article is pending submission for peer reveiw | Commun Biol 3, 81 (2020) | 10.1038/s42003-020-0809-4 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Focused Ion Beam Scanning Electron Microscope (FIB-SEM) imaging is a
technique that image materials section-by-section at nano-resolution, e.g.,5
nanometer width voxels. FIB-SEM is well suited for imaging ultrastructures in
cells. Unfortunately, typical setups will introduce a slight sub-pixel
translation from section to section typically referred to as drift. Over
multiple sections, drift compound to skew distance measures and geometric
structures significantly from the pre-imaged stage. Popular correction
approaches often involve standard image registration methods available in
packages such as ImageJ or similar software. These methods transform the images
to maximize the similarity between consecutive two-dimensional sections under
some measure. We show how these standard approaches will both significantly
underestimate the drift, as well as producing biased corrections as they tend
to align the images such that the normal of planar biological structures are
perpendicular to the sectioning direction causing poor or incorrect correction
of the images. In this paper, we present a highly accurate correction method
for estimating drift in isotropic electron microscope images with visible
vesicles.
| [
{
"created": "Tue, 2 Oct 2018 10:20:30 GMT",
"version": "v1"
}
] | 2020-08-06 | [
[
"Stephensen",
"Hans Jacob Teglbjærg",
""
],
[
"Darkner",
"Sune",
""
],
[
"Sporring",
"Jon",
""
]
] | Focused Ion Beam Scanning Electron Microscope (FIB-SEM) imaging is a technique that image materials section-by-section at nano-resolution, e.g.,5 nanometer width voxels. FIB-SEM is well suited for imaging ultrastructures in cells. Unfortunately, typical setups will introduce a slight sub-pixel translation from section to section typically referred to as drift. Over multiple sections, drift compound to skew distance measures and geometric structures significantly from the pre-imaged stage. Popular correction approaches often involve standard image registration methods available in packages such as ImageJ or similar software. These methods transform the images to maximize the similarity between consecutive two-dimensional sections under some measure. We show how these standard approaches will both significantly underestimate the drift, as well as producing biased corrections as they tend to align the images such that the normal of planar biological structures are perpendicular to the sectioning direction causing poor or incorrect correction of the images. In this paper, we present a highly accurate correction method for estimating drift in isotropic electron microscope images with visible vesicles. |
0709.3359 | Thomas R. Weikl | Thomas R. Weikl | Transition states in protein folding kinetics: Modeling Phi-values of
small beta-sheet proteins | 27 pages, 6 pages, 3 tables; to appear in Biophys. J | null | 10.1529/biophysj.107.109868 | null | q-bio.BM | null | Small single-domain proteins often exhibit only a single free-energy barrier,
or transition state, between the denatured and the native state. The folding
kinetics of these proteins is usually explored via mutational analysis. A
central question is which structural information on the transition state can be
derived from the mutational data. In this article, we model and structurally
interpret mutational Phi-values for two small beta-sheet proteins, the PIN and
the FBP WW domain. The native structure of these WW domains comprises two
beta-hairpins that form a three-stranded beta-sheet. In our model, we assume
that the transition state consists of two conformations in which either one of
the hairpins is formed. Such a transition state has been recently observed in
Molecular Dynamics folding-unfolding simulations of a small designed
three-stranded beta-sheet protein. We obtain good agreement with the
experimental data (i) by splitting up the mutation-induced free-energy changes
into terms for the two hairpins and for the small hydrophobic core of the
proteins, and (ii) by fitting a single parameter, the relative degree to which
hairpin 1 and 2 are formed in the transition state. The model helps to
understand how mutations affect the folding kinetics of WW domains, and
captures also negative Phi-values that have been difficult to interpret.
| [
{
"created": "Fri, 21 Sep 2007 09:11:17 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Weikl",
"Thomas R.",
""
]
] | Small single-domain proteins often exhibit only a single free-energy barrier, or transition state, between the denatured and the native state. The folding kinetics of these proteins is usually explored via mutational analysis. A central question is which structural information on the transition state can be derived from the mutational data. In this article, we model and structurally interpret mutational Phi-values for two small beta-sheet proteins, the PIN and the FBP WW domain. The native structure of these WW domains comprises two beta-hairpins that form a three-stranded beta-sheet. In our model, we assume that the transition state consists of two conformations in which either one of the hairpins is formed. Such a transition state has been recently observed in Molecular Dynamics folding-unfolding simulations of a small designed three-stranded beta-sheet protein. We obtain good agreement with the experimental data (i) by splitting up the mutation-induced free-energy changes into terms for the two hairpins and for the small hydrophobic core of the proteins, and (ii) by fitting a single parameter, the relative degree to which hairpin 1 and 2 are formed in the transition state. The model helps to understand how mutations affect the folding kinetics of WW domains, and captures also negative Phi-values that have been difficult to interpret. |
1703.05810 | Vikenty Mikheev | Vikenty Mikheev and Serge E. Miheev | Species Trees Forcing Parsimony to Fail | This article is an extended version of
http://ceur-ws.org/Vol-2254/10000206.pdf | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To the known fact that Parsimony method sometimes fails on the problem of
inferring species trees from gene trees, here we proved that no mater of what
topology the true 9-taxon and greater species tree is the only thing one needs
to break down Parsimony is to have in this species tree three consecutive inner
edges not going through the root but perhaps ending on it with lengths T1, T2,
T3 of some proportions. Obviously, the probability to meet these lengths is
growing in general with the size of species tree. Therefore, Parsimony may be
applied only when the described lengths of edges cannot be met in the tree.
| [
{
"created": "Thu, 16 Mar 2017 19:58:00 GMT",
"version": "v1"
},
{
"created": "Sat, 10 Aug 2019 02:31:48 GMT",
"version": "v2"
}
] | 2019-08-13 | [
[
"Mikheev",
"Vikenty",
""
],
[
"Miheev",
"Serge E.",
""
]
] | To the known fact that Parsimony method sometimes fails on the problem of inferring species trees from gene trees, here we proved that no mater of what topology the true 9-taxon and greater species tree is the only thing one needs to break down Parsimony is to have in this species tree three consecutive inner edges not going through the root but perhaps ending on it with lengths T1, T2, T3 of some proportions. Obviously, the probability to meet these lengths is growing in general with the size of species tree. Therefore, Parsimony may be applied only when the described lengths of edges cannot be met in the tree. |
1904.10498 | Izaak Neveln | Izaak D. Neveln, Amoolya Tirumalai, Simon Sponberg | Information Based Centralization of Locomotion in Animals and Robots | null | null | 10.1038/s41467-019-11613-y | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Movement in biology is often achieved with distributed control of coupled
subcomponents, e.g. muscles and limbs. Coupling could range from weak and
local, i.e. decentralized, to strong and global, i.e. centralized. We developed
a model-free measure of centralization that compares information shared between
control signals and both global and local states. A second measure, called
co-information, quantifies the net redundant information the control signal
shares with both states. We first validate our measures through simulations of
coupled oscillators and show that it successfully reconstructs the shift from
low to high coupling strengths. We then measure centralization in freely
running cockroaches. Surprisingly, extensor muscle activity in the middle leg
is more informative of movements of all legs combined than the movements of
that particular leg. Cockroach centralization successfully recapitulates a
specific model of a strongly coupled oscillator network previously used to
model cockroach leg kinematics. When segregated by stride frequency, slower
cockroach strides exhibit more shared information per stride between control
and output states than faster strides, indicative of an information bandwidth
limitation. However, centralization remains consistent between the two groups.
We then used a robotic model to show that centralization can be affected by
mechanical coupling independent of neural coupling. The mechanically coupled
bounding gait is decentralized and becomes more decentralized as mechanical
coupling decreases while internal parameters of control remain constant. The
results of these systems span a design space of centralization and
co-information that can be used to test biological hypotheses and advise the
design of robotic control.
| [
{
"created": "Wed, 17 Apr 2019 15:05:40 GMT",
"version": "v1"
}
] | 2019-09-11 | [
[
"Neveln",
"Izaak D.",
""
],
[
"Tirumalai",
"Amoolya",
""
],
[
"Sponberg",
"Simon",
""
]
] | Movement in biology is often achieved with distributed control of coupled subcomponents, e.g. muscles and limbs. Coupling could range from weak and local, i.e. decentralized, to strong and global, i.e. centralized. We developed a model-free measure of centralization that compares information shared between control signals and both global and local states. A second measure, called co-information, quantifies the net redundant information the control signal shares with both states. We first validate our measures through simulations of coupled oscillators and show that it successfully reconstructs the shift from low to high coupling strengths. We then measure centralization in freely running cockroaches. Surprisingly, extensor muscle activity in the middle leg is more informative of movements of all legs combined than the movements of that particular leg. Cockroach centralization successfully recapitulates a specific model of a strongly coupled oscillator network previously used to model cockroach leg kinematics. When segregated by stride frequency, slower cockroach strides exhibit more shared information per stride between control and output states than faster strides, indicative of an information bandwidth limitation. However, centralization remains consistent between the two groups. We then used a robotic model to show that centralization can be affected by mechanical coupling independent of neural coupling. The mechanically coupled bounding gait is decentralized and becomes more decentralized as mechanical coupling decreases while internal parameters of control remain constant. The results of these systems span a design space of centralization and co-information that can be used to test biological hypotheses and advise the design of robotic control. |
1903.10353 | Thitiya Theparod | Peter Neal and Thitiya Theparod | The basic reproduction number, $R_0$, in structured populations | 26 pages | null | null | null | q-bio.PE stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we provide a straightforward approach to defining and deriving
the key epidemiological quantity, the basic reproduction number, $R_0$, for
Markovian epidemics in structured populations. The methodology derived is
applicable to, and demonstrated on, both $SIR$ and $SIS$ epidemics and allows
for population as well as epidemic dynamics. The approach taken is to consider
the epidemic process as a multitype process by identifying and classifying the
different types of infectious units along with the infections from, and the
transitions between, infectious units. For the household model, we show that
our expression for $R_0$ agrees with earlier work despite the alternative
nature of the construction of the mean reproductive matrix, and hence, the
basic reproduction number.
| [
{
"created": "Fri, 22 Mar 2019 12:23:00 GMT",
"version": "v1"
}
] | 2019-03-26 | [
[
"Neal",
"Peter",
""
],
[
"Theparod",
"Thitiya",
""
]
] | In this paper, we provide a straightforward approach to defining and deriving the key epidemiological quantity, the basic reproduction number, $R_0$, for Markovian epidemics in structured populations. The methodology derived is applicable to, and demonstrated on, both $SIR$ and $SIS$ epidemics and allows for population as well as epidemic dynamics. The approach taken is to consider the epidemic process as a multitype process by identifying and classifying the different types of infectious units along with the infections from, and the transitions between, infectious units. For the household model, we show that our expression for $R_0$ agrees with earlier work despite the alternative nature of the construction of the mean reproductive matrix, and hence, the basic reproduction number. |
2206.07521 | Pau Clusella | Pau Clusella, Elif K\"oksal-Ers\"oz, Jordi Garcia-Ojalvo, Giulio
Ruffini | Comparison between an exact and a heuristic neural mass model with
second order synapses | null | null | 10.1007/s00422-022-00952-7 | null | q-bio.NC math.DS nlin.AO | http://creativecommons.org/licenses/by/4.0/ | Neural mass models (NMMs) are designed to reproduce the collective dynamics
of neuronal populations. A common framework for NMMs assumes heuristically that
the output firing rate of a neural population can be described by a static
nonlinear transfer function (NMM1). However, a recent exact mean-field theory
for quadratic integrate-and-fire (QIF) neurons challenges this view by showing
that the mean firing rate is not a static function of the neuronal state but
follows two coupled nonlinear differential equations (NMM2). Here we analyze
and compare these two descriptions in the presence of second-order synaptic
dynamics. First, we derive the mathematical equivalence between the two models
in the infinitely slow synapse limit, i.e., we show that NMM1 is an
approximation of NMM2 in this regime. Next, we evaluate the applicability of
this limit in the context of realistic physiological parameter values by
analyzing the dynamics of models with inhibitory or excitatory synapses. We
show that NMM1 fails to reproduce important dynamical features of the exact
model, such as the self-sustained oscillations of an inhibitory interneuron QIF
network. Furthermore, in the exact model but not in the limit one, stimulation
of a pyramidal cell population induces resonant oscillatory activity whose peak
frequency and amplitude increase with the self-coupling gain and the external
excitatory input. This may play a role in the enhanced response of densely
connected networks to weak uniform inputs, such as the electric fields produced
by non-invasive brain stimulation.
| [
{
"created": "Wed, 15 Jun 2022 13:14:11 GMT",
"version": "v1"
}
] | 2022-12-05 | [
[
"Clusella",
"Pau",
""
],
[
"Köksal-Ersöz",
"Elif",
""
],
[
"Garcia-Ojalvo",
"Jordi",
""
],
[
"Ruffini",
"Giulio",
""
]
] | Neural mass models (NMMs) are designed to reproduce the collective dynamics of neuronal populations. A common framework for NMMs assumes heuristically that the output firing rate of a neural population can be described by a static nonlinear transfer function (NMM1). However, a recent exact mean-field theory for quadratic integrate-and-fire (QIF) neurons challenges this view by showing that the mean firing rate is not a static function of the neuronal state but follows two coupled nonlinear differential equations (NMM2). Here we analyze and compare these two descriptions in the presence of second-order synaptic dynamics. First, we derive the mathematical equivalence between the two models in the infinitely slow synapse limit, i.e., we show that NMM1 is an approximation of NMM2 in this regime. Next, we evaluate the applicability of this limit in the context of realistic physiological parameter values by analyzing the dynamics of models with inhibitory or excitatory synapses. We show that NMM1 fails to reproduce important dynamical features of the exact model, such as the self-sustained oscillations of an inhibitory interneuron QIF network. Furthermore, in the exact model but not in the limit one, stimulation of a pyramidal cell population induces resonant oscillatory activity whose peak frequency and amplitude increase with the self-coupling gain and the external excitatory input. This may play a role in the enhanced response of densely connected networks to weak uniform inputs, such as the electric fields produced by non-invasive brain stimulation. |
1503.05971 | Valerio Capraro | Valerio Capraro and H\'el\`ene Barcelo | Group size effect on cooperation in one-shot social dilemmas II.
Curvilinear effect | Forthcoming in PLoS ONE | null | 10.1371/journal.pone.0131419 | null | q-bio.PE cs.GT physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a world in which many pressing global issues require large scale
cooperation, understanding the group size effect on cooperative behavior is a
topic of central importance. Yet, the nature of this effect remains largely
unknown, with lab experiments insisting that it is either positive or negative
or null, and field experiments suggesting that it is instead curvilinear. Here
we shed light on this apparent contradiction by considering a novel class of
public goods games inspired to the realistic scenario in which the natural
output limits of the public good imply that the benefit of cooperation
increases fast for early contributions and then decelerates. We report on a
large lab experiment providing evidence that, in this case, group size has a
curvilinear effect on cooperation, according to which intermediate-size groups
cooperate more than smaller groups and more than larger groups. In doing so,
our findings help fill the gap between lab experiments and field experiments
and suggest concrete ways to promote large scale cooperation among people.
| [
{
"created": "Fri, 20 Mar 2015 00:23:34 GMT",
"version": "v1"
},
{
"created": "Tue, 31 Mar 2015 13:21:46 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Jun 2015 17:09:13 GMT",
"version": "v3"
}
] | 2016-02-17 | [
[
"Capraro",
"Valerio",
""
],
[
"Barcelo",
"Hélène",
""
]
] | In a world in which many pressing global issues require large scale cooperation, understanding the group size effect on cooperative behavior is a topic of central importance. Yet, the nature of this effect remains largely unknown, with lab experiments insisting that it is either positive or negative or null, and field experiments suggesting that it is instead curvilinear. Here we shed light on this apparent contradiction by considering a novel class of public goods games inspired to the realistic scenario in which the natural output limits of the public good imply that the benefit of cooperation increases fast for early contributions and then decelerates. We report on a large lab experiment providing evidence that, in this case, group size has a curvilinear effect on cooperation, according to which intermediate-size groups cooperate more than smaller groups and more than larger groups. In doing so, our findings help fill the gap between lab experiments and field experiments and suggest concrete ways to promote large scale cooperation among people. |
q-bio/0405027 | Myoung Won Cho | Myoung Won Cho, Seunghwan Kim | General representation of collective neural dynamics with columnar
modularity | null | null | null | null | q-bio.NC | null | We exhibit a mathematical framework to represent the neural dynamics at
cortical level. Our description of neural dynamics with columnar and functional
modularity, named fibre bundle representation (FBM) method, is based both on
neuroscience and informatics, whereas they correspond with the conventional
formulas in statistical physics. In spite of complex interactions in neural
circuitry and various cortical modification rules per models, some significant
factors determine the typical phenomena in cortical dynamics. The FBM
representation method reveals them plainly and gives profit in building or
analyzing the cortical dynamic models. Not only the similarity in formulas, the
cortical dynamics can share the statistical properties with other physical
systems, which validated in primary visual maps. We apply our method to
proposed models in visual map formations, in addition our suggestion using the
lateral interaction scheme. In this paper, we will show that the neural dynamic
procedures can be treated through conventional physics expressions and
theories.
| [
{
"created": "Mon, 31 May 2004 12:36:13 GMT",
"version": "v1"
},
{
"created": "Sat, 7 Aug 2004 08:29:48 GMT",
"version": "v2"
},
{
"created": "Wed, 17 Nov 2004 12:38:31 GMT",
"version": "v3"
}
] | 2007-05-23 | [
[
"Cho",
"Myoung Won",
""
],
[
"Kim",
"Seunghwan",
""
]
] | We exhibit a mathematical framework to represent the neural dynamics at cortical level. Our description of neural dynamics with columnar and functional modularity, named fibre bundle representation (FBM) method, is based both on neuroscience and informatics, whereas they correspond with the conventional formulas in statistical physics. In spite of complex interactions in neural circuitry and various cortical modification rules per models, some significant factors determine the typical phenomena in cortical dynamics. The FBM representation method reveals them plainly and gives profit in building or analyzing the cortical dynamic models. Not only the similarity in formulas, the cortical dynamics can share the statistical properties with other physical systems, which validated in primary visual maps. We apply our method to proposed models in visual map formations, in addition our suggestion using the lateral interaction scheme. In this paper, we will show that the neural dynamic procedures can be treated through conventional physics expressions and theories. |
1806.09963 | Elke Kirschbaum | Elke Kirschbaum, Manuel Hau{\ss}mann, Steffen Wolf, Hannah Sonntag,
Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred
A. Hamprecht | LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium
imaging videos | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuronal assemblies, loosely defined as subsets of neurons with reoccurring
spatio-temporally coordinated activation patterns, or "motifs", are thought to
be building blocks of neural representations and information processing. We
here propose LeMoNADe, a new exploratory data analysis method that facilitates
hunting for motifs in calcium imaging videos, the dominant microscopic
functional imaging modality in neurophysiology. Our nonparametric method
extracts motifs directly from videos, bypassing the difficult intermediate step
of spike extraction. Our technique augments variational autoencoders with a
discrete stochastic node, and we show in detail how a differentiable
reparametrization and relaxation can be used. An evaluation on simulated data,
with available ground truth, reveals excellent quantitative performance. In
real video data acquired from brain slices, with no ground truth available,
LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses
for more focused biological investigations.
| [
{
"created": "Tue, 26 Jun 2018 13:21:48 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Oct 2018 05:44:00 GMT",
"version": "v2"
},
{
"created": "Fri, 22 Feb 2019 12:03:29 GMT",
"version": "v3"
}
] | 2019-02-25 | [
[
"Kirschbaum",
"Elke",
""
],
[
"Haußmann",
"Manuel",
""
],
[
"Wolf",
"Steffen",
""
],
[
"Sonntag",
"Hannah",
""
],
[
"Schneider",
"Justus",
""
],
[
"Elzoheiry",
"Shehabeldin",
""
],
[
"Kann",
"Oliver",
""
... | Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting for motifs in calcium imaging videos, the dominant microscopic functional imaging modality in neurophysiology. Our nonparametric method extracts motifs directly from videos, bypassing the difficult intermediate step of spike extraction. Our technique augments variational autoencoders with a discrete stochastic node, and we show in detail how a differentiable reparametrization and relaxation can be used. An evaluation on simulated data, with available ground truth, reveals excellent quantitative performance. In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations. |
1908.11739 | Dmitry Melnikov | Dmitry Melnikov and Alyson B. F. Neves | A Continuous Effective Model of the Protein Dynamics | 5 pages, 4 figures. This paper contains a discussion of an effective
field theory model in application to description of the protein structure. A
longer version containing technical details was submitted to the hep-ph
archive | null | null | null | q-bio.BM cond-mat.soft hep-ph hep-th physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theory of elastic rods can be used to describe certain geometric and
topological properties of the DNA molecules. A similar effective field theory
approach was previously suggested to describe the conformations and dynamics of
proteins. In this letter we report a detailed study of the basic features of a
version of the proposed model, which assumes proteins to be very long
continuous curves. In the most appealing case, the model is based on a
potential with a pair of minima corresponding to helical and strand-like
configurations of the curves. It allows to derive several predictions about the
geometric features of the molecules, and we show that the predictions are
compatible with the phenomenology. While the helices represent the ground state
configurations, the abundance of beta strands is controlled by a parameter,
which can either completely suppress their presence in a molecule, or make them
abundant. The few-parameter model investigated in the letter rather represents
a universality class of protein molecules. Generalizations accounting for the
discrete nature and inhomogeneity of the molecules presumably allow to model
realistic cases.
| [
{
"created": "Fri, 30 Aug 2019 13:48:18 GMT",
"version": "v1"
}
] | 2019-09-04 | [
[
"Melnikov",
"Dmitry",
""
],
[
"Neves",
"Alyson B. F.",
""
]
] | The theory of elastic rods can be used to describe certain geometric and topological properties of the DNA molecules. A similar effective field theory approach was previously suggested to describe the conformations and dynamics of proteins. In this letter we report a detailed study of the basic features of a version of the proposed model, which assumes proteins to be very long continuous curves. In the most appealing case, the model is based on a potential with a pair of minima corresponding to helical and strand-like configurations of the curves. It allows to derive several predictions about the geometric features of the molecules, and we show that the predictions are compatible with the phenomenology. While the helices represent the ground state configurations, the abundance of beta strands is controlled by a parameter, which can either completely suppress their presence in a molecule, or make them abundant. The few-parameter model investigated in the letter rather represents a universality class of protein molecules. Generalizations accounting for the discrete nature and inhomogeneity of the molecules presumably allow to model realistic cases. |
2405.20523 | Glen Pridham | Glen Pridham, Karthik K. Tennankore, Kenneth Rockwood, George Worthen,
and Andrew D. Rutenberg | Systems-level health of patients living with end-stage kidney disease
using standard lab values | 50 pages (15 for main, 35 supplemental), 10 figures in main | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a systems-level analysis of end-stage kidney disease (ESKD) with a
dynamical network analysis of 14 commonly measured blood-based biomarkers in
patients undergoing regular haemodialysis. Utilizing a validated pipeline for
declining homeostatic systems, our approach learns a dynamical model together
with an invertible transformation that simplifies the behaviour of observed
biomarkers into natural variables. Within the natural variables, we identified
two distinct dynamical behaviours: (i) stochastic accumulation, the random
accumulation of abnormal values, and (ii) mallostasis, a deterministic drift
towards worse health. These behaviours are identified by persistent
fluctuations indicating weak stability, or a gradual shift in homeostatic set
point, respectively. Both lead to worsening natural variable values, making the
natural variables salient survival predictors with preferred directions of
increasing risk. When this worsening is transformed back into observable
biomarkers, it generates a coherent spectrum of worsening medical signs
characteristic of a medical syndrome. Specifically, we found that small modules
of natural variables corresponded to two existing syndromes commonly afflicting
ESKD patients: protein-energy wasting and sepsis. We also identified new
prospective syndromes. Our findings suggest that natural variables are robust,
systems-level biomarkers, capturing the complex, holistic changes in health
associated with ESKD.
| [
{
"created": "Thu, 30 May 2024 22:49:48 GMT",
"version": "v1"
}
] | 2024-06-03 | [
[
"Pridham",
"Glen",
""
],
[
"Tennankore",
"Karthik K.",
""
],
[
"Rockwood",
"Kenneth",
""
],
[
"Worthen",
"George",
""
],
[
"Rutenberg",
"Andrew D.",
""
]
] | We present a systems-level analysis of end-stage kidney disease (ESKD) with a dynamical network analysis of 14 commonly measured blood-based biomarkers in patients undergoing regular haemodialysis. Utilizing a validated pipeline for declining homeostatic systems, our approach learns a dynamical model together with an invertible transformation that simplifies the behaviour of observed biomarkers into natural variables. Within the natural variables, we identified two distinct dynamical behaviours: (i) stochastic accumulation, the random accumulation of abnormal values, and (ii) mallostasis, a deterministic drift towards worse health. These behaviours are identified by persistent fluctuations indicating weak stability, or a gradual shift in homeostatic set point, respectively. Both lead to worsening natural variable values, making the natural variables salient survival predictors with preferred directions of increasing risk. When this worsening is transformed back into observable biomarkers, it generates a coherent spectrum of worsening medical signs characteristic of a medical syndrome. Specifically, we found that small modules of natural variables corresponded to two existing syndromes commonly afflicting ESKD patients: protein-energy wasting and sepsis. We also identified new prospective syndromes. Our findings suggest that natural variables are robust, systems-level biomarkers, capturing the complex, holistic changes in health associated with ESKD. |
2103.13467 | Egor Alimpiev | Egor Alimpiev, Noah A Rosenberg | A compendium of covariances and correlation coefficients of coalescent
tree properties | null | null | 10.1016/j.tpb.2021.09.008 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Gene genealogies are frequently studied by measuring properties such as their
height ($H$), length ($L$), sum of external branches ($E$), sum of internal
branches ($I$), and mean of their two basal branches ($B$), and the coalescence
times that contribute to the other genealogical features ($T$). These tree
properties and their relationships can provide insight into the effects of
population-genetic processes on genealogies and genetic sequences. Here, under
the coalescent model, we study the 15 correlations among pairs of features of
genealogical trees: $H_n$, $L_n$, $E_n$, $I_n$, $B_n$, and $T_k$ for a sample
of size $n$, with $2 \leq k \leq n$. We report high correlations among $H_n$,
$L_n$, $I_n,$ and $B_n$, with all pairwise correlations of these quantities
having values greater than or equal to $\sqrt{6} [6 \zeta(3) + 6 - \pi^2] / (
\pi \sqrt{18 + 9\pi^2 - \pi^4}) \approx 0.84930$ in the limit as $n \rightarrow
\infty$. Although $E_n$ has an expectation of 2 for all $n$ and $H_n$ has
expectation 2 in the limit as $n \rightarrow \infty$, their limiting
correlation is 0. The results contribute toward understanding features of the
shapes of coalescent trees.
| [
{
"created": "Wed, 24 Mar 2021 19:52:43 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Nov 2021 21:55:48 GMT",
"version": "v2"
}
] | 2022-05-24 | [
[
"Alimpiev",
"Egor",
""
],
[
"Rosenberg",
"Noah A",
""
]
] | Gene genealogies are frequently studied by measuring properties such as their height ($H$), length ($L$), sum of external branches ($E$), sum of internal branches ($I$), and mean of their two basal branches ($B$), and the coalescence times that contribute to the other genealogical features ($T$). These tree properties and their relationships can provide insight into the effects of population-genetic processes on genealogies and genetic sequences. Here, under the coalescent model, we study the 15 correlations among pairs of features of genealogical trees: $H_n$, $L_n$, $E_n$, $I_n$, $B_n$, and $T_k$ for a sample of size $n$, with $2 \leq k \leq n$. We report high correlations among $H_n$, $L_n$, $I_n,$ and $B_n$, with all pairwise correlations of these quantities having values greater than or equal to $\sqrt{6} [6 \zeta(3) + 6 - \pi^2] / ( \pi \sqrt{18 + 9\pi^2 - \pi^4}) \approx 0.84930$ in the limit as $n \rightarrow \infty$. Although $E_n$ has an expectation of 2 for all $n$ and $H_n$ has expectation 2 in the limit as $n \rightarrow \infty$, their limiting correlation is 0. The results contribute toward understanding features of the shapes of coalescent trees. |
q-bio/0310024 | Stefan Wuchty | S. Wuchty, Z.N. Oltvai and A.-L. Barabasi | Evolutionary conservation of motif constituents within the yeast protein
interaction network | 16 pages, 1 figure, 2 tables | Nature Genetics, 35(2), 176-179, (2003) | null | null | q-bio.GN cond-mat | null | Understanding why some cellular components are conserved across species,
while others evolve rapidly is a key question of modern biology. Here we
demonstrate that in S. cerevisiae proteins organized in cohesive patterns of
interactions are conserved to a significantly higher degree than those that do
not participate in such motifs. We find that the conservation of proteins
within distinct topological motifs correlates with the motif's
inter-connectedness and function and also depends on the structure of the
overall interactome topology. These findings indicate that motifs may represent
evolutionary conserved topological units of cellular networks molded in
accordance with the specific biological function in which they participate.
| [
{
"created": "Sun, 19 Oct 2003 02:36:18 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Wuchty",
"S.",
""
],
[
"Oltvai",
"Z. N.",
""
],
[
"Barabasi",
"A. -L.",
""
]
] | Understanding why some cellular components are conserved across species, while others evolve rapidly is a key question of modern biology. Here we demonstrate that in S. cerevisiae proteins organized in cohesive patterns of interactions are conserved to a significantly higher degree than those that do not participate in such motifs. We find that the conservation of proteins within distinct topological motifs correlates with the motif's inter-connectedness and function and also depends on the structure of the overall interactome topology. These findings indicate that motifs may represent evolutionary conserved topological units of cellular networks molded in accordance with the specific biological function in which they participate. |
1601.00716 | Edward Baskerville | Sarah Cobey, Edward B. Baskerville | Limits to causal inference with state-space reconstruction for
infectious disease | 32 pages, 7 main figures, 10 supplemental figures | null | 10.1371/journal.pone.0169050 | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Infectious diseases are notorious for their complex dynamics, which make it
difficult to fit models to test hypotheses. Methods based on state-space
reconstruction have been proposed to infer causal interactions in noisy,
nonlinear dynamical systems. These "model-free" methods are collectively known
as convergent cross-mapping (CCM). Although CCM has theoretical support,
natural systems routinely violate its assumptions. To identify the practical
limits of causal inference under CCM, we simulated the dynamics of two pathogen
strains with varying interaction strengths. The original method of CCM is
extremely sensitive to periodic fluctuations, inferring interactions between
independent strains that oscillate with similar frequencies. This sensitivity
vanishes with alternative criteria for inferring causality. However, CCM
remains sensitive to high levels of process noise and changes to the
deterministic attractor. This sensitivity is problematic because it remains
challenging to gauge noise and dynamical changes in natural systems, including
the quality of reconstructed attractors that underlie cross-mapping. We
illustrate these challenges by analyzing time series of reportable childhood
infections in New York City and Chicago during the pre-vaccine era. We comment
on the statistical and conceptual challenges that currently limit the use of
state-space reconstruction in causal inference.
| [
{
"created": "Tue, 5 Jan 2016 02:14:11 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jan 2016 18:37:23 GMT",
"version": "v2"
},
{
"created": "Sat, 29 Oct 2016 20:19:59 GMT",
"version": "v3"
}
] | 2017-02-08 | [
[
"Cobey",
"Sarah",
""
],
[
"Baskerville",
"Edward B.",
""
]
] | Infectious diseases are notorious for their complex dynamics, which make it difficult to fit models to test hypotheses. Methods based on state-space reconstruction have been proposed to infer causal interactions in noisy, nonlinear dynamical systems. These "model-free" methods are collectively known as convergent cross-mapping (CCM). Although CCM has theoretical support, natural systems routinely violate its assumptions. To identify the practical limits of causal inference under CCM, we simulated the dynamics of two pathogen strains with varying interaction strengths. The original method of CCM is extremely sensitive to periodic fluctuations, inferring interactions between independent strains that oscillate with similar frequencies. This sensitivity vanishes with alternative criteria for inferring causality. However, CCM remains sensitive to high levels of process noise and changes to the deterministic attractor. This sensitivity is problematic because it remains challenging to gauge noise and dynamical changes in natural systems, including the quality of reconstructed attractors that underlie cross-mapping. We illustrate these challenges by analyzing time series of reportable childhood infections in New York City and Chicago during the pre-vaccine era. We comment on the statistical and conceptual challenges that currently limit the use of state-space reconstruction in causal inference. |
1109.3888 | Daniel Kelleher | Daniel J. Kelleher, Tyler M. Reese, Dylan T. Yott, Antoni Brzoska | Analysing properties of the C. Elegans neural network: mathematically
modeling a biological system | 24 Pages, 6 Figures | null | null | null | q-bio.NC physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The brain is one of the most studied and highly complex systems in the
biological world. It is the information center behind all vertebrate and most
invertebrate life, and thus has become a major focus in current research. While
many of these studies have concentrated on studying the brain directly, our
focus is the structure of the brain itself: at its core an interconnected
network of nodes (neurons). A better understanding of the structural aspects of
the brain should elucidate some of its functional properties. In this paper we
analyze the brain of the nematode Caenorhabditis elegans. Consisting of only
302 neurons, it is one of the better-understood neural networks. Using a
Laplacian matrix of the 279-neuron "giant component" of the network, we use an
eigenvalue counting function to look for fractal-like self similarity. This
matrix representation is also used to plot (in eigenfunction coordinates) both
2 and 3 dimensional visualizations of the neural network. Further analysis
examines the small-world properties of the system, including average path
length and clustering coefficient. We then test for localization of
eigenfunctions, using graph energy and spacial variance. To better understand
these results, all of these calculations are also performed on random networks,
branching trees, and known fractals, as well as fractals which have been
"rewired" to have small-world properties. This analysis is one of many
stepping-stones in the research of neural networks. While many of the
structures and functions within the brain are known, understanding how the two
interact is also important. A firmer grasp on the structural properties of the
neural network is a key step in this process
| [
{
"created": "Sun, 18 Sep 2011 17:03:11 GMT",
"version": "v1"
}
] | 2015-03-19 | [
[
"Kelleher",
"Daniel J.",
""
],
[
"Reese",
"Tyler M.",
""
],
[
"Yott",
"Dylan T.",
""
],
[
"Brzoska",
"Antoni",
""
]
] | The brain is one of the most studied and highly complex systems in the biological world. It is the information center behind all vertebrate and most invertebrate life, and thus has become a major focus in current research. While many of these studies have concentrated on studying the brain directly, our focus is the structure of the brain itself: at its core an interconnected network of nodes (neurons). A better understanding of the structural aspects of the brain should elucidate some of its functional properties. In this paper we analyze the brain of the nematode Caenorhabditis elegans. Consisting of only 302 neurons, it is one of the better-understood neural networks. Using a Laplacian matrix of the 279-neuron "giant component" of the network, we use an eigenvalue counting function to look for fractal-like self similarity. This matrix representation is also used to plot (in eigenfunction coordinates) both 2 and 3 dimensional visualizations of the neural network. Further analysis examines the small-world properties of the system, including average path length and clustering coefficient. We then test for localization of eigenfunctions, using graph energy and spacial variance. To better understand these results, all of these calculations are also performed on random networks, branching trees, and known fractals, as well as fractals which have been "rewired" to have small-world properties. This analysis is one of many stepping-stones in the research of neural networks. While many of the structures and functions within the brain are known, understanding how the two interact is also important. A firmer grasp on the structural properties of the neural network is a key step in this process |
2404.17091 | Vladimir Reukov | Ummay Mowshome Jahan, Brianna Blevins, Sergiy Minko, Vladimir Reukov | Advancing Biomedical Applications: Antioxidant and Biocompatible Cerium
Oxide Nanoparticle-Integrated Poly-{\epsilon}- caprolactone Fibers | null | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reactive oxygen species (ROS), which are expressed at high levels in many
diseases, can be scavenged by cerium oxide nanoparticles (CeO2NPs). CeO2NPs can
cause significant cytotoxicity when administered directly to cells, but this
cytotoxicity can be reduced if CeO2NPs can be encapsulated in biocompatible
polymers. In this study, CeO2NPs were synthesized using a one-stage process,
then purified, characterized, and then encapsulated into an electrospun
poly-{\epsilon}-caprolactone (PCL) scaffold. The direct administration of
CeO2NPs to RAW 264.7 Macrophages resulted in reduced ROS levels but lower cell
viability. Conversely, the encapsulation of nanoceria in a PCL scaffold was
shown to lower ROS levels and improve cell survival. The study demonstrated an
effective technique for encapsulating nanoceria in PCL fiber and confirmed its
biocompatibility and efficacy. This system has the potential to be utilized for
developing tissue engineering scaffolds, targeted delivery of therapeutic
CeO2NPs, wound healing, and other biomedical applications.
| [
{
"created": "Fri, 26 Apr 2024 00:52:43 GMT",
"version": "v1"
}
] | 2024-04-29 | [
[
"Jahan",
"Ummay Mowshome",
""
],
[
"Blevins",
"Brianna",
""
],
[
"Minko",
"Sergiy",
""
],
[
"Reukov",
"Vladimir",
""
]
] | Reactive oxygen species (ROS), which are expressed at high levels in many diseases, can be scavenged by cerium oxide nanoparticles (CeO2NPs). CeO2NPs can cause significant cytotoxicity when administered directly to cells, but this cytotoxicity can be reduced if CeO2NPs can be encapsulated in biocompatible polymers. In this study, CeO2NPs were synthesized using a one-stage process, then purified, characterized, and then encapsulated into an electrospun poly-{\epsilon}-caprolactone (PCL) scaffold. The direct administration of CeO2NPs to RAW 264.7 Macrophages resulted in reduced ROS levels but lower cell viability. Conversely, the encapsulation of nanoceria in a PCL scaffold was shown to lower ROS levels and improve cell survival. The study demonstrated an effective technique for encapsulating nanoceria in PCL fiber and confirmed its biocompatibility and efficacy. This system has the potential to be utilized for developing tissue engineering scaffolds, targeted delivery of therapeutic CeO2NPs, wound healing, and other biomedical applications. |
1812.09538 | Andrea De Martino | Araks Martirosyan, Marco Del Giudice, Chiara Enrico Bena, Andrea
Pagnani, Carla Bosia, Andrea De Martino | Kinetic modelling of competition and depletion of shared miRNAs by
competing endogenous RNAs | review article, 29 pages, 7 figures | null | null | null | q-bio.MN cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-conding RNAs play a key role in the post-transcriptional regulation of
mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact
with their target RNAs through protein-mediated, sequence-specific binding,
giving rise to extended and highly heterogeneous miRNA-RNA interaction
networks. Within such networks, competition to bind miRNAs can generate an
effective positive coupling between their targets. Competing endogenous RNAs
(ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk.
Albeit potentially weak, ceRNA interactions can occur both dynamically,
affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA
networks as a whole can be implicated in the composition of the cell's
proteome. Many features of ceRNA interactions, including the conditions under
which they become significant, can be unraveled by mathematical and in silico
models. We review the understanding of the ceRNA effect obtained within such
frameworks, focusing on the methods employed to quantify it, its role in the
processing of gene expression noise, and how network topology can determine its
reach.
| [
{
"created": "Sat, 22 Dec 2018 14:54:11 GMT",
"version": "v1"
}
] | 2018-12-27 | [
[
"Martirosyan",
"Araks",
""
],
[
"Del Giudice",
"Marco",
""
],
[
"Bena",
"Chiara Enrico",
""
],
[
"Pagnani",
"Andrea",
""
],
[
"Bosia",
"Carla",
""
],
[
"De Martino",
"Andrea",
""
]
] | Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach. |
1401.5459 | Siqi Tian | Siqi Tian, Pablo Cordero, Wipapat Kladwang, Rhiju Das | Correcting a SHAPE-directed RNA structure by a mutate-map-rescue
approach | null | null | 10.1261/rna.044321.114 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The three-dimensional conformations of non-coding RNAs underpin their
biochemical functions but have largely eluded experimental characterization.
Here, we report that integrating a classic mutation/rescue strategy with
high-throughput chemical mapping enables rapid RNA structure inference with
unusually strong validation. We revisit a paradigmatic 16S rRNA domain for
which SHAPE (selective 2`-hydroxyl acylation with primer extension) suggested a
conformational change between apo- and holo-ribosome conformations.
Computational support estimates, data from alternative chemical probes, and
mutate-and-map (M2) experiments expose limitations of prior methodology and
instead give a near-crystallographic secondary structure. Systematic
interrogation of single base pairs via a high-throughput mutation/rescue
approach then permits incisive validation and refinement of the M2-based
secondary structure and further uncovers the functional conformation as an
excited state (25+/-5% population) accessible via a single-nucleotide register
shift. These results correct an erroneous SHAPE inference of a ribosomal
conformational change and suggest a general mutate-map-rescue approach for
dissecting RNA dynamic structure landscapes.
| [
{
"created": "Tue, 21 Jan 2014 20:47:46 GMT",
"version": "v1"
}
] | 2015-06-09 | [
[
"Tian",
"Siqi",
""
],
[
"Cordero",
"Pablo",
""
],
[
"Kladwang",
"Wipapat",
""
],
[
"Das",
"Rhiju",
""
]
] | The three-dimensional conformations of non-coding RNAs underpin their biochemical functions but have largely eluded experimental characterization. Here, we report that integrating a classic mutation/rescue strategy with high-throughput chemical mapping enables rapid RNA structure inference with unusually strong validation. We revisit a paradigmatic 16S rRNA domain for which SHAPE (selective 2`-hydroxyl acylation with primer extension) suggested a conformational change between apo- and holo-ribosome conformations. Computational support estimates, data from alternative chemical probes, and mutate-and-map (M2) experiments expose limitations of prior methodology and instead give a near-crystallographic secondary structure. Systematic interrogation of single base pairs via a high-throughput mutation/rescue approach then permits incisive validation and refinement of the M2-based secondary structure and further uncovers the functional conformation as an excited state (25+/-5% population) accessible via a single-nucleotide register shift. These results correct an erroneous SHAPE inference of a ribosomal conformational change and suggest a general mutate-map-rescue approach for dissecting RNA dynamic structure landscapes. |
1207.5184 | Himanshu Asnani | Himanshu Asnani, Dinesh Bharadia, Mainak Chowdhury, Idoia Ochoa, Itai
Sharon and Tsachy Weissman | Lossy Compression of Quality Values via Rate Distortion Theory | 7 Pages, 8 Figures, Submitted to Bioinformatics | null | null | null | q-bio.GN cs.IT math.IT q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Next Generation Sequencing technologies revolutionized many
fields in biology by enabling the fast and cheap sequencing of large amounts of
genomic data. The ever increasing sequencing capacities enabled by current
sequencing machines hold a lot of promise as for the future applications of
these technologies, but also create increasing computational challenges related
to the analysis and storage of these data. A typical sequencing data file may
occupy tens or even hundreds of gigabytes of disk space, prohibitively large
for many users. Raw sequencing data consists of both the DNA sequences (reads)
and per-base quality values that indicate the level of confidence in the
readout of these sequences. Quality values account for about half of the
required disk space in the commonly used FASTQ format and therefore their
compression can significantly reduce storage requirements and speed up analysis
and transmission of these data.
Results: In this paper we present a framework for the lossy compression of
the quality value sequences of genomic read files. Numerical experiments with
reference based alignment using these quality values suggest that we can
achieve significant compression with little compromise in performance for
several downstream applications of interest, as is consistent with our
theoretical analysis. Our framework also allows compression in a regime - below
one bit per quality value - for which there are no existing compressors.
| [
{
"created": "Sat, 21 Jul 2012 21:49:49 GMT",
"version": "v1"
}
] | 2012-07-24 | [
[
"Asnani",
"Himanshu",
""
],
[
"Bharadia",
"Dinesh",
""
],
[
"Chowdhury",
"Mainak",
""
],
[
"Ochoa",
"Idoia",
""
],
[
"Sharon",
"Itai",
""
],
[
"Weissman",
"Tsachy",
""
]
] | Motivation: Next Generation Sequencing technologies revolutionized many fields in biology by enabling the fast and cheap sequencing of large amounts of genomic data. The ever increasing sequencing capacities enabled by current sequencing machines hold a lot of promise as for the future applications of these technologies, but also create increasing computational challenges related to the analysis and storage of these data. A typical sequencing data file may occupy tens or even hundreds of gigabytes of disk space, prohibitively large for many users. Raw sequencing data consists of both the DNA sequences (reads) and per-base quality values that indicate the level of confidence in the readout of these sequences. Quality values account for about half of the required disk space in the commonly used FASTQ format and therefore their compression can significantly reduce storage requirements and speed up analysis and transmission of these data. Results: In this paper we present a framework for the lossy compression of the quality value sequences of genomic read files. Numerical experiments with reference based alignment using these quality values suggest that we can achieve significant compression with little compromise in performance for several downstream applications of interest, as is consistent with our theoretical analysis. Our framework also allows compression in a regime - below one bit per quality value - for which there are no existing compressors. |
1308.2190 | Matthew Jastrebski | Matthew Jastrebski, Joan Ponce, Daniel Burkow, Oyita Udiani, Dr. Leon
Arriola | Ticks, Deer, Mice, and a Touch of Sensitivity: A Recipe for Controlling
Lyme Disease | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Borrelia burgdorferi sensu stricto is a bacterial spirochete prevalent in the
Northeastern United States that causes Lyme disease. Lyme disease is the most
common arthropod-borne disease in the United States; affecting mice, deer,
humans and other mammals. The disease is spread by Ixodes Scapularis, a species
of tick whose primary food source are deer and mice. Reducing the population of
ticks feeding on both large and small mammals below some critical threshold can
decrease the prevalence of Lyme disease among humans. A simplified,
six-dimensional Susceptible-Infected, SI, model is used to capture the
mice-deer-tick dynamics while considering the impact of varying
population-specific death rates on infected population size. We analyzed the
stability of the models two equilibria, the unstable disease free equilibrium
and the endemic equilibrium. Static forward sensitivity analysis is conducted
on the basic reproduction number and the endemic equilibrium. A dynamic
approach was explored to observe change in the sensitivity of the death rates
over time. These analyses were conducted to determine the efficacy of changing
death rates in order to reduce prevalence of Lyme disease.
| [
{
"created": "Fri, 26 Jul 2013 20:53:28 GMT",
"version": "v1"
}
] | 2013-08-12 | [
[
"Jastrebski",
"Matthew",
""
],
[
"Ponce",
"Joan",
""
],
[
"Burkow",
"Daniel",
""
],
[
"Udiani",
"Oyita",
""
],
[
"Arriola",
"Dr. Leon",
""
]
] | Borrelia burgdorferi sensu stricto is a bacterial spirochete prevalent in the Northeastern United States that causes Lyme disease. Lyme disease is the most common arthropod-borne disease in the United States; affecting mice, deer, humans and other mammals. The disease is spread by Ixodes Scapularis, a species of tick whose primary food source are deer and mice. Reducing the population of ticks feeding on both large and small mammals below some critical threshold can decrease the prevalence of Lyme disease among humans. A simplified, six-dimensional Susceptible-Infected, SI, model is used to capture the mice-deer-tick dynamics while considering the impact of varying population-specific death rates on infected population size. We analyzed the stability of the models two equilibria, the unstable disease free equilibrium and the endemic equilibrium. Static forward sensitivity analysis is conducted on the basic reproduction number and the endemic equilibrium. A dynamic approach was explored to observe change in the sensitivity of the death rates over time. These analyses were conducted to determine the efficacy of changing death rates in order to reduce prevalence of Lyme disease. |
2104.00817 | Hyunjin Shim | Hyunjin Shim, Haridha Shivram, Shufei Lei, Jennifer A. Doudna, Jillian
F. Banfield | Diverse ATPase proteins in mobilomes constitute a large potential sink
for prokaryotic host ATP | 11 pages, 5 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Prokaryote mobilome genomes rely on host machineries for survival and
replication. Given that mobile genetic elements (MGEs) derive their energy from
host cells, we investigated the diversity of ATP-utilizing proteins in MGE
genomes to determine whether they might be associated with proteins that could
suppress related host proteins that consume host energy. A comprehensive search
of 353 huge phage genomes revealed that up to 9% of the proteins have ATPase
domains. For example, ATPase proteins constitute ~3% of the genomes of Lak
phages with ~550 kbp genomes that occur in the microbiomes of humans and other
animals. Statistical analysis shows the number of ATPase proteins increases
linearly with genome length, consistent with a large sink for host ATP during
replication of megaphages. Using metagenomic data from diverse environments, we
found 505 mobilome proteins with ATPase domains fused to diverse functional
domains. Among these composite ATPase proteins, 61.6% have known functional
domains that could contribute to host energy diversion during the mobilome life
cycle. As many have domains that are known to interact with nucleic acids and
proteins, we infer that numerous ATPase proteins are used during replication
and for protection from host immune systems. We found a set of uncharacterized
ATPase proteins with nuclease and protease activities, displaying unique domain
architectures that are energy intensive based on the presence of multiple
ATPase domains. In many cases, these composite ATPase proteins genomically
co-localize with small proteins in genomic contexts that are reminiscent of
toxin-antitoxin systems. Small proteins that function as inhibitors may be a
common strategy for control of cellular processes, thus could inspire the
development of new nucleic acid and protein manipulation tools, with diverse
biotechnological applications.
| [
{
"created": "Fri, 2 Apr 2021 00:07:06 GMT",
"version": "v1"
}
] | 2021-04-05 | [
[
"Shim",
"Hyunjin",
""
],
[
"Shivram",
"Haridha",
""
],
[
"Lei",
"Shufei",
""
],
[
"Doudna",
"Jennifer A.",
""
],
[
"Banfield",
"Jillian F.",
""
]
] | Prokaryote mobilome genomes rely on host machineries for survival and replication. Given that mobile genetic elements (MGEs) derive their energy from host cells, we investigated the diversity of ATP-utilizing proteins in MGE genomes to determine whether they might be associated with proteins that could suppress related host proteins that consume host energy. A comprehensive search of 353 huge phage genomes revealed that up to 9% of the proteins have ATPase domains. For example, ATPase proteins constitute ~3% of the genomes of Lak phages with ~550 kbp genomes that occur in the microbiomes of humans and other animals. Statistical analysis shows the number of ATPase proteins increases linearly with genome length, consistent with a large sink for host ATP during replication of megaphages. Using metagenomic data from diverse environments, we found 505 mobilome proteins with ATPase domains fused to diverse functional domains. Among these composite ATPase proteins, 61.6% have known functional domains that could contribute to host energy diversion during the mobilome life cycle. As many have domains that are known to interact with nucleic acids and proteins, we infer that numerous ATPase proteins are used during replication and for protection from host immune systems. We found a set of uncharacterized ATPase proteins with nuclease and protease activities, displaying unique domain architectures that are energy intensive based on the presence of multiple ATPase domains. In many cases, these composite ATPase proteins genomically co-localize with small proteins in genomic contexts that are reminiscent of toxin-antitoxin systems. Small proteins that function as inhibitors may be a common strategy for control of cellular processes, thus could inspire the development of new nucleic acid and protein manipulation tools, with diverse biotechnological applications. |
1905.03594 | Booma Sowkarthiga Balasubramani | Booma Sowkarthiga Balasubramani, Marco Nanni, Shin Imai, Isabel F.
Cruz | The Identification and Analysis of Indicators for Predicting Malarial
Incidence in Zimbabwe | 8 pages, 4 figures | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With over 50% of the country's population at risk of contracting malaria
despite the introduction of several measures to combat the disease, Zimbabwe is
one of the eight countries in the Malaria Elimination 8 platform of the
Southern African Development Community. Various indicators, including
temperature, population distribution, land cover, and access to hospitals
affect the incidence and spread of this disease. In this paper, we consider
different such indicators and present our analysis of their interaction (e.g.,
how the Plasmodium falciparum Parasite Rate (PfPR) affects the sickle cell
trait) and their effect on malaria incidence in Zimbabwe. We also discuss the
results of our preliminary experiments on predictive analytics of malaria
incidence based on the indicators we have considered.
| [
{
"created": "Tue, 7 May 2019 20:17:01 GMT",
"version": "v1"
}
] | 2019-05-10 | [
[
"Balasubramani",
"Booma Sowkarthiga",
""
],
[
"Nanni",
"Marco",
""
],
[
"Imai",
"Shin",
""
],
[
"Cruz",
"Isabel F.",
""
]
] | With over 50% of the country's population at risk of contracting malaria despite the introduction of several measures to combat the disease, Zimbabwe is one of the eight countries in the Malaria Elimination 8 platform of the Southern African Development Community. Various indicators, including temperature, population distribution, land cover, and access to hospitals affect the incidence and spread of this disease. In this paper, we consider different such indicators and present our analysis of their interaction (e.g., how the Plasmodium falciparum Parasite Rate (PfPR) affects the sickle cell trait) and their effect on malaria incidence in Zimbabwe. We also discuss the results of our preliminary experiments on predictive analytics of malaria incidence based on the indicators we have considered. |
1511.05478 | Hilde Wilkinson-Herbots Dr | Hilde Wilkinson-Herbots | A fast method to estimate speciation parameters in a model of isolation
with an initial period of gene flow and to test alternative evolutionary
scenarios | 16 pages, 5 figures, 3 tables, ancillary file: computer code in R | null | null | null | q-bio.PE q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a model of "isolation with an initial period of migration" (IIM),
where an ancestral population instantaneously split into two descendant
populations which exchanged migrants symmetrically at a constant rate for a
period of time but which are now completely isolated from each other. A method
of Maximum Likelihood estimation of the parameters of the model is implemented,
for data consisting of the number of nucleotide differences between two DNA
sequences at each of a large number of independent loci, using the explicit
analytical expressions for the likelihood obtained in Wilkinson-Herbots (2012).
The method is demonstrated on a large set of DNA sequence data from two species
of Drosophila, as well as on simulated data. The method is extremely fast,
returning parameter estimates in less than 1 minute for a data set consisting
of the numbers of differences between pairs of sequences from 10,000s of loci,
or in a small fraction of a second if all loci are trimmed to the same
estimated mutation rate. It is also illustrated how the maximized likelihood
can be used to quickly distinguish between competing models describing
alternative evolutionary scenarios, either by comparing AIC scores or by means
of likelihood ratio tests. The present implementation is for a simple version
of the model, but various extensions are possible and are briefly discussed.
| [
{
"created": "Tue, 17 Nov 2015 17:28:18 GMT",
"version": "v1"
}
] | 2015-11-18 | [
[
"Wilkinson-Herbots",
"Hilde",
""
]
] | We consider a model of "isolation with an initial period of migration" (IIM), where an ancestral population instantaneously split into two descendant populations which exchanged migrants symmetrically at a constant rate for a period of time but which are now completely isolated from each other. A method of Maximum Likelihood estimation of the parameters of the model is implemented, for data consisting of the number of nucleotide differences between two DNA sequences at each of a large number of independent loci, using the explicit analytical expressions for the likelihood obtained in Wilkinson-Herbots (2012). The method is demonstrated on a large set of DNA sequence data from two species of Drosophila, as well as on simulated data. The method is extremely fast, returning parameter estimates in less than 1 minute for a data set consisting of the numbers of differences between pairs of sequences from 10,000s of loci, or in a small fraction of a second if all loci are trimmed to the same estimated mutation rate. It is also illustrated how the maximized likelihood can be used to quickly distinguish between competing models describing alternative evolutionary scenarios, either by comparing AIC scores or by means of likelihood ratio tests. The present implementation is for a simple version of the model, but various extensions are possible and are briefly discussed. |
2205.10605 | Li Yang | Li Yang, Zhibin He, Changhe Li, Junwei Han, Dajiang Zhu, Tianming Liu,
Tuo Zhang | Brain Cortical Functional Gradients Predict Cortical Folding Patterns
via Attention Mesh Convolution | null | null | null | null | q-bio.NC cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | Since gyri and sulci, two basic anatomical building blocks of cortical
folding patterns, were suggested to bear different functional roles, a precise
mapping from brain function to gyro-sulcal patterns can provide profound
insights into both biological and artificial neural networks. However, there
lacks a generic theory and effective computational model so far, due to the
highly nonlinear relation between them, huge inter-individual variabilities and
a sophisticated description of brain function regions/networks distribution as
mosaics, such that spatial patterning of them has not been considered. we
adopted brain functional gradients derived from resting-state fMRI to embed the
"gradual" change of functional connectivity patterns, and developed a novel
attention mesh convolution model to predict cortical gyro-sulcal segmentation
maps on individual brains. The convolution on mesh considers the spatial
organization of functional gradients and folding patterns on a cortical sheet
and the newly designed channel attention block enhances the interpretability of
the contribution of different functional gradients to cortical folding
prediction. Experiments show that the prediction performance via our model
outperforms other state-of-the-art models. In addition, we found that the
dominant functional gradients contribute less to folding prediction. On the
activation maps of the last layer, some well-studied cortical landmarks are
found on the borders of, rather than within, the highly activated regions.
These results and findings suggest that a specifically designed artificial
neural network can improve the precision of the mapping between brain functions
and cortical folding patterns, and can provide valuable insight of brain
anatomy-function relation for neuroscience.
| [
{
"created": "Sat, 21 May 2022 14:08:53 GMT",
"version": "v1"
}
] | 2022-05-24 | [
[
"Yang",
"Li",
""
],
[
"He",
"Zhibin",
""
],
[
"Li",
"Changhe",
""
],
[
"Han",
"Junwei",
""
],
[
"Zhu",
"Dajiang",
""
],
[
"Liu",
"Tianming",
""
],
[
"Zhang",
"Tuo",
""
]
] | Since gyri and sulci, two basic anatomical building blocks of cortical folding patterns, were suggested to bear different functional roles, a precise mapping from brain function to gyro-sulcal patterns can provide profound insights into both biological and artificial neural networks. However, there lacks a generic theory and effective computational model so far, due to the highly nonlinear relation between them, huge inter-individual variabilities and a sophisticated description of brain function regions/networks distribution as mosaics, such that spatial patterning of them has not been considered. we adopted brain functional gradients derived from resting-state fMRI to embed the "gradual" change of functional connectivity patterns, and developed a novel attention mesh convolution model to predict cortical gyro-sulcal segmentation maps on individual brains. The convolution on mesh considers the spatial organization of functional gradients and folding patterns on a cortical sheet and the newly designed channel attention block enhances the interpretability of the contribution of different functional gradients to cortical folding prediction. Experiments show that the prediction performance via our model outperforms other state-of-the-art models. In addition, we found that the dominant functional gradients contribute less to folding prediction. On the activation maps of the last layer, some well-studied cortical landmarks are found on the borders of, rather than within, the highly activated regions. These results and findings suggest that a specifically designed artificial neural network can improve the precision of the mapping between brain functions and cortical folding patterns, and can provide valuable insight of brain anatomy-function relation for neuroscience. |
1911.08612 | Casey Cole | Casey A Cole, Caleb Parks, Julian Rachele, Homayoun Valafar | Improvements of the REDCRAFT Software Package | 7 pages, 5 figures, Int'l Conf. Bioinformatics and Computational
Biology (BIOCOMP'19), Las Vegas, NV, August 2019 | null | null | null | q-bio.BM cs.OH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional approaches to elucidation of protein structures by NMR
spectroscopy rely on distance restraints also known as nuclear Overhauser
effects (NOEs). The use of NOEs as the primary source of structure
determination by NMR spectroscopy is time consuming and expensive. Residual
Dipolar Couplings (RDCs) have become an alternate approach for structure
calculation by NMR spectroscopy. In previous works, the software package
REDCRAFT has been presented as a means of harnessing the information containing
in RDCs for structure calculation of proteins. In this work, we present
significant improvements to the REDCRAFT package including: refinement of the
decimation procedure, the inclusion of graphical user interface, adoption of
NEF standards, and addition of scripts for enhanced protein modeling options.
The improvements to REDCRAFT have resulted in the ability to fold proteins that
the previous versions were unable to fold. For instance, we report the results
of folding of the protein 1A1Z in the presence of highly erroneous data.
| [
{
"created": "Tue, 19 Nov 2019 22:18:24 GMT",
"version": "v1"
}
] | 2019-11-21 | [
[
"Cole",
"Casey A",
""
],
[
"Parks",
"Caleb",
""
],
[
"Rachele",
"Julian",
""
],
[
"Valafar",
"Homayoun",
""
]
] | Traditional approaches to elucidation of protein structures by NMR spectroscopy rely on distance restraints also known as nuclear Overhauser effects (NOEs). The use of NOEs as the primary source of structure determination by NMR spectroscopy is time consuming and expensive. Residual Dipolar Couplings (RDCs) have become an alternate approach for structure calculation by NMR spectroscopy. In previous works, the software package REDCRAFT has been presented as a means of harnessing the information containing in RDCs for structure calculation of proteins. In this work, we present significant improvements to the REDCRAFT package including: refinement of the decimation procedure, the inclusion of graphical user interface, adoption of NEF standards, and addition of scripts for enhanced protein modeling options. The improvements to REDCRAFT have resulted in the ability to fold proteins that the previous versions were unable to fold. For instance, we report the results of folding of the protein 1A1Z in the presence of highly erroneous data. |
2107.09761 | Lee Altenberg Ph.D. | Lee Altenberg, Susanne Still, and Christopher J. Watkins | The Evolution of Imitation Without Cultural Transmission | 31 pages, 15 figures, 1 table. v2: Miscellaneous revisions | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution and function of imitation have always been placed within the
confines of animal learning and associated with its crucial role in cultural
transmission and cultural evolution. Can imitation evolve as a form of
phenotypic plasticity in the absence of cultural transmission, in phenotypes
beyond behavior?
We investigate a model in which imitation is unbundled from cultural
transmission: an organism's adult phenotype is plastically altered by its
experiences as a juvenile of other juveniles' genetically determined traits.
The only information transmitted between generations is genetic.
We find that during a period of directional selection towards a phenotypic
optimum, natural selection favors modifiers which cause an organism to bias its
plastic phenotype in the direction opposite to the mean phenotype of the
population -- anti-imitation. As the population approaches the phenotypic
optimum and shifts into stabilizing selection, selection on the modifier
reverses and favors strong imitation of the population mean. Imitation can
evolve to overshoot the target and produce an evolutionary pathology where mean
fitness decreases. When purifying selection for an extreme phenotype is
modeled, only selection for anti-imitation occurs, even at a mutation-selection
balance.
Imitation and anti-imitation emerge from these models in the absence of any
goal representation, cognitive understanding of its purpose, or discernment of
any kind. These theoretical outcomes are all novel evolutionary and biological
phenomena, and we discuss their implications.
| [
{
"created": "Tue, 20 Jul 2021 20:45:57 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Oct 2021 08:44:22 GMT",
"version": "v2"
}
] | 2021-10-22 | [
[
"Altenberg",
"Lee",
""
],
[
"Still",
"Susanne",
""
],
[
"Watkins",
"Christopher J.",
""
]
] | The evolution and function of imitation have always been placed within the confines of animal learning and associated with its crucial role in cultural transmission and cultural evolution. Can imitation evolve as a form of phenotypic plasticity in the absence of cultural transmission, in phenotypes beyond behavior? We investigate a model in which imitation is unbundled from cultural transmission: an organism's adult phenotype is plastically altered by its experiences as a juvenile of other juveniles' genetically determined traits. The only information transmitted between generations is genetic. We find that during a period of directional selection towards a phenotypic optimum, natural selection favors modifiers which cause an organism to bias its plastic phenotype in the direction opposite to the mean phenotype of the population -- anti-imitation. As the population approaches the phenotypic optimum and shifts into stabilizing selection, selection on the modifier reverses and favors strong imitation of the population mean. Imitation can evolve to overshoot the target and produce an evolutionary pathology where mean fitness decreases. When purifying selection for an extreme phenotype is modeled, only selection for anti-imitation occurs, even at a mutation-selection balance. Imitation and anti-imitation emerge from these models in the absence of any goal representation, cognitive understanding of its purpose, or discernment of any kind. These theoretical outcomes are all novel evolutionary and biological phenomena, and we discuss their implications. |
1310.7672 | Liane Gabora | Diederik Aerts, Jan Broekaert and Liane Gabora | Intrinsic Contextuality as the Crux of Consciousness | 7 pages | In (K. Yasue, Ed.) Fundamental Approaches to Consciousness, Tokyo
'99. John Benjamins Publishing Company | null | null | q-bio.NC quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A stream of conscious experience is extremely contextual; it is impacted by
sensory stimuli, drives and emotions, and the web of associations that link,
directly or indirectly, the subject of experience to other elements of the
individual's worldview. The contextuality of one's conscious experience both
enhances and constrains the contextuality of one's behavior. Since we cannot
know first-hand the conscious experience of another, it is by way of behavioral
contextuality that we make judgements about whether or not, and to what extent,
a system is conscious. Thus we believe that a deep understanding of
contextuality is vital to the study of consciousness. Methods have been
developed for handling contextuality in the microworld of quantum particles.
Our goal has been to investigate the extent to which these methods can be used
to analyze contextuality in conscious experience. This work is the fledgling
efforts of a recently-initiated interdisciplinary collaboration.
| [
{
"created": "Tue, 29 Oct 2013 03:28:56 GMT",
"version": "v1"
}
] | 2013-10-30 | [
[
"Aerts",
"Diederik",
""
],
[
"Broekaert",
"Jan",
""
],
[
"Gabora",
"Liane",
""
]
] | A stream of conscious experience is extremely contextual; it is impacted by sensory stimuli, drives and emotions, and the web of associations that link, directly or indirectly, the subject of experience to other elements of the individual's worldview. The contextuality of one's conscious experience both enhances and constrains the contextuality of one's behavior. Since we cannot know first-hand the conscious experience of another, it is by way of behavioral contextuality that we make judgements about whether or not, and to what extent, a system is conscious. Thus we believe that a deep understanding of contextuality is vital to the study of consciousness. Methods have been developed for handling contextuality in the microworld of quantum particles. Our goal has been to investigate the extent to which these methods can be used to analyze contextuality in conscious experience. This work is the fledgling efforts of a recently-initiated interdisciplinary collaboration. |
0706.0349 | Nabanita Dasgupta-Schubert | N. Dasgupta-Schubert (UMSNH), S. Alexander (SWU), L. Sommer (SWU), T.
Whelan (UTPA), R. Alfaro Cuevas Villanueva (UMSNH), M. E. Mendez Lopez
(UMSNH), M. W. Persans (UTPA) | The Light Quanta Modulated Physiological Response of Brassica Juncea
Seedlings Subjected to Ni(II) Stress | 9 pages, 7 figures, PDF file only. Based on a lecture presented by N.
Dasgupta-Schubert at the ISEB/ESEB/JSEB International Symposium on
Environmental Biotechnology in Leipzig, Germany, July 9-13, 2006 | Engineering in the Life Sciences, 7(3), 259-267 (2007) | null | null | q-bio.OT | null | This work is a study of the inter-relationship between parameters that
principally affect metal up-take in the plant. The relationships between the
concentration of metal in the growth medium, Cs, the concentration of metal
absorbed by the plant, Cp, and the total biomass achieved, M, all of which are
factors relevant to the efficiency of phytoremediation of the plant, have been
investigated via the macro-physiological response of Brassica juncea seedlings
to Ni(II) stress. The factorial growth experiments treated the Ni(II)
concentration in the agar gel and the diurnal light quanta (DLQ) as
independently variable parameters. Observations included the evidence of light
enhancement of Ni toxicity at the root as well as at the whole plant level, the
shoot mass index as a possible indicator of shoot metal sequestration in B.
juncea, the logarithmic variation of Cp with Cs and the power-law dependence of
M on Cp. The sum total of these observations indicate that for the metal
accumulator B. juncea with regard to its capacity to accumulate Ni, the overall
metabolic nature of the plant is important; neither rapid biomass increase nor
a high metal concentration capability favor the removal of high metal mass from
the medium, but rather the plant with the moderate photosynthetically driven
biomass growth and moderate metal concentrations demonstrated the ability to
remove the maximum mass of metal from the medium. The implications of these
observations in the context of the perceived need in phytoremediation
engineering to maximize Cp and M simultaneously in the same plant, are
discussed.
| [
{
"created": "Sun, 3 Jun 2007 21:00:45 GMT",
"version": "v1"
}
] | 2007-06-05 | [
[
"Dasgupta-Schubert",
"N.",
"",
"UMSNH"
],
[
"Alexander",
"S.",
"",
"SWU"
],
[
"Sommer",
"L.",
"",
"SWU"
],
[
"Whelan",
"T.",
"",
"UTPA"
],
[
"Villanueva",
"R. Alfaro Cuevas",
"",
"UMSNH"
],
[
"Lopez",
"M. E... | This work is a study of the inter-relationship between parameters that principally affect metal up-take in the plant. The relationships between the concentration of metal in the growth medium, Cs, the concentration of metal absorbed by the plant, Cp, and the total biomass achieved, M, all of which are factors relevant to the efficiency of phytoremediation of the plant, have been investigated via the macro-physiological response of Brassica juncea seedlings to Ni(II) stress. The factorial growth experiments treated the Ni(II) concentration in the agar gel and the diurnal light quanta (DLQ) as independently variable parameters. Observations included the evidence of light enhancement of Ni toxicity at the root as well as at the whole plant level, the shoot mass index as a possible indicator of shoot metal sequestration in B. juncea, the logarithmic variation of Cp with Cs and the power-law dependence of M on Cp. The sum total of these observations indicate that for the metal accumulator B. juncea with regard to its capacity to accumulate Ni, the overall metabolic nature of the plant is important; neither rapid biomass increase nor a high metal concentration capability favor the removal of high metal mass from the medium, but rather the plant with the moderate photosynthetically driven biomass growth and moderate metal concentrations demonstrated the ability to remove the maximum mass of metal from the medium. The implications of these observations in the context of the perceived need in phytoremediation engineering to maximize Cp and M simultaneously in the same plant, are discussed. |
1201.3823 | Nils Becker | Nils B. Becker and Rosalind J. Allen and Pieter Rein ten Wolde | Non-Stationary Forward Flux Sampling | 18 pages, 10 figures | null | 10.1063/1.4704810 | null | q-bio.MN cond-mat.stat-mech physics.comp-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new method, Non-Stationary Forward Flux Sampling, that allows
efficient simulation of rare events in both stationary and non-stationary
stochastic systems. The method uses stochastic branching and pruning to achieve
uniform sampling of trajectories in phase space and time, leading to accurate
estimates for time-dependent switching propensities and time-dependent phase
space probability densities. The method is suitable for equilibrium or
non-equilibrium systems, in or out of stationary state, including non-Markovian
or externally driven systems. We demonstrate the validity of the technique by
applying it to a one-dimensional barrier crossing problem that can be solved
exactly, and show its usefulness by applying it to the time-dependent switching
of a genetic toggle switch.
| [
{
"created": "Wed, 18 Jan 2012 15:27:41 GMT",
"version": "v1"
}
] | 2015-06-03 | [
[
"Becker",
"Nils B.",
""
],
[
"Allen",
"Rosalind J.",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | We present a new method, Non-Stationary Forward Flux Sampling, that allows efficient simulation of rare events in both stationary and non-stationary stochastic systems. The method uses stochastic branching and pruning to achieve uniform sampling of trajectories in phase space and time, leading to accurate estimates for time-dependent switching propensities and time-dependent phase space probability densities. The method is suitable for equilibrium or non-equilibrium systems, in or out of stationary state, including non-Markovian or externally driven systems. We demonstrate the validity of the technique by applying it to a one-dimensional barrier crossing problem that can be solved exactly, and show its usefulness by applying it to the time-dependent switching of a genetic toggle switch. |
1711.05596 | Rolf Bader | Rolf Bader | Pitch and timbre discrimination at wave-to-spike transition in the
cochlea | 12 pages, 6 figures | null | null | null | q-bio.NC eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new definition of musical pitch is proposed. A Finite-Difference Time
Domain (FDTM) model of the cochlea is used to calculate spike trains caused by
tone complexes and by a recorded classical guitar tone. All harmonic tone
complexes, musical notes, show a narrow-band Interspike Interval (ISI) pattern
at the respective fundamental frequency of the tone complex. Still this
fundamental frequency is not only present at the bark band holding the
respective best frequency of this fundamental frequency, but rather at all bark
bands driven by the tone complex partials. This is caused by drop-outs in the
basically regular, periodic spike train in the respective bands. These
drop-outs are caused by the energy distribution in the wave form, where time
spans of low energy are not able to drive spikes. The presence of the
fundamental periodicity in all bark bands can be interpreted as pitch. Contrary
to pitch, timbre is represented as a wide distribution of different ISIs over
bark bands. The definition of pitch is shown to also works with residue
pitches. The spike drop-outs in times of low energy of the wave form also cause
undertones, integer multiple subdivisions in periodicity, but in no case
overtones can appear. This might explain the musical minor scale, which was
proposed to be caused by undertones already in 1880 by Hugo Riemann, still
until now without knowledge about any physical realization of such undertones.
| [
{
"created": "Wed, 15 Nov 2017 14:44:27 GMT",
"version": "v1"
}
] | 2017-11-16 | [
[
"Bader",
"Rolf",
""
]
] | A new definition of musical pitch is proposed. A Finite-Difference Time Domain (FDTM) model of the cochlea is used to calculate spike trains caused by tone complexes and by a recorded classical guitar tone. All harmonic tone complexes, musical notes, show a narrow-band Interspike Interval (ISI) pattern at the respective fundamental frequency of the tone complex. Still this fundamental frequency is not only present at the bark band holding the respective best frequency of this fundamental frequency, but rather at all bark bands driven by the tone complex partials. This is caused by drop-outs in the basically regular, periodic spike train in the respective bands. These drop-outs are caused by the energy distribution in the wave form, where time spans of low energy are not able to drive spikes. The presence of the fundamental periodicity in all bark bands can be interpreted as pitch. Contrary to pitch, timbre is represented as a wide distribution of different ISIs over bark bands. The definition of pitch is shown to also works with residue pitches. The spike drop-outs in times of low energy of the wave form also cause undertones, integer multiple subdivisions in periodicity, but in no case overtones can appear. This might explain the musical minor scale, which was proposed to be caused by undertones already in 1880 by Hugo Riemann, still until now without knowledge about any physical realization of such undertones. |
1706.03060 | Colby Long | Elizabeth Gross and Colby Long | Distinguishing Phylogenetic Networks | 20 pages, 10 figures | null | null | null | q-bio.PE math.AG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic networks are becoming increasingly popular in phylogenetics
since they have the ability to describe a wider range of evolutionary events
than their tree counterparts. In this paper, we study Markov models on
phylogenetic networks and their associated geometry. We restrict our attention
to large-cycle networks, networks with a single undirected cycle of length at
least four. Using tools from computational algebraic geometry, we show that the
semi-directed network topology is generically identifiable for Jukes-Cantor
large-cycle network models.
| [
{
"created": "Fri, 9 Jun 2017 17:59:27 GMT",
"version": "v1"
}
] | 2017-06-12 | [
[
"Gross",
"Elizabeth",
""
],
[
"Long",
"Colby",
""
]
] | Phylogenetic networks are becoming increasingly popular in phylogenetics since they have the ability to describe a wider range of evolutionary events than their tree counterparts. In this paper, we study Markov models on phylogenetic networks and their associated geometry. We restrict our attention to large-cycle networks, networks with a single undirected cycle of length at least four. Using tools from computational algebraic geometry, we show that the semi-directed network topology is generically identifiable for Jukes-Cantor large-cycle network models. |
2011.00378 | Kevin Heng | Kevin Heng, Christian L. Althaus | The approximately universal shapes of epidemic curves in the
Susceptible-Exposed-Infectious-Recovered (SEIR) model | This is a post-peer-review, pre-copyedit version of an article
published in Scientific Reports (Springer Nature;
www.nature.com/scientificreports). The final authenticated version is
available online at: https://doi.org/10.1038/s41598-020-76563-8 | null | 10.1038/s41598-020-76563-8 | null | q-bio.PE physics.bio-ph physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Compartmental transmission models have become an invaluable tool to study the
dynamics of infectious diseases. The Susceptible-Infectious-Recovered (SIR)
model is known to have an exact semi-analytical solution. In the current study,
the approach of Harko et al. (2014) is generalised to obtain an approximate
semi-analytical solution of the Susceptible-Exposed-Infectious-Recovered (SEIR)
model. The SEIR model curves have nearly the same shapes as the SIR ones, but
with a stretch factor applied to them across time that is related to the ratio
of the incubation to infectious periods. This finding implies an approximate
characteristic timescale, scaled by this stretch factor, that is universal to
all SEIR models, which only depends on the basic reproduction number and
initial fraction of the population that is infectious.
| [
{
"created": "Sat, 31 Oct 2020 22:31:52 GMT",
"version": "v1"
}
] | 2020-11-03 | [
[
"Heng",
"Kevin",
""
],
[
"Althaus",
"Christian L.",
""
]
] | Compartmental transmission models have become an invaluable tool to study the dynamics of infectious diseases. The Susceptible-Infectious-Recovered (SIR) model is known to have an exact semi-analytical solution. In the current study, the approach of Harko et al. (2014) is generalised to obtain an approximate semi-analytical solution of the Susceptible-Exposed-Infectious-Recovered (SEIR) model. The SEIR model curves have nearly the same shapes as the SIR ones, but with a stretch factor applied to them across time that is related to the ratio of the incubation to infectious periods. This finding implies an approximate characteristic timescale, scaled by this stretch factor, that is universal to all SEIR models, which only depends on the basic reproduction number and initial fraction of the population that is infectious. |
1912.11994 | Yuuki Matsushita | Yuuki Matsushita and Kunihiko Kaneko | Homeorhesis in Waddington's Landscape by Epigenetic Feedback Regulation | 10 pages, 7 figures | Phys. Rev. Research 2, 023083 (2020) | 10.1103/PhysRevResearch.2.023083 | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multicellular organisms, cells differentiate into several distinct types
during early development. Determination of each cellular state, along with the
ratio of each cell type, as well as the developmental course during cell
differentiation are highly regulated processes that are robust to noise and
environmental perturbations throughout development. Waddington metaphorically
depicted this robustness as the epigenetic landscape in which the robustness of
each cellular state is represented by each valley in the landscape. This
robustness is now conceptualized as an approach toward an attractor in a
gene-expression dynamical system. However, there is still an incomplete
understanding of the origin of landscape change, which is accompanied by
branching of valleys that corresponds to the differentiation process. Recent
progress in developmental biology has unveiled the molecular processes involved
in epigenetic modification, which will be a key to understanding the nature of
slow landscape change. Nevertheless, the contribution of the interplay between
gene expression and epigenetic modification to robust landscape changes, known
as homeorhesis, remains elusive. Here, we introduce a theoretical model that
combines epigenetic modification with gene expression dynamics driven by a
regulatory network. In this model, epigenetic modification changes the
feasibility of expression, i.e., the threshold for expression dynamics, and a
slow positive-feedback process from expression to the threshold level is
introduced. Under such epigenetic feedback, several fixed-point attractors with
distinct expression patterns are generated hierarchically shaping the
epigenetic landscape with successive branching of valleys. This theory provides
a quantitative framework for explaining homeorhesis in development as
postulated by Waddington, based on dynamical-system theory with slow feedback
reinforcement.
| [
{
"created": "Fri, 27 Dec 2019 05:28:39 GMT",
"version": "v1"
}
] | 2020-05-06 | [
[
"Matsushita",
"Yuuki",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] | In multicellular organisms, cells differentiate into several distinct types during early development. Determination of each cellular state, along with the ratio of each cell type, as well as the developmental course during cell differentiation are highly regulated processes that are robust to noise and environmental perturbations throughout development. Waddington metaphorically depicted this robustness as the epigenetic landscape in which the robustness of each cellular state is represented by each valley in the landscape. This robustness is now conceptualized as an approach toward an attractor in a gene-expression dynamical system. However, there is still an incomplete understanding of the origin of landscape change, which is accompanied by branching of valleys that corresponds to the differentiation process. Recent progress in developmental biology has unveiled the molecular processes involved in epigenetic modification, which will be a key to understanding the nature of slow landscape change. Nevertheless, the contribution of the interplay between gene expression and epigenetic modification to robust landscape changes, known as homeorhesis, remains elusive. Here, we introduce a theoretical model that combines epigenetic modification with gene expression dynamics driven by a regulatory network. In this model, epigenetic modification changes the feasibility of expression, i.e., the threshold for expression dynamics, and a slow positive-feedback process from expression to the threshold level is introduced. Under such epigenetic feedback, several fixed-point attractors with distinct expression patterns are generated hierarchically shaping the epigenetic landscape with successive branching of valleys. This theory provides a quantitative framework for explaining homeorhesis in development as postulated by Waddington, based on dynamical-system theory with slow feedback reinforcement. |
1711.01421 | I\'nigo Arandia-Romero | I\~nigo Arandia-Romero, Seiji Tanabe, Jan Drugowitsch, Adam Kohn,
Rub\'en Moreno-Bote | Multiplicative and additive modulation of neuronal tuning with
population activity affects encoded information | Main text: 34 pages, 7 figures. Supplementary information: 13 pages,
7 figures | Neuron, 2016 , Volume 89 , Issue 6 , 1305 - 1316 | 10.1016/j.neuron.2016.01.044 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerous studies have shown that neuronal responses are modulated by stimulus
properties, and also by the state of the local network. However, little is
known about how activity fluctuations of neuronal populations modulate the
sensory tuning of cells and affect their encoded information. We found that
fluctuations in ongoing and stimulus-evoked population activity in primate
visual cortex modulate the tuning of neurons in a multiplicative and additive
manner. While distributed on a continuum, neurons with stronger multiplicative
effects tended to have less additive modulation, and vice versa. The
information encoded by multiplicatively-modulated neurons increased with
greater population activity, while that of additively-modulated neurons
decreased. These effects offset each other, so that population activity had
little effect on total information. Our results thus suggest that intrinsic
activity fluctuations may act as a "traffic light" that determines which subset
of neurons are most informative.
| [
{
"created": "Sat, 4 Nov 2017 10:41:34 GMT",
"version": "v1"
}
] | 2017-11-07 | [
[
"Arandia-Romero",
"Iñigo",
""
],
[
"Tanabe",
"Seiji",
""
],
[
"Drugowitsch",
"Jan",
""
],
[
"Kohn",
"Adam",
""
],
[
"Moreno-Bote",
"Rubén",
""
]
] | Numerous studies have shown that neuronal responses are modulated by stimulus properties, and also by the state of the local network. However, little is known about how activity fluctuations of neuronal populations modulate the sensory tuning of cells and affect their encoded information. We found that fluctuations in ongoing and stimulus-evoked population activity in primate visual cortex modulate the tuning of neurons in a multiplicative and additive manner. While distributed on a continuum, neurons with stronger multiplicative effects tended to have less additive modulation, and vice versa. The information encoded by multiplicatively-modulated neurons increased with greater population activity, while that of additively-modulated neurons decreased. These effects offset each other, so that population activity had little effect on total information. Our results thus suggest that intrinsic activity fluctuations may act as a "traffic light" that determines which subset of neurons are most informative. |
1708.07062 | Paul Roberts | Paul A. Roberts, Ryan M. Huebinger, Emma Keen, Anne-Marie Krachler,
Sara Jabbari | Predictive modelling of a novel anti-adhesion therapy to combat
bacterial colonisation of burn wounds | Comments: 34 pages, 11 figures | null | 10.1371/journal.pcbi.1006071 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the development of new classes of antibiotics slows, bacterial resistance
to existing antibiotics is becoming an increasing problem. A potential solution
is to develop treatment strategies with an alternative mode of action. We
consider one such strategy: anti-adhesion therapy. Whereas antibiotics act
directly upon bacteria, either killing them or inhibiting their growth,
anti-adhesion therapy impedes the binding of bacteria to host cells. This
prevents bacteria from deploying their arsenal of virulence mechanisms, while
simultaneously rendering them more susceptible to natural and artificial
clearance. In this paper, we consider a particular form of anti-adhesion
therapy, involving biomimetic multivalent adhesion molecule (MAM) 7 coupled
polystyrene microbeads, which competitively inhibit the binding of bacteria to
host cells. We develop a mathematical model, formulated as a system of ordinary
differential equations, to describe inhibitor treatment of a Pseudomonas
aeruginosa burn wound infection in the rat. Benchmarking our model against in
vivo data from an ongoing experimental programme, we use the model to explain
bacteria population dynamics and to predict the efficacy of a range of
treatment strategies, with the aim of improving treatment outcome. The model
consists of two physical compartments: the epithelium and the exudate. It is
found that, when effective in reducing the bacterial burden, inhibitor
treatment operates both by preventing bacteria from binding to the epithelium
and by reducing the flux of daughter cells from the epithelium into the
exudate. Our model predicts that inhibitor treatment cannot eliminate the
bacterial burden when used in isolation; however, when combined with regular or
continuous debridement of the exudate, elimination is theoretically possible.
Lastly, we present ways to improve therapeutic efficacy, as predicted by our
mathematical model.
| [
{
"created": "Thu, 10 Aug 2017 09:56:31 GMT",
"version": "v1"
}
] | 2018-07-04 | [
[
"Roberts",
"Paul A.",
""
],
[
"Huebinger",
"Ryan M.",
""
],
[
"Keen",
"Emma",
""
],
[
"Krachler",
"Anne-Marie",
""
],
[
"Jabbari",
"Sara",
""
]
] | As the development of new classes of antibiotics slows, bacterial resistance to existing antibiotics is becoming an increasing problem. A potential solution is to develop treatment strategies with an alternative mode of action. We consider one such strategy: anti-adhesion therapy. Whereas antibiotics act directly upon bacteria, either killing them or inhibiting their growth, anti-adhesion therapy impedes the binding of bacteria to host cells. This prevents bacteria from deploying their arsenal of virulence mechanisms, while simultaneously rendering them more susceptible to natural and artificial clearance. In this paper, we consider a particular form of anti-adhesion therapy, involving biomimetic multivalent adhesion molecule (MAM) 7 coupled polystyrene microbeads, which competitively inhibit the binding of bacteria to host cells. We develop a mathematical model, formulated as a system of ordinary differential equations, to describe inhibitor treatment of a Pseudomonas aeruginosa burn wound infection in the rat. Benchmarking our model against in vivo data from an ongoing experimental programme, we use the model to explain bacteria population dynamics and to predict the efficacy of a range of treatment strategies, with the aim of improving treatment outcome. The model consists of two physical compartments: the epithelium and the exudate. It is found that, when effective in reducing the bacterial burden, inhibitor treatment operates both by preventing bacteria from binding to the epithelium and by reducing the flux of daughter cells from the epithelium into the exudate. Our model predicts that inhibitor treatment cannot eliminate the bacterial burden when used in isolation; however, when combined with regular or continuous debridement of the exudate, elimination is theoretically possible. Lastly, we present ways to improve therapeutic efficacy, as predicted by our mathematical model. |
1904.06156 | Ke Zuo | Peizhen Xie, Ke Zuo, Yu Zhang, Fangfang Li, Mingzhu Yin, Kai Lu | Interpretable Classification from Skin Cancer Histology Slides Using
Deep Learning: A Retrospective Multicenter Study | 6 pages,3 figures | null | null | null | q-bio.TO cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For diagnosing melanoma, hematoxylin and eosin (H&E) stained tissue slides
remains the gold standard. These images contain quantitative information in
different magnifications. In the present study, we investigated whether deep
convolutional neural networks can extract structural features of complex
tissues directly from these massive size images in a patched way. In order to
face the challenge arise from morphological diversity in histopathological
slides, we built a multicenter database of 2241 digital whole-slide images from
1321 patients from 2008 to 2018. We trained both ResNet50 and Vgg19 using over
9.95 million patches by transferring learning, and test performance with two
kinds of critical classifications: malignant melanomas versus benign nevi in
separate and mixed magnification; and distinguish among nevi in maximum
magnification. The CNNs achieves superior performance across both tasks,
demonstrating an AI capable of classifying skin cancer in the analysis from
histopathological images. For making the classifications reasonable, the
visualization of CNN representations is furthermore used to identify cells
between melanoma and nevi. Regions of interest (ROI) are also located which are
significantly helpful, giving pathologists more support of correctly diagnosis.
| [
{
"created": "Fri, 12 Apr 2019 11:17:37 GMT",
"version": "v1"
}
] | 2019-04-15 | [
[
"Xie",
"Peizhen",
""
],
[
"Zuo",
"Ke",
""
],
[
"Zhang",
"Yu",
""
],
[
"Li",
"Fangfang",
""
],
[
"Yin",
"Mingzhu",
""
],
[
"Lu",
"Kai",
""
]
] | For diagnosing melanoma, hematoxylin and eosin (H&E) stained tissue slides remains the gold standard. These images contain quantitative information in different magnifications. In the present study, we investigated whether deep convolutional neural networks can extract structural features of complex tissues directly from these massive size images in a patched way. In order to face the challenge arise from morphological diversity in histopathological slides, we built a multicenter database of 2241 digital whole-slide images from 1321 patients from 2008 to 2018. We trained both ResNet50 and Vgg19 using over 9.95 million patches by transferring learning, and test performance with two kinds of critical classifications: malignant melanomas versus benign nevi in separate and mixed magnification; and distinguish among nevi in maximum magnification. The CNNs achieves superior performance across both tasks, demonstrating an AI capable of classifying skin cancer in the analysis from histopathological images. For making the classifications reasonable, the visualization of CNN representations is furthermore used to identify cells between melanoma and nevi. Regions of interest (ROI) are also located which are significantly helpful, giving pathologists more support of correctly diagnosis. |
1007.5519 | Horacio Ceva | R.P.J. Perazzo, Laura Hern\'andez, Horacio Ceva, Enrique Burgos,
Jos\'e Ignacio Alvarez-Hamelin | Phylogenetic Proximity and Nestedness in Mutualistic Ecosystems | 12 pages, 7 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate how the pattern of contacts between species in mutualistic
ecosystems is affected by the phylogenetic proximity between the species of
each guild. We develop several theoretical tools to measure that effect and we
use them to examine some real mutualistic sytems. We aim at establishing the
role of such proximity in the emergence of a nested pattern of contacts. We
conclude that although phylogenetic proximity is compatible with nestedness it
can not be claimed to determine it. We find that nestedness can instead be
attributed to a general rule by which species tend to behave as generalists
holding contacts with counterparts that already have a large number of
contacts. A nested ecosystem generated by this rule, shows high phylogenetic
diversity. This is to say, the counterparts of species having similar degrees
are not phylogenetic neighbours.
| [
{
"created": "Fri, 30 Jul 2010 19:31:46 GMT",
"version": "v1"
}
] | 2010-08-02 | [
[
"Perazzo",
"R. P. J.",
""
],
[
"Hernández",
"Laura",
""
],
[
"Ceva",
"Horacio",
""
],
[
"Burgos",
"Enrique",
""
],
[
"Alvarez-Hamelin",
"José Ignacio",
""
]
] | We investigate how the pattern of contacts between species in mutualistic ecosystems is affected by the phylogenetic proximity between the species of each guild. We develop several theoretical tools to measure that effect and we use them to examine some real mutualistic sytems. We aim at establishing the role of such proximity in the emergence of a nested pattern of contacts. We conclude that although phylogenetic proximity is compatible with nestedness it can not be claimed to determine it. We find that nestedness can instead be attributed to a general rule by which species tend to behave as generalists holding contacts with counterparts that already have a large number of contacts. A nested ecosystem generated by this rule, shows high phylogenetic diversity. This is to say, the counterparts of species having similar degrees are not phylogenetic neighbours. |
1209.4306 | J. C. Phillips | J. C. Phillips | Punctuated evolution of influenza virus hemagglutinin (A/H1N1) under
opposing migration and vaccination pressures | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Influenza virus contains two highly variable envelope glycoproteins,
hemagglutinin (HA) and neuraminidase (NA). The structure and properties of HA,
which is responsible for binding the virus to the cell that is being infected,
change significantly when the virus is transmitted from avian or swine species
to humans. Previously we identified much smaller human individual evolutionary
amino acid mutational changes in NA, which cleaves sialic acid groups and is
required for influenza virus replication. We showed that these smaller changes
can be monitored very accurately across many Uniprot and NCBI strains using
hydropathicity scales to quantify the roughness of water film packages, which
increases gradually due to migration, but decreases abruptly under large-scale
vaccination pressures. Here we show that, while HA evolution is much more
complex, it still shows abrupt punctuation changes linked to those of NA. HA
exhibits proteinquakes, which resemble earthquakes and are related to
hydropathic shifting of sialic acid binding regions. HA proteinquakes based on
sialic acid interactions are required for optimal balance between the
receptor-binding and receptor-destroying activities of HA and NA for efficient
virus replication. Our comprehensive results present an historical (1945-2011)
panorama of HA evolution over thousands of strains, and are consistent with
many studies of HA and NA interactions based on a few mutations of a few
strains. While the common influenza virus discussed here has been rendered
almost harmless by decades of vaccination programs, the sequential decoding
lessons learned here are applicable to other viruses that are emerging as
powerful weapons for controlling and even curing common organ cancers. Those
engineered oncolytic drugs will be discussed in future papers.
| [
{
"created": "Wed, 19 Sep 2012 16:52:17 GMT",
"version": "v1"
}
] | 2012-09-20 | [
[
"Phillips",
"J. C.",
""
]
] | Influenza virus contains two highly variable envelope glycoproteins, hemagglutinin (HA) and neuraminidase (NA). The structure and properties of HA, which is responsible for binding the virus to the cell that is being infected, change significantly when the virus is transmitted from avian or swine species to humans. Previously we identified much smaller human individual evolutionary amino acid mutational changes in NA, which cleaves sialic acid groups and is required for influenza virus replication. We showed that these smaller changes can be monitored very accurately across many Uniprot and NCBI strains using hydropathicity scales to quantify the roughness of water film packages, which increases gradually due to migration, but decreases abruptly under large-scale vaccination pressures. Here we show that, while HA evolution is much more complex, it still shows abrupt punctuation changes linked to those of NA. HA exhibits proteinquakes, which resemble earthquakes and are related to hydropathic shifting of sialic acid binding regions. HA proteinquakes based on sialic acid interactions are required for optimal balance between the receptor-binding and receptor-destroying activities of HA and NA for efficient virus replication. Our comprehensive results present an historical (1945-2011) panorama of HA evolution over thousands of strains, and are consistent with many studies of HA and NA interactions based on a few mutations of a few strains. While the common influenza virus discussed here has been rendered almost harmless by decades of vaccination programs, the sequential decoding lessons learned here are applicable to other viruses that are emerging as powerful weapons for controlling and even curing common organ cancers. Those engineered oncolytic drugs will be discussed in future papers. |
1406.4355 | Marie Auger-M\'eth\'e | Marie Auger-M\'eth\'e, Andrew E. Derocher, Michael J. Plank, Edward A.
Codling, Mark A. Lewis | Differentiating the L\'evy walk from a composite correlated random walk | null | Methods in Ecology and Evolution (2015) 6:1179-1189 | 10.1111/2041-210X.12412 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 1. Understanding how to find targets with very limited information is a topic
of interest in many disciplines. In ecology, such research has often focused on
the development of two movement models: i) the L\'evy walk and; ii) the
composite correlated random walk and its associated area-restricted search
behaviour. Although the processes underlying these models differ, they can
produce similar movement patterns. Due to this similarity and because of their
disparate formulation, current methods cannot reliably differentiate between
these two models.
2. Here, we present a method that differentiates between the two models. It
consists of likelihood functions, including one for a hidden Markov model, and
associated statistical measures that assess the relative support for and
absolute fit of each model.
3. Using a simulation study, we show that our method can differentiate
between the two search models over a range of parameter values. Using the
movement data of two polar bears (\textit{Ursus maritimus}), we show that the
method can be applied to complex, real-world movement paths.
4. By providing the means to differentiate between the two most prominent
search models in the literature, and a framework that could be extended to
include other models, we facilitate further research into the strategies
animals use to find resources.
| [
{
"created": "Tue, 17 Jun 2014 17:43:35 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Jan 2015 21:33:58 GMT",
"version": "v2"
},
{
"created": "Mon, 27 Apr 2015 14:11:18 GMT",
"version": "v3"
}
] | 2015-11-30 | [
[
"Auger-Méthé",
"Marie",
""
],
[
"Derocher",
"Andrew E.",
""
],
[
"Plank",
"Michael J.",
""
],
[
"Codling",
"Edward A.",
""
],
[
"Lewis",
"Mark A.",
""
]
] | 1. Understanding how to find targets with very limited information is a topic of interest in many disciplines. In ecology, such research has often focused on the development of two movement models: i) the L\'evy walk and; ii) the composite correlated random walk and its associated area-restricted search behaviour. Although the processes underlying these models differ, they can produce similar movement patterns. Due to this similarity and because of their disparate formulation, current methods cannot reliably differentiate between these two models. 2. Here, we present a method that differentiates between the two models. It consists of likelihood functions, including one for a hidden Markov model, and associated statistical measures that assess the relative support for and absolute fit of each model. 3. Using a simulation study, we show that our method can differentiate between the two search models over a range of parameter values. Using the movement data of two polar bears (\textit{Ursus maritimus}), we show that the method can be applied to complex, real-world movement paths. 4. By providing the means to differentiate between the two most prominent search models in the literature, and a framework that could be extended to include other models, we facilitate further research into the strategies animals use to find resources. |
1801.06133 | Silke Bergeler | Silke Bergeler and Erwin Frey | Regulation of Pom cluster dynamics in Myxococcus xanthus | 17 pages, 6 figures, 10 pages supplemental material (including 12
figures and 1 table) | null | 10.1371/journal.pcbi.1006358 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Precise positioning of the cell division site is essential for the correct
segregation of the genetic material into the two daughter cells. In the
bacterium Myxococcus xanthus, the proteins PomX and PomY form a cluster on the
chromosome that performs a biased random walk to midcell and positively
regulates cell division there. PomZ, an ATPase, is necessary for tethering of
the cluster to the nucleoid and regulates its movement towards midcell. It has
remained unclear how the cluster dynamics change when the biochemical
parameters, such as the attachment rates of PomZ to the nucleoid and the
cluster, the ATP hydrolysis rate of PomZ or the mobility of PomZ dimers
interacting with the nucleoid and cluster, are varied. To answer these
questions, we investigate a one-dimensional model that includes the nucleoid,
the Pom cluster and the PomZ protein. We find that a mechanism based on the
diffusive PomZ fluxes on the nucleoid into the cluster can explain the latter's
midnucleoid localization for a broad parameter range. Furthermore, there is an
ATP hydrolysis rate that minimizes the time the cluster needs to reach
midnucleoid. If the dynamics of PomZ dimers on the nucleoid is slow relative to
the cluster's velocity, we observe oscillatory cluster movements around
midnucleoid. To understand midnucleoid localization, we developed a
semi-analytical approach that dissects the net movement of the cluster into its
components: the difference in PomZ fluxes into the cluster from either side,
the force exerted by a single PomZ dimer on the cluster and the effective
friction coefficient of the cluster. Importantly, we predict that the Pom
cluster oscillates around midnucleoid if the diffusivity of PomZ on the
nucleoid is reduced. A similar approach to that applied here may also prove
useful for cargo localization in ParABS systems.
| [
{
"created": "Thu, 18 Jan 2018 17:16:34 GMT",
"version": "v1"
}
] | 2018-09-05 | [
[
"Bergeler",
"Silke",
""
],
[
"Frey",
"Erwin",
""
]
] | Precise positioning of the cell division site is essential for the correct segregation of the genetic material into the two daughter cells. In the bacterium Myxococcus xanthus, the proteins PomX and PomY form a cluster on the chromosome that performs a biased random walk to midcell and positively regulates cell division there. PomZ, an ATPase, is necessary for tethering of the cluster to the nucleoid and regulates its movement towards midcell. It has remained unclear how the cluster dynamics change when the biochemical parameters, such as the attachment rates of PomZ to the nucleoid and the cluster, the ATP hydrolysis rate of PomZ or the mobility of PomZ dimers interacting with the nucleoid and cluster, are varied. To answer these questions, we investigate a one-dimensional model that includes the nucleoid, the Pom cluster and the PomZ protein. We find that a mechanism based on the diffusive PomZ fluxes on the nucleoid into the cluster can explain the latter's midnucleoid localization for a broad parameter range. Furthermore, there is an ATP hydrolysis rate that minimizes the time the cluster needs to reach midnucleoid. If the dynamics of PomZ dimers on the nucleoid is slow relative to the cluster's velocity, we observe oscillatory cluster movements around midnucleoid. To understand midnucleoid localization, we developed a semi-analytical approach that dissects the net movement of the cluster into its components: the difference in PomZ fluxes into the cluster from either side, the force exerted by a single PomZ dimer on the cluster and the effective friction coefficient of the cluster. Importantly, we predict that the Pom cluster oscillates around midnucleoid if the diffusivity of PomZ on the nucleoid is reduced. A similar approach to that applied here may also prove useful for cargo localization in ParABS systems. |
2101.08660 | Erhard Scholz | Matthias Kreck, Erhard Scholz | Studying the course of Covid-19 by a recursive delay approach | 66 pages, 67 figures Changes in v2:Correction of formulas pp. 5, 6,
7, 20, general case of dark model, p_c \neq p_d included, tautological model
for eta_7 added | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-sa/4.0/ | In an earlier paper we proposed a recursive model for epidemics; in the
present paper we generalize this model to include the asymptomatic or
unrecorded symptomatic people, which we call {\em dark people} (dark sector).
We call this the SEPAR$_d$-model. A delay differential equation version of the
model is added; it allows a better comparison to other models. We carry this
out by a comparison with the classical SIR model and indicate why we believe
that the SEPAR$_d$ model may work better for Covid-19 than other approaches. In
the second part of the paper we explain how to deal with the data provided by
the JHU, in particular we explain how to derive central model parameters from
the data. Other parameters, like the size of the dark sector, are less
accessible and have to be estimated more roughly, at best by results of
representative serological studies which are accessible, however, only for a
few countries. We start our country studies with Switzerland where such data
are available. Then we apply the model to a collection of other countries,
three European ones (Germany, France, Sweden), the three most stricken
countries from three other continents (USA, Brazil, India). Finally we show
that even the aggregated world data can be well represented by our approach. At
the end of the paper we discuss the use of the model. Perhaps the most striking
application is that it allows a quantitative analysis of the influence of the
time until people are sent to quarantine or hospital. This suggests that
imposing means to shorten this time is a powerful tool to flatten the curves
| [
{
"created": "Sun, 17 Jan 2021 23:58:12 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Feb 2021 22:57:33 GMT",
"version": "v2"
}
] | 2021-03-01 | [
[
"Kreck",
"Matthias",
""
],
[
"Scholz",
"Erhard",
""
]
] | In an earlier paper we proposed a recursive model for epidemics; in the present paper we generalize this model to include the asymptomatic or unrecorded symptomatic people, which we call {\em dark people} (dark sector). We call this the SEPAR$_d$-model. A delay differential equation version of the model is added; it allows a better comparison to other models. We carry this out by a comparison with the classical SIR model and indicate why we believe that the SEPAR$_d$ model may work better for Covid-19 than other approaches. In the second part of the paper we explain how to deal with the data provided by the JHU, in particular we explain how to derive central model parameters from the data. Other parameters, like the size of the dark sector, are less accessible and have to be estimated more roughly, at best by results of representative serological studies which are accessible, however, only for a few countries. We start our country studies with Switzerland where such data are available. Then we apply the model to a collection of other countries, three European ones (Germany, France, Sweden), the three most stricken countries from three other continents (USA, Brazil, India). Finally we show that even the aggregated world data can be well represented by our approach. At the end of the paper we discuss the use of the model. Perhaps the most striking application is that it allows a quantitative analysis of the influence of the time until people are sent to quarantine or hospital. This suggests that imposing means to shorten this time is a powerful tool to flatten the curves |
2010.00957 | Kaspar Rufibach | Steven Sun and Hans-Jochen Weber and Emily Butler and Kaspar Rufibach
and Satrajit Roychoudhury | Estimands in Hematologic Oncology Trials | 5 tables, 1 figure | Pharm. Stat., 2021, 20, 793-805 | 10.1002/pst.2108 | null | q-bio.OT stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The estimand framework included in the addendum to the ICH E9 guideline
facilitates discussions to ensure alignment between the key question of
interest, the analysis, and interpretation. Therapeutic knowledge and drug
mechanism play a crucial role in determining the strategy and defining the
estimand for clinical trial designs. Clinical trials in patients with
hematological malignancies often present unique challenges for trial design due
to complexity of treatment options and existence of potential curative but
highly risky procedures, e.g. stem cell transplant or treatment sequence across
different phases (induction, consolidation, maintenance). Here, we illustrate
how to apply the estimand framework in hematological clinical trials and how
the estimand framework can address potential difficulties in trial result
interpretation.
This paper is a result of a cross-industry collaboration to connect the
International Conference on Harmonisation (ICH) E9 addendum concepts to
applications. Three randomized phase 3 trials will be used to consider common
challenges including intercurrent events in hematologic oncology trials to
illustrate different scientific questions and the consequences of the estimand
choice for trial design, data collection, analysis, and interpretation.
Template language for describing estimand in both study protocols and
statistical analysis plans is suggested for statisticians' reference.
| [
{
"created": "Thu, 1 Oct 2020 15:38:48 GMT",
"version": "v1"
}
] | 2023-04-17 | [
[
"Sun",
"Steven",
""
],
[
"Weber",
"Hans-Jochen",
""
],
[
"Butler",
"Emily",
""
],
[
"Rufibach",
"Kaspar",
""
],
[
"Roychoudhury",
"Satrajit",
""
]
] | The estimand framework included in the addendum to the ICH E9 guideline facilitates discussions to ensure alignment between the key question of interest, the analysis, and interpretation. Therapeutic knowledge and drug mechanism play a crucial role in determining the strategy and defining the estimand for clinical trial designs. Clinical trials in patients with hematological malignancies often present unique challenges for trial design due to complexity of treatment options and existence of potential curative but highly risky procedures, e.g. stem cell transplant or treatment sequence across different phases (induction, consolidation, maintenance). Here, we illustrate how to apply the estimand framework in hematological clinical trials and how the estimand framework can address potential difficulties in trial result interpretation. This paper is a result of a cross-industry collaboration to connect the International Conference on Harmonisation (ICH) E9 addendum concepts to applications. Three randomized phase 3 trials will be used to consider common challenges including intercurrent events in hematologic oncology trials to illustrate different scientific questions and the consequences of the estimand choice for trial design, data collection, analysis, and interpretation. Template language for describing estimand in both study protocols and statistical analysis plans is suggested for statisticians' reference. |
1508.01509 | Suyan Tian | Lei Zhang, Linlin Wang, Pu Tian, Suyan Tian | Pathway-based feature selection algorithms identify genes discriminating
patients with multiple sclerosis apart from controls | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Introduction The focus of analyzing data from microarray experiments and
extracting biological insight from such data has experienced a shift from
identification of individual genes in association with a phenotype to that of
biological pathways or gene sets. Meanwhile, feature selection algorithm
becomes imperative to cope with the high dimensional nature of many modeling
tasks in bioinformatics. Many feature selection algorithms use information
contained within a gene set as a biological priori, and select relevant
features by incorporating such information. Thus, an integration of gene set
analysis with feature selection is highly desired. Significance analysis of
microarray to gene-set reduction analysis (SAM-GSR) algorithm is a novel
direction of gene set analysis, aiming at further reduction of gene set into a
core subset. Here, we explore the feature selection trait possessed by SAM-GSR
and then modify SAM-GSR specifically to better fulfill this role. Results and
Conclusions Training on a multiple sclerosis (MS) microarray data using both
SAM-GSR and our modification of SAM-GSR, excellent discriminative performance
on an independent test set was achieved. To conclude, absorbing biological
information from a gene set may be helpful for classification and feature
selection. Discussion Given the fact the complete pathway information is far
from completeness, a statistical method capable of constructing biologically
meaningful gene networks is in demand. The basic requirement is that interplay
among genes must be taken into account.
| [
{
"created": "Thu, 6 Aug 2015 07:20:08 GMT",
"version": "v1"
}
] | 2015-08-10 | [
[
"Zhang",
"Lei",
""
],
[
"Wang",
"Linlin",
""
],
[
"Tian",
"Pu",
""
],
[
"Tian",
"Suyan",
""
]
] | Introduction The focus of analyzing data from microarray experiments and extracting biological insight from such data has experienced a shift from identification of individual genes in association with a phenotype to that of biological pathways or gene sets. Meanwhile, feature selection algorithm becomes imperative to cope with the high dimensional nature of many modeling tasks in bioinformatics. Many feature selection algorithms use information contained within a gene set as a biological priori, and select relevant features by incorporating such information. Thus, an integration of gene set analysis with feature selection is highly desired. Significance analysis of microarray to gene-set reduction analysis (SAM-GSR) algorithm is a novel direction of gene set analysis, aiming at further reduction of gene set into a core subset. Here, we explore the feature selection trait possessed by SAM-GSR and then modify SAM-GSR specifically to better fulfill this role. Results and Conclusions Training on a multiple sclerosis (MS) microarray data using both SAM-GSR and our modification of SAM-GSR, excellent discriminative performance on an independent test set was achieved. To conclude, absorbing biological information from a gene set may be helpful for classification and feature selection. Discussion Given the fact the complete pathway information is far from completeness, a statistical method capable of constructing biologically meaningful gene networks is in demand. The basic requirement is that interplay among genes must be taken into account. |
1508.07527 | Aziz Mezlini | Aziz M. Mezlini, Fabio Fuligni, Adam Shlien and Anna Goldenberg | Combining exome and gene expression datasets in one graphical model of
disease to empower the discovery of disease mechanisms | null | null | null | null | q-bio.GN cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying genes associated with complex human diseases is one of the main
challenges of human genetics and computational medicine. To answer this
question, millions of genetic variants get screened to identify a few of
importance. To increase the power of identifying genes associated with diseases
and to account for other potential sources of protein function aberrations, we
propose a novel factor-graph based model, where much of the biological
knowledge is incorporated through factors and priors. Our extensive simulations
show that our method has superior sensitivity and precision compared to
variant-aggregating and differential expression methods. Our integrative
approach was able to identify important genes in breast cancer, identifying
genes that had coding aberrations in some patients and regulatory abnormalities
in others, emphasizing the importance of data integration to explain the
disease in a larger number of patients.
| [
{
"created": "Sun, 30 Aug 2015 03:08:39 GMT",
"version": "v1"
}
] | 2015-09-01 | [
[
"Mezlini",
"Aziz M.",
""
],
[
"Fuligni",
"Fabio",
""
],
[
"Shlien",
"Adam",
""
],
[
"Goldenberg",
"Anna",
""
]
] | Identifying genes associated with complex human diseases is one of the main challenges of human genetics and computational medicine. To answer this question, millions of genetic variants get screened to identify a few of importance. To increase the power of identifying genes associated with diseases and to account for other potential sources of protein function aberrations, we propose a novel factor-graph based model, where much of the biological knowledge is incorporated through factors and priors. Our extensive simulations show that our method has superior sensitivity and precision compared to variant-aggregating and differential expression methods. Our integrative approach was able to identify important genes in breast cancer, identifying genes that had coding aberrations in some patients and regulatory abnormalities in others, emphasizing the importance of data integration to explain the disease in a larger number of patients. |
1312.3023 | Lisette dePillis | L.G. dePillis, H. Savage, A.E. Radunskaya | Mathematical Model of Colorectal Cancer with Monoclonal Antibody
Treatments | 28 pages plus 11 page appendix; 9 figures | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new mathematical model of colorectal cancer growth and its
response to monoclonal-antibody (mAb) therapy. Although promising, most mAb
drugs are still in trial phases, and the possible variations in the dosing
schedules of those currently approved for use have not yet been thoroughly
explored. To investigate the effectiveness of current mAb treatment schedules,
and to test hypothetical treatment strategies, we have created a system of
nonlinear ordinary differential equations (ODE) to model colorectal cancer
growth and treatment. The model includes tumor cells, elements of the host's
immune response, and treatments. Model treatments include the chemotherapy
agent irinotecan and one of two monoclonal antibodies - cetuximab, which is
FDA-approved for colorectal cancer, and panitumumab, which is still being
evaluated in clinical trials. The model incorporates patient-specific
parameters to account for individual variations in immune system strength and
in medication efficacy against the tumor. We have simulated outcomes for groups
of virtual patients on treatment protocols for which clinical trial data are
available, using a range of biologically reasonable patient-specific parameter
values. Our results closely match clinical trial results for these protocols.
We also simulated experimental dosing schedules, and have found new schedules
which, in our simulations, reduce tumor size more effectively than current
treatment schedules. Additionally, we examined the system's equilibria and
sensitivity to parameter values. In the absence of treatment, tumor evolution
is most affected by the intrinsic tumor growth rate and carrying capacity. When
treatment is introduced, tumor growth is most affected by drug-specific PK/PD
parameters.
| [
{
"created": "Wed, 11 Dec 2013 02:46:11 GMT",
"version": "v1"
}
] | 2013-12-12 | [
[
"dePillis",
"L. G.",
""
],
[
"Savage",
"H.",
""
],
[
"Radunskaya",
"A. E.",
""
]
] | We present a new mathematical model of colorectal cancer growth and its response to monoclonal-antibody (mAb) therapy. Although promising, most mAb drugs are still in trial phases, and the possible variations in the dosing schedules of those currently approved for use have not yet been thoroughly explored. To investigate the effectiveness of current mAb treatment schedules, and to test hypothetical treatment strategies, we have created a system of nonlinear ordinary differential equations (ODE) to model colorectal cancer growth and treatment. The model includes tumor cells, elements of the host's immune response, and treatments. Model treatments include the chemotherapy agent irinotecan and one of two monoclonal antibodies - cetuximab, which is FDA-approved for colorectal cancer, and panitumumab, which is still being evaluated in clinical trials. The model incorporates patient-specific parameters to account for individual variations in immune system strength and in medication efficacy against the tumor. We have simulated outcomes for groups of virtual patients on treatment protocols for which clinical trial data are available, using a range of biologically reasonable patient-specific parameter values. Our results closely match clinical trial results for these protocols. We also simulated experimental dosing schedules, and have found new schedules which, in our simulations, reduce tumor size more effectively than current treatment schedules. Additionally, we examined the system's equilibria and sensitivity to parameter values. In the absence of treatment, tumor evolution is most affected by the intrinsic tumor growth rate and carrying capacity. When treatment is introduced, tumor growth is most affected by drug-specific PK/PD parameters. |
1411.4556 | Kamran Kaveh | Kamran Kaveh, Natalia Komarova, Mohammad Kohandel | The duality of spatial death-birth and birth-death processes and
limitations of the isothermal theorem | 32 pages, 10 figures | Royal Society Open Science 11/2014; 2(4) | 10.1098/rsos.140465 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolutionary models on graphs, as an extension of the Moran process, have two
major implementations: birth-death (BD) models (or the invasion process) and
death- birth (DB) models (or voter models). The isothermal theorem states that
the fixation probability of mutants in a large group of graph structures (known
as isothermal graphs, which include regular graphs) coincides with that for the
mixed population. This result has been proven by Lieberman et al (Nature 433:
312-316, 2005) in the case of BD processes, where mutants differ from the wild
types by their birth rate (and not by their death rate). In this paper we
discuss to what extent the isothermal theorem can be formulated for DB
processes, proving that it only holds for mutants that differ from the wild
type by their death rate (and not by their birth rate). For more general BD and
DB processes with arbitrary birth and death rates of mutants, we show that the
fixation probabilities of mutants are different from those obtained in the
mass-action populations. We focus on spatial lattices and show that the
difference between BD and DB processes on 1D and 2D lattices are non-small even
for large population sizes. We support these results with a generating function
approach that can be generalized to arbitrary graph structures. Finally, we
discuss several biological applications of the results.
| [
{
"created": "Mon, 17 Nov 2014 17:21:36 GMT",
"version": "v1"
}
] | 2015-05-19 | [
[
"Kaveh",
"Kamran",
""
],
[
"Komarova",
"Natalia",
""
],
[
"Kohandel",
"Mohammad",
""
]
] | Evolutionary models on graphs, as an extension of the Moran process, have two major implementations: birth-death (BD) models (or the invasion process) and death- birth (DB) models (or voter models). The isothermal theorem states that the fixation probability of mutants in a large group of graph structures (known as isothermal graphs, which include regular graphs) coincides with that for the mixed population. This result has been proven by Lieberman et al (Nature 433: 312-316, 2005) in the case of BD processes, where mutants differ from the wild types by their birth rate (and not by their death rate). In this paper we discuss to what extent the isothermal theorem can be formulated for DB processes, proving that it only holds for mutants that differ from the wild type by their death rate (and not by their birth rate). For more general BD and DB processes with arbitrary birth and death rates of mutants, we show that the fixation probabilities of mutants are different from those obtained in the mass-action populations. We focus on spatial lattices and show that the difference between BD and DB processes on 1D and 2D lattices are non-small even for large population sizes. We support these results with a generating function approach that can be generalized to arbitrary graph structures. Finally, we discuss several biological applications of the results. |
2312.02305 | Victor Manuel Hidalgo | V\'ictor Manuel Hidalgo, Carlos Andr\'es Bazaes, Juan-Carlos Letelier | Effects of a mixed reality headset on the delay of visually evoked
potentials | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Virtual and mixed reality (VR, MR) technologies offer a powerful solution for
on-the-ground flight training curricula. While these technologies offer safer
and cheaper instructional programs, it is still unclear how they impact
neuronal brain dynamics. Indeed, MR simulations engage students in a strange
mix of incongruous visual, somatosensory and vestibular sensory input.
Characterizing brain dynamics during MR simulation is important for
understanding cognitive processes during virtual flight training. To this end,
we studies the delays introduced in the neuronal stream from the retina to the
visual cortex when presented with visual stimuli using a Varjo-XR3 headset. We
recorded cortical visual evoked potentials (VEPs) from 6 subjects under two
conditions. First, we recorded normal VEPs triggered by short flashes. Second,
we recorded VEPs triggered by an internal image of the flashes produced by the
Varjo-XR3 headset. All subjects had used the headset before and were familiar
with immersive experiences. Our results show mixed-reality stimulation imposes
a small, but consistent, 4 [ms] processing delay in the N2-VEP component during
MR stimulation as compared to direct stimulation. Also we found that VEP
amplitudes during MR stimulation were also decreased. These results suggest
that visual cognition during mixed-reality training is delayed, not only by the
unavoidalbe hardware/software processing delays of the headset and the attached
computer, but also by an extra biological delay induced by the headset's
limited visual display in terms of the image intensity and contrast. As flight
training is a demanding task, this study measures visual signal latency to
better understand how MR affects the sensation of immersion.
| [
{
"created": "Mon, 4 Dec 2023 19:39:32 GMT",
"version": "v1"
}
] | 2023-12-06 | [
[
"Hidalgo",
"Víctor Manuel",
""
],
[
"Bazaes",
"Carlos Andrés",
""
],
[
"Letelier",
"Juan-Carlos",
""
]
] | Virtual and mixed reality (VR, MR) technologies offer a powerful solution for on-the-ground flight training curricula. While these technologies offer safer and cheaper instructional programs, it is still unclear how they impact neuronal brain dynamics. Indeed, MR simulations engage students in a strange mix of incongruous visual, somatosensory and vestibular sensory input. Characterizing brain dynamics during MR simulation is important for understanding cognitive processes during virtual flight training. To this end, we studies the delays introduced in the neuronal stream from the retina to the visual cortex when presented with visual stimuli using a Varjo-XR3 headset. We recorded cortical visual evoked potentials (VEPs) from 6 subjects under two conditions. First, we recorded normal VEPs triggered by short flashes. Second, we recorded VEPs triggered by an internal image of the flashes produced by the Varjo-XR3 headset. All subjects had used the headset before and were familiar with immersive experiences. Our results show mixed-reality stimulation imposes a small, but consistent, 4 [ms] processing delay in the N2-VEP component during MR stimulation as compared to direct stimulation. Also we found that VEP amplitudes during MR stimulation were also decreased. These results suggest that visual cognition during mixed-reality training is delayed, not only by the unavoidalbe hardware/software processing delays of the headset and the attached computer, but also by an extra biological delay induced by the headset's limited visual display in terms of the image intensity and contrast. As flight training is a demanding task, this study measures visual signal latency to better understand how MR affects the sensation of immersion. |
1709.04300 | Archana Ram | Archana Ram, Andrew Lo | Is Smaller Better: A Proposal To Consider Bacteria For Biologically
Inspired Modeling | null | null | null | null | q-bio.NC cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bacteria are easily characterizable model organisms with an impressively
complicated set of capabilities. Among their capabilities is quorum sensing, a
detailed cell-cell signaling system that may have a common origin with
eukaryotic cell-cell signaling. Not only are the two phenomena similar, but
quorum sensing, as is the case with any bacterial phenomenon when compared to
eukaryotes, is also easier to study in depth than eukaryotic cell-cell
signaling. This ease of study is a contrast to the only partially understood
cellular dynamics of neurons. Here we review the literature on the strikingly
neuron-like qualities of bacterial colonies and biofilms, including ion-based
and hormonal signaling, and action potential-like behavior. This allows them to
feasibly act as an analog for neurons that could produce more detailed and more
accurate biologically-based computational models. Using bacteria as the basis
for biologically feasible computational models may allow models to better
harness the tremendous ability of biological organisms to make decisions and
process information. Additionally, principles gleaned from bacterial function
have the potential to influence computational efforts divorced from biology,
just as neuronal function has in the abstract influenced countless machine
learning efforts.
| [
{
"created": "Tue, 12 Sep 2017 00:16:30 GMT",
"version": "v1"
}
] | 2017-09-14 | [
[
"Ram",
"Archana",
""
],
[
"Lo",
"Andrew",
""
]
] | Bacteria are easily characterizable model organisms with an impressively complicated set of capabilities. Among their capabilities is quorum sensing, a detailed cell-cell signaling system that may have a common origin with eukaryotic cell-cell signaling. Not only are the two phenomena similar, but quorum sensing, as is the case with any bacterial phenomenon when compared to eukaryotes, is also easier to study in depth than eukaryotic cell-cell signaling. This ease of study is a contrast to the only partially understood cellular dynamics of neurons. Here we review the literature on the strikingly neuron-like qualities of bacterial colonies and biofilms, including ion-based and hormonal signaling, and action potential-like behavior. This allows them to feasibly act as an analog for neurons that could produce more detailed and more accurate biologically-based computational models. Using bacteria as the basis for biologically feasible computational models may allow models to better harness the tremendous ability of biological organisms to make decisions and process information. Additionally, principles gleaned from bacterial function have the potential to influence computational efforts divorced from biology, just as neuronal function has in the abstract influenced countless machine learning efforts. |
1702.00354 | Jason Kim | Jason Kim, Jonathan M. Soffer, Ari E. Kahn, Jean M. Vettel, Fabio
Pasqualetti, Danielle S. Bassett | Topological Principles of Control in Dynamical Network Systems | 7 figures, Supplement | null | 10.1038/nphys4268 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Networked systems display complex patterns of interactions between a large
number of components. In physical networks, these interactions often occur
along structural connections that link components in a hard-wired connection
topology, supporting a variety of system-wide dynamical behaviors such as
synchronization. While descriptions of these behaviors are important, they are
only a first step towards understanding the relationship between network
topology and system behavior, and harnessing that relationship to optimally
control the system's function. Here, we use linear network control theory to
analytically relate the topology of a subset of structural connections (those
linking driver nodes to non-driver nodes) to the minimum energy required to
control networked systems. As opposed to the numerical computations of control
energy, our accurate closed-form expressions yield general structural features
in networks that require significantly more or less energy to control,
providing topological principles for the design and modification of network
behavior. To illustrate the utility of the mathematics, we apply this approach
to high-resolution connectomes recently reconstructed from drosophila, mouse,
and human brains. We use these principles to show that connectomes of
increasingly complex species are wired to reduce control energy. We then use
the analytical expressions we derive to perform targeted manipulation of the
brain's control profile by removing single edges in the network, a manipulation
that is accessible to current clinical techniques in patients with neurological
disorders. Cross-species comparisons suggest an advantage of the human brain in
supporting diverse network dynamics with small energetic costs, while remaining
unexpectedly robust to perturbations. Our results ground the expectation of a
system's dynamical behavior in its network architecture.
| [
{
"created": "Wed, 1 Feb 2017 16:55:42 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Feb 2017 05:31:08 GMT",
"version": "v2"
}
] | 2018-04-03 | [
[
"Kim",
"Jason",
""
],
[
"Soffer",
"Jonathan M.",
""
],
[
"Kahn",
"Ari E.",
""
],
[
"Vettel",
"Jean M.",
""
],
[
"Pasqualetti",
"Fabio",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Networked systems display complex patterns of interactions between a large number of components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding the relationship between network topology and system behavior, and harnessing that relationship to optimally control the system's function. Here, we use linear network control theory to analytically relate the topology of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. As opposed to the numerical computations of control energy, our accurate closed-form expressions yield general structural features in networks that require significantly more or less energy to control, providing topological principles for the design and modification of network behavior. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from drosophila, mouse, and human brains. We use these principles to show that connectomes of increasingly complex species are wired to reduce control energy. We then use the analytical expressions we derive to perform targeted manipulation of the brain's control profile by removing single edges in the network, a manipulation that is accessible to current clinical techniques in patients with neurological disorders. Cross-species comparisons suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs, while remaining unexpectedly robust to perturbations. Our results ground the expectation of a system's dynamical behavior in its network architecture. |
1911.09352 | Thomas R. Weikl | Thomas R. Weikl, Jinglei Hu, Batuhan Kav, and Bartosz Rozycki | Binding and segregation of proteins in membrane adhesion: Theory,
modelling, and simulations | Review article with 39 pages and 11 figures in "Advances in
Biomembranes and Lipid Self-Assembly", Volume 30 (2019) | null | null | null | q-bio.SC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The adhesion of biomembranes is mediated by the binding of membrane-anchored
receptor and ligand proteins. The proteins can only bind if the separation
between apposing membranes is sufficiently close to the length of the protein
complexes, which leads to an interplay between protein binding and membrane
shape. In this article, we review current models of biomembrane adhesion and
novel insights obtained from the models. Theory and simulations with
elastic-membrane and coarse-grained molecular models of biomembrane adhesion
indicate that the binding of proteins in membrane adhesion strongly depends on
nanoscale shape fluctuations of the apposing membranes, which results in
binding cooperativity. A length mismatch between protein complexes leads to
repulsive interactions that are caused by membrane bending and act as a driving
force for the length-based segregation of proteins during membrane adhesion.
| [
{
"created": "Thu, 21 Nov 2019 09:02:39 GMT",
"version": "v1"
}
] | 2019-11-22 | [
[
"Weikl",
"Thomas R.",
""
],
[
"Hu",
"Jinglei",
""
],
[
"Kav",
"Batuhan",
""
],
[
"Rozycki",
"Bartosz",
""
]
] | The adhesion of biomembranes is mediated by the binding of membrane-anchored receptor and ligand proteins. The proteins can only bind if the separation between apposing membranes is sufficiently close to the length of the protein complexes, which leads to an interplay between protein binding and membrane shape. In this article, we review current models of biomembrane adhesion and novel insights obtained from the models. Theory and simulations with elastic-membrane and coarse-grained molecular models of biomembrane adhesion indicate that the binding of proteins in membrane adhesion strongly depends on nanoscale shape fluctuations of the apposing membranes, which results in binding cooperativity. A length mismatch between protein complexes leads to repulsive interactions that are caused by membrane bending and act as a driving force for the length-based segregation of proteins during membrane adhesion. |
q-bio/0609025 | Cristian Micheletti | Vincenzo Carnevale, Simone Raugei, Cristian Micheletti and Paolo
Carloni | Convergent dynamics in the protease enzymatic superfamily | 13 pages, 6 figures | J. Am. Chem. Soc. 128, 9766-9772 (2006) | 10.1021/ja060896t | null | q-bio.BM cond-mat.soft physics.bio-ph | null | Proteases regulate various aspects of the life cycle in all organisms by
cleaving specific peptide bonds. Their action is so central for biochemical
processes that at least 2% of any known genome encodes for proteolytic enzymes.
Here we show that selected proteases pairs, despite differences in oligomeric
state, catalytic residues and fold, share a common structural organization of
functionally relevant regions which are further shown to undergo similar
concerted movements. The structural and dynamical similarities found
pervasively across evolutionarily distant clans point to common mechanisms for
peptide hydrolysis.
| [
{
"created": "Fri, 15 Sep 2006 18:10:32 GMT",
"version": "v1"
}
] | 2013-06-07 | [
[
"Carnevale",
"Vincenzo",
""
],
[
"Raugei",
"Simone",
""
],
[
"Micheletti",
"Cristian",
""
],
[
"Carloni",
"Paolo",
""
]
] | Proteases regulate various aspects of the life cycle in all organisms by cleaving specific peptide bonds. Their action is so central for biochemical processes that at least 2% of any known genome encodes for proteolytic enzymes. Here we show that selected proteases pairs, despite differences in oligomeric state, catalytic residues and fold, share a common structural organization of functionally relevant regions which are further shown to undergo similar concerted movements. The structural and dynamical similarities found pervasively across evolutionarily distant clans point to common mechanisms for peptide hydrolysis. |
2208.06013 | Anne-Florence Bitbol | Richard Servajean and Anne-Florence Bitbol | Impact of population size on early adaptation in rugged fitness
landscapes | 36 pages, 18 figures, 2 tables | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to stochastic fluctuations arising from finite population size, known as
genetic drift, the ability of a population to explore a rugged fitness
landscape depends on its size. In the weak mutation regime, while the mean
steady-state fitness increases with population size, we find that the height of
the first fitness peak encountered when starting from a random genotype
displays various behaviors versus population size, even among small and simple
rugged landscapes. We show that the accessibility of the different fitness
peaks is key to determining whether this height overall increases or decreases
with population size. Furthermore, there is often a finite population size that
maximizes the height of the first fitness peak encountered when starting from a
random genotype. This holds across various classes of model rugged landscapes
with sparse peaks, and in some experimental and experimentally-inspired ones.
Thus, early adaptation in rugged fitness landscapes can be more efficient and
predictable for relatively small population sizes than in the large-size limit.
| [
{
"created": "Thu, 11 Aug 2022 19:17:29 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Dec 2022 14:16:45 GMT",
"version": "v2"
}
] | 2022-12-05 | [
[
"Servajean",
"Richard",
""
],
[
"Bitbol",
"Anne-Florence",
""
]
] | Due to stochastic fluctuations arising from finite population size, known as genetic drift, the ability of a population to explore a rugged fitness landscape depends on its size. In the weak mutation regime, while the mean steady-state fitness increases with population size, we find that the height of the first fitness peak encountered when starting from a random genotype displays various behaviors versus population size, even among small and simple rugged landscapes. We show that the accessibility of the different fitness peaks is key to determining whether this height overall increases or decreases with population size. Furthermore, there is often a finite population size that maximizes the height of the first fitness peak encountered when starting from a random genotype. This holds across various classes of model rugged landscapes with sparse peaks, and in some experimental and experimentally-inspired ones. Thus, early adaptation in rugged fitness landscapes can be more efficient and predictable for relatively small population sizes than in the large-size limit. |
2108.11025 | Chathika Gunaratne | Chathika Gunaratne, Rene Reyes, Erik Hemberg, Una-May O'Reilly | Evaluating Efficacy of Indoor Non-Pharmaceutical Interventions against
COVID-19 Outbreaks with a Coupled Spatial-SIR Agent-Based Simulation
Framework | null | null | null | null | q-bio.PE cs.SI q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Contagious respiratory diseases, such as COVID-19, depend on sufficiently
prolonged exposures for the successful transmission of the underlying pathogen.
It is important for organizations to evaluate the efficacy of interventions
aiming at mitigating viral transmission among their personnel. We have
developed a operational risk assessment simulation framework that couples a
spatial agent-based model of movement with a SIR epidemiological model to
assess the relative risks of different intervention strategies. By applying our
model on MIT's STATA building, we assess the impacts of three possible
dimensions of intervention: one-way vs unrestricted movement, population size
allowed onsite, and frequency of leaving designated work location for breaks.
We find that there is no significant impact made by one-way movement
restrictions over unrestricted movement. Instead, we find that a combination of
lowering the number of individuals admitted below the current recommendations
and advising individuals to reduce the frequency at which they leave their
workstations lowers the likelihood of highly connected individuals within the
contact networks that emerge, which in turn lowers the overall risk of
infection. We discover three classes of possible interventions based on their
epidemiological effects. By assuming a direct relationship between data on
secondary attack rates and transmissibility in the SIR model, we compare
relative infection risk of four respiratory diseases, MERS, SARS, COVID-19, and
Measles, within the simulated area, and recommend appropriate intervention
guidelines.
| [
{
"created": "Wed, 25 Aug 2021 03:14:35 GMT",
"version": "v1"
}
] | 2021-08-26 | [
[
"Gunaratne",
"Chathika",
""
],
[
"Reyes",
"Rene",
""
],
[
"Hemberg",
"Erik",
""
],
[
"O'Reilly",
"Una-May",
""
]
] | Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important for organizations to evaluate the efficacy of interventions aiming at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with a SIR epidemiological model to assess the relative risks of different intervention strategies. By applying our model on MIT's STATA building, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that a combination of lowering the number of individuals admitted below the current recommendations and advising individuals to reduce the frequency at which they leave their workstations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the SIR model, we compare relative infection risk of four respiratory diseases, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines. |
1107.3531 | Elisenda Feliu | Elisenda Feliu, Carsten Wiuf | Variable elimination in post-translational modification reaction
networks with mass-action kinetics | null | null | null | null | q-bio.MN math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We define a subclass of Chemical Reaction Networks called Post-Translational
Modification systems. Important biological examples of such systems include
MAPK cascades and two-component systems which are well-studied experimentally
as well as theoretically. The steady states of such a system are solutions to a
system of polynomial equations with as many variables as equations. Even for
small systems the task of finding the solutions is daunting. We develop a
mathematical framework based on the notion of a cut, which provides a linear
elimination procedure to reduce the number of variables in the system. The
steady states are parameterized algebraically by a set of "core" variables, and
the non-negative steady states correspond to non-negative values of the core
variables. Further, minimal cuts are the connected components in the species
graph and provide conservation laws. A criterion for when a set of independent
conservation laws can be derived from cuts is given.
| [
{
"created": "Mon, 18 Jul 2011 19:26:04 GMT",
"version": "v1"
}
] | 2011-07-19 | [
[
"Feliu",
"Elisenda",
""
],
[
"Wiuf",
"Carsten",
""
]
] | We define a subclass of Chemical Reaction Networks called Post-Translational Modification systems. Important biological examples of such systems include MAPK cascades and two-component systems which are well-studied experimentally as well as theoretically. The steady states of such a system are solutions to a system of polynomial equations with as many variables as equations. Even for small systems the task of finding the solutions is daunting. We develop a mathematical framework based on the notion of a cut, which provides a linear elimination procedure to reduce the number of variables in the system. The steady states are parameterized algebraically by a set of "core" variables, and the non-negative steady states correspond to non-negative values of the core variables. Further, minimal cuts are the connected components in the species graph and provide conservation laws. A criterion for when a set of independent conservation laws can be derived from cuts is given. |
q-bio/0612046 | Lior Pachter | Lior Pachter | An introduction to reconstructing ancestral genomes | Expanded lecture notes from the AMS short course on modeling and
simulation of biological networks held in San Antonio, TX January 2006. To
appear in the Proceedings of Symposia in Applied Mathematics, AMS Short
Course Subseries | null | null | null | q-bio.GN q-bio.QM | null | Recent advances in high-throughput genomics technologies have resulted in the
sequencing of large numbers of (near) complete genomes. These genome sequences
are being mined for important functional elements, such as genes. They are also
being compared and contrasted in order to identify other functional sequences,
such as those involved in the regulation of genes. In cases where DNA sequences
from different organisms can be determined to have originated from a common
ancestor, it is natural to try to infer the an- cestral sequences. The
reconstruction of ancestral genomes can lead to insights about genome
evolution, and the origins and diversity of function. There are a number of
interesting foundational questions associated with reconstructing ancestral
genomes: Which statistical models for evolution should be used for making
inferences about ancestral sequences? How should extant genomes be compared in
order to facilitate ancestral reconstruction? Which portions of ancestral
genomes can be reconstructed reliably, and what are the limits of ancestral
reconstruction? We discuss recent progress on some of these questions, offer
some of our own opinions, and highlight interesting mathematics, statistics,
and computer science problems.
| [
{
"created": "Mon, 25 Dec 2006 21:20:34 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Pachter",
"Lior",
""
]
] | Recent advances in high-throughput genomics technologies have resulted in the sequencing of large numbers of (near) complete genomes. These genome sequences are being mined for important functional elements, such as genes. They are also being compared and contrasted in order to identify other functional sequences, such as those involved in the regulation of genes. In cases where DNA sequences from different organisms can be determined to have originated from a common ancestor, it is natural to try to infer the an- cestral sequences. The reconstruction of ancestral genomes can lead to insights about genome evolution, and the origins and diversity of function. There are a number of interesting foundational questions associated with reconstructing ancestral genomes: Which statistical models for evolution should be used for making inferences about ancestral sequences? How should extant genomes be compared in order to facilitate ancestral reconstruction? Which portions of ancestral genomes can be reconstructed reliably, and what are the limits of ancestral reconstruction? We discuss recent progress on some of these questions, offer some of our own opinions, and highlight interesting mathematics, statistics, and computer science problems. |
1803.10318 | Gaurav Gupta | Gaurav Gupta, Sergio Pequito, Paul Bogdan | Re-thinking EEG-based non-invasive brain interfaces: modeling and
analysis | 12 pages, 16 figures, ICCPS-18 | null | null | null | q-bio.NC cs.HC cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Brain interfaces are cyber-physical systems that aim to harvest information
from the (physical) brain through sensing mechanisms, extract information about
the underlying processes, and decide/actuate accordingly. Nonetheless, the
brain interfaces are still in their infancy, but reaching to their maturity
quickly as several initiatives are released to push forward their development
(e.g., NeuraLink by Elon Musk and `typing-by-brain' by Facebook). This has
motivated us to revisit the design of EEG-based non-invasive brain interfaces.
Specifically, current methodologies entail a highly skilled neuro-functional
approach and evidence-based \emph{a priori} knowledge about specific signal
features and their interpretation from a neuro-physiological point of view.
Hereafter, we propose to demystify such approaches, as we propose to leverage
new time-varying complex network models that equip us with a fractal dynamical
characterization of the underlying processes. Subsequently, the parameters of
the proposed complex network models can be explained from a system's
perspective, and, consecutively, used for classification using machine learning
algorithms and/or actuation laws determined using control system's theory.
Besides, the proposed system identification methods and techniques have
computational complexities comparable with those currently used in EEG-based
brain interfaces, which enable comparable online performances. Furthermore, we
foresee that the proposed models and approaches are also valid using other
invasive and non-invasive technologies. Finally, we illustrate and
experimentally evaluate this approach on real EEG-datasets to assess and
validate the proposed methodology. The classification accuracies are high even
on having less number of training samples.
| [
{
"created": "Tue, 27 Mar 2018 20:54:29 GMT",
"version": "v1"
}
] | 2018-03-29 | [
[
"Gupta",
"Gaurav",
""
],
[
"Pequito",
"Sergio",
""
],
[
"Bogdan",
"Paul",
""
]
] | Brain interfaces are cyber-physical systems that aim to harvest information from the (physical) brain through sensing mechanisms, extract information about the underlying processes, and decide/actuate accordingly. Nonetheless, the brain interfaces are still in their infancy, but reaching to their maturity quickly as several initiatives are released to push forward their development (e.g., NeuraLink by Elon Musk and `typing-by-brain' by Facebook). This has motivated us to revisit the design of EEG-based non-invasive brain interfaces. Specifically, current methodologies entail a highly skilled neuro-functional approach and evidence-based \emph{a priori} knowledge about specific signal features and their interpretation from a neuro-physiological point of view. Hereafter, we propose to demystify such approaches, as we propose to leverage new time-varying complex network models that equip us with a fractal dynamical characterization of the underlying processes. Subsequently, the parameters of the proposed complex network models can be explained from a system's perspective, and, consecutively, used for classification using machine learning algorithms and/or actuation laws determined using control system's theory. Besides, the proposed system identification methods and techniques have computational complexities comparable with those currently used in EEG-based brain interfaces, which enable comparable online performances. Furthermore, we foresee that the proposed models and approaches are also valid using other invasive and non-invasive technologies. Finally, we illustrate and experimentally evaluate this approach on real EEG-datasets to assess and validate the proposed methodology. The classification accuracies are high even on having less number of training samples. |
1711.04702 | Deisy Morselli Gysi | Deisy Morselli Gysi, Andre Voigt, Tiago de Miranda Fragoso, Eivind
Almaas and Katja Nowick | wTO: an R package for computing weighted topological overlap and
consensus networks with an integrated visualization tool | null | null | 10.1186/s12859-018-2351-7 | null | q-bio.MN stat.AP stat.CO stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network analyses, such as of gene co-expression networks, metabolic networks
and ecological networks have become a central approach for the systems-level
study of biological data. Several software packages exist for generating and
analyzing such networks, either from correlation scores or the absolute value
of a transformed score called weighted topological overlap (wTO). However,
since gene regulatory processes can up- or down-regulate genes, it is of great
interest to explicitly consider both positive and negative correlations when
constructing a gene co-expression network. Here, we present an R package for
calculating the wTO, that, in contrast to existing packages, explicitly
addresses the sign of the wTO values, and is thus especially valuable for the
analysis of gene regulatory networks. The package includes the calculation of
p-values (raw and adjusted) for each pairwise gene score. Our package also
allows the calculation of networks from time series (without replicates). Since
networks from independent datasets (biological repeats or related studies) are
not the same due to technical and biological noise in the data, we
additionally, incorporated a novel method for calculating a consensus network
(CN) from two or more networks into our R package. We compare our new wTO
package to state of art packages and demonstrate the application of the wTO and
CN functions using 3 independently derived datasets from healthy human
pre-frontal cortex samples. To showcase an example for the time series
application we utilized a metagenomics data set. In this work, we developed a
software package that allows the computation of wTO networks, CNs and a
visualization tool in the R statistical environment. It is publicly available
on CRAN repositories under the GPL-2 Open Source License
(https://cran.r-project.org/web/packages/wTO/).
| [
{
"created": "Mon, 13 Nov 2017 16:54:57 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Sep 2018 19:44:12 GMT",
"version": "v2"
}
] | 2021-04-26 | [
[
"Gysi",
"Deisy Morselli",
""
],
[
"Voigt",
"Andre",
""
],
[
"Fragoso",
"Tiago de Miranda",
""
],
[
"Almaas",
"Eivind",
""
],
[
"Nowick",
"Katja",
""
]
] | Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network. Here, we present an R package for calculating the wTO, that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set. In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL-2 Open Source License (https://cran.r-project.org/web/packages/wTO/). |
q-bio/0411023 | Peng-Ye Wang | Ping Xie, Shuo-Xing Dou, and Peng-Ye Wang | Model for processive movement of dynein | 16 pages, 5 figures | null | null | null | q-bio.BM | null | A model for the processive movement of dynein is presented based on
experimental observations available. In the model, the change from strong
microtubule-binding to weak binding of dynein is determined naturally by the
variation of the relative orientation between the two interacting surfaces of
the stalk tip and the microtubule as the stalk rotates from the ADP.Vi-state
orientation to the apo-state orientation. This means that the puzzling
communication from the ATP binding site in the globular head to the MT-binding
site in the tip of the stalk, which is prerequisite in the conventional model,
is not required. Using the present model, the previous experimental results,
such as (i) the step size of a dynein being an integer times of the period of
the MT lattice, (ii) the dependence of the step size on load, i.e., the step
size decreasing with the increase of load, and (iii) the stall force being
proportional to [ATP] at low [ATP] and becoming saturated at high [ATP], are
well explained.
| [
{
"created": "Thu, 11 Nov 2004 01:51:12 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Xie",
"Ping",
""
],
[
"Dou",
"Shuo-Xing",
""
],
[
"Wang",
"Peng-Ye",
""
]
] | A model for the processive movement of dynein is presented based on experimental observations available. In the model, the change from strong microtubule-binding to weak binding of dynein is determined naturally by the variation of the relative orientation between the two interacting surfaces of the stalk tip and the microtubule as the stalk rotates from the ADP.Vi-state orientation to the apo-state orientation. This means that the puzzling communication from the ATP binding site in the globular head to the MT-binding site in the tip of the stalk, which is prerequisite in the conventional model, is not required. Using the present model, the previous experimental results, such as (i) the step size of a dynein being an integer times of the period of the MT lattice, (ii) the dependence of the step size on load, i.e., the step size decreasing with the increase of load, and (iii) the stall force being proportional to [ATP] at low [ATP] and becoming saturated at high [ATP], are well explained. |
1709.09526 | Yukitaka Ishimoto | Yukitaka Ishimoto, Kaoru Sugimura | A mechanical model for diversified insect wing margin shapes | latex 21 pages, 9 figures, pre-revised version with the same content
for journal submission | Journal of Theoretical Biology 427 (2017) 17-27 | 10.1016/j.jtbi.2017.05.026 | null | q-bio.TO cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The wings in different insect species are morphologically distinct with
regards to their size, outer contour (margin) shape, venation, and
pigmentation. The basis of the diversity of wing margin shapes remains unknown,
despite the fact that gene networks governing the Drosophila wing development
have been well characterised. Among the different types of wing margin shapes,
smoothly curved contour is the most frequently found and implies the existence
of a highly organised, multicellular mechanical structure. Here, we developed a
mechanical model for diversified insect wing margin shapes, in which
non-uniform bending stiffness of the wing margin is considered. We showed that
a variety of spatial distribution of the bending stiffness could reproduce
diverse wing margin shapes. Moreover, the inference of the distribution of the
bending stiffness from experimental images indicates a common spatial profile
among insects tested. We further studied the effect of the intrinsic tension of
the wing blade on the margin shape and on the inferred bending stiffness.
Finally, we implemented the bending stiffness of the wing margin in the cell
vertex model of the wing blade, and confirmed that the hybrid model retains the
essential feature of the margin model. We propose that in addition to
morphogenetic processes in the wing blade, the spatial profile of the bending
stiffness in the wing margin can play a pivotal role in shaping insect wings.
| [
{
"created": "Wed, 27 Sep 2017 13:58:50 GMT",
"version": "v1"
}
] | 2017-09-28 | [
[
"Ishimoto",
"Yukitaka",
""
],
[
"Sugimura",
"Kaoru",
""
]
] | The wings in different insect species are morphologically distinct with regards to their size, outer contour (margin) shape, venation, and pigmentation. The basis of the diversity of wing margin shapes remains unknown, despite the fact that gene networks governing the Drosophila wing development have been well characterised. Among the different types of wing margin shapes, smoothly curved contour is the most frequently found and implies the existence of a highly organised, multicellular mechanical structure. Here, we developed a mechanical model for diversified insect wing margin shapes, in which non-uniform bending stiffness of the wing margin is considered. We showed that a variety of spatial distribution of the bending stiffness could reproduce diverse wing margin shapes. Moreover, the inference of the distribution of the bending stiffness from experimental images indicates a common spatial profile among insects tested. We further studied the effect of the intrinsic tension of the wing blade on the margin shape and on the inferred bending stiffness. Finally, we implemented the bending stiffness of the wing margin in the cell vertex model of the wing blade, and confirmed that the hybrid model retains the essential feature of the margin model. We propose that in addition to morphogenetic processes in the wing blade, the spatial profile of the bending stiffness in the wing margin can play a pivotal role in shaping insect wings. |
2112.03466 | Ryan Renslow | Sean M. Colby, Christine H. Chang, Jessica L. Bade, Jamie R. Nunez,
Madison R. Blumer, Daniel J. Orton, Kent J. Bloodsworth, Ernesto S. Nakayasu,
Richard D. Smith, Yehia M. Ibrahim, Ryan S. Renslow, Thomas O. Metz | DEIMoS: an open-source tool for processing high-dimensional mass
spectrometry data | null | null | null | null | q-bio.QM q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | We present DEIMoS: Data Extraction for Integrated Multidimensional
Spectrometry, a Python application programming interface (API) and command-line
tool for high-dimensional mass spectrometry data analysis workflows that offers
ease of development and access to efficient algorithmic implementations.
Functionality includes feature detection, feature alignment, collision cross
section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution,
with the output comprising detected features aligned across study samples and
characterized by mass, CCS, tandem mass spectra, and isotopic signature.
Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition
instrumentation; algorithm implementations simultaneously utilize all
dimensions to (i) offer greater separation between features, thus improving
detection sensitivity, (ii) increase alignment/feature matching confidence
among datasets, and (iii) mitigate convolution artifacts in tandem mass
spectra. We demonstrate DEIMoS with LC-IMS-MS/MS data to illustrate the
advantages of a multidimensional approach in each data processing step.
| [
{
"created": "Tue, 7 Dec 2021 03:14:58 GMT",
"version": "v1"
}
] | 2021-12-08 | [
[
"Colby",
"Sean M.",
""
],
[
"Chang",
"Christine H.",
""
],
[
"Bade",
"Jessica L.",
""
],
[
"Nunez",
"Jamie R.",
""
],
[
"Blumer",
"Madison R.",
""
],
[
"Orton",
"Daniel J.",
""
],
[
"Bloodsworth",
"Kent J.",
... | We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among datasets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS data to illustrate the advantages of a multidimensional approach in each data processing step. |
1803.09565 | Alexander Titus | Alexander J. Titus, Audrey Flower, Patrick Hagerty, Paul Gamble,
Charlie Lewis, Todd Stavish, Kevin P. OConnell, Greg Shipley, and Stephanie
M. Rogers | SIG-DB: leveraging homomorphic encryption to Securely Interrogate
privately held Genomic DataBases | 38 pages, 3 figures, 4 tables, 1 supplemental table, 7 supplemental
figures | PLoS Computational Biology; 2018 Sep 4; 14(9):e1006454 | 10.1371/journal.pcbi.1006454 | PMID: 30180163 | q-bio.QM cs.CR cs.DB q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genomic data are becoming increasingly valuable as we develop methods to
utilize the information at scale and gain a greater understanding of how
genetic information relates to biological function. Advances in synthetic
biology and the decreased cost of sequencing are increasing the amount of
privately held genomic data. As the quantity and value of private genomic data
grows, so does the incentive to acquire and protect such data, which creates a
need to store and process these data securely. We present an algorithm for the
Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm
enables databases of genomic sequences to be searched with an encrypted query
sequence without revealing the query sequence to the Database Owner or any of
the database sequences to the Querier. SIG-DB is the first application of its
kind to take advantage of locality-sensitive hashing and homomorphic encryption
to allow generalized sequence-to-sequence comparisons of genomic data.
| [
{
"created": "Mon, 26 Mar 2018 13:09:12 GMT",
"version": "v1"
}
] | 2018-09-13 | [
[
"Titus",
"Alexander J.",
""
],
[
"Flower",
"Audrey",
""
],
[
"Hagerty",
"Patrick",
""
],
[
"Gamble",
"Paul",
""
],
[
"Lewis",
"Charlie",
""
],
[
"Stavish",
"Todd",
""
],
[
"OConnell",
"Kevin P.",
""
],
... | Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to biological function. Advances in synthetic biology and the decreased cost of sequencing are increasing the amount of privately held genomic data. As the quantity and value of private genomic data grows, so does the incentive to acquire and protect such data, which creates a need to store and process these data securely. We present an algorithm for the Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm enables databases of genomic sequences to be searched with an encrypted query sequence without revealing the query sequence to the Database Owner or any of the database sequences to the Querier. SIG-DB is the first application of its kind to take advantage of locality-sensitive hashing and homomorphic encryption to allow generalized sequence-to-sequence comparisons of genomic data. |
2407.21080 | Yuanyuan Wei | Yuanyuan Wei, Xianxian Liu, Changran Xu, Guoxun Zhang, Wu Yuan, Ho-Pui
Ho, and Mingkun Xu | Artificial Intelligence Enhanced Digital Nucleic Acid Amplification
Testing for Precision Medicine and Molecular Diagnostics | Review article. 46 Pages. 6 Figures. 4 Tables | null | null | null | q-bio.QM eess.IV | http://creativecommons.org/licenses/by/4.0/ | The precise quantification of nucleic acids is pivotal in molecular biology,
underscored by the rising prominence of nucleic acid amplification tests (NAAT)
in diagnosing infectious diseases and conducting genomic studies. This review
examines recent advancements in digital Polymerase Chain Reaction (dPCR) and
digital Loop-mediated Isothermal Amplification (dLAMP), which surpass the
limitations of traditional NAAT by offering absolute quantification and
enhanced sensitivity. In this review, we summarize the compelling advancements
of dNNAT in addressing pressing public health issues, especially during the
COVID-19 pandemic. Further, we explore the transformative role of artificial
intelligence (AI) in enhancing dNAAT image analysis, which not only improves
efficiency and accuracy but also addresses traditional constraints related to
cost, complexity, and data interpretation. In encompassing the state-of-the-art
(SOTA) development and potential of both software and hardware, the
all-encompassing Point-of-Care Testing (POCT) systems cast new light on
benefits including higher throughput, label-free detection, and expanded
multiplex analyses. While acknowledging the enhancement of AI-enhanced dNAAT
technology, this review aims to both fill critical gaps in the existing
technologies through comparative assessments and offer a balanced perspective
on the current trajectory, including attendant challenges and future
directions. Leveraging AI, next-generation dPCR and dLAMP technologies promises
integration into clinical practice, improving personalized medicine, real-time
epidemic surveillance, and global diagnostic accessibility.
| [
{
"created": "Tue, 30 Jul 2024 01:17:29 GMT",
"version": "v1"
}
] | 2024-08-01 | [
[
"Wei",
"Yuanyuan",
""
],
[
"Liu",
"Xianxian",
""
],
[
"Xu",
"Changran",
""
],
[
"Zhang",
"Guoxun",
""
],
[
"Yuan",
"Wu",
""
],
[
"Ho",
"Ho-Pui",
""
],
[
"Xu",
"Mingkun",
""
]
] | The precise quantification of nucleic acids is pivotal in molecular biology, underscored by the rising prominence of nucleic acid amplification tests (NAAT) in diagnosing infectious diseases and conducting genomic studies. This review examines recent advancements in digital Polymerase Chain Reaction (dPCR) and digital Loop-mediated Isothermal Amplification (dLAMP), which surpass the limitations of traditional NAAT by offering absolute quantification and enhanced sensitivity. In this review, we summarize the compelling advancements of dNNAT in addressing pressing public health issues, especially during the COVID-19 pandemic. Further, we explore the transformative role of artificial intelligence (AI) in enhancing dNAAT image analysis, which not only improves efficiency and accuracy but also addresses traditional constraints related to cost, complexity, and data interpretation. In encompassing the state-of-the-art (SOTA) development and potential of both software and hardware, the all-encompassing Point-of-Care Testing (POCT) systems cast new light on benefits including higher throughput, label-free detection, and expanded multiplex analyses. While acknowledging the enhancement of AI-enhanced dNAAT technology, this review aims to both fill critical gaps in the existing technologies through comparative assessments and offer a balanced perspective on the current trajectory, including attendant challenges and future directions. Leveraging AI, next-generation dPCR and dLAMP technologies promises integration into clinical practice, improving personalized medicine, real-time epidemic surveillance, and global diagnostic accessibility. |
2402.19035 | Sergei Kozyrev | S. V. Kozyrev | Lotka-Volterra Model with Mutations and Generative Adversarial Networks | 13 pages | Theoretical and Mathematical Physics, 218(2): 276--284 (2024) | 10.1134/S0040577924020077 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A model of population genetics of the Lotka-Volterra type with mutations on a
statistical manifold is introduced. Mutations in the model are described by
diffusion on a statistical manifold with a generator in the form of a
Laplace-Beltrami operator with a Fisher-Rao metric, that is, the model combines
population genetics and information geometry. This model describes a
generalization of the model of machine learning theory, the model of generative
adversarial network (GAN), to the case of populations of generative adversarial
networks. The introduced model describes the control of overfitting for
generative adversarial networks.
| [
{
"created": "Thu, 29 Feb 2024 11:00:19 GMT",
"version": "v1"
}
] | 2024-03-01 | [
[
"Kozyrev",
"S. V.",
""
]
] | A model of population genetics of the Lotka-Volterra type with mutations on a statistical manifold is introduced. Mutations in the model are described by diffusion on a statistical manifold with a generator in the form of a Laplace-Beltrami operator with a Fisher-Rao metric, that is, the model combines population genetics and information geometry. This model describes a generalization of the model of machine learning theory, the model of generative adversarial network (GAN), to the case of populations of generative adversarial networks. The introduced model describes the control of overfitting for generative adversarial networks. |
2210.15271 | Yetao Wu | Yetao Wu, Han Liu, Jie Yan, Xiaolin Hu | Drug repositioning for Alzheimer's disease with transfer learning | 13 pages, 1 figure | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep Learning and DRUG-seq (Digital RNA with perturbation of genes) have
attracted attention in drug discovery. However, the public DRUG-seq dataset is
too small to be used for directly training a deep learning neural network from
scratch. Inspired by the transfer learning technique, we pretrain a drug
efficacy prediction neural network model with the Library of Integrated
Network-based Cell-Signature (LINCS) L1000 data and then use human neural cell
DRUG-seq data to fine-tune it. After training, the model is used for virtual
screening to find potential drugs for Alzheimer's disease (AD) treatment.
Finally, we find 27 potential drugs for AD treatment including Irsogladine
(PDE4 inhibitor), Tasquinimod (HDAC4 selective inhibitor), Suprofen (dual
COX-1/COX-2 inhibitor) et al.
| [
{
"created": "Thu, 27 Oct 2022 08:56:44 GMT",
"version": "v1"
}
] | 2022-10-28 | [
[
"Wu",
"Yetao",
""
],
[
"Liu",
"Han",
""
],
[
"Yan",
"Jie",
""
],
[
"Hu",
"Xiaolin",
""
]
] | Deep Learning and DRUG-seq (Digital RNA with perturbation of genes) have attracted attention in drug discovery. However, the public DRUG-seq dataset is too small to be used for directly training a deep learning neural network from scratch. Inspired by the transfer learning technique, we pretrain a drug efficacy prediction neural network model with the Library of Integrated Network-based Cell-Signature (LINCS) L1000 data and then use human neural cell DRUG-seq data to fine-tune it. After training, the model is used for virtual screening to find potential drugs for Alzheimer's disease (AD) treatment. Finally, we find 27 potential drugs for AD treatment including Irsogladine (PDE4 inhibitor), Tasquinimod (HDAC4 selective inhibitor), Suprofen (dual COX-1/COX-2 inhibitor) et al. |
1901.07467 | Chengyuan Liu | Chengyuan Liu, Josep Vehi, Nick Oliver, Pantelis Georgiou and Pau
Herrero | Enhancing Blood Glucose Prediction with Meal Absorption and Physical
Exercise Information | 10 pages, 5 figures, 8 tables and one appendix | null | null | null | q-bio.TO cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: Numerous glucose prediction algorithm have been proposed to
empower type 1 diabetes (T1D) management. Most of these algorithms only account
for input such as glucose, insulin and carbohydrate, which limits their
performance. Here, we present a novel glucose prediction algorithm which, in
addition to standard inputs, accounts for meal absorption and physical exercise
information to enhance prediction accuracy. Methods: a compartmental model of
glucose-insulin dynamics combined with a deconvolution technique for state
estimation is employed for glucose prediction. In silico data corresponding
from the 10 adult subjects of UVa-Padova simulator, and clinical data from 10
adults with T1D were used. Finally, a comparison against a validated glucose
prediction algorithm based on a latent variable with exogenous input (LVX)
model is provided. Results: For a prediction horizon of 60 minutes, accounting
for meal absorption and physical exercise improved glucose forecasting
accuracy. In particular, root mean square error (mg/dL) went from 26.68 to
23.89, p<0.001 (in silico data); and from 37.02 to 35.96, p<0.001 (clinical
data - only meal information). Such improvement in accuracy was translated into
significant improvements on hypoglycaemia and hyperglycaemia prediction.
Finally, the performance of the proposed algorithm is statistically superior to
that of the LVX algorithm (26.68 vs. 32.80, p<0.001 (in silico data); 37.02 vs.
49.17, p<0.01 (clinical data). Conclusion: Taking into account meal absorption
and physical exercise information improves glucose prediction accuracy.
| [
{
"created": "Thu, 13 Dec 2018 18:29:34 GMT",
"version": "v1"
}
] | 2019-01-23 | [
[
"Liu",
"Chengyuan",
""
],
[
"Vehi",
"Josep",
""
],
[
"Oliver",
"Nick",
""
],
[
"Georgiou",
"Pantelis",
""
],
[
"Herrero",
"Pau",
""
]
] | Objective: Numerous glucose prediction algorithm have been proposed to empower type 1 diabetes (T1D) management. Most of these algorithms only account for input such as glucose, insulin and carbohydrate, which limits their performance. Here, we present a novel glucose prediction algorithm which, in addition to standard inputs, accounts for meal absorption and physical exercise information to enhance prediction accuracy. Methods: a compartmental model of glucose-insulin dynamics combined with a deconvolution technique for state estimation is employed for glucose prediction. In silico data corresponding from the 10 adult subjects of UVa-Padova simulator, and clinical data from 10 adults with T1D were used. Finally, a comparison against a validated glucose prediction algorithm based on a latent variable with exogenous input (LVX) model is provided. Results: For a prediction horizon of 60 minutes, accounting for meal absorption and physical exercise improved glucose forecasting accuracy. In particular, root mean square error (mg/dL) went from 26.68 to 23.89, p<0.001 (in silico data); and from 37.02 to 35.96, p<0.001 (clinical data - only meal information). Such improvement in accuracy was translated into significant improvements on hypoglycaemia and hyperglycaemia prediction. Finally, the performance of the proposed algorithm is statistically superior to that of the LVX algorithm (26.68 vs. 32.80, p<0.001 (in silico data); 37.02 vs. 49.17, p<0.01 (clinical data). Conclusion: Taking into account meal absorption and physical exercise information improves glucose prediction accuracy. |
2211.09276 | Uwe C. T\"auber | Mohamed Swailem and Uwe C. T\"auber (Virginia Tech) | Lotka-Volterra predator-prey model with periodically varying carrying
capacity | 18 pages, 20 figures, to appear in Phys. Rev. E (2023) | Phys. Rev. E 107 (2023) 064144 | 10.1103/PhysRevE.107.064144 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the stochastic spatial Lotka-Volterra (LV) model for predator-prey
interaction subject to a periodically varying carrying capacity. The LV model
with on-site lattice occupation restrictions that represent finite food
resources for the prey exhibits a continuous active-to-absorbing phase
transition. The active phase is sustained by spatio-temporal patterns in the
form of pursuit and evasion waves. Monte Carlo simulations on a two-dimensional
lattice are utilized to investigate the effect of seasonal variations of the
environment on species coexistence. The results of our simulations are also
compared to a mean-field analysis. We find that the parameter region of
predator and prey coexistence is enlarged relative to the stationary situation
when the carrying capacity varies periodically. The stationary regime of our
periodically varying LV system shows qualitative agreement between the
stochastic model and the mean-field approximation. However, under periodic
carrying capacity switching environments, the mean-field rate equations predict
period-doubling scenarios that are washed out by internal reaction noise in the
stochastic lattice model. Utilizing visual representations of the lattice
simulations and dynamical correlation functions, we study how the pursuit and
evasion waves are affected by ensuing resonance effects. Correlation function
measurements indicate a time delay in the response of the system to sudden
changes in the environment. Resonance features are observed in our simulations
that cause prolonged persistent spatial correlations. Different effective
static environments are explored in the extreme limits of fast- and slow
periodic switching. The analysis of the mean-field equations in the
fast-switching regime enables a semi-quantitative description of the stationary
state.
| [
{
"created": "Thu, 17 Nov 2022 00:37:07 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Feb 2023 10:43:58 GMT",
"version": "v2"
},
{
"created": "Mon, 12 Jun 2023 15:42:02 GMT",
"version": "v3"
}
] | 2023-07-07 | [
[
"Swailem",
"Mohamed",
"",
"Virginia Tech"
],
[
"Täuber",
"Uwe C.",
"",
"Virginia Tech"
]
] | We study the stochastic spatial Lotka-Volterra (LV) model for predator-prey interaction subject to a periodically varying carrying capacity. The LV model with on-site lattice occupation restrictions that represent finite food resources for the prey exhibits a continuous active-to-absorbing phase transition. The active phase is sustained by spatio-temporal patterns in the form of pursuit and evasion waves. Monte Carlo simulations on a two-dimensional lattice are utilized to investigate the effect of seasonal variations of the environment on species coexistence. The results of our simulations are also compared to a mean-field analysis. We find that the parameter region of predator and prey coexistence is enlarged relative to the stationary situation when the carrying capacity varies periodically. The stationary regime of our periodically varying LV system shows qualitative agreement between the stochastic model and the mean-field approximation. However, under periodic carrying capacity switching environments, the mean-field rate equations predict period-doubling scenarios that are washed out by internal reaction noise in the stochastic lattice model. Utilizing visual representations of the lattice simulations and dynamical correlation functions, we study how the pursuit and evasion waves are affected by ensuing resonance effects. Correlation function measurements indicate a time delay in the response of the system to sudden changes in the environment. Resonance features are observed in our simulations that cause prolonged persistent spatial correlations. Different effective static environments are explored in the extreme limits of fast- and slow periodic switching. The analysis of the mean-field equations in the fast-switching regime enables a semi-quantitative description of the stationary state. |
1605.04519 | Enrico Catalano | Enrico Catalano | Role of phytochemicals in the chemoprevention of tumors | null | null | null | null | q-bio.BM q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Phytochemicals are plant-derived secondary metabolites, which may exert many
biological activities in humans, including anticancer properties. Although
recent findings appear to support their role in cancer prevention and
treatment, this issue is still controversial. Anti-cancer activity of
phytochemicals mainly depends on their multi-target mechanism of action,
including antimutagenic, antioxidant and antiproliferative activities.
Furthermore, they may modulate the host immune response to cancer, reducing
inflammatory microenvironment and enhancing lymphocyte onco-surveillance. Since
carcinogenesis is multi-factorial and involves several signaling pathways,
multi-targeted agents as phytochemicals may represent promising anticancer
compounds. This narrative review aims to analyze the current literature on
phytochemicals highlighting their specific targets on carcinogenic molecular
pathways and their chemopreventive role. A full comprehension of their activity
at molecular and cellular levels will contribute for a better understanding of
phytochemical clinical efficacy, thus promoting the identification of new
effective plant-derived therapeutics.
| [
{
"created": "Sun, 15 May 2016 10:55:57 GMT",
"version": "v1"
}
] | 2016-05-17 | [
[
"Catalano",
"Enrico",
""
]
] | Phytochemicals are plant-derived secondary metabolites, which may exert many biological activities in humans, including anticancer properties. Although recent findings appear to support their role in cancer prevention and treatment, this issue is still controversial. Anti-cancer activity of phytochemicals mainly depends on their multi-target mechanism of action, including antimutagenic, antioxidant and antiproliferative activities. Furthermore, they may modulate the host immune response to cancer, reducing inflammatory microenvironment and enhancing lymphocyte onco-surveillance. Since carcinogenesis is multi-factorial and involves several signaling pathways, multi-targeted agents as phytochemicals may represent promising anticancer compounds. This narrative review aims to analyze the current literature on phytochemicals highlighting their specific targets on carcinogenic molecular pathways and their chemopreventive role. A full comprehension of their activity at molecular and cellular levels will contribute for a better understanding of phytochemical clinical efficacy, thus promoting the identification of new effective plant-derived therapeutics. |
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