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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1502.00820 | Sedat G\"undo\u{g}du Mr | Sedat Gundogdu | A study on the characteristics of introduced Pearl Mullet population
(Chalcalburnus Tarichi, Pallas, 1811) in Lake Er\c{c}ek | 8 pages with 3 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/publicdomain/ | This study examines features such as the population structure, growth and
reproduction of the pearl mullet captured in the Lake Ercek. Between the dates
January 2008 and January 2010 a total number of 527 individual were sampled
through using trammel net and mesh size of 20,22 and 24 mm of fish net. The
fork length, total and gonad weight, sex and fecundity of the pearl mullet
which were captured has been identified; their age determination has been done
through their operculum and otolith; their condition factor and gonadosomatic
index value has been estimated.
| [
{
"created": "Tue, 3 Feb 2015 11:39:04 GMT",
"version": "v1"
}
] | 2015-02-04 | [
[
"Gundogdu",
"Sedat",
""
]
] | This study examines features such as the population structure, growth and reproduction of the pearl mullet captured in the Lake Ercek. Between the dates January 2008 and January 2010 a total number of 527 individual were sampled through using trammel net and mesh size of 20,22 and 24 mm of fish net. The fork length, total and gonad weight, sex and fecundity of the pearl mullet which were captured has been identified; their age determination has been done through their operculum and otolith; their condition factor and gonadosomatic index value has been estimated. |
2010.15065 | Amir Shanehsazzadeh | Amir Shanehsazzadeh, David Belanger, David Dohan | Fixed-Length Protein Embeddings using Contextual Lenses | null | null | null | null | q-bio.BM cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Basic Local Alignment Search Tool (BLAST) is currently the most popular
method for searching databases of biological sequences. BLAST compares
sequences via similarity defined by a weighted edit distance, which results in
it being computationally expensive. As opposed to working with edit distance, a
vector similarity approach can be accelerated substantially using modern
hardware or hashing techniques. Such an approach would require fixed-length
embeddings for biological sequences. There has been recent interest in learning
fixed-length protein embeddings using deep learning models under the hypothesis
that the hidden layers of supervised or semi-supervised models could produce
potentially useful vector embeddings. We consider transformer (BERT) protein
language models that are pretrained on the TrEMBL data set and learn
fixed-length embeddings on top of them with contextual lenses. The embeddings
are trained to predict the family a protein belongs to for sequences in the
Pfam database. We show that for nearest-neighbor family classification,
pretraining offers a noticeable boost in performance and that the corresponding
learned embeddings are competitive with BLAST. Furthermore, we show that the
raw transformer embeddings, obtained via static pooling, do not perform well on
nearest-neighbor family classification, which suggests that learning embeddings
in a supervised manner via contextual lenses may be a compute-efficient
alternative to fine-tuning.
| [
{
"created": "Thu, 15 Oct 2020 14:54:55 GMT",
"version": "v1"
}
] | 2020-10-29 | [
[
"Shanehsazzadeh",
"Amir",
""
],
[
"Belanger",
"David",
""
],
[
"Dohan",
"David",
""
]
] | The Basic Local Alignment Search Tool (BLAST) is currently the most popular method for searching databases of biological sequences. BLAST compares sequences via similarity defined by a weighted edit distance, which results in it being computationally expensive. As opposed to working with edit distance, a vector similarity approach can be accelerated substantially using modern hardware or hashing techniques. Such an approach would require fixed-length embeddings for biological sequences. There has been recent interest in learning fixed-length protein embeddings using deep learning models under the hypothesis that the hidden layers of supervised or semi-supervised models could produce potentially useful vector embeddings. We consider transformer (BERT) protein language models that are pretrained on the TrEMBL data set and learn fixed-length embeddings on top of them with contextual lenses. The embeddings are trained to predict the family a protein belongs to for sequences in the Pfam database. We show that for nearest-neighbor family classification, pretraining offers a noticeable boost in performance and that the corresponding learned embeddings are competitive with BLAST. Furthermore, we show that the raw transformer embeddings, obtained via static pooling, do not perform well on nearest-neighbor family classification, which suggests that learning embeddings in a supervised manner via contextual lenses may be a compute-efficient alternative to fine-tuning. |
q-bio/0507017 | Mathieu Andro | Pascal Deynat (DMPA) | New data on the systematics and interrelationships of sawfishes
(Elasmobranchii, Batoidea, Pristiformes) | null | Journal of Fish Biology 66 (2005) 1447-1458 | 10.1111/j.0022-1112.2005.00695.x | null | q-bio.PE | null | New characters based on the arrangement and morphology of dermal denticles
show that sawfishes can be divided into two distinctive groups. The first
group, comprising the knifetooth sawfish Anoxypristis cuspidata, is
characterized by tricuspid denticles variably located on both dorsal and
ventral parts of the body. The second group is represented by species of the
genus Pristis, showing an uniform and homogenous dermal covering of
monocuspidate denticles on both dorsal and ventral sides of the body and within
the buccopharyngeal cavity. Pristis is further divided into two subgroups: the
first comprises species with denticles lacking any keels and furrows (the
smalltooth sawfish Pristis pectinata, the green sawfish Pristis zijsron and the
dwarf sawfish Pristis clavata); the second comprises species with denticles
presenting keels and furrows well differentiated on their anterior part (the
common sawfish Pristis pristis, the largetooth sawfish Pristis perotteti and
the greattooth sawfish Pristis microdon). This investigation of the dermal
covering provides results which agree with studies that separate the same two
species groups of Pristis on the basis of other morphological data.
| [
{
"created": "Tue, 12 Jul 2005 14:07:01 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Deynat",
"Pascal",
"",
"DMPA"
]
] | New characters based on the arrangement and morphology of dermal denticles show that sawfishes can be divided into two distinctive groups. The first group, comprising the knifetooth sawfish Anoxypristis cuspidata, is characterized by tricuspid denticles variably located on both dorsal and ventral parts of the body. The second group is represented by species of the genus Pristis, showing an uniform and homogenous dermal covering of monocuspidate denticles on both dorsal and ventral sides of the body and within the buccopharyngeal cavity. Pristis is further divided into two subgroups: the first comprises species with denticles lacking any keels and furrows (the smalltooth sawfish Pristis pectinata, the green sawfish Pristis zijsron and the dwarf sawfish Pristis clavata); the second comprises species with denticles presenting keels and furrows well differentiated on their anterior part (the common sawfish Pristis pristis, the largetooth sawfish Pristis perotteti and the greattooth sawfish Pristis microdon). This investigation of the dermal covering provides results which agree with studies that separate the same two species groups of Pristis on the basis of other morphological data. |
0904.2252 | Hao Ge | Hao Ge, Min Qian | Boolean Network Approach to Negative Feedback Loops of the p53 Pathways:
Synchronized Dynamics and Stochastic Limit Cycles | 20 pages, 9 figures; in Journal of Computational Biology 2009 | null | null | null | q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deterministic and stochastic Boolean network models are build for the
dynamics of negative feedback loops of the p53 pathways. It is shown that the
main function of the negative feedback in the p53 pathways is to keep p53 at a
low steady state level, and each sequence of protein states in the negative
feedback loops, is globally attracted to a closed cycle of the p53 dynamics
after being perturbed by outside signal (e.g. DNA damage). Our theoretical and
numerical studies show that both the biological stationary state and the
biological oscillation after being perturbed are stable for a wide range of
noise level. Applying the mathematical circulation theory of Markov chains, we
investigate their stochastic synchronized dynamics and by comparing the network
dynamics of the stochastic model with its corresponding deterministic network
counterpart, a dominant circulation in the stochastic model is the natural
generalization of the deterministic limit cycle in the deterministic system.
Moreover, the period of the main peak in the power spectrum, which is in common
use to characterize the synchronized dynamics, perfectly corresponds to the
number of states in the main cycle with dominant circulation. Such a large
separation in the magnitude of the circulations, between a dominant, main cycle
and the rest, gives rise to the stochastic synchronization phenomenon.
| [
{
"created": "Wed, 15 Apr 2009 07:35:16 GMT",
"version": "v1"
}
] | 2009-04-16 | [
[
"Ge",
"Hao",
""
],
[
"Qian",
"Min",
""
]
] | Deterministic and stochastic Boolean network models are build for the dynamics of negative feedback loops of the p53 pathways. It is shown that the main function of the negative feedback in the p53 pathways is to keep p53 at a low steady state level, and each sequence of protein states in the negative feedback loops, is globally attracted to a closed cycle of the p53 dynamics after being perturbed by outside signal (e.g. DNA damage). Our theoretical and numerical studies show that both the biological stationary state and the biological oscillation after being perturbed are stable for a wide range of noise level. Applying the mathematical circulation theory of Markov chains, we investigate their stochastic synchronized dynamics and by comparing the network dynamics of the stochastic model with its corresponding deterministic network counterpart, a dominant circulation in the stochastic model is the natural generalization of the deterministic limit cycle in the deterministic system. Moreover, the period of the main peak in the power spectrum, which is in common use to characterize the synchronized dynamics, perfectly corresponds to the number of states in the main cycle with dominant circulation. Such a large separation in the magnitude of the circulations, between a dominant, main cycle and the rest, gives rise to the stochastic synchronization phenomenon. |
2103.13114 | Mar\'ia Vallet-Regi | N. Gomez-Cerezo, L. Casarrubios, M. Saiz-Pardo, L. Ortega, D. de
Pablo, I. Diaz-Guemes, B. Fernandez-Tome, S. Enciso, F.M. Sanchez-Margallo,
M.T. Portoles, D. Arcos, M. Vallet-Regi | Mesoporous bioactive glass/e-polycaprolactone scaffolds promote bone
regeneration in osteoporotic sheep | 25 pages | Acta Biomaterialia 90 (2019) 3693-402 | 10.1016/j.actbio.2019.04.019 | null | q-bio.TO physics.bio-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Macroporous scaffolds made of a SiO2-CaO-P2O5 mesoporous bioactive glass
(MBG) and epolycaprolactone (PCL) have been prepared by robocasting. These
scaffolds showed an excellent in vitro biocompatibility in contact with
osteoblast like cells (Saos 2) and osteoclasts derived from RAW 264.7
macrophages. In vivo studies were carried out by implantation into cavitary
defects drilled in osteoporotic sheep. The scaffolds evidenced excellent bone
regeneration properties, promoting new bone formation at both the peripheral
and the inner parts of the scaffolds, thick trabeculae, high vascularization
and high presence of osteoblasts and osteoclasts. In order to evaluate the
effects of the local release of an antiosteoporotic drug, 1% (%wt) of
zoledronic acid was incorporated to the scaffolds. The scaffolds loaded with
zoledronic acid induced apoptosis in Saos 2 cells, impeded osteoclast
differentiation in a time dependent manner and inhibited bone healing,
promoting an intense inflammatory response in osteoporotic sheep.
| [
{
"created": "Wed, 24 Mar 2021 11:42:27 GMT",
"version": "v1"
}
] | 2021-03-25 | [
[
"Gomez-Cerezo",
"N.",
""
],
[
"Casarrubios",
"L.",
""
],
[
"Saiz-Pardo",
"M.",
""
],
[
"Ortega",
"L.",
""
],
[
"de Pablo",
"D.",
""
],
[
"Diaz-Guemes",
"I.",
""
],
[
"Fernandez-Tome",
"B.",
""
],
[
"Enciso",
"S.",
""
],
[
"Sanchez-Margallo",
"F. M.",
""
],
[
"Portoles",
"M. T.",
""
],
[
"Arcos",
"D.",
""
],
[
"Vallet-Regi",
"M.",
""
]
] | Macroporous scaffolds made of a SiO2-CaO-P2O5 mesoporous bioactive glass (MBG) and epolycaprolactone (PCL) have been prepared by robocasting. These scaffolds showed an excellent in vitro biocompatibility in contact with osteoblast like cells (Saos 2) and osteoclasts derived from RAW 264.7 macrophages. In vivo studies were carried out by implantation into cavitary defects drilled in osteoporotic sheep. The scaffolds evidenced excellent bone regeneration properties, promoting new bone formation at both the peripheral and the inner parts of the scaffolds, thick trabeculae, high vascularization and high presence of osteoblasts and osteoclasts. In order to evaluate the effects of the local release of an antiosteoporotic drug, 1% (%wt) of zoledronic acid was incorporated to the scaffolds. The scaffolds loaded with zoledronic acid induced apoptosis in Saos 2 cells, impeded osteoclast differentiation in a time dependent manner and inhibited bone healing, promoting an intense inflammatory response in osteoporotic sheep. |
1411.7635 | Simone Carlo Surace | Simone Carlo Surace, Jean-Pascal Pfister | A statistical model for in vivo neuronal dynamics | 31 pages, 10 figures | null | 10.1371/journal.pone.0142435 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single neuron models have a long tradition in computational neuroscience.
Detailed biophysical models such as the Hodgkin-Huxley model as well as
simplified neuron models such as the class of integrate-and-fire models relate
the input current to the membrane potential of the neuron. Those types of
models have been extensively fitted to in vitro data where the input current is
controlled. Those models are however of little use when it comes to
characterize intracellular in vivo recordings since the input to the neuron is
not known. Here we propose a novel single neuron model that characterizes the
statistical properties of in vivo recordings. More specifically, we propose a
stochastic process where the subthreshold membrane potential follows a Gaussian
process and the spike emission intensity depends nonlinearly on the membrane
potential as well as the spiking history. We first show that the model has a
rich dynamical repertoire since it can capture arbitrary subthreshold
autocovariance functions, firing-rate adaptations as well as arbitrary shapes
of the action potential. We then show that this model can be efficiently fitted
to data without overfitting. Finally, we show that this model can be used to
characterize and therefore precisely compare various intracellular in vivo
recordings from different animals and experimental conditions.
| [
{
"created": "Thu, 27 Nov 2014 16:10:19 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Apr 2015 15:21:04 GMT",
"version": "v2"
},
{
"created": "Fri, 23 Oct 2015 13:13:20 GMT",
"version": "v3"
}
] | 2016-11-02 | [
[
"Surace",
"Simone Carlo",
""
],
[
"Pfister",
"Jean-Pascal",
""
]
] | Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. Finally, we show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions. |
2003.12028 | Ying-Cheng Lai | Yong-Shang Long, Zheng-Meng Zhai, Li-Lei Han, Jie Kang, Yi-Lin Li,
Zhao-Hua Lin, Lang Zeng, Da-Yu Wu, Chang-Qing Hao, Ming Tang, Zonghua Liu,
and Ying-Cheng Lai | Quantitative assessment of the role of undocumented infection in the
2019 novel coronavirus (COVID-19) pandemic | 20 pages, 7 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An urgent problem in controlling COVID-19 spreading is to understand the role
of undocumented infection. We develop a five-state model for COVID-19, taking
into account the unique features of the novel coronavirus, with key parameters
determined by the government reports and mathematical optimization. Tests using
data from China, South Korea, Italy, and Iran indicate that the model is
capable of generating accurate prediction of the daily accumulated number of
confirmed cases and is entirely suitable for real-time prediction. The
drastically disparate testing and diagnostic standards/policies among different
countries lead to large variations in the estimated parameter values such as
the duration of the outbreak, but such uncertainties have little effect on the
occurrence time of the inflection point as predicted by the model, indicating
its reliability and robustness. Model prediction for Italy suggests that
insufficient government action leading to a large fraction of undocumented
infection plays an important role in the abnormally high mortality in that
country. With the data currently available from United Kingdom, our model
predicts catastrophic epidemic scenarios in the country if the government did
not impose strict travel and social distancing restrictions. A key finding is
that, if the percentage of undocumented infection exceeds a threshold, a
non-negligible hidden population can exist even after the the epidemic has been
deemed over, implying the likelihood of future outbreaks should the currently
imposed strict government actions be relaxed. This could make COVID-19 evolving
into a long-term epidemic or a community disease a real possibility, suggesting
the necessity to conduct universal testing and monitoring to identify the
hidden individuals.
| [
{
"created": "Thu, 26 Mar 2020 16:50:16 GMT",
"version": "v1"
}
] | 2020-03-27 | [
[
"Long",
"Yong-Shang",
""
],
[
"Zhai",
"Zheng-Meng",
""
],
[
"Han",
"Li-Lei",
""
],
[
"Kang",
"Jie",
""
],
[
"Li",
"Yi-Lin",
""
],
[
"Lin",
"Zhao-Hua",
""
],
[
"Zeng",
"Lang",
""
],
[
"Wu",
"Da-Yu",
""
],
[
"Hao",
"Chang-Qing",
""
],
[
"Tang",
"Ming",
""
],
[
"Liu",
"Zonghua",
""
],
[
"Lai",
"Ying-Cheng",
""
]
] | An urgent problem in controlling COVID-19 spreading is to understand the role of undocumented infection. We develop a five-state model for COVID-19, taking into account the unique features of the novel coronavirus, with key parameters determined by the government reports and mathematical optimization. Tests using data from China, South Korea, Italy, and Iran indicate that the model is capable of generating accurate prediction of the daily accumulated number of confirmed cases and is entirely suitable for real-time prediction. The drastically disparate testing and diagnostic standards/policies among different countries lead to large variations in the estimated parameter values such as the duration of the outbreak, but such uncertainties have little effect on the occurrence time of the inflection point as predicted by the model, indicating its reliability and robustness. Model prediction for Italy suggests that insufficient government action leading to a large fraction of undocumented infection plays an important role in the abnormally high mortality in that country. With the data currently available from United Kingdom, our model predicts catastrophic epidemic scenarios in the country if the government did not impose strict travel and social distancing restrictions. A key finding is that, if the percentage of undocumented infection exceeds a threshold, a non-negligible hidden population can exist even after the the epidemic has been deemed over, implying the likelihood of future outbreaks should the currently imposed strict government actions be relaxed. This could make COVID-19 evolving into a long-term epidemic or a community disease a real possibility, suggesting the necessity to conduct universal testing and monitoring to identify the hidden individuals. |
2008.00828 | Vasiliki Bitsouni | Vasiliki Bitsouni, Nikolaos Gialelis and Ioannis G. Stratis | A model for the outbreak of COVID-19: Vaccine effectiveness in a case
study of Italy | null | null | null | null | q-bio.PE math.DS physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a compartmental mathematical model with demography for the spread
of the COVID-19 disease, considering also asymptomatic infectious individuals.
We compute the basic reproductive ratio of the model and study the local and
global stability for it. We solve the model numerically based on the case of
Italy. We propose a vaccination model and we derive threshold conditions for
preventing infection spread in the case of imperfect vaccines.
| [
{
"created": "Fri, 24 Jul 2020 12:41:17 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Nov 2020 17:11:56 GMT",
"version": "v2"
}
] | 2020-11-17 | [
[
"Bitsouni",
"Vasiliki",
""
],
[
"Gialelis",
"Nikolaos",
""
],
[
"Stratis",
"Ioannis G.",
""
]
] | We present a compartmental mathematical model with demography for the spread of the COVID-19 disease, considering also asymptomatic infectious individuals. We compute the basic reproductive ratio of the model and study the local and global stability for it. We solve the model numerically based on the case of Italy. We propose a vaccination model and we derive threshold conditions for preventing infection spread in the case of imperfect vaccines. |
2405.16870 | Ulrich S. Schwarz | Valentin W\"ossner, Oliver M. Drozdowski, Falko Ziebert, Ulrich S.
Schwarz (Heidelberg University) | Active gel model for one-dimensional cell migration coupling actin flow
and adhesion dynamics | Revtex, 33 pages, 7 figures, minor revisions to original version | null | null | null | q-bio.CB cond-mat.soft physics.bio-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Migration of animal cells is based on the interplay between actin
polymerization at the front, adhesion along the cell-substrate interface, and
actomyosin contractility at the back. Active gel theory has been used before to
demonstrate that actomyosin contractility is sufficient for polarization and
self-sustained cell migration in the absence of external cues, but did not
consider the dynamics of adhesion. Likewise, migration models based on the
mechanosensitive dynamics of adhesion receptors usually do not include the
global dynamics of intracellular flow. Here we show that both aspects can be
combined in a minimal active gel model for one-dimensional cell migration with
dynamic adhesion. This model demonstrates that load sharing between the
adhesion receptors leads to symmetry breaking, with stronger adhesion at the
front, and that bistability of migration arises for intermediate adhesiveness.
Local variations in adhesiveness are sufficient to switch between sessile and
motile states, in qualitative agreement with experiments.
| [
{
"created": "Mon, 27 May 2024 06:37:21 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Jul 2024 15:25:17 GMT",
"version": "v2"
}
] | 2024-07-10 | [
[
"Wössner",
"Valentin",
"",
"Heidelberg University"
],
[
"Drozdowski",
"Oliver M.",
"",
"Heidelberg University"
],
[
"Ziebert",
"Falko",
"",
"Heidelberg University"
],
[
"Schwarz",
"Ulrich S.",
"",
"Heidelberg University"
]
] | Migration of animal cells is based on the interplay between actin polymerization at the front, adhesion along the cell-substrate interface, and actomyosin contractility at the back. Active gel theory has been used before to demonstrate that actomyosin contractility is sufficient for polarization and self-sustained cell migration in the absence of external cues, but did not consider the dynamics of adhesion. Likewise, migration models based on the mechanosensitive dynamics of adhesion receptors usually do not include the global dynamics of intracellular flow. Here we show that both aspects can be combined in a minimal active gel model for one-dimensional cell migration with dynamic adhesion. This model demonstrates that load sharing between the adhesion receptors leads to symmetry breaking, with stronger adhesion at the front, and that bistability of migration arises for intermediate adhesiveness. Local variations in adhesiveness are sufficient to switch between sessile and motile states, in qualitative agreement with experiments. |
1808.01359 | Zhanyu Ma | Hong Yu and Zhanyu Ma | Deep Neural Network for Analysis of DNA Methylation Data | Techinical Report | null | null | null | q-bio.GN q-bio.QM stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many researches demonstrated that the DNA methylation, which occurs in the
context of a CpG, has strong correlation with diseases, including cancer. There
is a strong interest in analyzing the DNA methylation data to find how to
distinguish different subtypes of the tumor. However, the conventional
statistical methods are not suitable for analyzing the highly dimensional DNA
methylation data with bounded support. In order to explicitly capture the
properties of the data, we design a deep neural network, which composes of
several stacked binary restricted Boltzmann machines, to learn the low
dimensional deep features of the DNA methylation data. Experiments show these
features perform best in breast cancer DNA methylation data cluster analysis,
comparing with some state-of-the-art methods.
| [
{
"created": "Thu, 2 Aug 2018 13:50:29 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Jan 2020 08:52:05 GMT",
"version": "v2"
}
] | 2020-01-29 | [
[
"Yu",
"Hong",
""
],
[
"Ma",
"Zhanyu",
""
]
] | Many researches demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer. There is a strong interest in analyzing the DNA methylation data to find how to distinguish different subtypes of the tumor. However, the conventional statistical methods are not suitable for analyzing the highly dimensional DNA methylation data with bounded support. In order to explicitly capture the properties of the data, we design a deep neural network, which composes of several stacked binary restricted Boltzmann machines, to learn the low dimensional deep features of the DNA methylation data. Experiments show these features perform best in breast cancer DNA methylation data cluster analysis, comparing with some state-of-the-art methods. |
q-bio/0409028 | Jacek Miekisz | Maciej Bukowski and Jacek Miekisz | Evolutionary and asymptotic stability in symmetric multi-player games | 21 pages, to appear in Int. J. Game Theory | null | null | null | q-bio.PE | null | We provide a classification of symmetric three-player games with two
strategies and investigate evolutionary and asymptotic stability (in the
replicator dynamics) of their Nash equilibria. We discuss similarities and
differences between two-player and multi-player games. In particular, we
construct examples which exhibit a novel behavior not found in two-player
games.
| [
{
"created": "Fri, 24 Sep 2004 11:59:45 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bukowski",
"Maciej",
""
],
[
"Miekisz",
"Jacek",
""
]
] | We provide a classification of symmetric three-player games with two strategies and investigate evolutionary and asymptotic stability (in the replicator dynamics) of their Nash equilibria. We discuss similarities and differences between two-player and multi-player games. In particular, we construct examples which exhibit a novel behavior not found in two-player games. |
2107.11477 | Peter A. V. DiBerardino | Peter A. V. DiBerardino, Alexandre L. S. Filipowicz, James Danckert,
Britt Anderson | Plinko: Eliciting beliefs to build better models of statistical learning
and mental model updating | Partial rewrite. Added references and further discussion of
background and results. Results unchanged | null | null | null | q-bio.NC cs.AI q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Prior beliefs are central to Bayesian accounts of cognition, but many of
these accounts do not directly measure priors. More specifically, initial
states of belief heavily influence how new information is assumed to be
utilized when updating a particular model. Despite this, prior and posterior
beliefs are either inferred from sequential participant actions or elicited
through impoverished means. We had participants play a version of the game
"Plinko", to first elicit individual participant priors in a theoretically
agnostic manner. Subsequent learning and updating of participant beliefs was
then directly measured. We show that participants hold a variety of priors that
cluster around prototypical probability distributions that in turn influence
learning. In follow-up experiments we show that participant priors are stable
over time and that the ability to update beliefs is influenced by a simple
environmental manipulation (i.e. a short break). This data reveals the
importance of directly measuring participant beliefs rather than assuming or
inferring them as has been widely done in the literature to date. The Plinko
game provides a flexible and fecund means for examining statistical learning
and mental model updating.
| [
{
"created": "Fri, 23 Jul 2021 22:27:30 GMT",
"version": "v1"
},
{
"created": "Sat, 8 Jan 2022 00:38:42 GMT",
"version": "v2"
}
] | 2022-01-11 | [
[
"DiBerardino",
"Peter A. V.",
""
],
[
"Filipowicz",
"Alexandre L. S.",
""
],
[
"Danckert",
"James",
""
],
[
"Anderson",
"Britt",
""
]
] | Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when updating a particular model. Despite this, prior and posterior beliefs are either inferred from sequential participant actions or elicited through impoverished means. We had participants play a version of the game "Plinko", to first elicit individual participant priors in a theoretically agnostic manner. Subsequent learning and updating of participant beliefs was then directly measured. We show that participants hold a variety of priors that cluster around prototypical probability distributions that in turn influence learning. In follow-up experiments we show that participant priors are stable over time and that the ability to update beliefs is influenced by a simple environmental manipulation (i.e. a short break). This data reveals the importance of directly measuring participant beliefs rather than assuming or inferring them as has been widely done in the literature to date. The Plinko game provides a flexible and fecund means for examining statistical learning and mental model updating. |
1906.07444 | Thierry Mora | Jacopo Marchi, Michael L\"assig, Thierry Mora, Aleksandra M. Walczak | Multi-lineage evolution in viral populations driven by host immune
systems | null | Pathogens 2019, 8(3), 115 | 10.3390/pathogens8030115 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Viruses evolve in the background of host immune systems that exert selective
pressure and drive viral evolutionary trajectories. This interaction leads to
different evolutionary patterns in antigenic space. Examples observed in nature
include the effectively one-dimensional escape characteristic of influenza A
and the prolonged coexistence of lineages in influenza B. Here we use an
evolutionary model for viruses in the presence of immune host systems with
finite memory to delineate parameter regimes of these patterns in a in
two-dimensional antigenic space. We find that for small effective mutation
rates and mutation jump ranges, a single lineage is the only stable solution.
Large effective mutation rates combined with large mutational jumps in
antigenic space lead to multiple stably co-existing lineages over prolonged
evolutionary periods. These results combined with observations from data
constrain the parameter regimes for the adaptation of viruses, including
influenza.
| [
{
"created": "Tue, 18 Jun 2019 08:44:58 GMT",
"version": "v1"
}
] | 2020-11-20 | [
[
"Marchi",
"Jacopo",
""
],
[
"Lässig",
"Michael",
""
],
[
"Mora",
"Thierry",
""
],
[
"Walczak",
"Aleksandra M.",
""
]
] | Viruses evolve in the background of host immune systems that exert selective pressure and drive viral evolutionary trajectories. This interaction leads to different evolutionary patterns in antigenic space. Examples observed in nature include the effectively one-dimensional escape characteristic of influenza A and the prolonged coexistence of lineages in influenza B. Here we use an evolutionary model for viruses in the presence of immune host systems with finite memory to delineate parameter regimes of these patterns in a in two-dimensional antigenic space. We find that for small effective mutation rates and mutation jump ranges, a single lineage is the only stable solution. Large effective mutation rates combined with large mutational jumps in antigenic space lead to multiple stably co-existing lineages over prolonged evolutionary periods. These results combined with observations from data constrain the parameter regimes for the adaptation of viruses, including influenza. |
2311.00136 | Antonis Antoniades | Antonis Antoniades, Yiyi Yu, Joseph Canzano, William Wang, Spencer
LaVere Smith | Neuroformer: Multimodal and Multitask Generative Pretraining for Brain
Data | 9 pages for main paper. 22 pages in total. 13 figures, 1 table | null | null | null | q-bio.NC cs.LG cs.NE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | State-of-the-art systems neuroscience experiments yield large-scale
multimodal data, and these data sets require new tools for analysis. Inspired
by the success of large pretrained models in vision and language domains, we
reframe the analysis of large-scale, cellular-resolution neuronal spiking data
into an autoregressive spatiotemporal generation problem. Neuroformer is a
multimodal, multitask generative pretrained transformer (GPT) model that is
specifically designed to handle the intricacies of data in systems
neuroscience. It scales linearly with feature size, can process an arbitrary
number of modalities, and is adaptable to downstream tasks, such as predicting
behavior. We first trained Neuroformer on simulated datasets, and found that it
both accurately predicted simulated neuronal circuit activity, and also
intrinsically inferred the underlying neural circuit connectivity, including
direction. When pretrained to decode neural responses, the model predicted the
behavior of a mouse with only few-shot fine-tuning, suggesting that the model
begins learning how to do so directly from the neural representations
themselves, without any explicit supervision. We used an ablation study to show
that joint training on neuronal responses and behavior boosted performance,
highlighting the model's ability to associate behavioral and neural
representations in an unsupervised manner. These findings show that Neuroformer
can analyze neural datasets and their emergent properties, informing the
development of models and hypotheses associated with the brain.
| [
{
"created": "Tue, 31 Oct 2023 20:17:32 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Nov 2023 21:18:26 GMT",
"version": "v2"
},
{
"created": "Wed, 8 Nov 2023 19:48:12 GMT",
"version": "v3"
},
{
"created": "Fri, 15 Mar 2024 22:07:06 GMT",
"version": "v4"
}
] | 2024-03-19 | [
[
"Antoniades",
"Antonis",
""
],
[
"Yu",
"Yiyi",
""
],
[
"Canzano",
"Joseph",
""
],
[
"Wang",
"William",
""
],
[
"Smith",
"Spencer LaVere",
""
]
] | State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis. Inspired by the success of large pretrained models in vision and language domains, we reframe the analysis of large-scale, cellular-resolution neuronal spiking data into an autoregressive spatiotemporal generation problem. Neuroformer is a multimodal, multitask generative pretrained transformer (GPT) model that is specifically designed to handle the intricacies of data in systems neuroscience. It scales linearly with feature size, can process an arbitrary number of modalities, and is adaptable to downstream tasks, such as predicting behavior. We first trained Neuroformer on simulated datasets, and found that it both accurately predicted simulated neuronal circuit activity, and also intrinsically inferred the underlying neural circuit connectivity, including direction. When pretrained to decode neural responses, the model predicted the behavior of a mouse with only few-shot fine-tuning, suggesting that the model begins learning how to do so directly from the neural representations themselves, without any explicit supervision. We used an ablation study to show that joint training on neuronal responses and behavior boosted performance, highlighting the model's ability to associate behavioral and neural representations in an unsupervised manner. These findings show that Neuroformer can analyze neural datasets and their emergent properties, informing the development of models and hypotheses associated with the brain. |
2305.07488 | Andreas Evers Dr. | Andreas Evers, Shipra Malhotra, Vanita D. Sood | In Silico Approaches to Deliver Better Antibodies by Design: The Past,
the Present and the Future | 35 pages, 8 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The recognition of the importance of drug-like properties beyond potency to
reduce clinical attrition of biologics has driven significant progress in the
development of in vitro and in silico tools for developability assessment of
antibody sequences. It is now routine to identify and eliminate or optimize
antibody hits with poor developability profiles. To further accelerate
discovery timelines and reduce clinical and non-clinical development attrition
rates, more proactive in silico approaches to design sequence spaces with
favorable developability profiles are required. From pragmatically
front-loading structure based drug design for developability, to combining next
generation sequencing with machine learning to shape screening libraries, to
adapting the use of artificial intelligence and deep learning for
immunoglobulins, we review herein progressively more proactive approaches to
developability by design.
| [
{
"created": "Fri, 12 May 2023 13:59:09 GMT",
"version": "v1"
}
] | 2023-05-15 | [
[
"Evers",
"Andreas",
""
],
[
"Malhotra",
"Shipra",
""
],
[
"Sood",
"Vanita D.",
""
]
] | The recognition of the importance of drug-like properties beyond potency to reduce clinical attrition of biologics has driven significant progress in the development of in vitro and in silico tools for developability assessment of antibody sequences. It is now routine to identify and eliminate or optimize antibody hits with poor developability profiles. To further accelerate discovery timelines and reduce clinical and non-clinical development attrition rates, more proactive in silico approaches to design sequence spaces with favorable developability profiles are required. From pragmatically front-loading structure based drug design for developability, to combining next generation sequencing with machine learning to shape screening libraries, to adapting the use of artificial intelligence and deep learning for immunoglobulins, we review herein progressively more proactive approaches to developability by design. |
1605.09353 | Maria Luisa Saggio | Maria Luisa Saggio, Andreas Spiegler, Christophe Bernard, Viktor K.
Jirsa | Fast-slow bursters in the unfolding of a high codimension singularity
and the ultra-slow transitions of classes | 22 pages, 15 figures | null | null | null | q-bio.NC math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bursting is a phenomenon found in a variety of physical and biological
systems. For example, in neuroscience, bursting is believed to play a key role
in the way information is transferred in the nervous system. In this work, we
propose a model that, appropriately tuned, can display several types of
bursting behaviors. The model contains two subsystems acting at different
timescales. For the fast subsystem we use the planar unfolding of a high
codimension singularity. In its bifurcation diagram, we locate paths that
underly the right sequence of bifurcations necessary for bursting. The slow
subsystem steers the fast one back and forth along these paths leading to
bursting behavior. The model is able to produce almost all the classes of
bursting predicted for systems with a planar fast subsystems. Transitions
between classes can be obtained through an ultra-slow modulation of the model's
parameters. A detailed exploration of the parameter space allows predicting
possible transitions. This provides a single framework to understand the
coexistence of diverse bursting patterns in physical and biological systems or
in models.
| [
{
"created": "Mon, 30 May 2016 18:55:42 GMT",
"version": "v1"
}
] | 2016-05-31 | [
[
"Saggio",
"Maria Luisa",
""
],
[
"Spiegler",
"Andreas",
""
],
[
"Bernard",
"Christophe",
""
],
[
"Jirsa",
"Viktor K.",
""
]
] | Bursting is a phenomenon found in a variety of physical and biological systems. For example, in neuroscience, bursting is believed to play a key role in the way information is transferred in the nervous system. In this work, we propose a model that, appropriately tuned, can display several types of bursting behaviors. The model contains two subsystems acting at different timescales. For the fast subsystem we use the planar unfolding of a high codimension singularity. In its bifurcation diagram, we locate paths that underly the right sequence of bifurcations necessary for bursting. The slow subsystem steers the fast one back and forth along these paths leading to bursting behavior. The model is able to produce almost all the classes of bursting predicted for systems with a planar fast subsystems. Transitions between classes can be obtained through an ultra-slow modulation of the model's parameters. A detailed exploration of the parameter space allows predicting possible transitions. This provides a single framework to understand the coexistence of diverse bursting patterns in physical and biological systems or in models. |
2312.00074 | Ga\"el Beaun\'ee | Ga\"el Beaun\'ee, Pauline Ezanno, Alain Joly, Pierre Nicolas,
Elisabeta Vergu | Inference using a composite-likelihood approximation for stochastic
metapopulation model of disease spread | null | null | null | null | q-bio.PE q-bio.QM stat.CO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Spatio-temporal pathogen spread is often partially observed at the
metapopulation scale. Available data correspond to proxies and are incomplete,
censored and heterogeneous. Moreover, representing such biological systems
often leads to complex stochastic models. Such complexity together with data
characteristics make the analysis of these systems a challenge. Our objective
was to develop a new inference procedure to estimate key parameters of
stochastic metapopulation models of animal disease spread from longitudinal and
spatial datasets, while accurately accounting for characteristics of census
data. We applied our procedure to provide new knowledge on the regional spread
of \emph{Mycobacterium avium} subsp. \emph{paratuberculosis} (\emph{Map}),
which causes bovine paratuberculosis, a worldwide endemic disease. \emph{Map}
spread between herds through trade movements was modeled with a stochastic
mechanistic model. Comprehensive data from 2005 to 2013 on cattle movements in
12,857 dairy herds in Brittany (western France) and partial data on animal
infection status in 2,278 herds sampled from 2007 to 2013 were used. Inference
was performed using a new criterion based on a Monte-Carlo approximation of a
composite likelihood, coupled to a numerical optimization algorithm
(Nelder-Mead Simplex-like). Our criterion showed a clear superiority to
alternative ones in identifying the right parameter values, as assessed by an
empirical identifiability on simulated data. Point estimates and profile
likelihoods allowed us to establish the initial state of the system, identify
the risk of pathogen introduction from outside the metapopulation, and confirm
the assumption of the low sensitivity of the diagnostic test. Our inference
procedure could easily be applied to other spatio-temporal infection dynamics,
especially when ABC-like methods face challenges in defining relevant summary
statistics.
| [
{
"created": "Wed, 29 Nov 2023 22:52:26 GMT",
"version": "v1"
}
] | 2023-12-04 | [
[
"Beaunée",
"Gaël",
""
],
[
"Ezanno",
"Pauline",
""
],
[
"Joly",
"Alain",
""
],
[
"Nicolas",
"Pierre",
""
],
[
"Vergu",
"Elisabeta",
""
]
] | Spatio-temporal pathogen spread is often partially observed at the metapopulation scale. Available data correspond to proxies and are incomplete, censored and heterogeneous. Moreover, representing such biological systems often leads to complex stochastic models. Such complexity together with data characteristics make the analysis of these systems a challenge. Our objective was to develop a new inference procedure to estimate key parameters of stochastic metapopulation models of animal disease spread from longitudinal and spatial datasets, while accurately accounting for characteristics of census data. We applied our procedure to provide new knowledge on the regional spread of \emph{Mycobacterium avium} subsp. \emph{paratuberculosis} (\emph{Map}), which causes bovine paratuberculosis, a worldwide endemic disease. \emph{Map} spread between herds through trade movements was modeled with a stochastic mechanistic model. Comprehensive data from 2005 to 2013 on cattle movements in 12,857 dairy herds in Brittany (western France) and partial data on animal infection status in 2,278 herds sampled from 2007 to 2013 were used. Inference was performed using a new criterion based on a Monte-Carlo approximation of a composite likelihood, coupled to a numerical optimization algorithm (Nelder-Mead Simplex-like). Our criterion showed a clear superiority to alternative ones in identifying the right parameter values, as assessed by an empirical identifiability on simulated data. Point estimates and profile likelihoods allowed us to establish the initial state of the system, identify the risk of pathogen introduction from outside the metapopulation, and confirm the assumption of the low sensitivity of the diagnostic test. Our inference procedure could easily be applied to other spatio-temporal infection dynamics, especially when ABC-like methods face challenges in defining relevant summary statistics. |
2401.08626 | Lukas Schumacher | Lukas Schumacher, Martin Schnuerch, Andreas Voss, Stefan T. Radev | Validation and Comparison of Non-Stationary Cognitive Models: A
Diffusion Model Application | null | null | null | null | q-bio.NC stat.ME | http://creativecommons.org/licenses/by-sa/4.0/ | Cognitive processes undergo various fluctuations and transient states across
different temporal scales. Superstatistics are emerging as a flexible framework
for incorporating such non-stationary dynamics into existing cognitive model
classes. In this work, we provide the first experimental validation of
superstatistics and formal comparison of four non-stationary diffusion decision
models in a specifically designed perceptual decision-making task. Task
difficulty and speed-accuracy trade-off were systematically manipulated to
induce expected changes in model parameters. To validate our models, we assess
whether the inferred parameter trajectories align with the patterns and
sequences of the experimental manipulations. To address computational
challenges, we present novel deep learning techniques for amortized Bayesian
estimation and comparison of models with time-varying parameters. Our findings
indicate that transition models incorporating both gradual and abrupt parameter
shifts provide the best fit to the empirical data. Moreover, we find that the
inferred parameter trajectories closely mirror the sequence of experimental
manipulations. Posterior re-simulations further underscore the ability of the
models to faithfully reproduce critical data patterns. Accordingly, our results
suggest that the inferred non-stationary dynamics may reflect actual changes in
the targeted psychological constructs. We argue that our initial experimental
validation paves the way for the widespread application of superstatistics in
cognitive modeling and beyond.
| [
{
"created": "Thu, 7 Dec 2023 20:17:09 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Jan 2024 12:27:39 GMT",
"version": "v2"
}
] | 2024-01-29 | [
[
"Schumacher",
"Lukas",
""
],
[
"Schnuerch",
"Martin",
""
],
[
"Voss",
"Andreas",
""
],
[
"Radev",
"Stefan T.",
""
]
] | Cognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond. |
2010.07455 | Ameya Harmalkar | Ameya Harmalkar and Jeffrey J. Gray | Advances to tackle backbone flexibility in protein docking | null | null | 10.1016/j.sbi.2020.11.011 | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Computational docking methods can provide structural models of
protein-protein complexes, but protein backbone flexibility upon association
often thwarts accurate predictions. In recent blind challenges, medium or high
accuracy models were submitted in less than 20% of the "difficult" targets
(with significant backbone change or uncertainty). Here, we describe recent
developments in protein-protein docking and highlight advances that tackle
backbone flexibility. In molecular dynamics and Monte Carlo approaches,
enhanced sampling techniques have reduced time-scale limitations. Internal
coordinate formulations can now capture realistic motions of monomers and
complexes using harmonic dynamics. And machine learning approaches adaptively
guide docking trajectories or generate novel binding site predictions from deep
neural networks trained on protein interfaces. These tools poise the field to
break through the longstanding challenge of correctly predicting complex
structures with significant conformational change.
| [
{
"created": "Thu, 15 Oct 2020 00:53:35 GMT",
"version": "v1"
}
] | 2020-12-29 | [
[
"Harmalkar",
"Ameya",
""
],
[
"Gray",
"Jeffrey J.",
""
]
] | Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the "difficult" targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change. |
1103.5490 | Andre X. C. N. Valente | A. X. C. N. Valente and Stephen S. Fong | High-Throughput Biologically Optimized Search Engineering Approach to
Synthetic Biology | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synthetic Biology is the new engineering-based approach to biology that
includes applications of designing complex biological devices. At present, it
is not yet clear what will emerge as the defining principles of Synthetic
Biology. One proposed approach is to build Synthetic Biology around the
classical engineering principles of standardization, modularity/decoupling and
abstraction/modeling to facilitate component-based design. In this article we
suggest and discuss an alternative paradigm, which we call High-throughput
Biologically Optimized Search Engineering (HT-BOSE). Stemming from directed
evolution, in HT-BOSE the focal point is a biological knowledge based rational
optimization of the search process in the space of device design possibilities.
The HT-BOSE approach may also be relevant in other contexts and we briefly
highlight how it could be applicable to the development of multi-drug cocktails
in a biomedical setting.
| [
{
"created": "Mon, 28 Mar 2011 21:17:25 GMT",
"version": "v1"
}
] | 2011-03-30 | [
[
"Valente",
"A. X. C. N.",
""
],
[
"Fong",
"Stephen S.",
""
]
] | Synthetic Biology is the new engineering-based approach to biology that includes applications of designing complex biological devices. At present, it is not yet clear what will emerge as the defining principles of Synthetic Biology. One proposed approach is to build Synthetic Biology around the classical engineering principles of standardization, modularity/decoupling and abstraction/modeling to facilitate component-based design. In this article we suggest and discuss an alternative paradigm, which we call High-throughput Biologically Optimized Search Engineering (HT-BOSE). Stemming from directed evolution, in HT-BOSE the focal point is a biological knowledge based rational optimization of the search process in the space of device design possibilities. The HT-BOSE approach may also be relevant in other contexts and we briefly highlight how it could be applicable to the development of multi-drug cocktails in a biomedical setting. |
1604.08508 | Timoth\'ee Proix Ph.D. | Timoth\'ee Proix, Fabrice Bartolomei, Maxime Guye, Viktor K. Jirsa | Individual structural connectivity defines propagation networks in
partial epilepsy | 30 pages; 10 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural network oscillations are a fundamental mechanism for cognition,
perception and consciousness. Consequently, perturbations of network activity
play an important role in the pathophysiology of brain disorders. When
structural information from non-invasive brain imaging is merged with
mathematical modeling, then generative brain network models constitute
in-silico platforms for the exploration of causal mechanisms of brain function
and clinical hypothesis testing. We here demonstrate along the example of
drug-resistant epilepsy that patient-specific virtual brain models derived from
diffusion MRI have sufficient predictive power to improve diagnosis and surgery
outcome. In partial epilepsy, seizures originate in a local network, the
so-called epileptogenic zone, before recruiting other close or distant brain
regions. We create personalized large-scale brain networks for 15 patients and
simulate the individual seizure propagation patterns. Model validation is
performed against the presurgical stereotactic electroencephalography (SEEG)
data and the standard-of-care clinical evaluation. We demonstrate that the
individual brain models account for the temporal variability in patient seizure
propagation patterns and explain the variability in postsurgical success. Our
results show that individual variations in structural connectivity, when linked
with mathematical dynamic models, have the capacity to explain changes in
spatiotemporal organization of brain dynamics as observed in network-based
brain disorders, thus opening up avenues towards discovery of novel clinical
interventions.
| [
{
"created": "Thu, 28 Apr 2016 16:42:53 GMT",
"version": "v1"
}
] | 2016-04-29 | [
[
"Proix",
"Timothée",
""
],
[
"Bartolomei",
"Fabrice",
""
],
[
"Guye",
"Maxime",
""
],
[
"Jirsa",
"Viktor K.",
""
]
] | Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modeling, then generative brain network models constitute in-silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate along the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion MRI have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography (SEEG) data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the temporal variability in patient seizure propagation patterns and explain the variability in postsurgical success. Our results show that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics as observed in network-based brain disorders, thus opening up avenues towards discovery of novel clinical interventions. |
2404.12763 | Vince Grolmusz | D\'aniel Heged\H{u}s and Vince Grolmusz | The Length and the Width of the Human Brain Circuit Connections are
Strongly Correlated | null | null | null | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | The correlations of several fundamental properties of human brain connections
are investigated in a consensus connectome, constructed from 1064 braingraphs,
each on 1015 vertices, corresponding to 1015 anatomical brain areas. The
properties examined include the edge length, the fiber number, or edge width,
meaning the number of discovered axon bundles forming the edge and the
occurrence number of the edge, meaning the number of individual braingraphs
where the edge exists. By using our previously published robust braingraphs at
\url{https://braingraph.org}, we have prepared a single consensus graph from
the data and compared the statistical similarity of the edge occurrence
numbers, edge lengths, and fiber counts of the edges. We have found a strong
positive Spearman correlation between the edge occurrence numbers and the fiber
count numbers, showing that statistically, the most frequent cerebral
connections have the largest widths, i.e., the fiber number. We have found a
negative Spearman correlation between the fiber lengths and fiber counts,
showing that, typically, the shortest edges are the widest or strongest by
their fiber counts. We have also found a negative Spearman correlation between
the occurrence numbers and the edge lengths: it shows that typically, the long
edges are infrequent, and the frequent edges are short.
| [
{
"created": "Fri, 19 Apr 2024 10:08:57 GMT",
"version": "v1"
}
] | 2024-04-22 | [
[
"Hegedűs",
"Dániel",
""
],
[
"Grolmusz",
"Vince",
""
]
] | The correlations of several fundamental properties of human brain connections are investigated in a consensus connectome, constructed from 1064 braingraphs, each on 1015 vertices, corresponding to 1015 anatomical brain areas. The properties examined include the edge length, the fiber number, or edge width, meaning the number of discovered axon bundles forming the edge and the occurrence number of the edge, meaning the number of individual braingraphs where the edge exists. By using our previously published robust braingraphs at \url{https://braingraph.org}, we have prepared a single consensus graph from the data and compared the statistical similarity of the edge occurrence numbers, edge lengths, and fiber counts of the edges. We have found a strong positive Spearman correlation between the edge occurrence numbers and the fiber count numbers, showing that statistically, the most frequent cerebral connections have the largest widths, i.e., the fiber number. We have found a negative Spearman correlation between the fiber lengths and fiber counts, showing that, typically, the shortest edges are the widest or strongest by their fiber counts. We have also found a negative Spearman correlation between the occurrence numbers and the edge lengths: it shows that typically, the long edges are infrequent, and the frequent edges are short. |
q-bio/0410007 | Paulo Alexandre de Castro | Roberto N. Onody, Paulo A. de Castro | Self-Organized Criticality, Optimization and Biodiversity | 3 pages, 3 figures, published in to International Journal of Modern
Physics C | International Journal of Modern Physics C 14, 911-916 (2002) | null | null | q-bio.PE | null | By driven to extinction species less or poorly adapted, the Darwinian
evolutionary theory is intrinsically an optimization theory. We investigate two
optimization algorithms with such evolutionary characteristics: the Bak-Sneppen
and the Extremal Optimization. By comparing their mean fitness in the steady
state regime, we conclude that the Bak-Sneppen dynamics is more efficient than
the Extremal Optimization if the parameter $\tau$ is in the interval
$[0,0.86]$. The determination of the spatial correlation and the probability
distribution of the avalanches show that the Extremal Optimization dynamics
does not lead the system into a critical self-organized state. Trough a
discrete form of the Bak-Sneppen model we argument that biodiversity is an
essential requisite to preserve the self-organized criticality.
| [
{
"created": "Wed, 6 Oct 2004 03:32:27 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Onody",
"Roberto N.",
""
],
[
"de Castro",
"Paulo A.",
""
]
] | By driven to extinction species less or poorly adapted, the Darwinian evolutionary theory is intrinsically an optimization theory. We investigate two optimization algorithms with such evolutionary characteristics: the Bak-Sneppen and the Extremal Optimization. By comparing their mean fitness in the steady state regime, we conclude that the Bak-Sneppen dynamics is more efficient than the Extremal Optimization if the parameter $\tau$ is in the interval $[0,0.86]$. The determination of the spatial correlation and the probability distribution of the avalanches show that the Extremal Optimization dynamics does not lead the system into a critical self-organized state. Trough a discrete form of the Bak-Sneppen model we argument that biodiversity is an essential requisite to preserve the self-organized criticality. |
0710.5142 | Lior Pachter | Kord Eickmeyer, Peter Huggins, Lior Pachter and Ruriko Yoshida | On the optimality of the neighbor-joining algorithm | null | null | null | null | q-bio.QM q-bio.PE | null | The popular neighbor-joining (NJ) algorithm used in phylogenetics is a greedy
algorithm for finding the balanced minimum evolution (BME) tree associated to a
dissimilarity map. From this point of view, NJ is ``optimal'' when the
algorithm outputs the tree which minimizes the balanced minimum evolution
criterion. We use the fact that the NJ tree topology and the BME tree topology
are determined by polyhedral subdivisions of the spaces of dissimilarity maps
${\R}_{+}^{n \choose 2}$ to study the optimality of the neighbor-joining
algorithm. In particular, we investigate and compare the polyhedral
subdivisions for $n \leq 8$. A key requirement is the measurement of volumes of
spherical polytopes in high dimension, which we obtain using a combination of
Monte Carlo methods and polyhedral algorithms. We show that highly unrelated
trees can be co-optimal in BME reconstruction, and that NJ regions are not
convex. We obtain the $l_2$ radius for neighbor-joining for $n=5$ and we
conjecture that the ability of the neighbor-joining algorithm to recover the
BME tree depends on the diameter of the BME tree.
| [
{
"created": "Fri, 26 Oct 2007 16:53:34 GMT",
"version": "v1"
}
] | 2007-10-29 | [
[
"Eickmeyer",
"Kord",
""
],
[
"Huggins",
"Peter",
""
],
[
"Pachter",
"Lior",
""
],
[
"Yoshida",
"Ruriko",
""
]
] | The popular neighbor-joining (NJ) algorithm used in phylogenetics is a greedy algorithm for finding the balanced minimum evolution (BME) tree associated to a dissimilarity map. From this point of view, NJ is ``optimal'' when the algorithm outputs the tree which minimizes the balanced minimum evolution criterion. We use the fact that the NJ tree topology and the BME tree topology are determined by polyhedral subdivisions of the spaces of dissimilarity maps ${\R}_{+}^{n \choose 2}$ to study the optimality of the neighbor-joining algorithm. In particular, we investigate and compare the polyhedral subdivisions for $n \leq 8$. A key requirement is the measurement of volumes of spherical polytopes in high dimension, which we obtain using a combination of Monte Carlo methods and polyhedral algorithms. We show that highly unrelated trees can be co-optimal in BME reconstruction, and that NJ regions are not convex. We obtain the $l_2$ radius for neighbor-joining for $n=5$ and we conjecture that the ability of the neighbor-joining algorithm to recover the BME tree depends on the diameter of the BME tree. |
1212.0661 | \"Umit Seren | \"Umit Seren (1), Bjarni J. Vilhj\'almsson (1,2), Matthew W. Horton
(1,3), Dazhe Meng (4), Petar Forai (1), Yu S. Huang (4), Quan Long (1),
Vincent Segura (5), Magnus Nordborg (1,2) ((1) Gregor Mendel, Institute
Austrian Academy of Sciences, (2) Molecular and Computational Biology,
University of Southern California, (3) Department of Ecology and Evolution,
University of Chicago, (4) Center for Neurobehavioral Genetics, Semel
Institute, University of California Los Angeles, (5) INRA, France) | GWAPP: A Web Application for Genome-wide Association Mapping in A.
thaliana | Submitted to The Plant Cell (http://www.plantcell.org/) 42 pages with
15 figures | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Arabidopsis thaliana is an important model organism for understanding the
genetics and molecular biology of plants. Its highly selfing nature, together
with other important features, such as small size, short generation time, small
genome size, and wide geographic distribution, make it an ideal model organism
for understanding natural variation. Genome-wide association studies (GWAS)
have proven a useful technique for identifying genetic loci responsible for
natural variation in A. thaliana. Previously genotyped accessions (natural
inbred lines) can be grown in replicate under different conditions, and
phenotyped for different traits. These important features greatly simplify
association mapping of traits and allow for systematic dissection of the
genetics of natural variation by the entire Arabidopsis community. To
facilitate this, we present GWAPP, an interactive web-based application for
conducting GWAS in A. thaliana. Using an efficient Python implementation of a
linear mixed model, traits measured for a subset of 1386 publicly available
ecotypes can be uploaded and mapped with an efficient mixed model and other
methods in just a couple of minutes. GWAPP features an extensive, interactive,
and a user-friendly interface that includes interactive manhattan plots and
interactive local and genome-wide LD plots. It facilitates exploratory data
analysis by implementing features such as the inclusion of candidate SNPs in
the model as cofactors.
| [
{
"created": "Tue, 4 Dec 2012 10:06:56 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Dec 2012 15:31:27 GMT",
"version": "v2"
}
] | 2013-08-12 | [
[
"Seren",
"Ümit",
""
],
[
"Vilhjálmsson",
"Bjarni J.",
""
],
[
"Horton",
"Matthew W.",
""
],
[
"Meng",
"Dazhe",
""
],
[
"Forai",
"Petar",
""
],
[
"Huang",
"Yu S.",
""
],
[
"Long",
"Quan",
""
],
[
"Segura",
"Vincent",
""
],
[
"Nordborg",
"Magnus",
""
]
] | Arabidopsis thaliana is an important model organism for understanding the genetics and molecular biology of plants. Its highly selfing nature, together with other important features, such as small size, short generation time, small genome size, and wide geographic distribution, make it an ideal model organism for understanding natural variation. Genome-wide association studies (GWAS) have proven a useful technique for identifying genetic loci responsible for natural variation in A. thaliana. Previously genotyped accessions (natural inbred lines) can be grown in replicate under different conditions, and phenotyped for different traits. These important features greatly simplify association mapping of traits and allow for systematic dissection of the genetics of natural variation by the entire Arabidopsis community. To facilitate this, we present GWAPP, an interactive web-based application for conducting GWAS in A. thaliana. Using an efficient Python implementation of a linear mixed model, traits measured for a subset of 1386 publicly available ecotypes can be uploaded and mapped with an efficient mixed model and other methods in just a couple of minutes. GWAPP features an extensive, interactive, and a user-friendly interface that includes interactive manhattan plots and interactive local and genome-wide LD plots. It facilitates exploratory data analysis by implementing features such as the inclusion of candidate SNPs in the model as cofactors. |
2211.07507 | Andrew Hannum | Andrew Hannum, Mario A. Lopez, Sa\'ul A. Blanco, Richard F. Betzel | High-Accuracy Machine Learning Techniques for Functional Connectome
Fingerprinting and Cognitive State Decoding | 18 pages | null | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The human brain is a complex network comprised of functionally and
anatomically interconnected brain regions. A growing number of studies have
suggested that empirical estimates of brain networks may be useful for
discovery of biomarkers of disease and cognitive state. A prerequisite for
realizing this aim, however, is that brain networks also serve as reliable
markers of an individual. Here, using Human Connectome Project data, we build
upon recent studies examining brain-based fingerprints of individual subjects
and cognitive states based on cognitively-demanding tasks that assess, for
example, working memory, theory of mind, and motor function. Our approach
achieves accuracy of up to 99\% for both identification of the subject of an
fMRI scan, and for classification of the cognitive state of a previously-unseen
subject in a scan. More broadly, we explore the accuracy and reliability of
five different machine learning techniques on subject fingerprinting and
cognitive state decoding objectives, using functional connectivity data from
fMRI scans of a high number of subjects (865) across a number of cognitive
states (8). These results represent an advance on existing techniques for
functional connectivity-based brain fingerprinting and state decoding.
Additionally, 16 different pre-processing pipelines are compared in order to
characterize the effects of different aspects of the production of functional
connectomes (FCs) on the accuracy of subject and task classification, and to
identify possible confounds.
| [
{
"created": "Mon, 14 Nov 2022 16:41:51 GMT",
"version": "v1"
}
] | 2022-11-15 | [
[
"Hannum",
"Andrew",
""
],
[
"Lopez",
"Mario A.",
""
],
[
"Blanco",
"Saúl A.",
""
],
[
"Betzel",
"Richard F.",
""
]
] | The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as reliable markers of an individual. Here, using Human Connectome Project data, we build upon recent studies examining brain-based fingerprints of individual subjects and cognitive states based on cognitively-demanding tasks that assess, for example, working memory, theory of mind, and motor function. Our approach achieves accuracy of up to 99\% for both identification of the subject of an fMRI scan, and for classification of the cognitive state of a previously-unseen subject in a scan. More broadly, we explore the accuracy and reliability of five different machine learning techniques on subject fingerprinting and cognitive state decoding objectives, using functional connectivity data from fMRI scans of a high number of subjects (865) across a number of cognitive states (8). These results represent an advance on existing techniques for functional connectivity-based brain fingerprinting and state decoding. Additionally, 16 different pre-processing pipelines are compared in order to characterize the effects of different aspects of the production of functional connectomes (FCs) on the accuracy of subject and task classification, and to identify possible confounds. |
2401.01039 | Vitaly Chernik | Vitaly Chernik, Pavel Buklemishev | The numerical solution of the free-boundary cell motility problem | null | null | null | null | q-bio.CB q-bio.TO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The cell motility problem has been investigated for a long time. Today, many
biologists, physicists, and mathematicians are looking for new research
instruments for this process. A simple 2D model of a free-boundary cell moving
on a homogeneous isotropic surface is presented in the paper. It describes the
dynamics of the complex actomyosin liquid, whose special properties influence
the boundary dynamics and cell motility. The model consists of a system of
equations with the free boundary domain and contains a non-local term. In this
work, we present a numerical solution of this problem.
| [
{
"created": "Fri, 6 Oct 2023 13:44:40 GMT",
"version": "v1"
}
] | 2024-01-03 | [
[
"Chernik",
"Vitaly",
""
],
[
"Buklemishev",
"Pavel",
""
]
] | The cell motility problem has been investigated for a long time. Today, many biologists, physicists, and mathematicians are looking for new research instruments for this process. A simple 2D model of a free-boundary cell moving on a homogeneous isotropic surface is presented in the paper. It describes the dynamics of the complex actomyosin liquid, whose special properties influence the boundary dynamics and cell motility. The model consists of a system of equations with the free boundary domain and contains a non-local term. In this work, we present a numerical solution of this problem. |
1902.03526 | Guang Song | Guang Song | Use Symmetry to Elucidate the Roles of Global Shape and Local
Interactions in Protein Dynamics and Cooperativity | 31 pages, 5 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Shape had been intuitively recognized to play a dominant role in determining
the global motion patterns of bio-molecular assemblies. However, it is not
clear exactly how shape determines the motion patterns. What about the local
interactions that hold a structure together to a certain shape? The
contributions of global shape and local interactions usually mix together and
are difficult to tease part. In this work, we use symmetry to elucidate the
distinct roles of global shape and local interactions in protein dynamics.
Symmetric complexes provide an ideal platform for this task since in them the
effects of local interactions and global shape are separable, allowing their
distinct roles to be identified. Our key findings based on symmetric assemblies
are: (i) the motion patterns of each subunit are determined primarily by
intra-subunit interactions (IRSi), and secondarily by inter-subunit
interactions (IESi); (ii) the motion patterns of the whole assembly are fully
dictated by the global symmetry/shape and have nothing to do with local iESi or
IRSi. This is followed by a discussion on how the findings may be generalized
to complexes in any shape, with or without symmetry.
| [
{
"created": "Sun, 10 Feb 2019 02:08:28 GMT",
"version": "v1"
}
] | 2019-02-12 | [
[
"Song",
"Guang",
""
]
] | Shape had been intuitively recognized to play a dominant role in determining the global motion patterns of bio-molecular assemblies. However, it is not clear exactly how shape determines the motion patterns. What about the local interactions that hold a structure together to a certain shape? The contributions of global shape and local interactions usually mix together and are difficult to tease part. In this work, we use symmetry to elucidate the distinct roles of global shape and local interactions in protein dynamics. Symmetric complexes provide an ideal platform for this task since in them the effects of local interactions and global shape are separable, allowing their distinct roles to be identified. Our key findings based on symmetric assemblies are: (i) the motion patterns of each subunit are determined primarily by intra-subunit interactions (IRSi), and secondarily by inter-subunit interactions (IESi); (ii) the motion patterns of the whole assembly are fully dictated by the global symmetry/shape and have nothing to do with local iESi or IRSi. This is followed by a discussion on how the findings may be generalized to complexes in any shape, with or without symmetry. |
1609.05517 | Andrew Eckford | Andrew W. Eckford, Kenneth A. Loparo, and Peter J. Thomas | Finite-State Channel Models for Signal Transduction in Neural Systems | Accepted for publication in 2016 IEEE International Conference on
Acoustics, Speech, and Signal Processing, Shanghai, China | null | 10.1109/ICASSP.2016.7472889 | null | q-bio.QM cs.IT math.IT q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Information theory provides powerful tools for understanding communication
systems. This analysis can be applied to intercellular signal transduction,
which is a means of chemical communication among cells and microbes. We discuss
how to apply information-theoretic analysis to ligand-receptor systems, which
form the signal carrier and receiver in intercellular signal transduction
channels. We also discuss the applications of these results to neuroscience.
| [
{
"created": "Sun, 18 Sep 2016 17:25:05 GMT",
"version": "v1"
}
] | 2016-11-15 | [
[
"Eckford",
"Andrew W.",
""
],
[
"Loparo",
"Kenneth A.",
""
],
[
"Thomas",
"Peter J.",
""
]
] | Information theory provides powerful tools for understanding communication systems. This analysis can be applied to intercellular signal transduction, which is a means of chemical communication among cells and microbes. We discuss how to apply information-theoretic analysis to ligand-receptor systems, which form the signal carrier and receiver in intercellular signal transduction channels. We also discuss the applications of these results to neuroscience. |
1606.09348 | Lei Zhao | Lei Zhao, Xia Li, Ning Zhang, Shu-Dong Zhang, Ting-Shuang Yi, Hong Ma,
Zhen-Hua Guo, De-Zhu Li | Phylogenomic Analyses of Large-scale Nuclear Genes Provide New Insights
into the Evolutionary Relationships within the Rosids | null | null | null | null | q-bio.PE | http://creativecommons.org/publicdomain/zero/1.0/ | The Rosids is one of the largest groups of flowering plants, with 140
families and ~70,000 species. Previous phylogenetic studies of the rosids have
primarily utilized organelle genes that likely differ in evolutionary histories
from nuclear genes. To better understand the evolutionary history of rosids, it
is necessary to investigate their phylogenetic relationships using nuclear
genes. Here, we employed large-scale phylogenomic datasets composed of nuclear
genes, including 891 clusters of putative orthologous genes. Combined with
comprehensive taxon sampling covering 63 species representing 14 out of the 17
orders, we reconstructed the rosids phylogeny with coalescence and
concatenation methods, yielding similar tree topologies from all datasets.
However, these topologies did not agree on the placement of Zygophyllales.
Through comprehensive analyses, we found that missing data and gene tree
heterogeneity were potential factors that may mislead concatenation methods, in
particular, large amounts of missing data under high gene tree heterogeneity.
Our results provided new insights into the deep phylogenetic relationships of
the rosids, and demonstrated that coalescence methods may effectively resolve
the phylogenetic relationships of the rosids with missing data under high gene
tree heterogeneity.
| [
{
"created": "Thu, 30 Jun 2016 05:22:54 GMT",
"version": "v1"
}
] | 2016-07-01 | [
[
"Zhao",
"Lei",
""
],
[
"Li",
"Xia",
""
],
[
"Zhang",
"Ning",
""
],
[
"Zhang",
"Shu-Dong",
""
],
[
"Yi",
"Ting-Shuang",
""
],
[
"Ma",
"Hong",
""
],
[
"Guo",
"Zhen-Hua",
""
],
[
"Li",
"De-Zhu",
""
]
] | The Rosids is one of the largest groups of flowering plants, with 140 families and ~70,000 species. Previous phylogenetic studies of the rosids have primarily utilized organelle genes that likely differ in evolutionary histories from nuclear genes. To better understand the evolutionary history of rosids, it is necessary to investigate their phylogenetic relationships using nuclear genes. Here, we employed large-scale phylogenomic datasets composed of nuclear genes, including 891 clusters of putative orthologous genes. Combined with comprehensive taxon sampling covering 63 species representing 14 out of the 17 orders, we reconstructed the rosids phylogeny with coalescence and concatenation methods, yielding similar tree topologies from all datasets. However, these topologies did not agree on the placement of Zygophyllales. Through comprehensive analyses, we found that missing data and gene tree heterogeneity were potential factors that may mislead concatenation methods, in particular, large amounts of missing data under high gene tree heterogeneity. Our results provided new insights into the deep phylogenetic relationships of the rosids, and demonstrated that coalescence methods may effectively resolve the phylogenetic relationships of the rosids with missing data under high gene tree heterogeneity. |
q-bio/0312009 | Kwang-Il Goh | K.-I. Goh, B. Kahng, and D. Kim (Seoul National University) | Evolution of the Protein Interaction Network of Budding Yeast: Role of
the Protein Family Compatibility Constraint | 5 pages, 5 figures, 1 table. Title changed. Final version published
in JKPS | J. Korean Phys. Soc. 46, 551 (2005) | null | null | q-bio.MN cond-mat | null | Understanding of how protein interaction networks (PIN) of living organisms
have evolved or are organized can be the first stepping stone in unveiling how
life works on a fundamental ground. Here we introduce a hybrid network model
composed of the yeast PIN and the protein family interaction network. The
essential ingredient of the model includes the protein family identity and its
robustness under evolution, as well as the three previously proposed ones: gene
duplication, divergence, and mutation. We investigate diverse structural
properties of our model with parameter values relevant to yeast, finding that
the model successfully reproduces the empirical data.
| [
{
"created": "Mon, 8 Dec 2003 15:15:42 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Mar 2004 16:10:16 GMT",
"version": "v2"
},
{
"created": "Wed, 16 Feb 2005 05:43:15 GMT",
"version": "v3"
}
] | 2007-05-23 | [
[
"Goh",
"K. -I.",
"",
"Seoul National University"
],
[
"Kahng",
"B.",
"",
"Seoul National University"
],
[
"Kim",
"D.",
"",
"Seoul National University"
]
] | Understanding of how protein interaction networks (PIN) of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce a hybrid network model composed of the yeast PIN and the protein family interaction network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed ones: gene duplication, divergence, and mutation. We investigate diverse structural properties of our model with parameter values relevant to yeast, finding that the model successfully reproduces the empirical data. |
1408.6777 | William Levy Ph. D. | William B. Levy, Toby Berger, Ilya A. Fleidervish | Neural computation at the thermal limit | 2 figures | null | null | null | q-bio.NC physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Although several measurements and analyses support the idea that the brain is
energy-optimized, there is one disturbing, contradictory observation: In
theory, computation limited by thermal noise can occur as cheaply as ~$2.9\cdot
10^{-21}$ joules per bit (kTln2). Unfortunately, for a neuron the ostensible
discrepancy from this minimum is startling - ignoring inhibition the
discrepancy is $10^7$ times this amount and taking inhibition into account
$>10^9$. Here we point out that what has been defined as neural computation is
actually a combination of computation and neural communication: the
communication costs, transmission from each excitatory postsynaptic activation
to the S4-gating-charges of the fast Na+ channels of the initial segment
(fNa's), dominate the joule-costs. Making this distinction between
communication to the initial segment and computation at the initial segment
(i.e., adding up of the activated fNa's) implies that the size of the average
synaptic event reaching the fNa's is the size of the standard deviation of the
thermal noise. Moreover, defining computation as the addition of activated
fNa's, yields a biophysically plausible mechanism for approaching the desired
minimum. This mechanism, requiring something like the electrical engineer's
equalizer (not much more than the action potential generating conductances),
only operates at threshold. This active filter modifies the last few synaptic
excitations, providing barely enough energy to allow the last sub-threshold
gating charge to transport. That is, the last, threshold-achieving S4-subunit
activation requires an energy that matches the information being provided by
the last few synaptic events, a ratio that is near kTln2 joules per bit.
| [
{
"created": "Thu, 28 Aug 2014 17:02:11 GMT",
"version": "v1"
},
{
"created": "Fri, 21 Nov 2014 01:55:14 GMT",
"version": "v2"
}
] | 2014-11-24 | [
[
"Levy",
"William B.",
""
],
[
"Berger",
"Toby",
""
],
[
"Fleidervish",
"Ilya A.",
""
]
] | Although several measurements and analyses support the idea that the brain is energy-optimized, there is one disturbing, contradictory observation: In theory, computation limited by thermal noise can occur as cheaply as ~$2.9\cdot 10^{-21}$ joules per bit (kTln2). Unfortunately, for a neuron the ostensible discrepancy from this minimum is startling - ignoring inhibition the discrepancy is $10^7$ times this amount and taking inhibition into account $>10^9$. Here we point out that what has been defined as neural computation is actually a combination of computation and neural communication: the communication costs, transmission from each excitatory postsynaptic activation to the S4-gating-charges of the fast Na+ channels of the initial segment (fNa's), dominate the joule-costs. Making this distinction between communication to the initial segment and computation at the initial segment (i.e., adding up of the activated fNa's) implies that the size of the average synaptic event reaching the fNa's is the size of the standard deviation of the thermal noise. Moreover, defining computation as the addition of activated fNa's, yields a biophysically plausible mechanism for approaching the desired minimum. This mechanism, requiring something like the electrical engineer's equalizer (not much more than the action potential generating conductances), only operates at threshold. This active filter modifies the last few synaptic excitations, providing barely enough energy to allow the last sub-threshold gating charge to transport. That is, the last, threshold-achieving S4-subunit activation requires an energy that matches the information being provided by the last few synaptic events, a ratio that is near kTln2 joules per bit. |
q-bio/0404015 | Markus Porto | Markus Porto, Ugo Bastolla, H. Eduardo Roman, and Michele Vendruscolo | Reconstruction of protein structures from a vectorial representation | 4 pages, 1 figure | Phys. Rev. Lett. 92, 218101 (2004) | 10.1103/PhysRevLett.92.218101 | null | q-bio.BM | null | We show that the contact map of the native structure of globular proteins can
be reconstructed starting from the sole knowledge of the contact map's
principal eigenvector, and present an exact algorithm for this purpose. Our
algorithm yields a unique contact map for all 221 globular structures of
PDBselect25 of length $N \le 120$. We also show that the reconstructed contact
maps allow in turn for the accurate reconstruction of the three-dimensional
structure. These results indicate that the reduced vectorial representation
provided by the principal eigenvector of the contact map is equivalent to the
protein structure itself. This representation is expected to provide a useful
tool in bioinformatics algorithms for protein structure comparison and
alignment, as well as a promising intermediate step towards protein structure
prediction.
| [
{
"created": "Tue, 13 Apr 2004 22:07:06 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Porto",
"Markus",
""
],
[
"Bastolla",
"Ugo",
""
],
[
"Roman",
"H. Eduardo",
""
],
[
"Vendruscolo",
"Michele",
""
]
] | We show that the contact map of the native structure of globular proteins can be reconstructed starting from the sole knowledge of the contact map's principal eigenvector, and present an exact algorithm for this purpose. Our algorithm yields a unique contact map for all 221 globular structures of PDBselect25 of length $N \le 120$. We also show that the reconstructed contact maps allow in turn for the accurate reconstruction of the three-dimensional structure. These results indicate that the reduced vectorial representation provided by the principal eigenvector of the contact map is equivalent to the protein structure itself. This representation is expected to provide a useful tool in bioinformatics algorithms for protein structure comparison and alignment, as well as a promising intermediate step towards protein structure prediction. |
1701.04893 | Benjamin Dunn | Benjamin Dunn, Daniel Wennberg, Ziwei Huang, Yasser Roudi | Grid cells show field-to-field variability and this explains the
aperiodic response of inhibitory interneurons | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Research on network mechanisms and coding properties of grid cells assume
that the firing rate of a grid cell in each of its fields is the same.
Furthermore, proposed network models predict spatial regularities in the firing
of inhibitory interneurons that are inconsistent with experimental data. In
this paper, by analyzing the response of grid cells recorded from rats during
free navigation, we first show that there are strong variations in the mean
firing rate of the fields of individual grid cells and thus show that the data
is inconsistent with the theoretical models that predict similar peak
magnitudes. We then build a two population excitatory-inhibitory network model
in which sparse spatially selective input to the excitatory cells, presumed to
arise from e.g. salient external stimuli, hippocampus or a combination of both,
leads to the variability in the firing field amplitudes of grid cells. We show
that, when combined with appropriate connectivity between the excitatory and
inhibitory neurons, the variability in the firing field amplitudes of grid
cells results in inhibitory neurons that do not exhibit regular spatial firing,
consistent with experimental data. Finally, we show that even if the spatial
positions of the fields are maintained, variations in the firing rates of the
fields of grid cells are enough to cause remapping of hippocampal cells.
| [
{
"created": "Tue, 17 Jan 2017 22:40:59 GMT",
"version": "v1"
}
] | 2017-01-19 | [
[
"Dunn",
"Benjamin",
""
],
[
"Wennberg",
"Daniel",
""
],
[
"Huang",
"Ziwei",
""
],
[
"Roudi",
"Yasser",
""
]
] | Research on network mechanisms and coding properties of grid cells assume that the firing rate of a grid cell in each of its fields is the same. Furthermore, proposed network models predict spatial regularities in the firing of inhibitory interneurons that are inconsistent with experimental data. In this paper, by analyzing the response of grid cells recorded from rats during free navigation, we first show that there are strong variations in the mean firing rate of the fields of individual grid cells and thus show that the data is inconsistent with the theoretical models that predict similar peak magnitudes. We then build a two population excitatory-inhibitory network model in which sparse spatially selective input to the excitatory cells, presumed to arise from e.g. salient external stimuli, hippocampus or a combination of both, leads to the variability in the firing field amplitudes of grid cells. We show that, when combined with appropriate connectivity between the excitatory and inhibitory neurons, the variability in the firing field amplitudes of grid cells results in inhibitory neurons that do not exhibit regular spatial firing, consistent with experimental data. Finally, we show that even if the spatial positions of the fields are maintained, variations in the firing rates of the fields of grid cells are enough to cause remapping of hippocampal cells. |
1305.7390 | Adam Freedman | Adam H. Freedman, Rena M. Schweizer, Ilan Gronau, Eunjung Han, Diego
Ortega-Del Vecchyo, Pedro M. Silva, Marco Galaverni, Zhenxin Fan, Peter Marx,
Belen Lorente-Galdos, Holly Beale, Oscar Ramirez, Farhad Hormozdiari, Can
Alkan, Carles Vil\`a, Kevin Squire, Eli Geffen, Josip Kusak, Adam R. Boyko,
Heidi G. Parker, Clarence Lee, Vasisht Tadigotla, Adam Siepel, Carlos D.
Bustamante, Timothy T. Harkins, Stanley F. Nelson, Elaine A. Ostrander, Tomas
Marques-Bonet, Robert K. Wayne, John Novembre | Genome Sequencing Highlights Genes Under Selection and the Dynamic Early
History of Dogs | 24 pages, 5 figures. To download the Supporting Information file, use
the following link:
https://www.dropbox.com/s/2yoytspv1iods7s/Freedman_etal_SupportingInfo_arxiv.pdf | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To identify genetic changes underlying dog domestication and reconstruct
their early evolutionary history, we analyzed novel high-quality genome
sequences of three gray wolves, one from each of three putative centers of dog
domestication, two ancient dog lineages (Basenji and Dingo) and a golden jackal
as an outgroup. We find dogs and wolves diverged through a dynamic process
involving population bottlenecks in both lineages and post-divergence gene
flow, which confounds previous inferences of dog origins. In dogs, the
domestication bottleneck was severe involving a 17 to 49-fold reduction in
population size, a much stronger bottleneck than estimated previously from less
intensive sequencing efforts. A sharp bottleneck in wolves occurred soon after
their divergence from dogs, implying that the pool of diversity from which dogs
arose was far larger than represented by modern wolf populations. Conditional
on mutation rate, we narrow the plausible range for the date of initial dog
domestication to an interval from 11 to 16 thousand years ago. This period
predates the rise of agriculture, implying that the earliest dogs arose
alongside hunter-gathers rather than agriculturists. Regarding the geographic
origin of dogs, we find that surprisingly, none of the extant wolf lineages
from putative domestication centers are more closely related to dogs, and the
sampled wolves instead form a sister monophyletic clade. This result, in
combination with our finding of dog-wolf admixture during the process of
domestication, suggests a re-evaluation of past hypotheses of dog origin is
necessary. Finally, we also detect signatures of selection, including evidence
for selection on genes implicated in morphology, metabolism, and neural
development. Uniquely, we find support for selective sweeps at regulatory sites
suggesting gene regulatory changes played a critical role in dog domestication.
| [
{
"created": "Fri, 31 May 2013 13:17:04 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Jun 2013 04:23:59 GMT",
"version": "v2"
}
] | 2013-06-05 | [
[
"Freedman",
"Adam H.",
""
],
[
"Schweizer",
"Rena M.",
""
],
[
"Gronau",
"Ilan",
""
],
[
"Han",
"Eunjung",
""
],
[
"Vecchyo",
"Diego Ortega-Del",
""
],
[
"Silva",
"Pedro M.",
""
],
[
"Galaverni",
"Marco",
""
],
[
"Fan",
"Zhenxin",
""
],
[
"Marx",
"Peter",
""
],
[
"Lorente-Galdos",
"Belen",
""
],
[
"Beale",
"Holly",
""
],
[
"Ramirez",
"Oscar",
""
],
[
"Hormozdiari",
"Farhad",
""
],
[
"Alkan",
"Can",
""
],
[
"Vilà",
"Carles",
""
],
[
"Squire",
"Kevin",
""
],
[
"Geffen",
"Eli",
""
],
[
"Kusak",
"Josip",
""
],
[
"Boyko",
"Adam R.",
""
],
[
"Parker",
"Heidi G.",
""
],
[
"Lee",
"Clarence",
""
],
[
"Tadigotla",
"Vasisht",
""
],
[
"Siepel",
"Adam",
""
],
[
"Bustamante",
"Carlos D.",
""
],
[
"Harkins",
"Timothy T.",
""
],
[
"Nelson",
"Stanley F.",
""
],
[
"Ostrander",
"Elaine A.",
""
],
[
"Marques-Bonet",
"Tomas",
""
],
[
"Wayne",
"Robert K.",
""
],
[
"Novembre",
"John",
""
]
] | To identify genetic changes underlying dog domestication and reconstruct their early evolutionary history, we analyzed novel high-quality genome sequences of three gray wolves, one from each of three putative centers of dog domestication, two ancient dog lineages (Basenji and Dingo) and a golden jackal as an outgroup. We find dogs and wolves diverged through a dynamic process involving population bottlenecks in both lineages and post-divergence gene flow, which confounds previous inferences of dog origins. In dogs, the domestication bottleneck was severe involving a 17 to 49-fold reduction in population size, a much stronger bottleneck than estimated previously from less intensive sequencing efforts. A sharp bottleneck in wolves occurred soon after their divergence from dogs, implying that the pool of diversity from which dogs arose was far larger than represented by modern wolf populations. Conditional on mutation rate, we narrow the plausible range for the date of initial dog domestication to an interval from 11 to 16 thousand years ago. This period predates the rise of agriculture, implying that the earliest dogs arose alongside hunter-gathers rather than agriculturists. Regarding the geographic origin of dogs, we find that surprisingly, none of the extant wolf lineages from putative domestication centers are more closely related to dogs, and the sampled wolves instead form a sister monophyletic clade. This result, in combination with our finding of dog-wolf admixture during the process of domestication, suggests a re-evaluation of past hypotheses of dog origin is necessary. Finally, we also detect signatures of selection, including evidence for selection on genes implicated in morphology, metabolism, and neural development. Uniquely, we find support for selective sweeps at regulatory sites suggesting gene regulatory changes played a critical role in dog domestication. |
2009.12855 | Ryan Blything | Ryan Blything, Valerio Biscione, Ivan I. Vankov, Casimir J.H. Ludwig,
and Jeffrey S. Bowers | The human visual system and CNNs can both support robust online
translation tolerance following extreme displacements | Main manuscript contains 5 figures plus 2 tables. SI contains 2
tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual translation tolerance refers to our capacity to recognize objects over
a wide range of different retinal locations. Although translation is perhaps
the simplest spatial transform that the visual system needs to cope with, the
extent to which the human visual system can identify objects at previously
unseen locations is unclear, with some studies reporting near complete
invariance over 10{\deg} and other reporting zero invariance at 4{\deg} of
visual angle. Similarly, there is confusion regarding the extent of translation
tolerance in computational models of vision, as well as the degree of match
between human and model performance. Here we report a series of eye-tracking
studies (total N=70) demonstrating that novel objects trained at one retinal
location can be recognized at high accuracy rates following translations up to
18{\deg}. We also show that standard deep convolutional networks (DCNNs)
support our findings when pretrained to classify another set of stimuli across
a range of locations, or when a Global Average Pooling (GAP) layer is added to
produce larger receptive fields. Our findings provide a strong constraint for
theories of human vision and help explain inconsistent findings previously
reported with CNNs.
| [
{
"created": "Sun, 27 Sep 2020 14:33:32 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Dec 2020 09:59:58 GMT",
"version": "v2"
}
] | 2020-12-09 | [
[
"Blything",
"Ryan",
""
],
[
"Biscione",
"Valerio",
""
],
[
"Vankov",
"Ivan I.",
""
],
[
"Ludwig",
"Casimir J. H.",
""
],
[
"Bowers",
"Jeffrey S.",
""
]
] | Visual translation tolerance refers to our capacity to recognize objects over a wide range of different retinal locations. Although translation is perhaps the simplest spatial transform that the visual system needs to cope with, the extent to which the human visual system can identify objects at previously unseen locations is unclear, with some studies reporting near complete invariance over 10{\deg} and other reporting zero invariance at 4{\deg} of visual angle. Similarly, there is confusion regarding the extent of translation tolerance in computational models of vision, as well as the degree of match between human and model performance. Here we report a series of eye-tracking studies (total N=70) demonstrating that novel objects trained at one retinal location can be recognized at high accuracy rates following translations up to 18{\deg}. We also show that standard deep convolutional networks (DCNNs) support our findings when pretrained to classify another set of stimuli across a range of locations, or when a Global Average Pooling (GAP) layer is added to produce larger receptive fields. Our findings provide a strong constraint for theories of human vision and help explain inconsistent findings previously reported with CNNs. |
2308.00678 | Takuya Matsuyama | Takuya Matsuyama, Kota S Sasaki, Shinji Nishimoto | Applicability of scaling laws to vision encoding models | 7 pages, 3 figures | null | null | null | q-bio.NC cs.AI cs.CV | http://creativecommons.org/licenses/by/4.0/ | In this paper, we investigated how to build a high-performance vision
encoding model to predict brain activity as part of our participation in the
Algonauts Project 2023 Challenge. The challenge provided brain activity
recorded by functional MRI (fMRI) while participants viewed images. Several
vision models with parameter sizes ranging from 86M to 4.3B were used to build
predictive models. To build highly accurate models, we focused our analysis on
two main aspects: (1) How does the sample size of the fMRI training set change
the prediction accuracy? (2) How does the prediction accuracy across the visual
cortex vary with the parameter size of the vision models? The results show that
as the sample size used during training increases, the prediction accuracy
improves according to the scaling law. Similarly, we found that as the
parameter size of the vision models increases, the prediction accuracy improves
according to the scaling law. These results suggest that increasing the sample
size of the fMRI training set and the parameter size of visual models may
contribute to more accurate visual models of the brain and lead to a better
understanding of visual neuroscience.
| [
{
"created": "Tue, 1 Aug 2023 17:31:14 GMT",
"version": "v1"
}
] | 2023-08-02 | [
[
"Matsuyama",
"Takuya",
""
],
[
"Sasaki",
"Kota S",
""
],
[
"Nishimoto",
"Shinji",
""
]
] | In this paper, we investigated how to build a high-performance vision encoding model to predict brain activity as part of our participation in the Algonauts Project 2023 Challenge. The challenge provided brain activity recorded by functional MRI (fMRI) while participants viewed images. Several vision models with parameter sizes ranging from 86M to 4.3B were used to build predictive models. To build highly accurate models, we focused our analysis on two main aspects: (1) How does the sample size of the fMRI training set change the prediction accuracy? (2) How does the prediction accuracy across the visual cortex vary with the parameter size of the vision models? The results show that as the sample size used during training increases, the prediction accuracy improves according to the scaling law. Similarly, we found that as the parameter size of the vision models increases, the prediction accuracy improves according to the scaling law. These results suggest that increasing the sample size of the fMRI training set and the parameter size of visual models may contribute to more accurate visual models of the brain and lead to a better understanding of visual neuroscience. |
q-bio/0502032 | Martin Howard | Martin Howard and Karsten Kruse | Cellular organization by self-organization : mechanisms and models for
Min protein dynamics | 20 pages, 1 figure | J. Cell Biol. 168 533-536 (2005) | 10.1083/jcb.200411122 | null | q-bio.SC cond-mat.soft | null | We use the oscillating Min proteins of Escherichia coli as a prototype system
to illustrate the current state and potential of modeling protein dynamics in
space and time. We demonstrate how a theoretical approach has led to striking
new insights into the mechanisms of self-organization in bacterial cells and
indicate how these ideas may be applicable to more complex structure formation
in eukaryotic cells.
| [
{
"created": "Wed, 23 Feb 2005 14:04:14 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Howard",
"Martin",
""
],
[
"Kruse",
"Karsten",
""
]
] | We use the oscillating Min proteins of Escherichia coli as a prototype system to illustrate the current state and potential of modeling protein dynamics in space and time. We demonstrate how a theoretical approach has led to striking new insights into the mechanisms of self-organization in bacterial cells and indicate how these ideas may be applicable to more complex structure formation in eukaryotic cells. |
1811.07838 | Akram Yazdani PhD | Akram Yazdani, Azam Yazdani, Ra\'ul M\'endez Gir\'aldez, David
Aguilar, Luca Sartore | A Multi-Trait Approach Identified Genetic Variants Including a Rare
Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heart failure is a major cause for premature death. Given heterogeneity of
the heart failure syndrome, identifying genetic determinants of cardiac
function and structure may provide greater insights into heart failure. Despite
progress in understanding the genetic basis of heart failure through genome
wide association studies, heritability of heart failure is not well understood.
Gaining further insights into mechanisms that contribute to heart failure
requires systematic approaches that go beyond single trait analysis. We
integrated Bayesian multi-trait approach and Bayesian networks for the analysis
of 10 correlated traits of cardiac structure and function measured for 3387
individuals with whole exome sequence data. While using single-trait based
approaches did not find any significant genetic variant, applying the
integrative Bayesian multi-trait approach, we identified 3 novel variants
located in genes, RGS3, CHD3, and MRPL38 with significant impact on the cardiac
traits such as left ventricular volume index, parasternal long axis
interventricular septum thickness, and mean left ventricular wall thickness.
Among these, the rare variant NC_000009.11:g.116346115C>A (rs144636307) in RGS3
showed pleiotropic effect on left ventricular mass index, left ventricular
volume index and Maximum left atrial anterior-posterior diameter while RGS3 can
inhibit TGF-beta signaling associated with left ventricle dilation and systolic
dysfunction.
| [
{
"created": "Mon, 19 Nov 2018 17:53:31 GMT",
"version": "v1"
}
] | 2018-11-20 | [
[
"Yazdani",
"Akram",
""
],
[
"Yazdani",
"Azam",
""
],
[
"Giráldez",
"Raúl Méndez",
""
],
[
"Aguilar",
"David",
""
],
[
"Sartore",
"Luca",
""
]
] | Heart failure is a major cause for premature death. Given heterogeneity of the heart failure syndrome, identifying genetic determinants of cardiac function and structure may provide greater insights into heart failure. Despite progress in understanding the genetic basis of heart failure through genome wide association studies, heritability of heart failure is not well understood. Gaining further insights into mechanisms that contribute to heart failure requires systematic approaches that go beyond single trait analysis. We integrated Bayesian multi-trait approach and Bayesian networks for the analysis of 10 correlated traits of cardiac structure and function measured for 3387 individuals with whole exome sequence data. While using single-trait based approaches did not find any significant genetic variant, applying the integrative Bayesian multi-trait approach, we identified 3 novel variants located in genes, RGS3, CHD3, and MRPL38 with significant impact on the cardiac traits such as left ventricular volume index, parasternal long axis interventricular septum thickness, and mean left ventricular wall thickness. Among these, the rare variant NC_000009.11:g.116346115C>A (rs144636307) in RGS3 showed pleiotropic effect on left ventricular mass index, left ventricular volume index and Maximum left atrial anterior-posterior diameter while RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction. |
1211.7320 | Stanley Lazic | Stanley E. Lazic and Laurent Essioux | Improving basic and translational science by accounting for
litter-to-litter variation in animal models | http://www.biomedcentral.com/1471-2202/14/37/abstract | BMC Neuroscience 2013, 14:37 | 10.1186/1471-2202-14-37 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Animals from the same litter are often more alike compared with
animals from different litters. This litter-to-litter variation, or "litter
effects", can influence the results in addition to the experimental factors of
interest. Furthermore, an experimental treatment can be applied to whole
litters rather than to individual offspring. For example, in the valproic acid
(VPA) model of autism, VPA is administered to pregnant females thereby inducing
the disease phenotype in the offspring. With this type of experiment the sample
size is the number of litters and not the total number of offspring. If such
experiments are not appropriately designed and analysed, the results can be
severely biased as well as extremely underpowered.
Results: A review of the VPA literature showed that only 9% (3/34) of studies
correctly determined that the experimental unit (n) was the litter and
therefore made valid statistical inferences. In addition, litter effects
accounted for up to 61% (p <0.001) of the variation in behavioural outcomes,
which was larger than the treatment effects. In addition, few studies reported
using randomisation (12%) or blinding (18%), and none indicated that a sample
size calculation or power analysis had been conducted.
Conclusions: Litter effects are common, large, and ignoring them can make
replication of findings difficult and can contribute to the low rate of
translating preclinical in vivo studies into successful therapies. Only a
minority of studies reported using rigorous experimental methods, which is
consistent with much of the preclinical in vivo literature.
| [
{
"created": "Fri, 30 Nov 2012 17:32:28 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Mar 2013 18:05:28 GMT",
"version": "v2"
}
] | 2013-03-25 | [
[
"Lazic",
"Stanley E.",
""
],
[
"Essioux",
"Laurent",
""
]
] | Background: Animals from the same litter are often more alike compared with animals from different litters. This litter-to-litter variation, or "litter effects", can influence the results in addition to the experimental factors of interest. Furthermore, an experimental treatment can be applied to whole litters rather than to individual offspring. For example, in the valproic acid (VPA) model of autism, VPA is administered to pregnant females thereby inducing the disease phenotype in the offspring. With this type of experiment the sample size is the number of litters and not the total number of offspring. If such experiments are not appropriately designed and analysed, the results can be severely biased as well as extremely underpowered. Results: A review of the VPA literature showed that only 9% (3/34) of studies correctly determined that the experimental unit (n) was the litter and therefore made valid statistical inferences. In addition, litter effects accounted for up to 61% (p <0.001) of the variation in behavioural outcomes, which was larger than the treatment effects. In addition, few studies reported using randomisation (12%) or blinding (18%), and none indicated that a sample size calculation or power analysis had been conducted. Conclusions: Litter effects are common, large, and ignoring them can make replication of findings difficult and can contribute to the low rate of translating preclinical in vivo studies into successful therapies. Only a minority of studies reported using rigorous experimental methods, which is consistent with much of the preclinical in vivo literature. |
0805.0634 | Frederick Matsen IV | Frederick A. Matsen and Steven N. Evans | To what extent does genealogical ancestry imply genetic ancestry? | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent statistical and computational analyses have shown that a genealogical
most recent common ancestor (MRCA) may have lived in the recent past. However,
coalescent-based approaches show that genetic most recent common ancestors for
a given non-recombining locus are typically much more ancient. It is not
immediately clear how these two perspectives interact. This paper investigates
relationships between the number of descendant alleles of an ancestor allele
and the number of genealogical descendants of the individual who possessed that
allele for a simple diploid genetic model extending the genealogical model of
Joseph Chang.
| [
{
"created": "Tue, 6 May 2008 02:51:05 GMT",
"version": "v1"
},
{
"created": "Wed, 14 May 2008 21:14:49 GMT",
"version": "v2"
}
] | 2008-05-15 | [
[
"Matsen",
"Frederick A.",
""
],
[
"Evans",
"Steven N.",
""
]
] | Recent statistical and computational analyses have shown that a genealogical most recent common ancestor (MRCA) may have lived in the recent past. However, coalescent-based approaches show that genetic most recent common ancestors for a given non-recombining locus are typically much more ancient. It is not immediately clear how these two perspectives interact. This paper investigates relationships between the number of descendant alleles of an ancestor allele and the number of genealogical descendants of the individual who possessed that allele for a simple diploid genetic model extending the genealogical model of Joseph Chang. |
2111.08000 | Emil Iftekhar | Viola Priesemann (1), Eberhard Bodenschatz (1), Sandra Ciesek (2), Eva
Grill (3), Emil N. Iftekhar (1), Christian Karagiannidis (4), Andr\'e Karch
(5), Mirjam Kretzschmar (6), Berit Lange (7), Sebastian A. M\"uller (8), Kai
Nagel (8), Armin Nassehi (9), Mathias W. Pletz (10), Barbara Prainsack (11),
Ulrike Protzer (12), Leif Erik Sander (13), Andreas Schuppert (14), Anita
Sch\"obel (15), Klaus \"Uberla (16), Carsten Watzl (17), Hajo Zeeb (18) ((1)
Max-Planck-Institut f\"ur Dynamik und Selbstorganisation, G\"ottingen, (2)
Universit\"atsklinikum Frankfurt, Goethe-Universit\"at, Frankfurt, (3)
Institut f\"ur Medizinische Informationsverarbeitung, Biometrie und
Epidemiologie, Ludwig-Maximilians-Universit\"at M\"unchen (LMU), M\"unchen,
(4) Lungenklinik K\"oln-Merheim, Universit\"at Witten/ Herdecke, (5)
Westf\"alische Wilhelms-Universit\"at M\"unster, M\"unster, (6) University
Medical Center Utrecht, Utrecht, Die Niederlande, (7) Epidemiologie,
Helmholtz-Zentrum f\"ur Infektionsforschung, Braunschweig, (8) Fachgebiet
Verkehrssystemplanung und Verkehrstelematik, Technische Universit\"at (TU)
Berlin, Berlin, (9) Institut f\"ur Soziologie,
Ludwig-Maximilians-Universit\"at M\"unchen (LMU), M\"unchen, (10) Institut
f\"ur Infektionsmedizin und Krankenhaushygiene, Universit\"atsklinikum Jena,
Jena, (11) Institut f\"ur Politikwissenschaft, Universit\"at Wien, Wien,
\"Osterreich, (12) Institut f\"ur Virologie, Technische Universit\"at
M\"unchen / Helmholtz Zentrum M\"unchen, M\"unchen, (13) Medizinische Klinik
mit Schwerpunkt Infektiologie und Pneumologie, Charit\'e -
Universit\"atsmedizin Berlin, Berlin, (14) RWTH Aachen /
Universit\"atsklinikum Aachen, Aachen, (15) Fraunhofer-Institut f\"ur Techno-
und Wirtschaftsmathematik (ITWM), Kaiserslautern und Fachbereich Mathematik,
TU Kaiserslautern, (16) Virologisches Institut, Universit\"atsklinikum
Erlangen, Erlangen, (17) Leibniz Institut f\"ur Arbeitsforschung (IfADo), TU
Dortmund, Dortmund, (18) Leibniz Institut f\"ur Pr\"aventionsforschung und
Epidemiologe-BIPS, Bremen) | Nachhaltige Strategien gegen die COVID-19-Pandemie in Deutschland im
Winter 2021/2022 | in German, Extensive expert assessment on COVID-19 response policies
for the winter 2021/22 | null | null | null | q-bio.OT physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | In this position paper, a large group of interdisciplinary experts outlines
response strategies against the spread of SARS-CoV-2 in the winter of 2021/2022
in Germany. We review the current state of the COVID-19 pandemic, from
incidence and vaccination efficacy to hospital capacity. Building on this
situation assessment, we illustrate various possible scenarios for the winter,
and detail the mechanisms and effectiveness of the non-pharmaceutical
interventions, vaccination, and booster. With this assessment, we want to
provide orientation for decision makers about the progress and mitigation of
COVID-19.
| [
{
"created": "Wed, 10 Nov 2021 17:41:13 GMT",
"version": "v1"
}
] | 2021-11-16 | [
[
"Priesemann",
"Viola",
""
],
[
"Bodenschatz",
"Eberhard",
""
],
[
"Ciesek",
"Sandra",
""
],
[
"Grill",
"Eva",
""
],
[
"Iftekhar",
"Emil N.",
""
],
[
"Karagiannidis",
"Christian",
""
],
[
"Karch",
"André",
""
],
[
"Kretzschmar",
"Mirjam",
""
],
[
"Lange",
"Berit",
""
],
[
"Müller",
"Sebastian A.",
""
],
[
"Nagel",
"Kai",
""
],
[
"Nassehi",
"Armin",
""
],
[
"Pletz",
"Mathias W.",
""
],
[
"Prainsack",
"Barbara",
""
],
[
"Protzer",
"Ulrike",
""
],
[
"Sander",
"Leif Erik",
""
],
[
"Schuppert",
"Andreas",
""
],
[
"Schöbel",
"Anita",
""
],
[
"Überla",
"Klaus",
""
],
[
"Watzl",
"Carsten",
""
],
[
"Zeeb",
"Hajo",
""
]
] | In this position paper, a large group of interdisciplinary experts outlines response strategies against the spread of SARS-CoV-2 in the winter of 2021/2022 in Germany. We review the current state of the COVID-19 pandemic, from incidence and vaccination efficacy to hospital capacity. Building on this situation assessment, we illustrate various possible scenarios for the winter, and detail the mechanisms and effectiveness of the non-pharmaceutical interventions, vaccination, and booster. With this assessment, we want to provide orientation for decision makers about the progress and mitigation of COVID-19. |
1110.0276 | Fang-Chieh Chou | Fang-Chieh Chou, Parin Sripakdeevong, Sergey M. Dibrov, Thomas Hermann
and Rhiju Das | Correcting pervasive errors in RNA crystallography through enumerative
structure prediction | null | null | 10.1038/nmeth.2262 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Three-dimensional RNA models fitted into crystallographic density maps
exhibit pervasive conformational ambiguities, geometric errors and steric
clashes. To address these problems, we present enumerative real-space
refinement assisted by electron density under Rosetta (ERRASER), coupled to
Python-based hierarchical environment for integrated 'xtallography' (PHENIX)
diffraction-based refinement. On 24 data sets, ERRASER automatically corrects
the majority of MolProbity-assessed errors, improves the average Rfree factor,
resolves functionally important discrepancies in noncanonical structure and
refines low-resolution models to better match higher-resolution models.
| [
{
"created": "Mon, 3 Oct 2011 06:54:10 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Jun 2012 07:09:13 GMT",
"version": "v2"
},
{
"created": "Mon, 3 Dec 2012 01:16:47 GMT",
"version": "v3"
}
] | 2012-12-04 | [
[
"Chou",
"Fang-Chieh",
""
],
[
"Sripakdeevong",
"Parin",
""
],
[
"Dibrov",
"Sergey M.",
""
],
[
"Hermann",
"Thomas",
""
],
[
"Das",
"Rhiju",
""
]
] | Three-dimensional RNA models fitted into crystallographic density maps exhibit pervasive conformational ambiguities, geometric errors and steric clashes. To address these problems, we present enumerative real-space refinement assisted by electron density under Rosetta (ERRASER), coupled to Python-based hierarchical environment for integrated 'xtallography' (PHENIX) diffraction-based refinement. On 24 data sets, ERRASER automatically corrects the majority of MolProbity-assessed errors, improves the average Rfree factor, resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models. |
1607.03687 | Daniele Marinazzo | Frederik van de Steen, Luca Faes, Esin Karahan, Jitkomut Songsiri,
Pedro Antonio Valdes Sosa, Daniele Marinazzo | Critical comments on EEG sensor space dynamical connectivity analysis | null | null | null | null | q-bio.NC stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many different analysis techniques have been developed and applied to EEG
recordings that allow one to investigate how different brain areas interact.
One particular class of methods, based on the linear parametric representation
of multiple interacting time series, is widely used to study causal
connectivity in the brain. However, the results obtained by these methods
should be interpreted with great care. The goal of this paper is to show, both
theoretically and using simulations, that results obtained by applying causal
connectivity measures on the sensor (scalp) time series do not allow
interpretation in terms of interacting brain sources. This is because 1) the
channel locations cannot be seen as an approximation of a source's anatomical
location and 2) spurious connectivity can occur between sensors. Although many
measures of causal connectivity derived from EEG sensor time series are
affected by the latter, here we will focus on the well-known time domain index
of Granger causality (GC) and on the frequency domain directed transfer
function (DTF). Using the state-space framework and designing two simulation
studies we show that mixing effects caused by volume conduction can lead to
spurious connections, detected either by time domain GC or by DTF. Therefore,
GC/DTF causal connectivity measures should be computed at the source level, or
derived within analysis frameworks that model the effects of volume conduction.
Since mixing effects can also occur in the source space, it is advised to
combine source space analysis with connectivity measures that are robust to
mixing.
| [
{
"created": "Wed, 13 Jul 2016 11:54:08 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Nov 2016 13:18:45 GMT",
"version": "v2"
}
] | 2016-11-09 | [
[
"van de Steen",
"Frederik",
""
],
[
"Faes",
"Luca",
""
],
[
"Karahan",
"Esin",
""
],
[
"Songsiri",
"Jitkomut",
""
],
[
"Sosa",
"Pedro Antonio Valdes",
""
],
[
"Marinazzo",
"Daniele",
""
]
] | Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal connectivity in the brain. However, the results obtained by these methods should be interpreted with great care. The goal of this paper is to show, both theoretically and using simulations, that results obtained by applying causal connectivity measures on the sensor (scalp) time series do not allow interpretation in terms of interacting brain sources. This is because 1) the channel locations cannot be seen as an approximation of a source's anatomical location and 2) spurious connectivity can occur between sensors. Although many measures of causal connectivity derived from EEG sensor time series are affected by the latter, here we will focus on the well-known time domain index of Granger causality (GC) and on the frequency domain directed transfer function (DTF). Using the state-space framework and designing two simulation studies we show that mixing effects caused by volume conduction can lead to spurious connections, detected either by time domain GC or by DTF. Therefore, GC/DTF causal connectivity measures should be computed at the source level, or derived within analysis frameworks that model the effects of volume conduction. Since mixing effects can also occur in the source space, it is advised to combine source space analysis with connectivity measures that are robust to mixing. |
0808.3099 | David Pincus | D. L. Pincus, S. S. Cho, C. Hyeon, D. Thirumalai | Minimal models for proteins and RNA: From folding to function | 60 pages, 11 figures | null | null | null | q-bio.BM cond-mat.soft q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a panoramic view of the utility of coarse-grained (CG) models to
study folding and functions of proteins and RNA. Drawing largely on the methods
developed in our group over the last twenty years, we describe a number of key
applications ranging from folding of proteins with disulfide bonds to functions
of molecular machines. After presenting the theoretical basis that justifies
the use of CG models, we explore the biophysical basis for the emergence of a
finite number of folds from lattice models. The lattice model simulations of
approach to the folded state show that non-native interactions are relevant
only early in the folding process - a finding that rationalizes the success of
structure-based models that emphasize native interactions. Applications of
off-lattice $C_{\alpha}$ and models that explicitly consider side chains
($C_{\alpha}$-SCM) to folding of $\beta$-hairpin and effects of macromolecular
crowding are briefly discussed. Successful application of a new class of
off-lattice model, referred to as the Self-Organized Polymer (SOP), is shown by
describing the response of Green Fluorescent Protein (GFP) to mechanical force.
The utility of the SOP model is further illustrated by applications that
clarify the functions of the chaperonin GroEL and motion of the molecular motor
kinesin. We also present two distinct models for RNA, namely, the Three Site
Interaction (TIS) model and the SOP model, that probe forced unfolding and
force quench refolding of a simple hairpin and {\it Azoarcus} ribozyme. The
predictions based on the SOP model show that force-induced unfolding pathways
of the ribozyme can be dramatically changed by varying the loading rate. We
conclude with a discussion of future prospects for the use of coarse-grained
models in addressing problems of outstanding interest in biology.
| [
{
"created": "Fri, 22 Aug 2008 15:05:10 GMT",
"version": "v1"
}
] | 2008-08-25 | [
[
"Pincus",
"D. L.",
""
],
[
"Cho",
"S. S.",
""
],
[
"Hyeon",
"C.",
""
],
[
"Thirumalai",
"D.",
""
]
] | We present a panoramic view of the utility of coarse-grained (CG) models to study folding and functions of proteins and RNA. Drawing largely on the methods developed in our group over the last twenty years, we describe a number of key applications ranging from folding of proteins with disulfide bonds to functions of molecular machines. After presenting the theoretical basis that justifies the use of CG models, we explore the biophysical basis for the emergence of a finite number of folds from lattice models. The lattice model simulations of approach to the folded state show that non-native interactions are relevant only early in the folding process - a finding that rationalizes the success of structure-based models that emphasize native interactions. Applications of off-lattice $C_{\alpha}$ and models that explicitly consider side chains ($C_{\alpha}$-SCM) to folding of $\beta$-hairpin and effects of macromolecular crowding are briefly discussed. Successful application of a new class of off-lattice model, referred to as the Self-Organized Polymer (SOP), is shown by describing the response of Green Fluorescent Protein (GFP) to mechanical force. The utility of the SOP model is further illustrated by applications that clarify the functions of the chaperonin GroEL and motion of the molecular motor kinesin. We also present two distinct models for RNA, namely, the Three Site Interaction (TIS) model and the SOP model, that probe forced unfolding and force quench refolding of a simple hairpin and {\it Azoarcus} ribozyme. The predictions based on the SOP model show that force-induced unfolding pathways of the ribozyme can be dramatically changed by varying the loading rate. We conclude with a discussion of future prospects for the use of coarse-grained models in addressing problems of outstanding interest in biology. |
1512.05420 | Christoph Feinauer | Christoph Feinauer, Hendrik Szurmant, Martin Weigt, Andrea Pagnani | Inter-protein sequence co-evolution predicts known physical interactions
in bacterial ribosomes and the trp operon | null | PLoS ONE 11(2): e0149166 (2016) | 10.1371/journal.pone.0149166 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interaction between proteins is a fundamental mechanism that underlies
virtually all biological processes. Many important interactions are conserved
across a large variety of species. The need to maintain interaction leads to a
high degree of co-evolution between residues in the interface between partner
proteins. The inference of protein-protein interaction networks from the
rapidly growing sequence databases is one of the most formidable tasks in
systems biology today. We propose here a novel approach based on the
Direct-Coupling Analysis of the co-evolution between inter-protein residue
pairs. We use ribosomal and trp operon proteins as test cases: For the small
resp. large ribosomal subunit our approach predicts protein-interaction
partners at a true-positive rate of 70% resp. 90% within the first 10
predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all
predictions. In the trp operon, it assigns the two largest interaction scores
to the only two interactions experimentally known. On the level of residue
interactions we show that for both the small and the large ribosomal subunit
our approach predicts interacting residues in the system with a true positive
rate of 60% and 85% in the first 20 predictions. We use artificial data to show
that the performance of our approach depends crucially on the size of the joint
multiple sequence alignments and analyze how many sequences would be necessary
for a perfect prediction if the sequences were sampled from the same model that
we use for prediction. Given the performance of our approach on the test data
we speculate that it can be used to detect new interactions, especially in the
light of the rapid growth of available sequence data.
| [
{
"created": "Thu, 17 Dec 2015 00:22:17 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Feb 2016 10:54:32 GMT",
"version": "v2"
}
] | 2016-02-25 | [
[
"Feinauer",
"Christoph",
""
],
[
"Szurmant",
"Hendrik",
""
],
[
"Weigt",
"Martin",
""
],
[
"Pagnani",
"Andrea",
""
]
] | Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data. |
1410.8799 | Jannis Schuecker | Jannis Schuecker, Markus Diesmann and Moritz Helias | Reduction of colored noise in excitable systems to white noise and
dynamic boundary conditions | null | null | null | null | q-bio.NC cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A recent study on the effect of colored driving noise on the escape from a
metastable state derives an analytic expression of the transfer function of the
leaky integrate-and-fire neuron model subject to colored noise. Here we present
an alternative derivation of the results, taking into account time-dependent
boundary conditions explicitly. This systematic approach may facilitate future
extensions beyond first order perturbation theory. The analogy of the quantum
harmonic oscillator to the LIF neuron model subject to white noise enables a
derivation of the well known transfer function simpler than the original
approach. We offer a pedagogical presentation including all intermediate steps
of the calculations.
| [
{
"created": "Thu, 23 Oct 2014 12:40:42 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Feb 2015 14:16:23 GMT",
"version": "v2"
},
{
"created": "Tue, 13 Oct 2015 11:50:55 GMT",
"version": "v3"
}
] | 2015-10-14 | [
[
"Schuecker",
"Jannis",
""
],
[
"Diesmann",
"Markus",
""
],
[
"Helias",
"Moritz",
""
]
] | A recent study on the effect of colored driving noise on the escape from a metastable state derives an analytic expression of the transfer function of the leaky integrate-and-fire neuron model subject to colored noise. Here we present an alternative derivation of the results, taking into account time-dependent boundary conditions explicitly. This systematic approach may facilitate future extensions beyond first order perturbation theory. The analogy of the quantum harmonic oscillator to the LIF neuron model subject to white noise enables a derivation of the well known transfer function simpler than the original approach. We offer a pedagogical presentation including all intermediate steps of the calculations. |
2108.12759 | Eric Berlow | Eric Berlow, Spencer Canon, Kaustuv DeBiswas, David Gurman, Shawna
Jacoby, Lizbet Simmons, Andy Walshe, Rich Williams, Tiffany Yuan, and Mark
Runco | Creative Diversity: Patterns in the Creative Habits of ~10,000 People | 13 pages, 5 Figures, 1 Box, 2 Tables, Supplementary Information | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-sa/4.0/ | Despite popular media interest in uncovering the creative habits of
successful people, there is a surprising paucity of empirical research on the
diversity of tendencies and preferences people have when engaged in creative
work. We developed a simple survey that characterized 42 creative habits along
21 independent dimensions. Data from 9,633 respondents revealed seven 'Creative
Species', or clusters of people with combinations of creative habits that tend
to co-occur more than expected by chance. These emergent clusters where
relatively stable to random subsampling of the population and to variation in
model parameters. The seven Creative Species self-sorted along a primary
gradient from those characterized by more 'deliberate' creative habits (e.g.,
Monotasker, Risk Averse, Routine Seeker, Tenacious, Make it Happen) to those
characterized by more 'open' creative habits (e.g. Multitasker, Risk Friendly,
Novelty Seeker, Reframer, Let it Happen). A weaker second gradient was defined
by outward and rational vs inward and intuitive creators. For the subset of
respondents with data about their broad professional discipline (Art, Science,
and Business) and gender, some groups were more (or less) common in some
Creative Species than expected by chance, but the absolute magnitude of these
differences were generally small; and knowing the discipline or gender of a
person was not a good single predictor of their creative preferences or
tendencies. Together these results suggest that independent of discipline or
gender, people vary widely in the habits, behaviors, and contexts in which they
feel most creative. Understanding creative diversity is critical for improving
the creative performance of both individuals and collaborative teams.
| [
{
"created": "Sun, 29 Aug 2021 06:34:02 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Sep 2021 21:18:27 GMT",
"version": "v2"
}
] | 2021-09-06 | [
[
"Berlow",
"Eric",
""
],
[
"Canon",
"Spencer",
""
],
[
"DeBiswas",
"Kaustuv",
""
],
[
"Gurman",
"David",
""
],
[
"Jacoby",
"Shawna",
""
],
[
"Simmons",
"Lizbet",
""
],
[
"Walshe",
"Andy",
""
],
[
"Williams",
"Rich",
""
],
[
"Yuan",
"Tiffany",
""
],
[
"Runco",
"Mark",
""
]
] | Despite popular media interest in uncovering the creative habits of successful people, there is a surprising paucity of empirical research on the diversity of tendencies and preferences people have when engaged in creative work. We developed a simple survey that characterized 42 creative habits along 21 independent dimensions. Data from 9,633 respondents revealed seven 'Creative Species', or clusters of people with combinations of creative habits that tend to co-occur more than expected by chance. These emergent clusters where relatively stable to random subsampling of the population and to variation in model parameters. The seven Creative Species self-sorted along a primary gradient from those characterized by more 'deliberate' creative habits (e.g., Monotasker, Risk Averse, Routine Seeker, Tenacious, Make it Happen) to those characterized by more 'open' creative habits (e.g. Multitasker, Risk Friendly, Novelty Seeker, Reframer, Let it Happen). A weaker second gradient was defined by outward and rational vs inward and intuitive creators. For the subset of respondents with data about their broad professional discipline (Art, Science, and Business) and gender, some groups were more (or less) common in some Creative Species than expected by chance, but the absolute magnitude of these differences were generally small; and knowing the discipline or gender of a person was not a good single predictor of their creative preferences or tendencies. Together these results suggest that independent of discipline or gender, people vary widely in the habits, behaviors, and contexts in which they feel most creative. Understanding creative diversity is critical for improving the creative performance of both individuals and collaborative teams. |
2103.04856 | Anna Song | Anna Song | Generation of tubular and membranous shape textures with curvature
functionals | 25 pages, 13 figures | null | null | null | q-bio.QM cond-mat.soft math.DG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tubular and membranous shapes display a wide range of morphologies that are
difficult to analyze within a common framework. By generalizing the classical
Helfrich energy of biomembranes, we model them as solutions to a curvature
optimization problem in which the principal curvatures may play asymmetric
roles. We then give a novel phase-field formulation to approximate this
geometric problem, and study its Gamma-limsup convergence. This results in an
efficient GPU algorithm that we validate on well-known minimizers of the
Willmore energy; the software for the implementation of our algorithm is freely
available online. Exploring the space of parameters reveals that this
comprehensive framework leads to a wide continuum of shape textures. This first
step towards a unifying theory will have several implications, in biology for
quantifying tubular shapes or designing bio-mimetic scaffolds, but also in
computer graphics or architecture.
| [
{
"created": "Mon, 8 Mar 2021 16:06:31 GMT",
"version": "v1"
}
] | 2021-03-10 | [
[
"Song",
"Anna",
""
]
] | Tubular and membranous shapes display a wide range of morphologies that are difficult to analyze within a common framework. By generalizing the classical Helfrich energy of biomembranes, we model them as solutions to a curvature optimization problem in which the principal curvatures may play asymmetric roles. We then give a novel phase-field formulation to approximate this geometric problem, and study its Gamma-limsup convergence. This results in an efficient GPU algorithm that we validate on well-known minimizers of the Willmore energy; the software for the implementation of our algorithm is freely available online. Exploring the space of parameters reveals that this comprehensive framework leads to a wide continuum of shape textures. This first step towards a unifying theory will have several implications, in biology for quantifying tubular shapes or designing bio-mimetic scaffolds, but also in computer graphics or architecture. |
q-bio/0512029 | Guido Tiana | A. Amatori, J.Ferkinghoff-Borg, G. Tiana, and R. A. Broglia | What thermodynamic features characterize good and bad folders? Results
from a simplified off-lattice protein model | null | null | 10.1103/PhysRevE.73.061905 | null | q-bio.BM | null | The thermodynamics of the small SH3 protein domain is studied by means of a
simplified model where each bead-like amino acid interacts with the others
through a contact potential controlled by a 20x20 random matrix. Good folding
sequences, characterized by a low native energy, display three main
thermodynamical phases, namely a coil-like phase, an unfolded globule and a
folded phase (plus other two phases, namely frozen and random coil, populated
only at extremes temperatures). Interestingly, the unfolded globule has some
regions already structured. Poorly designed sequences, on the other hand,
display a wide transition from the random coil to a frozen state. The
comparison with the analytic theory of heteropolymers is discussed.
| [
{
"created": "Mon, 12 Dec 2005 18:30:32 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Amatori",
"A.",
""
],
[
"Ferkinghoff-Borg",
"J.",
""
],
[
"Tiana",
"G.",
""
],
[
"Broglia",
"R. A.",
""
]
] | The thermodynamics of the small SH3 protein domain is studied by means of a simplified model where each bead-like amino acid interacts with the others through a contact potential controlled by a 20x20 random matrix. Good folding sequences, characterized by a low native energy, display three main thermodynamical phases, namely a coil-like phase, an unfolded globule and a folded phase (plus other two phases, namely frozen and random coil, populated only at extremes temperatures). Interestingly, the unfolded globule has some regions already structured. Poorly designed sequences, on the other hand, display a wide transition from the random coil to a frozen state. The comparison with the analytic theory of heteropolymers is discussed. |
1709.08021 | Sanzo Miyazawa | Sanzo Miyazawa | Prediction of Structures and Interactions from Genome Information | 35 pages, 4 Tables, and 1 figures. In 2018, this manuscript with the
short version of appendix has been published as the chapter 9 of a book,
"Integrative Structural Biology with Hybrid Methods" edited by Haruki
Nakamura as one of the book series: "Advances in Experimental Medicine and
Biology 1105" from Springer Nature Singapore Pte Ltd.;
https://doi.org/10.1007/978-981-13-2200-6_9 | In: H. Nakamura (ed.) Integrative Structural Biology with Hybrid
Methods, Advances in Experimental Medicine and Biology 1105, chap. 9.
Springer Nature Singapore Pte Ltd. (2018) | 10.1007/978-981-13-2200-6_9 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting three dimensional residue-residue contacts from evolutionary
information in protein sequences was attempted already in the early 1990s.
However, contact prediction accuracies of methods evaluated in CASP experiments
before CASP11 remained quite low, typically with $<20$% true positives.
Recently, contact prediction has been significantly improved to the level that
an accurate three dimensional model of a large protein can be generated on the
basis of predicted contacts. This improvement was attained by disentangling
direct from indirect correlations in amino acid covariations or cosubstitutions
between sites in protein evolution. Here, we review statistical methods for
extracting causative correlations and various approaches to describe protein
structure, complex, and flexibility based on predicted contacts.
| [
{
"created": "Sat, 23 Sep 2017 08:44:31 GMT",
"version": "v1"
},
{
"created": "Sat, 13 Oct 2018 02:43:32 GMT",
"version": "v2"
}
] | 2018-10-16 | [
[
"Miyazawa",
"Sanzo",
""
]
] | Predicting three dimensional residue-residue contacts from evolutionary information in protein sequences was attempted already in the early 1990s. However, contact prediction accuracies of methods evaluated in CASP experiments before CASP11 remained quite low, typically with $<20$% true positives. Recently, contact prediction has been significantly improved to the level that an accurate three dimensional model of a large protein can be generated on the basis of predicted contacts. This improvement was attained by disentangling direct from indirect correlations in amino acid covariations or cosubstitutions between sites in protein evolution. Here, we review statistical methods for extracting causative correlations and various approaches to describe protein structure, complex, and flexibility based on predicted contacts. |
1909.09965 | Xuguang Xi | Hai-Lei Guo, Wei-Fei Chen, Stephane Rety, Na-Nv Liu, Ze-Yu Song,
Yan-Xue Dai, Xi-Miao Hou, Shuo-Xing Dou and Xu-Guang Xi | DHX36-mediated G-quadruplex unfolding is ATP-independent? | null | null | null | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chen et al. solved the crystal structure of bovine DHX36 bound to a DNA with
a G-quadruplex (G4) and a single-stranded DNA segment. They believed that the
mechanism they proposed may represent a general model for describing how a
G4-unfolding helicase recognizes and unfolds G4 DNA. Their conclusion is
interesting, however, we noticed that their linear DNA substrate (DNAMyc) that
harbors a Myc-promoter-derived G4-forming sequence was directly used without
pre-folding. This raises the question whether the structure they obtained
really reflects DHX36-mediated G4 recognition and unfolding, or just only
represents a DHX36-binding-induced quasi-folded G4 structure. By a combination
of polymerase extension, DMS footprinting, stopped-flow, and smFRET assays, we
obtained clear evidences that do not support their ATP-independent one-base
translocation structural model. We further revealed that the oscillation of
FRET signal they observed should correspond to a repetitive G4 binding, but not
unfolding, by DHX36.
| [
{
"created": "Sun, 22 Sep 2019 08:32:51 GMT",
"version": "v1"
}
] | 2019-09-24 | [
[
"Guo",
"Hai-Lei",
""
],
[
"Chen",
"Wei-Fei",
""
],
[
"Rety",
"Stephane",
""
],
[
"Liu",
"Na-Nv",
""
],
[
"Song",
"Ze-Yu",
""
],
[
"Dai",
"Yan-Xue",
""
],
[
"Hou",
"Xi-Miao",
""
],
[
"Dou",
"Shuo-Xing",
""
],
[
"Xi",
"Xu-Guang",
""
]
] | Chen et al. solved the crystal structure of bovine DHX36 bound to a DNA with a G-quadruplex (G4) and a single-stranded DNA segment. They believed that the mechanism they proposed may represent a general model for describing how a G4-unfolding helicase recognizes and unfolds G4 DNA. Their conclusion is interesting, however, we noticed that their linear DNA substrate (DNAMyc) that harbors a Myc-promoter-derived G4-forming sequence was directly used without pre-folding. This raises the question whether the structure they obtained really reflects DHX36-mediated G4 recognition and unfolding, or just only represents a DHX36-binding-induced quasi-folded G4 structure. By a combination of polymerase extension, DMS footprinting, stopped-flow, and smFRET assays, we obtained clear evidences that do not support their ATP-independent one-base translocation structural model. We further revealed that the oscillation of FRET signal they observed should correspond to a repetitive G4 binding, but not unfolding, by DHX36. |
1811.07661 | Michael Margaliot | Itzik Nanikashvili, Yoram Zarai, Alexander Ovseevich, Tamir Tuller,
and Michael Margaliot | Networks of ribosome flow models for modeling and analyzing
intracellular traffic | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ribosome flow model with input and output (RFMIO) is a deterministic
dynamical system that has been used to study the flow of ribosomes during mRNA
translation. The RFMIO and its variants encapsulate important properties that
are relevant to modeling ribosome flow such as the possible evolution of
"traffic jams" and non-homogeneous elongation rates along the mRNA molecule,
and can also be used for studying additional intracellular processes such as
transcription, transport, and more. Here we consider networks of interconnected
RFMIOs as a fundamental tool for modeling, analyzing and re-engineering the
complex mechanisms of protein production. In these networks, the output of each
RFMIO may be divided, using connection weights, between several inputs of other
RFMIOs. We show that under quite general feedback connections the network has
two important properties: (1) it admits a unique steady-state and every
trajectory converges to this steady-state, and (2) the problem of how to
determine the connection weights so that the network steady-state output is
maximized is a convex optimization problem. These mathematical properties make
these networks highly suitable as models of various phenomena: property (1)
means that the behavior is predictable and ordered, and property (2) means that
determining the optimal weights is numerically tractable even for large-scale
networks. For the specific case of a feed-forward network of RFMIOs we prove an
additional useful property, namely, that there exists a spectral representation
for the network steady-state, and thus it can be determined without any
numerical simulations of the dynamics. We describe the implications of these
result to several fundamental biological phenomena and biotechnological
objectives.
| [
{
"created": "Mon, 19 Nov 2018 13:07:42 GMT",
"version": "v1"
}
] | 2018-11-20 | [
[
"Nanikashvili",
"Itzik",
""
],
[
"Zarai",
"Yoram",
""
],
[
"Ovseevich",
"Alexander",
""
],
[
"Tuller",
"Tamir",
""
],
[
"Margaliot",
"Michael",
""
]
] | The ribosome flow model with input and output (RFMIO) is a deterministic dynamical system that has been used to study the flow of ribosomes during mRNA translation. The RFMIO and its variants encapsulate important properties that are relevant to modeling ribosome flow such as the possible evolution of "traffic jams" and non-homogeneous elongation rates along the mRNA molecule, and can also be used for studying additional intracellular processes such as transcription, transport, and more. Here we consider networks of interconnected RFMIOs as a fundamental tool for modeling, analyzing and re-engineering the complex mechanisms of protein production. In these networks, the output of each RFMIO may be divided, using connection weights, between several inputs of other RFMIOs. We show that under quite general feedback connections the network has two important properties: (1) it admits a unique steady-state and every trajectory converges to this steady-state, and (2) the problem of how to determine the connection weights so that the network steady-state output is maximized is a convex optimization problem. These mathematical properties make these networks highly suitable as models of various phenomena: property (1) means that the behavior is predictable and ordered, and property (2) means that determining the optimal weights is numerically tractable even for large-scale networks. For the specific case of a feed-forward network of RFMIOs we prove an additional useful property, namely, that there exists a spectral representation for the network steady-state, and thus it can be determined without any numerical simulations of the dynamics. We describe the implications of these result to several fundamental biological phenomena and biotechnological objectives. |
1203.6832 | Lucilla de Arcangelis | Lucilla de Arcangelis and Hans J. Herrmann | Activity-dependent neuronal model on complex networks | 9 pages, 8 figures | published on Frontiers in Physiology vol3, 62 (2012) | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuronal avalanches are a novel mode of activity in neuronal networks,
experimentally found in vitro and in vivo, and exhibit a robust critical
behaviour: These avalanches are characterized by a power law distribution for
the size and duration, features found in other problems in the context of the
physics of complex systems. We present a recent model inspired in
self-organized criticality, which consists of an electrical network with
threshold firing, refractory period and activity-dependent synaptic plasticity.
The model reproduces the critical behaviour of the distribution of avalanche
sizes and durations measured experimentally. Moreover, the power spectra of the
electrical signal reproduce very robustly the power law behaviour found in
human electroencephalogram (EEG) spectra. We implement this model on a variety
of complex networks, i.e. regular, small-world and scale-free and verify the
robustness of the critical behaviour.
| [
{
"created": "Fri, 30 Mar 2012 14:46:47 GMT",
"version": "v1"
}
] | 2012-04-02 | [
[
"de Arcangelis",
"Lucilla",
""
],
[
"Herrmann",
"Hans J.",
""
]
] | Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour: These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems. We present a recent model inspired in self-organized criticality, which consists of an electrical network with threshold firing, refractory period and activity-dependent synaptic plasticity. The model reproduces the critical behaviour of the distribution of avalanche sizes and durations measured experimentally. Moreover, the power spectra of the electrical signal reproduce very robustly the power law behaviour found in human electroencephalogram (EEG) spectra. We implement this model on a variety of complex networks, i.e. regular, small-world and scale-free and verify the robustness of the critical behaviour. |
2111.04208 | Fabrizio Pucci Dr. | Fabrizio Pucci, Martin Schwersensky, Marianne Rooman | AI challenges for predicting the impact of mutations on protein
stability | null | null | null | null | q-bio.MN cs.LG physics.bio-ph q-bio.BM q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Stability is a key ingredient of protein fitness and its modification through
targeted mutations has applications in various fields such as protein
engineering, drug design and deleterious variant interpretation. Many studies
have been devoted over the past decades to building new, more effective methods
for predicting the impact of mutations on protein stability, based on the
latest developments in artificial intelligence (AI). We discuss their features,
algorithms, computational efficiency, and accuracy estimated on an independent
test set. We focus on a critical analysis of their limitations, the recurrent
biases towards the training set, their generalizability and interpretability.
We found that the accuracy of the predictors has stagnated at around 1 kcal/mol
for over 15 years. We conclude by discussing the challenges that need to be
addressed to reach improved performance.
| [
{
"created": "Mon, 8 Nov 2021 00:10:56 GMT",
"version": "v1"
}
] | 2021-11-09 | [
[
"Pucci",
"Fabrizio",
""
],
[
"Schwersensky",
"Martin",
""
],
[
"Rooman",
"Marianne",
""
]
] | Stability is a key ingredient of protein fitness and its modification through targeted mutations has applications in various fields such as protein engineering, drug design and deleterious variant interpretation. Many studies have been devoted over the past decades to building new, more effective methods for predicting the impact of mutations on protein stability, based on the latest developments in artificial intelligence (AI). We discuss their features, algorithms, computational efficiency, and accuracy estimated on an independent test set. We focus on a critical analysis of their limitations, the recurrent biases towards the training set, their generalizability and interpretability. We found that the accuracy of the predictors has stagnated at around 1 kcal/mol for over 15 years. We conclude by discussing the challenges that need to be addressed to reach improved performance. |
2401.17411 | Alejandro Leon PhD | Martha Lorena Avenda\~no-Garrido, Carlos Alberto Hern\'andez-Linares,
Brenda Zarah\'i Medina-P\'erez, Varsovia Hern\'andez, Porfirio Toledo,
Alejandro Le\'on | Identification of spatial dynamic patterns of behavior using weighted
Voronoi diagrams | 10 pages, 4 figures, 2 tables, Submitted to 16th Mexican Conference
on Pattern Recognition 2024 | null | 10.1007/978-3-031-62836-8_1 | null | q-bio.OT physics.bio-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This study proposes an innovative approach to analyze spatial patterns of
behavior by integrating information in weighted Voronoi diagrams. The objective
of the research is to analyze the temporal distribution of an experimental
subject in different regions of a given space, with the aim of identifying
significant areas of interest. The methodology employed involves dividing the
experimental space, determining representative points, and assigning weights
based on the cumulative time the subject spends in each region. This process
results in a set of generator points along with their respective weights, thus
defining the Voronoi diagram. The study also presents a detailed and advanced
perspective for understanding spatial behavioral patterns in experimental
contexts.
| [
{
"created": "Tue, 30 Jan 2024 20:00:49 GMT",
"version": "v1"
},
{
"created": "Sun, 16 Jun 2024 21:20:53 GMT",
"version": "v2"
}
] | 2024-06-18 | [
[
"Avendaño-Garrido",
"Martha Lorena",
""
],
[
"Hernández-Linares",
"Carlos Alberto",
""
],
[
"Medina-Pérez",
"Brenda Zarahí",
""
],
[
"Hernández",
"Varsovia",
""
],
[
"Toledo",
"Porfirio",
""
],
[
"León",
"Alejandro",
""
]
] | This study proposes an innovative approach to analyze spatial patterns of behavior by integrating information in weighted Voronoi diagrams. The objective of the research is to analyze the temporal distribution of an experimental subject in different regions of a given space, with the aim of identifying significant areas of interest. The methodology employed involves dividing the experimental space, determining representative points, and assigning weights based on the cumulative time the subject spends in each region. This process results in a set of generator points along with their respective weights, thus defining the Voronoi diagram. The study also presents a detailed and advanced perspective for understanding spatial behavioral patterns in experimental contexts. |
1206.6640 | Supratim Sengupta | Supratim Sengupta, Julien Derr, Anirban Sain and Andrew D. Rutenberg | Stuttering Min oscillations within E. coli bacteria: A stochastic
polymerization model | 21 pages, 7 figures, missing unit for k_f inserted | Phys. Biol. 9 (2012) 056003 | 10.1088/1478-3975/9/5/056003 | null | q-bio.SC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have developed a 3D off-lattice stochastic polymerization model to study
subcellular oscillation of Min proteins in the bacteria Escherichia coli, and
used it to investigate the experimental phenomenon of Min oscillation
stuttering. Stuttering was affected by the rate of immediate rebinding of MinE
released from depolymerizing filament tips (processivity), protection of
depolymerizing filament tips from MinD binding, and fragmentation of MinD
filaments due to MinE. Each of processivity, protection, and fragmentation
reduces stuttering, speeds oscillations, and reduces MinD filament lengths.
Neither processivity or tip-protection were, on their own, sufficient to
produce fast stutter-free oscillations. While filament fragmentation could, on
its own, lead to fast oscillations with infrequent stuttering; high levels of
fragmentation degraded oscillations. The infrequent stuttering observed in
standard Min oscillations are consistent with short filaments of MinD, while we
expect that mutants that exhibit higher stuttering frequencies will exhibit
longer MinD filaments. Increased stuttering rate may be a useful diagnostic to
find observable MinD polymerization in experimental conditions.
| [
{
"created": "Thu, 28 Jun 2012 11:26:09 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Jul 2012 07:14:26 GMT",
"version": "v2"
}
] | 2015-06-05 | [
[
"Sengupta",
"Supratim",
""
],
[
"Derr",
"Julien",
""
],
[
"Sain",
"Anirban",
""
],
[
"Rutenberg",
"Andrew D.",
""
]
] | We have developed a 3D off-lattice stochastic polymerization model to study subcellular oscillation of Min proteins in the bacteria Escherichia coli, and used it to investigate the experimental phenomenon of Min oscillation stuttering. Stuttering was affected by the rate of immediate rebinding of MinE released from depolymerizing filament tips (processivity), protection of depolymerizing filament tips from MinD binding, and fragmentation of MinD filaments due to MinE. Each of processivity, protection, and fragmentation reduces stuttering, speeds oscillations, and reduces MinD filament lengths. Neither processivity or tip-protection were, on their own, sufficient to produce fast stutter-free oscillations. While filament fragmentation could, on its own, lead to fast oscillations with infrequent stuttering; high levels of fragmentation degraded oscillations. The infrequent stuttering observed in standard Min oscillations are consistent with short filaments of MinD, while we expect that mutants that exhibit higher stuttering frequencies will exhibit longer MinD filaments. Increased stuttering rate may be a useful diagnostic to find observable MinD polymerization in experimental conditions. |
1602.02308 | Michael Margaliot | Yoram Zarai and Michael Margaliot and Eduardo D. Sontag and Tamir
Tuller | Controllability analysis and control synthesis for the ribosome flow
model | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ribosomal density along the coding region of the mRNA molecule affect
various fundamental intracellular phenomena including: protein production
rates, organismal fitness, ribosomal drop off, and co-translational protein
folding. Thus, regulating translation in order to obtain a desired ribosomal
profile along the mRNA molecule is an important biological problem. We study
this problem using a model for mRNA translation, called the ribosome flow model
(RFM). In the RFM, the mRNA molecule is modeled as chain of n sites. The n
state-variables describe the ribosomal density profile along the mRNA molecule,
whereas the transition rates from each site to the next are controlled by n+1
positive constants. To study the problem of controlling the density profile, we
consider some or all of the transition rates as time-varying controls. We
consider the following problem: given an initial and a desired ribosomal
density profile, determine the time-varying values of the transition rates that
steer the RFM to this density profile, if they exist. Specifically, we consider
two control problems. In the first, all transition rates can be regulated and
the goal is to steer the ribosomal density profile and the protein production
rate from a given initial value to a desired value. In the second, a single
transition rate is controlled and the goal is to steer the production rate to a
desired value. In the first case, we show that the system is controllable, i.e.
the control is powerful enough to steer the RFM to any desired value, and we
provide closed-form expressions for constant control functions (or transition
rates) asymptotically steering the RFM to the desired value. For the second
problem, we show that the production rate can be steered to any desired value
in a feasible region determined by the other, constant transition rates. We
discuss some of the biological implications of these results.
| [
{
"created": "Sat, 6 Feb 2016 20:47:13 GMT",
"version": "v1"
},
{
"created": "Wed, 17 May 2017 21:07:56 GMT",
"version": "v2"
}
] | 2017-05-19 | [
[
"Zarai",
"Yoram",
""
],
[
"Margaliot",
"Michael",
""
],
[
"Sontag",
"Eduardo D.",
""
],
[
"Tuller",
"Tamir",
""
]
] | The ribosomal density along the coding region of the mRNA molecule affect various fundamental intracellular phenomena including: protein production rates, organismal fitness, ribosomal drop off, and co-translational protein folding. Thus, regulating translation in order to obtain a desired ribosomal profile along the mRNA molecule is an important biological problem. We study this problem using a model for mRNA translation, called the ribosome flow model (RFM). In the RFM, the mRNA molecule is modeled as chain of n sites. The n state-variables describe the ribosomal density profile along the mRNA molecule, whereas the transition rates from each site to the next are controlled by n+1 positive constants. To study the problem of controlling the density profile, we consider some or all of the transition rates as time-varying controls. We consider the following problem: given an initial and a desired ribosomal density profile, determine the time-varying values of the transition rates that steer the RFM to this density profile, if they exist. Specifically, we consider two control problems. In the first, all transition rates can be regulated and the goal is to steer the ribosomal density profile and the protein production rate from a given initial value to a desired value. In the second, a single transition rate is controlled and the goal is to steer the production rate to a desired value. In the first case, we show that the system is controllable, i.e. the control is powerful enough to steer the RFM to any desired value, and we provide closed-form expressions for constant control functions (or transition rates) asymptotically steering the RFM to the desired value. For the second problem, we show that the production rate can be steered to any desired value in a feasible region determined by the other, constant transition rates. We discuss some of the biological implications of these results. |
1004.2073 | David Haws | David C. Haws and Terrell Hodge and Ruriko Yoshida | Optimality of the Neighbor Joining Algorithm and Faces of the Balanced
Minimum Evolution Polytope | 24 pages,4 figure | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Balanced minimum evolution (BME) is a statistically consistent distance-based
method to reconstruct a phylogenetic tree from an alignment of molecular data.
In 2000, Pauplin showed that the BME method is equivalent to optimizing a
linear functional over the BME polytope, the convex hull of the BME vectors
obtained from Pauplin's formula applied to all binary trees. The BME method is
related to the Neighbor Joining (NJ) algorithm, now known to be a greedy
optimization of the BME principle. Further, the NJ and BME algorithms have been
studied previously to understand when the NJ Algorithm returns a BME tree for
small numbers of taxa. In this paper we aim to elucidate the structure of the
BME polytope and strengthen knowledge of the connection between the BME method
and NJ Algorithm. We first prove that any subtree-prune-regraft move from a
binary tree to another binary tree corresponds to an edge of the BME polytope.
Moreover, we describe an entire family of faces parametrized by disjoint
clades. We show that these {\em clade-faces} are smaller dimensional BME
polytopes themselves. Finally, we show that for any order of joining nodes to
form a tree, there exists an associated distance matrix (i.e., dissimilarity
map) for which the NJ Algorithm returns the BME tree. More strongly, we show
that the BME cone and every NJ cone associated to a tree $T$ have an
intersection of positive measure.
| [
{
"created": "Mon, 12 Apr 2010 22:26:03 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Feb 2011 20:07:01 GMT",
"version": "v2"
}
] | 2015-03-14 | [
[
"Haws",
"David C.",
""
],
[
"Hodge",
"Terrell",
""
],
[
"Yoshida",
"Ruriko",
""
]
] | Balanced minimum evolution (BME) is a statistically consistent distance-based method to reconstruct a phylogenetic tree from an alignment of molecular data. In 2000, Pauplin showed that the BME method is equivalent to optimizing a linear functional over the BME polytope, the convex hull of the BME vectors obtained from Pauplin's formula applied to all binary trees. The BME method is related to the Neighbor Joining (NJ) algorithm, now known to be a greedy optimization of the BME principle. Further, the NJ and BME algorithms have been studied previously to understand when the NJ Algorithm returns a BME tree for small numbers of taxa. In this paper we aim to elucidate the structure of the BME polytope and strengthen knowledge of the connection between the BME method and NJ Algorithm. We first prove that any subtree-prune-regraft move from a binary tree to another binary tree corresponds to an edge of the BME polytope. Moreover, we describe an entire family of faces parametrized by disjoint clades. We show that these {\em clade-faces} are smaller dimensional BME polytopes themselves. Finally, we show that for any order of joining nodes to form a tree, there exists an associated distance matrix (i.e., dissimilarity map) for which the NJ Algorithm returns the BME tree. More strongly, we show that the BME cone and every NJ cone associated to a tree $T$ have an intersection of positive measure. |
2201.05496 | Benedikt Feldotto | Benedikt Feldotto, Cristian Soare, Alois Knoll, Piyanee Sriya, Sarah
Astill, Marc de Kamps and Samit Chakrabarty | Evaluating Muscle Synergies with EMG Data and Physics Simulation in the
Neurorobotics Platform | 15 pages, 10 figures | null | null | null | q-bio.NC cs.OH | http://creativecommons.org/licenses/by/4.0/ | Although we can measure muscle activity and analyze their activation
patterns, we understand little about how individual muscles affect the joint
torque generated. It is known that they are controlled by circuits in the
spinal cord, a system much less well understood than the cortex. Knowing the
contribution of the muscles towards a joint torque would improve our
understanding of human limb control. We present a novel framework to examine
the control of biomechanics using physics simulations informed by
electromyography (EMG) data. These signals drive a virtual musculoskeletal
model in the Neurorobotics Platform (NRP), which we then use to evaluate
resulting joint torques. We use our framework to analyze raw EMG data collected
during an isometric knee extension study to identify synergies that drive a
musculoskeletal lower limb model. The resulting knee torques are used as a
reference for genetic algorithms (GA) to generate new simulated activation
patterns. On the platform the GA finds solutions that generate torques matching
those observed. Possible solutions include synergies that are similar to those
extracted from the human study. In addition, the GA finds activation patterns
that are different from the the biological ones while still producing the same
knee torque. The NRP forms a highly modular integrated simulation platform
allowing these in silico experiments. We argue that our framework allows for
research of the neurobiomechanical control of muscles during tasks, which would
otherwise not be possible.
| [
{
"created": "Fri, 14 Jan 2022 15:03:34 GMT",
"version": "v1"
}
] | 2022-01-19 | [
[
"Feldotto",
"Benedikt",
""
],
[
"Soare",
"Cristian",
""
],
[
"Knoll",
"Alois",
""
],
[
"Sriya",
"Piyanee",
""
],
[
"Astill",
"Sarah",
""
],
[
"de Kamps",
"Marc",
""
],
[
"Chakrabarty",
"Samit",
""
]
] | Although we can measure muscle activity and analyze their activation patterns, we understand little about how individual muscles affect the joint torque generated. It is known that they are controlled by circuits in the spinal cord, a system much less well understood than the cortex. Knowing the contribution of the muscles towards a joint torque would improve our understanding of human limb control. We present a novel framework to examine the control of biomechanics using physics simulations informed by electromyography (EMG) data. These signals drive a virtual musculoskeletal model in the Neurorobotics Platform (NRP), which we then use to evaluate resulting joint torques. We use our framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model. The resulting knee torques are used as a reference for genetic algorithms (GA) to generate new simulated activation patterns. On the platform the GA finds solutions that generate torques matching those observed. Possible solutions include synergies that are similar to those extracted from the human study. In addition, the GA finds activation patterns that are different from the the biological ones while still producing the same knee torque. The NRP forms a highly modular integrated simulation platform allowing these in silico experiments. We argue that our framework allows for research of the neurobiomechanical control of muscles during tasks, which would otherwise not be possible. |
1503.02019 | William J. Tyler | Jerel K. Mueller, Wynn Legon, and William J. Tyler | Analysis of Transcranial Focused Ultrasound Beam Profile Sensitivity for
Neuromodulation of the Human Brain | 32 pages, 14 figures, and 3 tables | null | null | null | q-bio.TO q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective. While ultrasound is largely established for use in diagnostic
imaging and heating therapies, its application for neuromodulation is
relatively new and not well understood. The objective of the present study was
to investigate issues related to interactions between focused acoustic beams
and brain tissues to better understand possible limitations of transcranial
ultrasound for neuromodulation. Approach. A computational model of transcranial
focused ultrasound was constructed and validated against bench top experimental
data. The models were then incrementally extended to address and investigate a
number of issues related to the use of ultrasound for neuromodulation. These
included the effect of variations in skull geometry and gyral anatomy, as well
as the effect of transmission across multiple tissue and media layers, such as
scalp, skull, CSF, and gray/white matter on ultrasound insertion behavior. In
addition, a sensitivity analysis was run to characterize the influence of
acoustic properties of intracranial tissues. Finally, the heating associated
with ultrasonic stimulation waveforms designed for neuromodulation was modeled.
Main results. Depending on factors such as acoustic frequency, the insertion
behavior of a transcranial focused ultrasound beam is only subtly influenced by
the geometry and acoustic properties of the underlying tissues. Significance.
These issues are critical for the refinement of device design and the overall
advancement of ultrasound methods for noninvasive neuromodulation.
| [
{
"created": "Fri, 6 Mar 2015 17:11:44 GMT",
"version": "v1"
}
] | 2015-03-09 | [
[
"Mueller",
"Jerel K.",
""
],
[
"Legon",
"Wynn",
""
],
[
"Tyler",
"William J.",
""
]
] | Objective. While ultrasound is largely established for use in diagnostic imaging and heating therapies, its application for neuromodulation is relatively new and not well understood. The objective of the present study was to investigate issues related to interactions between focused acoustic beams and brain tissues to better understand possible limitations of transcranial ultrasound for neuromodulation. Approach. A computational model of transcranial focused ultrasound was constructed and validated against bench top experimental data. The models were then incrementally extended to address and investigate a number of issues related to the use of ultrasound for neuromodulation. These included the effect of variations in skull geometry and gyral anatomy, as well as the effect of transmission across multiple tissue and media layers, such as scalp, skull, CSF, and gray/white matter on ultrasound insertion behavior. In addition, a sensitivity analysis was run to characterize the influence of acoustic properties of intracranial tissues. Finally, the heating associated with ultrasonic stimulation waveforms designed for neuromodulation was modeled. Main results. Depending on factors such as acoustic frequency, the insertion behavior of a transcranial focused ultrasound beam is only subtly influenced by the geometry and acoustic properties of the underlying tissues. Significance. These issues are critical for the refinement of device design and the overall advancement of ultrasound methods for noninvasive neuromodulation. |
1906.00862 | Dr. Biplab Chattopadhyay | Biplab Chattopadhyay and Nirmalendu Hui | Immunopathogenesis in Psoriasis through a Density-type Mathematical
Model | 11 pages, 9 figures, 1 table | WSEAS Transactions on Mathematics, Issue 5, Volume 11, May 2012,
pp 440 - 450 | null | null | q-bio.TO q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Disease psoriasis occurs as chronic inflammation of skin and appears as scaly
red lesions on skin surface. Advent of several immunosuppressive drugs
established that the disease stems from immuno-pathogenic disorder in human
blood. Cell biological as well as clinical research on the disease reveals that
the helper T-cells and other Leucocytes, responsible for human immunity, may
lead to psoriasis pathogenesis if produced in plenty at locations close to the
dermal region. Research findings also showed that a complex, self-sustaining
(cytokine and related) proteins network play important role in disease
maturation by actually leading to a huge proliferation of epidermal
keratinocytes. Disease pathogenesis is identified with such hyperproliferation
leading to flaking of skin surface (psoriatic plaques). An excessive generation
of nitric oxide by proliferated keratinocytes, through a complex chain of
bio-chemical events, is causal to the scaliness of psoriatic plaques.
Considering these immunopathogenic mechanisms, we propose and analyse a
mathematical time differential model for the disease psoriasis. Outcomes of
analysis are consistent with existing cell biological and clinical findings
with some new predictions which could be tested further.
| [
{
"created": "Mon, 3 Jun 2019 15:17:49 GMT",
"version": "v1"
}
] | 2019-06-04 | [
[
"Chattopadhyay",
"Biplab",
""
],
[
"Hui",
"Nirmalendu",
""
]
] | Disease psoriasis occurs as chronic inflammation of skin and appears as scaly red lesions on skin surface. Advent of several immunosuppressive drugs established that the disease stems from immuno-pathogenic disorder in human blood. Cell biological as well as clinical research on the disease reveals that the helper T-cells and other Leucocytes, responsible for human immunity, may lead to psoriasis pathogenesis if produced in plenty at locations close to the dermal region. Research findings also showed that a complex, self-sustaining (cytokine and related) proteins network play important role in disease maturation by actually leading to a huge proliferation of epidermal keratinocytes. Disease pathogenesis is identified with such hyperproliferation leading to flaking of skin surface (psoriatic plaques). An excessive generation of nitric oxide by proliferated keratinocytes, through a complex chain of bio-chemical events, is causal to the scaliness of psoriatic plaques. Considering these immunopathogenic mechanisms, we propose and analyse a mathematical time differential model for the disease psoriasis. Outcomes of analysis are consistent with existing cell biological and clinical findings with some new predictions which could be tested further. |
1206.4400 | Shantanav Chakraborty | Shantanav Chakraborty, Naman Joshi, Anop Singh, Ety Mittal | Finding the Ion Current Density of Microtubules by defining a potential
function for the same and Solving the time independent Schrodinger Equation | 9 pages, 6 figures, 1 table | null | null | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we model a microtubule based on its dimer resolution
structure. First, the fundamental structural components were studied and then
electrostatic potential function for a single monomer was calculated.
Subsequently, the potential function inside a single monomer due to a ring of
dimers was obtained. Considering the potential due to
protofilament-protofilament interaction with a monomer in a B crystal structure
of a microtubule, we obtain a double well potential wall. Quantum mechanically
the ions can pass through this wall because of the Tunnelling effect. We solve
the time independent Schrodinger Equation, calculate the transmission
efficiency of ion flow and use the latter in the calculation of ion current
density.
| [
{
"created": "Wed, 20 Jun 2012 07:24:57 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Jun 2012 13:48:34 GMT",
"version": "v2"
}
] | 2012-06-27 | [
[
"Chakraborty",
"Shantanav",
""
],
[
"Joshi",
"Naman",
""
],
[
"Singh",
"Anop",
""
],
[
"Mittal",
"Ety",
""
]
] | In this paper, we model a microtubule based on its dimer resolution structure. First, the fundamental structural components were studied and then electrostatic potential function for a single monomer was calculated. Subsequently, the potential function inside a single monomer due to a ring of dimers was obtained. Considering the potential due to protofilament-protofilament interaction with a monomer in a B crystal structure of a microtubule, we obtain a double well potential wall. Quantum mechanically the ions can pass through this wall because of the Tunnelling effect. We solve the time independent Schrodinger Equation, calculate the transmission efficiency of ion flow and use the latter in the calculation of ion current density. |
q-bio/0512031 | Martin Rost | Tobias Merkle, Martin Rost and Wolfgang Alt | Egocentric Path Integration Models and their Application to Desert
Arthropods | 30 pages, 8 figures. Journal of Theoretical Biology (online
available, paper in press) | null | null | null | q-bio.NC q-bio.OT | null | Path integration enables desert arthropods to find back to their nest on the
shortest track from any position. To perform path integration successfully,
speeds and turning angles along the preceding outbound path have to be measured
continuously and combined to determine an internal {\em global vector} leading
back home at any time. A number of experiments have given an idea how
arthropods might use allothetic or idiothetic signals to perceive their
orientation and moving speed. We systematically review the four possible model
descriptions of mathematically precise path integration, whereby we favour and
elaborate the hitherto not used variant of egocentric cartesian coordinates.
Its simple and intuitive structure is demonstrated in comparison to the other
models. Measuring two speeds, the forward moving speed and the angular turning
rate, and implementing them into a linear system of differential equations
provides the necessary information during outbound route, reorientation process
and return path. In addition, we propose several possible types of systematic
errors that can cause deviations from the correct homeward course. Deviations
have been observed for several species of desert arthropods in different
experiments, but their origin is still under debate. Using our egocentric path
integration model we propose simple error indices depending on path geometry
that will allow future experiments to rule out or corroborate certain error
types.
| [
{
"created": "Wed, 14 Dec 2005 14:43:58 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Merkle",
"Tobias",
""
],
[
"Rost",
"Martin",
""
],
[
"Alt",
"Wolfgang",
""
]
] | Path integration enables desert arthropods to find back to their nest on the shortest track from any position. To perform path integration successfully, speeds and turning angles along the preceding outbound path have to be measured continuously and combined to determine an internal {\em global vector} leading back home at any time. A number of experiments have given an idea how arthropods might use allothetic or idiothetic signals to perceive their orientation and moving speed. We systematically review the four possible model descriptions of mathematically precise path integration, whereby we favour and elaborate the hitherto not used variant of egocentric cartesian coordinates. Its simple and intuitive structure is demonstrated in comparison to the other models. Measuring two speeds, the forward moving speed and the angular turning rate, and implementing them into a linear system of differential equations provides the necessary information during outbound route, reorientation process and return path. In addition, we propose several possible types of systematic errors that can cause deviations from the correct homeward course. Deviations have been observed for several species of desert arthropods in different experiments, but their origin is still under debate. Using our egocentric path integration model we propose simple error indices depending on path geometry that will allow future experiments to rule out or corroborate certain error types. |
2006.04181 | Sona Vasudevan | Takashi Kitani, Sushma C. Maddipatla, Ramya Madupuri, James N.
Baraniuk, Christopher Greco, Jonathan Hartman and Sona Vasudevan | In Search of Newer Targets for IBD: A Systems and a Network Medicine
Approach | 14 pages | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Introduction: Crohn's disease and ulcerative colitis, both under the umbrella
of inflammatory bowel diseases (IBD), involve many distinct molecular
processes. The difference in their molecular processes is studied by using the
different genes involved in each disease, and it is explored further for drug
targeting and drug repurposing. Methods: The initial set of genes was obtained
by mining published literature and several curated databases. The identified
genes were then subject to Systems and Network analysis to reveal their
molecular processes and shed some light on their pathogenesis. Such
methodologies have identified newer targets and drugs that can be repurposed.
Results: We use a Systems and Network Medicine approach to understand the
mechanism of actions of genes involved in IBD. From an initial set of genes
mined from literature and curated databases, we used the Multi-Steiner Tree
algorithm implemented within the CoVex systems medicine platform to expand each
disease module by incorporating candidate genes with significant connections to
the disease-related seed genes. Such expanded disease modules will identify a
larger set of potential targets and drugs. We used the Closeness Centrality
algorithm implemented within CoVex to search for newer targets and repurposable
drugs. Through a network medicine approach, we provide a mechanistic view of
the diseases and point to newer drugs and targets. Conclusion: We demonstrate
that the Systems and Network Medicine approach is a powerful way to understand
diseases and understand their mechanisms of action.
| [
{
"created": "Sun, 7 Jun 2020 15:35:13 GMT",
"version": "v1"
},
{
"created": "Tue, 11 May 2021 00:20:33 GMT",
"version": "v2"
}
] | 2021-05-12 | [
[
"Kitani",
"Takashi",
""
],
[
"Maddipatla",
"Sushma C.",
""
],
[
"Madupuri",
"Ramya",
""
],
[
"Baraniuk",
"James N.",
""
],
[
"Greco",
"Christopher",
""
],
[
"Hartman",
"Jonathan",
""
],
[
"Vasudevan",
"Sona",
""
]
] | Introduction: Crohn's disease and ulcerative colitis, both under the umbrella of inflammatory bowel diseases (IBD), involve many distinct molecular processes. The difference in their molecular processes is studied by using the different genes involved in each disease, and it is explored further for drug targeting and drug repurposing. Methods: The initial set of genes was obtained by mining published literature and several curated databases. The identified genes were then subject to Systems and Network analysis to reveal their molecular processes and shed some light on their pathogenesis. Such methodologies have identified newer targets and drugs that can be repurposed. Results: We use a Systems and Network Medicine approach to understand the mechanism of actions of genes involved in IBD. From an initial set of genes mined from literature and curated databases, we used the Multi-Steiner Tree algorithm implemented within the CoVex systems medicine platform to expand each disease module by incorporating candidate genes with significant connections to the disease-related seed genes. Such expanded disease modules will identify a larger set of potential targets and drugs. We used the Closeness Centrality algorithm implemented within CoVex to search for newer targets and repurposable drugs. Through a network medicine approach, we provide a mechanistic view of the diseases and point to newer drugs and targets. Conclusion: We demonstrate that the Systems and Network Medicine approach is a powerful way to understand diseases and understand their mechanisms of action. |
2001.08332 | Megan Morrison | Megan Morrison, Charles Fieseler, and J. Nathan Kutz | Nonlinear control in the nematode C. elegans | null | null | null | null | q-bio.NC math.DS physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Recent whole-brain calcium imaging recordings of the nematode C. elegans have
demonstrated that neural activity is dominated by dynamics on a low-dimensional
manifold that can be clustered according to behavioral states. Despite progress
in modeling the dynamics with linear or locally linear models, it remains
unclear how a single network of neurons can produce the observed features. In
particular, there are multiple clusters, or fixed points, observed in the data
which cannot be characterized by a single linear model. We propose a nonlinear
control model which is global and parameterized by only four free parameters
that match the features displayed by the low-dimensional C. elegans neural
activity. In addition to reproducing the average probability distribution of
the data, long and short time-scale changes in transition statistics can be
characterized via changes in a single parameter. Some of these macro-scale
transitions have experimental correlates to single neuro-modulators that seem
to act as biological controls, allowing this model to generate testable
hypotheses about the effect of these neuro-modulators on the global dynamics.
The theory provides an elegant characterization of the neuron population
dynamics in C. elegans. Moreover, the mathematical structure of the nonlinear
control framework provides a paradigm that can be generalized to more complex
systems with an arbitrary number of behavioral states.
| [
{
"created": "Thu, 23 Jan 2020 01:35:14 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Feb 2020 20:51:16 GMT",
"version": "v2"
}
] | 2020-02-05 | [
[
"Morrison",
"Megan",
""
],
[
"Fieseler",
"Charles",
""
],
[
"Kutz",
"J. Nathan",
""
]
] | Recent whole-brain calcium imaging recordings of the nematode C. elegans have demonstrated that neural activity is dominated by dynamics on a low-dimensional manifold that can be clustered according to behavioral states. Despite progress in modeling the dynamics with linear or locally linear models, it remains unclear how a single network of neurons can produce the observed features. In particular, there are multiple clusters, or fixed points, observed in the data which cannot be characterized by a single linear model. We propose a nonlinear control model which is global and parameterized by only four free parameters that match the features displayed by the low-dimensional C. elegans neural activity. In addition to reproducing the average probability distribution of the data, long and short time-scale changes in transition statistics can be characterized via changes in a single parameter. Some of these macro-scale transitions have experimental correlates to single neuro-modulators that seem to act as biological controls, allowing this model to generate testable hypotheses about the effect of these neuro-modulators on the global dynamics. The theory provides an elegant characterization of the neuron population dynamics in C. elegans. Moreover, the mathematical structure of the nonlinear control framework provides a paradigm that can be generalized to more complex systems with an arbitrary number of behavioral states. |
1310.1912 | Joshua Weitz | Joshua S. Weitz | Let my people go (home) to Spain: a genealogical model of Jewish
identities since 1492 | 6 page, 4 figures | null | 10.1371/journal.pone.0085673 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Spanish government recently announced an official fast-track path to
citizenship for any individual who is Jewish and whose ancestors were expelled
from Spain during the inquisition-related dislocation of Spanish Jews in 1492.
It would seem that this policy targets a small subset of the global Jewish
population, i.e., restricted to individuals who retain cultural practices
associated with ancestral origins in Spain. However, the central contribution
of this manuscript is to demonstrate how and why the policy is far more likely
to apply to a very large fraction (i.e., the vast majority) of Jews. This claim
is supported using a series of genealogical models that include transmissable
"identities" and preferential intra-group mating. Model analysis reveals that
even when intra-group mating is strong and even if only a small subset of a
present-day population retains cultural practices typically associated with
that of an ancestral group, it is highly likely that nearly all members of that
population have direct geneaological links to that ancestral group, given
sufficient number of generations have elapsed. The basis for this conclusion is
that not having a link to an ancestral group must be a property of all of an
individual's ancestors, the probability of which declines (nearly)
superexponentially with each successive generation. These findings highlight
unexpected incongruities induced by genealogical dynamics between present-day
and ancestral identities.
| [
{
"created": "Mon, 7 Oct 2013 22:23:00 GMT",
"version": "v1"
}
] | 2014-02-05 | [
[
"Weitz",
"Joshua S.",
""
]
] | The Spanish government recently announced an official fast-track path to citizenship for any individual who is Jewish and whose ancestors were expelled from Spain during the inquisition-related dislocation of Spanish Jews in 1492. It would seem that this policy targets a small subset of the global Jewish population, i.e., restricted to individuals who retain cultural practices associated with ancestral origins in Spain. However, the central contribution of this manuscript is to demonstrate how and why the policy is far more likely to apply to a very large fraction (i.e., the vast majority) of Jews. This claim is supported using a series of genealogical models that include transmissable "identities" and preferential intra-group mating. Model analysis reveals that even when intra-group mating is strong and even if only a small subset of a present-day population retains cultural practices typically associated with that of an ancestral group, it is highly likely that nearly all members of that population have direct geneaological links to that ancestral group, given sufficient number of generations have elapsed. The basis for this conclusion is that not having a link to an ancestral group must be a property of all of an individual's ancestors, the probability of which declines (nearly) superexponentially with each successive generation. These findings highlight unexpected incongruities induced by genealogical dynamics between present-day and ancestral identities. |
2405.08397 | Laval Julien | Julien Laval (BAOBAB), Aymeric Gaudin (BAOBAB), Vincent Frouin
(BAOBAB), Jessica Dubois (UNIACT), Andrea Gondova (UNIACT), Jean-Fran\c{c}ois
Mangin (BAOBAB), Jo\"el Chavas (BAOBAB), Denis Rivi\`ere (BAOBAB) | Self-supervised contrastive learning unveils cortical folding pattern
linked to prematurity | null | MIDL 2024, Jul 2024, Paris, France | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Brain folding patterns have been reported to carry clinically relevant
information. The brain folds mainly during the last trimester of pregnancy, and
the process might be durably disturbed by preterm birth. Yet little is known
about preterm-specific patterns. In this work, we train a self-supervised model
(SimCLR) on the UKBioBank cohort (21070 adults) to represent the right superior
temporal sulcus (STS) region and apply it to sulci images of 374 babies from
the dHCP database, containing preterms and full-terms, and acquired at 40 weeks
post-menstrual age. We find a lower variability in the preterm embeddings,
supported by the identification of a knob pattern, missing in the extremely
preterm population.
| [
{
"created": "Tue, 14 May 2024 07:49:52 GMT",
"version": "v1"
}
] | 2024-05-15 | [
[
"Laval",
"Julien",
"",
"BAOBAB"
],
[
"Gaudin",
"Aymeric",
"",
"BAOBAB"
],
[
"Frouin",
"Vincent",
"",
"BAOBAB"
],
[
"Dubois",
"Jessica",
"",
"UNIACT"
],
[
"Gondova",
"Andrea",
"",
"UNIACT"
],
[
"Mangin",
"Jean-François",
"",
"BAOBAB"
],
[
"Chavas",
"Joël",
"",
"BAOBAB"
],
[
"Rivière",
"Denis",
"",
"BAOBAB"
]
] | Brain folding patterns have been reported to carry clinically relevant information. The brain folds mainly during the last trimester of pregnancy, and the process might be durably disturbed by preterm birth. Yet little is known about preterm-specific patterns. In this work, we train a self-supervised model (SimCLR) on the UKBioBank cohort (21070 adults) to represent the right superior temporal sulcus (STS) region and apply it to sulci images of 374 babies from the dHCP database, containing preterms and full-terms, and acquired at 40 weeks post-menstrual age. We find a lower variability in the preterm embeddings, supported by the identification of a knob pattern, missing in the extremely preterm population. |
1103.0668 | Wei Wei | Wei Wei and Fred Wolf | Spike Onset Dynamics and Response Speed in Neuronal Populations | null | Phys. Rev. Lett. 106, 088102 (2011) | 10.1103/PhysRevLett.106.088102 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies of cortical neurons driven by fluctuating currents revealed
cutoff frequencies for action potential encoding of several hundred Hz.
Theoretical studies of biophysical neuron models have predicted a much lower
cutoff frequency of the order of average firing rate or the inverse membrane
time constant. The biophysical origin of the observed high cutoff frequencies
is thus not well understood. Here we introduce a neuron model with dynamical
action potential generation, in which the linear response can be analytically
calculated for uncorrelated synaptic noise. We find that the cutoff frequencies
increase to very large values when the time scale of action potential
initiation becomes short.
| [
{
"created": "Thu, 3 Mar 2011 12:37:57 GMT",
"version": "v1"
}
] | 2015-05-27 | [
[
"Wei",
"Wei",
""
],
[
"Wolf",
"Fred",
""
]
] | Recent studies of cortical neurons driven by fluctuating currents revealed cutoff frequencies for action potential encoding of several hundred Hz. Theoretical studies of biophysical neuron models have predicted a much lower cutoff frequency of the order of average firing rate or the inverse membrane time constant. The biophysical origin of the observed high cutoff frequencies is thus not well understood. Here we introduce a neuron model with dynamical action potential generation, in which the linear response can be analytically calculated for uncorrelated synaptic noise. We find that the cutoff frequencies increase to very large values when the time scale of action potential initiation becomes short. |
2406.09947 | Sarah Grube | Sarah Grube, Maximilian Neidhardt, Anna-Katarina Herrmann, Johanna
Sprenger, Kian Abdolazizi, Sarah Latus, Christian J. Cyron, Alexander
Schlaefer | A Calibration Approach for Elasticity Estimation with Medical Tools | Submitted to CURAC 2023 | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Soft tissue elasticity is directly related to different stages of diseases
and can be used for tissue identification during minimally invasive procedures.
By palpating a tissue with a robot in a minimally invasive fashion
force-displacement curves can be acquired. However, force-displacement curves
strongly depend on the tool geometry which is often complex in the case of
medical tools. Hence, a tool calibration procedure is desired to directly map
force-displacement curves to the corresponding tissue elasticity.We present an
experimental setup for calibrating medical tools with a robot. First, we
propose to estimate the elasticity of gelatin phantoms by spherical indentation
with a state-of-the-art contact model. We estimate force-displacement curves
for different gelatin elasticities and temperatures. Our experiments
demonstrate that gelatin elasticity is highly dependent on temperature, which
can lead to an elasticity offset if not considered. Second, we propose to use a
more complex material model, e.g., a neural network, that can be trained with
the determined elasticities. Considering the temperature of the gelatin sample
we can represent different elasticities per phantom and thereby increase our
training data.We report elasticity values ranging from 10 to 40 kPa for a 10%
gelatin phantom, depending on temperature.
| [
{
"created": "Fri, 14 Jun 2024 11:47:57 GMT",
"version": "v1"
}
] | 2024-06-17 | [
[
"Grube",
"Sarah",
""
],
[
"Neidhardt",
"Maximilian",
""
],
[
"Herrmann",
"Anna-Katarina",
""
],
[
"Sprenger",
"Johanna",
""
],
[
"Abdolazizi",
"Kian",
""
],
[
"Latus",
"Sarah",
""
],
[
"Cyron",
"Christian J.",
""
],
[
"Schlaefer",
"Alexander",
""
]
] | Soft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired. However, force-displacement curves strongly depend on the tool geometry which is often complex in the case of medical tools. Hence, a tool calibration procedure is desired to directly map force-displacement curves to the corresponding tissue elasticity.We present an experimental setup for calibrating medical tools with a robot. First, we propose to estimate the elasticity of gelatin phantoms by spherical indentation with a state-of-the-art contact model. We estimate force-displacement curves for different gelatin elasticities and temperatures. Our experiments demonstrate that gelatin elasticity is highly dependent on temperature, which can lead to an elasticity offset if not considered. Second, we propose to use a more complex material model, e.g., a neural network, that can be trained with the determined elasticities. Considering the temperature of the gelatin sample we can represent different elasticities per phantom and thereby increase our training data.We report elasticity values ranging from 10 to 40 kPa for a 10% gelatin phantom, depending on temperature. |
1811.10693 | Ali Gholami | Ali Gholami, Mohammad Ali Maddah-Ali, Seyed Abolfazl Motahari | Private Shotgun DNA Sequencing | 20 pages with 3 figures | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current techniques in sequencing a genome allow a service provider (e.g. a
sequencing company) to have full access to the genome information, and thus the
privacy of individuals regarding their lifetime secret is violated. In this
paper, we introduce the problem of private DNA sequencing, where the goal is to
keep the DNA sequence private to the sequencer. We propose an architecture,
where the task of reading fragments of DNA and the task of DNA assembly are
separated, the former is done at the sequencer(s), and the later is completed
at a local trusted data collector. To satisfy the privacy constraint at the
sequencer and reconstruction condition at the data collector, we create an
information gap between these two relying on two techniques: (i) we use more
than one non-colluding sequencer, all reporting the read fragments to the
single data collector, (ii) adding the fragments of some known DNA molecules,
which are still unknown to the sequencers, to the pool. We prove that these two
techniques provide enough freedom to satisfy both conditions at the same time.
| [
{
"created": "Fri, 23 Nov 2018 13:20:48 GMT",
"version": "v1"
}
] | 2018-11-28 | [
[
"Gholami",
"Ali",
""
],
[
"Maddah-Ali",
"Mohammad Ali",
""
],
[
"Motahari",
"Seyed Abolfazl",
""
]
] | Current techniques in sequencing a genome allow a service provider (e.g. a sequencing company) to have full access to the genome information, and thus the privacy of individuals regarding their lifetime secret is violated. In this paper, we introduce the problem of private DNA sequencing, where the goal is to keep the DNA sequence private to the sequencer. We propose an architecture, where the task of reading fragments of DNA and the task of DNA assembly are separated, the former is done at the sequencer(s), and the later is completed at a local trusted data collector. To satisfy the privacy constraint at the sequencer and reconstruction condition at the data collector, we create an information gap between these two relying on two techniques: (i) we use more than one non-colluding sequencer, all reporting the read fragments to the single data collector, (ii) adding the fragments of some known DNA molecules, which are still unknown to the sequencers, to the pool. We prove that these two techniques provide enough freedom to satisfy both conditions at the same time. |
1308.2969 | Charles Fisher | Charles K. Fisher, Pankaj Mehta | A phase transition between the niche and neutral regimes in ecology | null | null | 10.1073/pnas.1405637111 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An ongoing debate in ecology concerns the impacts of ecological drift and
selection on community assembly. Here, we show that there is a sharp phase
transition in diverse ecological communities between a selection dominated
regime (the niche phase) and a drift dominated regime (the neutral phase).
Simulations and analytic arguments show that the niche phase is favored in
communities with large population sizes and relatively constant environments,
whereas the neutral phase is favored in communities with small population sizes
and fluctuating environments. Our results demonstrate how apparently neutral
populations may arise even in communities inhabited by species with varying
traits.
| [
{
"created": "Tue, 13 Aug 2013 20:01:39 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Mar 2014 17:17:52 GMT",
"version": "v2"
}
] | 2015-06-16 | [
[
"Fisher",
"Charles K.",
""
],
[
"Mehta",
"Pankaj",
""
]
] | An ongoing debate in ecology concerns the impacts of ecological drift and selection on community assembly. Here, we show that there is a sharp phase transition in diverse ecological communities between a selection dominated regime (the niche phase) and a drift dominated regime (the neutral phase). Simulations and analytic arguments show that the niche phase is favored in communities with large population sizes and relatively constant environments, whereas the neutral phase is favored in communities with small population sizes and fluctuating environments. Our results demonstrate how apparently neutral populations may arise even in communities inhabited by species with varying traits. |
1007.1092 | Ulrich S. Schwarz | A. Besser and U.S. Schwarz (Bioquant, University of Heidelberg,
Germany) | Hysteresis in the cell response to time-dependent substrate stiffness | Revtex, 4 PDF figures | Biophysical Journal, Volume 99, Issue 1, L10-L12, 7 July 2010 | 10.1016/j.bpj.2010.04.008 | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mechanical cues like the rigidity of the substrate are main determinants for
the decision making of adherent cells. Here we use a mechano-chemical model to
predict the cellular response to varying substrate stiffness. The model
equations combine the mechanics of contractile actin filament bundles with a
model for the Rho-signaling pathway triggered by forces at cell-matrix
contacts. A bifurcation analysis of cellular contractility as a function of
substrate stiffness reveals a bistable response, thus defining a lower
threshold of stiffness, below which cells are not able to build up contractile
forces, and an upper threshold of stiffness, above which cells are always in a
strongly contracted state. Using the full dynamical model, we predict that
rate-dependent hysteresis will occur in the cellular traction forces when cells
are exposed to substrates of time-dependent stiffness.
| [
{
"created": "Wed, 7 Jul 2010 10:06:35 GMT",
"version": "v1"
}
] | 2010-07-08 | [
[
"Besser",
"A.",
"",
"Bioquant, University of Heidelberg,\n Germany"
],
[
"Schwarz",
"U. S.",
"",
"Bioquant, University of Heidelberg,\n Germany"
]
] | Mechanical cues like the rigidity of the substrate are main determinants for the decision making of adherent cells. Here we use a mechano-chemical model to predict the cellular response to varying substrate stiffness. The model equations combine the mechanics of contractile actin filament bundles with a model for the Rho-signaling pathway triggered by forces at cell-matrix contacts. A bifurcation analysis of cellular contractility as a function of substrate stiffness reveals a bistable response, thus defining a lower threshold of stiffness, below which cells are not able to build up contractile forces, and an upper threshold of stiffness, above which cells are always in a strongly contracted state. Using the full dynamical model, we predict that rate-dependent hysteresis will occur in the cellular traction forces when cells are exposed to substrates of time-dependent stiffness. |
1611.06185 | Keith Alexander | Keith Alexander, Alexander J Taylor and Mark R Dennis | Proteins analysed as virtual knots | 12 pages main text and 8 pages supplementary information, including 5
figures, 3 supplementary figures and 1 supplementary table | null | null | null | q-bio.BM math.GT physics.comp-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Long, flexible physical filaments are naturally tangled and knotted, from
macroscopic string down to long-chain molecules. The existence of knotting in a
filament naturally affects its configuration and properties, and may be very
stable or disappear rapidly under manipulation and interaction. Knotting has
been previously identified in protein backbone chains, for which these
mechanical constraints are of fundamental importance to their molecular
functionality, despite their being open curves in which the knots are not
mathematically well defined; knotting can only be identified by closing the
termini of the chain somehow. We introduce a new method for resolving knotting
in open curves using virtual knots, a wider class of topological objects that
do not require a classical closure and so naturally capture the topological
ambiguity inherent in open curves. We describe the results of analysing
proteins in the Protein Data Bank by this new scheme, recovering and extending
previous knotting results, and identifying topological interest in some new
cases. The statistics of virtual knots in protein chains are compared with
those of open random walks and Hamiltonian subchains on cubic lattices,
identifying a regime of open curves in which the virtual knotting description
is likely to be important.
| [
{
"created": "Fri, 18 Nov 2016 18:04:41 GMT",
"version": "v1"
}
] | 2016-11-21 | [
[
"Alexander",
"Keith",
""
],
[
"Taylor",
"Alexander J",
""
],
[
"Dennis",
"Mark R",
""
]
] | Long, flexible physical filaments are naturally tangled and knotted, from macroscopic string down to long-chain molecules. The existence of knotting in a filament naturally affects its configuration and properties, and may be very stable or disappear rapidly under manipulation and interaction. Knotting has been previously identified in protein backbone chains, for which these mechanical constraints are of fundamental importance to their molecular functionality, despite their being open curves in which the knots are not mathematically well defined; knotting can only be identified by closing the termini of the chain somehow. We introduce a new method for resolving knotting in open curves using virtual knots, a wider class of topological objects that do not require a classical closure and so naturally capture the topological ambiguity inherent in open curves. We describe the results of analysing proteins in the Protein Data Bank by this new scheme, recovering and extending previous knotting results, and identifying topological interest in some new cases. The statistics of virtual knots in protein chains are compared with those of open random walks and Hamiltonian subchains on cubic lattices, identifying a regime of open curves in which the virtual knotting description is likely to be important. |
2004.02006 | Alfonso Vivanco Lira | A. Vivanco-Lira and R. Luna-Banenelli | Stochastic and nonstochastic descriptions of the 2019-2020 measles
outbreak worldwide with an emphasis in Mexico | 31 pages, 11 figures | null | null | null | q-bio.PE q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Measles is an infectious disease caused by the Morbilivirus Measles Virus
which has accompanied the human race since the 4th millennium BC, it is a
disease usually concerning the paediatric population and in the past, before
the advent of vaccination, almost all the population suffered from it, and in
some cases the complications derived from this disease, such as central nervous
involvement. Vaccination changed the course of the disease worldwide and
diminished the associated comorbidities and mortality; in Mexico the
vaccination program commenced in the decade of the 1970s and was successful in
preventing peaks of infection. Nevertheless, due to various factors, has the
world seen measles outbreaks once more, this commencing in the year 2019 and
extending towards the year 2020. Here we make account of the biology and the
pathophysiology of the viral infection, and present three models: one
concerning the dynamics of the cases by means of a continuous method and a
discrete stochastic model; one concerning the cellular compartmentalization
behaviour of the virus, that is the viral tropism towards certain cell types in
the host and the tendencies in extended or complicated infection; the last one
concerning geographic behaviour of the virus, regarding in particular the
tendencies in Mexico City, those involved at a global scale, and finally a
model providing a prediction of the viral genotypes' distribution worldwide.
| [
{
"created": "Sat, 4 Apr 2020 20:05:06 GMT",
"version": "v1"
}
] | 2020-04-07 | [
[
"Vivanco-Lira",
"A.",
""
],
[
"Luna-Banenelli",
"R.",
""
]
] | Measles is an infectious disease caused by the Morbilivirus Measles Virus which has accompanied the human race since the 4th millennium BC, it is a disease usually concerning the paediatric population and in the past, before the advent of vaccination, almost all the population suffered from it, and in some cases the complications derived from this disease, such as central nervous involvement. Vaccination changed the course of the disease worldwide and diminished the associated comorbidities and mortality; in Mexico the vaccination program commenced in the decade of the 1970s and was successful in preventing peaks of infection. Nevertheless, due to various factors, has the world seen measles outbreaks once more, this commencing in the year 2019 and extending towards the year 2020. Here we make account of the biology and the pathophysiology of the viral infection, and present three models: one concerning the dynamics of the cases by means of a continuous method and a discrete stochastic model; one concerning the cellular compartmentalization behaviour of the virus, that is the viral tropism towards certain cell types in the host and the tendencies in extended or complicated infection; the last one concerning geographic behaviour of the virus, regarding in particular the tendencies in Mexico City, those involved at a global scale, and finally a model providing a prediction of the viral genotypes' distribution worldwide. |
2003.02573 | Suparna Roychowdhury Dr. | Sourav Chowdhury, Sourabh Kumar Manna, Suparna Roychowdhury, Indranath
Chaudhuri | Mathematical Model of ingested glucose in Glucose-Insulin Regulation | 12 pages, 8 figures and 3 tables, accepted for publication in Journal
of Applied and Computational Mathematics | null | null | null | q-bio.TO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here, we develop a mathematical model for glucose-insulin regulatory system.
The model includes a new parameter which is the amount of ingested glucose.
Ingested glucose is an external glucose source coming from digested food. We
assume that the external glucose or ingested glucose decays exponentially with
time. We establish a system of three linear ordinary differential equations
with this new parameter, derive stability analysis and the solution of this
model.
| [
{
"created": "Thu, 5 Mar 2020 12:44:25 GMT",
"version": "v1"
}
] | 2020-03-06 | [
[
"Chowdhury",
"Sourav",
""
],
[
"Manna",
"Sourabh Kumar",
""
],
[
"Roychowdhury",
"Suparna",
""
],
[
"Chaudhuri",
"Indranath",
""
]
] | Here, we develop a mathematical model for glucose-insulin regulatory system. The model includes a new parameter which is the amount of ingested glucose. Ingested glucose is an external glucose source coming from digested food. We assume that the external glucose or ingested glucose decays exponentially with time. We establish a system of three linear ordinary differential equations with this new parameter, derive stability analysis and the solution of this model. |
2302.01270 | Yuji Hirono | Yuji Hirono, Hyukpyo Hong, and Jae Kyoung Kim | Robust Perfect Adaptation of Reaction Fluxes Ensured by Network Topology | 5 pages, 2 figures (Supplemental Material: 14 pages, 1 figure) | null | null | RIKEN-iTHEMS-Report-23 | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Maintaining stability in an uncertain environment is essential for proper
functioning of living systems. Robust perfect adaptation (RPA) is a property of
a system that generates an output at a fixed level even after fluctuations in
input stimulus without fine-tuning parameters, and it is important to
understand how this feature is implemented through biochemical networks. The
existing literature has mainly focused on RPA of the concentration of a chosen
chemical species, and no generic analysis has been made on RPA of reaction
fluxes, that play an equally important role. Here, we identify structural
conditions on reaction networks under which all the reaction fluxes exhibit RPA
against the perturbation of the parameters inside a subnetwork. Based on this
understanding, we give a recipe for obtaining a simpler reaction network, from
which we can fully recover the steady-state reaction fluxes of the original
system. This helps us identify key parameters that determine the fluxes and
study the properties of complex reaction networks using a smaller one without
losing any information about steady-state reaction fluxes.
| [
{
"created": "Thu, 2 Feb 2023 17:58:19 GMT",
"version": "v1"
}
] | 2023-02-03 | [
[
"Hirono",
"Yuji",
""
],
[
"Hong",
"Hyukpyo",
""
],
[
"Kim",
"Jae Kyoung",
""
]
] | Maintaining stability in an uncertain environment is essential for proper functioning of living systems. Robust perfect adaptation (RPA) is a property of a system that generates an output at a fixed level even after fluctuations in input stimulus without fine-tuning parameters, and it is important to understand how this feature is implemented through biochemical networks. The existing literature has mainly focused on RPA of the concentration of a chosen chemical species, and no generic analysis has been made on RPA of reaction fluxes, that play an equally important role. Here, we identify structural conditions on reaction networks under which all the reaction fluxes exhibit RPA against the perturbation of the parameters inside a subnetwork. Based on this understanding, we give a recipe for obtaining a simpler reaction network, from which we can fully recover the steady-state reaction fluxes of the original system. This helps us identify key parameters that determine the fluxes and study the properties of complex reaction networks using a smaller one without losing any information about steady-state reaction fluxes. |
2112.05816 | Sanjukta Krishnagopal | Sanjukta Krishnagopal and Peter Latham | Encoding priors in the brain: a reinforcement learning model for mouse
decision making | Permission required from the data organization for posting of paper.
Will repost when the review is complete. Please contact author for additional
details/questions | null | null | null | q-bio.NC cs.LG physics.data-an | http://creativecommons.org/licenses/by/4.0/ | In two-alternative forced choice tasks, prior knowledge can improve
performance, especially when operating near the psychophysical threshold. For
instance, if subjects know that one choice is much more likely than the other,
they can make that choice when evidence is weak. A common hypothesis for these
kinds of tasks is that the prior is stored in neural activity. Here we propose
a different hypothesis: the prior is stored in synaptic strengths. We study the
International Brain Laboratory task, in which a grating appears on either the
right or left side of a screen, and a mouse has to move a wheel to bring the
grating to the center. The grating is often low in contrast which makes the
task relatively difficult, and the prior probability that the grating appears
on the right is either 80% or 20%, in (unsignaled) blocks of about 50 trials.
We model this as a reinforcement learning task, using a feedforward neural
network to map states to actions, and adjust the weights of the network to
maximize reward, learning via policy gradient. Our model uses an internal state
that stores an estimate of the grating and confidence, and follows Bayesian
updates, and can switch between engaged and disengaged states to mimic animal
behavior. This model reproduces the main experimental finding - that the
psychometric curve with respect to contrast shifts after a block switch in
about 10 trials. Also, as seen in the experiments, in our model the difference
in neuronal activity in the right and left blocks is small - it is virtually
impossible to decode block structure from activity on single trials if noise is
about 2%. The hypothesis that priors are stored in weights is difficult to
test, but the technology to do so should be available in the not so distant
future.
| [
{
"created": "Fri, 10 Dec 2021 20:16:36 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Dec 2021 00:27:14 GMT",
"version": "v2"
}
] | 2021-12-22 | [
[
"Krishnagopal",
"Sanjukta",
""
],
[
"Latham",
"Peter",
""
]
] | In two-alternative forced choice tasks, prior knowledge can improve performance, especially when operating near the psychophysical threshold. For instance, if subjects know that one choice is much more likely than the other, they can make that choice when evidence is weak. A common hypothesis for these kinds of tasks is that the prior is stored in neural activity. Here we propose a different hypothesis: the prior is stored in synaptic strengths. We study the International Brain Laboratory task, in which a grating appears on either the right or left side of a screen, and a mouse has to move a wheel to bring the grating to the center. The grating is often low in contrast which makes the task relatively difficult, and the prior probability that the grating appears on the right is either 80% or 20%, in (unsignaled) blocks of about 50 trials. We model this as a reinforcement learning task, using a feedforward neural network to map states to actions, and adjust the weights of the network to maximize reward, learning via policy gradient. Our model uses an internal state that stores an estimate of the grating and confidence, and follows Bayesian updates, and can switch between engaged and disengaged states to mimic animal behavior. This model reproduces the main experimental finding - that the psychometric curve with respect to contrast shifts after a block switch in about 10 trials. Also, as seen in the experiments, in our model the difference in neuronal activity in the right and left blocks is small - it is virtually impossible to decode block structure from activity on single trials if noise is about 2%. The hypothesis that priors are stored in weights is difficult to test, but the technology to do so should be available in the not so distant future. |
1511.00320 | Ali Bakhshinejad | Ali Bakhshinejad | A short review on techniques for processes and process simulation of
scaffold-free tissue engineering | null | null | null | null | q-bio.QM q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The invention of three-dimensional printers has led to major innovations in
tissue engineering. They have enabled the printing of complex geometries such
as those that occur in natural tissues, that were not possible with traditional
manufacturing techniques. Tissue engineering in particular deals with printing
bio-compatible material that may be infused with live cells. Thus additional
complexity is incurred because the live cells can migrate and proliferate and
thus change the printed geometry. One of the important issues is the prediction
of geometry and possibly mechanical properties of the steady state tissue. In
this short review, we will provide an overview of different tissue engineering
processes that are currently available. Furthermore, we will review two
important techniques, namely, Cellular Potts Model (CPM), and Cellular Particle
Dynamics (CPD) that have been used to predict the steady state of printed
tissue.
| [
{
"created": "Sun, 1 Nov 2015 22:17:43 GMT",
"version": "v1"
}
] | 2015-11-23 | [
[
"Bakhshinejad",
"Ali",
""
]
] | The invention of three-dimensional printers has led to major innovations in tissue engineering. They have enabled the printing of complex geometries such as those that occur in natural tissues, that were not possible with traditional manufacturing techniques. Tissue engineering in particular deals with printing bio-compatible material that may be infused with live cells. Thus additional complexity is incurred because the live cells can migrate and proliferate and thus change the printed geometry. One of the important issues is the prediction of geometry and possibly mechanical properties of the steady state tissue. In this short review, we will provide an overview of different tissue engineering processes that are currently available. Furthermore, we will review two important techniques, namely, Cellular Potts Model (CPM), and Cellular Particle Dynamics (CPD) that have been used to predict the steady state of printed tissue. |
1706.09478 | Nash Rochman | Nash Rochman, Dan Popescu, Sean X. Sun | Erg(r)odicity: Hidden Bias and the Growthrate Gain | 17 pages, 4 figures | null | 10.1088/1478-3975/aab0e6 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many single-cell observables are highly heterogeneous. A part of this
heterogeneity stems from age-related phenomena: the fact that there is a
nonuniform distribution of cells with different ages. This has led to a renewed
interest in analytic methodologies including use of the "von Foerster equation"
for predicting population growth and cell age distributions. Here we discuss
how some of the most popular implementations of this machinery assume a strong
condition on the ergodicity of the cell cycle duration ensemble. We show that
one common definition for the term ergodicity, "a single individual observed
over many generations recapitulates the behavior of the entire ensemble" is
implied by the other, "the probability of observing any state is conserved
across time and over all individuals" in an ensemble with a fixed number of
individuals but that this is not true when the ensemble is growing. We further
explore the impact of generational correlations between cell cycle durations on
the population growth rate. Finally, we explore the "growth rate gain" - the
phenomenon that variations in the cell cycle duration lead to an improved
population-level growth rate - in this context. We highlight that,
fundamentally, this effect is due to asymmetric division.
| [
{
"created": "Wed, 28 Jun 2017 20:35:45 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Apr 2018 14:29:12 GMT",
"version": "v2"
}
] | 2018-04-18 | [
[
"Rochman",
"Nash",
""
],
[
"Popescu",
"Dan",
""
],
[
"Sun",
"Sean X.",
""
]
] | Many single-cell observables are highly heterogeneous. A part of this heterogeneity stems from age-related phenomena: the fact that there is a nonuniform distribution of cells with different ages. This has led to a renewed interest in analytic methodologies including use of the "von Foerster equation" for predicting population growth and cell age distributions. Here we discuss how some of the most popular implementations of this machinery assume a strong condition on the ergodicity of the cell cycle duration ensemble. We show that one common definition for the term ergodicity, "a single individual observed over many generations recapitulates the behavior of the entire ensemble" is implied by the other, "the probability of observing any state is conserved across time and over all individuals" in an ensemble with a fixed number of individuals but that this is not true when the ensemble is growing. We further explore the impact of generational correlations between cell cycle durations on the population growth rate. Finally, we explore the "growth rate gain" - the phenomenon that variations in the cell cycle duration lead to an improved population-level growth rate - in this context. We highlight that, fundamentally, this effect is due to asymmetric division. |
1601.01288 | Georgi Kapitanov | Georgi I. Kapitanov, Xiayi Wang, Bruce P. Ayati, Marc J. Brouillette,
James A. Martin | Linking Cellular and Mechanical Processes in Articular Cartilage Lesion
Formation: A Mathematical Model | null | null | 10.3389/fbioe.2016.00080 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A severe application of stress on articular cartilage can initiate a cascade
of biochemical reactions that can lead to the development of osteoarthritis. We
constructed a multiscale mathematical model of the process with three
components: cellular, chemical, and mechanical. The cellular component
describes the different chondrocyte states according to the chemicals these
cells release. The chemical component models the change in concentrations of
those chemicals. The mechanical component contains a simulation of pressure
application onto a cartilage explant and the resulting strains that initiate
the biochemical processes. The model creates a framework for incorporating
explicit mechanics, simulated by finite element analysis, into a theoretical
biology framework.
| [
{
"created": "Wed, 6 Jan 2016 19:42:20 GMT",
"version": "v1"
}
] | 2023-02-14 | [
[
"Kapitanov",
"Georgi I.",
""
],
[
"Wang",
"Xiayi",
""
],
[
"Ayati",
"Bruce P.",
""
],
[
"Brouillette",
"Marc J.",
""
],
[
"Martin",
"James A.",
""
]
] | A severe application of stress on articular cartilage can initiate a cascade of biochemical reactions that can lead to the development of osteoarthritis. We constructed a multiscale mathematical model of the process with three components: cellular, chemical, and mechanical. The cellular component describes the different chondrocyte states according to the chemicals these cells release. The chemical component models the change in concentrations of those chemicals. The mechanical component contains a simulation of pressure application onto a cartilage explant and the resulting strains that initiate the biochemical processes. The model creates a framework for incorporating explicit mechanics, simulated by finite element analysis, into a theoretical biology framework. |
2206.07797 | Tom\'as S. Grigera | Sabrina Camargo, Daniel A. Martin, Eyisto J. Aguilar Trejo, Aylen de
Florian, Maciej A. Nowak, Sergio A. Cannas, Tomas S. Grigera, and Dante R.
Chialvo | Scale-free correlations in the dynamics of a small (N ~ 10000) cortical
network | 8 pages, 6 figures | Phys. Rev. E 108, 034302 (2023) | 10.1103/PhysRevE.108.034302 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of novel opto-genetics technology allows the recording of brain
activity with a resolution never seen before. The characterisation of these
very large data sets offers new challenges as well as unique theory-testing
opportunities. Here we discuss whether the spatial and temporal correlation of
the collective activity of thousands of neurons are tangled as predicted by the
theory of critical phenomena. The analysis shows that both, the correlation
length $\xi$ and the correlation time $\tau$ scale as predicted as a function
of the system size. With some peculiarities that we discuss, the analysis
uncovers new evidence consistent with the view that the large scale brain
cortical dynamics corresponds to critical phenomena.
| [
{
"created": "Wed, 15 Jun 2022 20:21:18 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Sep 2023 17:44:35 GMT",
"version": "v2"
}
] | 2023-09-14 | [
[
"Camargo",
"Sabrina",
""
],
[
"Martin",
"Daniel A.",
""
],
[
"Trejo",
"Eyisto J. Aguilar",
""
],
[
"de Florian",
"Aylen",
""
],
[
"Nowak",
"Maciej A.",
""
],
[
"Cannas",
"Sergio A.",
""
],
[
"Grigera",
"Tomas S.",
""
],
[
"Chialvo",
"Dante R.",
""
]
] | The advent of novel opto-genetics technology allows the recording of brain activity with a resolution never seen before. The characterisation of these very large data sets offers new challenges as well as unique theory-testing opportunities. Here we discuss whether the spatial and temporal correlation of the collective activity of thousands of neurons are tangled as predicted by the theory of critical phenomena. The analysis shows that both, the correlation length $\xi$ and the correlation time $\tau$ scale as predicted as a function of the system size. With some peculiarities that we discuss, the analysis uncovers new evidence consistent with the view that the large scale brain cortical dynamics corresponds to critical phenomena. |
1512.05007 | Anca Radulescu | Anca Radulescu, Rachel Marra | A mathematical model of reward and executive circuitry in obsessive
compulsive disorde | 16 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The neuronal circuit that controls obsessive and compulsive behaviors
involves a complex network of brain regions (some with known involvement in
reward processing). Among these are cortical regions, the striatum and the
thalamus (which compose the CSTC pathway), limbic areas such as the amygdala
and the hippocampus, and well as dopamine pathways. Abnormal dynamic behavior
in this brain network is a hallmark feature of patients with increased anxiety
and motor activity, like the ones affected by OCD. There is currently no clear
understanding of precisely what mechanisms generates these behaviors.
We attempt to investigate a collection of connectivity hypotheses of OCD by
means of a computational model of the brain circuitry that governs reward and
motion execution. Mathematically, we use methods from ordinary differential
equations and continuous time dynamical systems. We use classical analytical
methods as well as computational approaches to study phenomena in the phase
plane (e.g., behavior of the system's solutions when given certain initial
conditions) and in the parameter space (e.g., sensitive dependence of initial
conditions).
We find that different obsessive-compulsive subtypes may correspond to
different abnormalities in the network connectivity profiles. We suggest that
it is combinations of parameters (connectivity strengths between regions),
rather the than the value of any one parameter taken independently, that
provides the best basis for predicting behavior, and for understanding the
heterogeneity of the illness.
| [
{
"created": "Tue, 15 Dec 2015 23:17:55 GMT",
"version": "v1"
}
] | 2015-12-17 | [
[
"Radulescu",
"Anca",
""
],
[
"Marra",
"Rachel",
""
]
] | The neuronal circuit that controls obsessive and compulsive behaviors involves a complex network of brain regions (some with known involvement in reward processing). Among these are cortical regions, the striatum and the thalamus (which compose the CSTC pathway), limbic areas such as the amygdala and the hippocampus, and well as dopamine pathways. Abnormal dynamic behavior in this brain network is a hallmark feature of patients with increased anxiety and motor activity, like the ones affected by OCD. There is currently no clear understanding of precisely what mechanisms generates these behaviors. We attempt to investigate a collection of connectivity hypotheses of OCD by means of a computational model of the brain circuitry that governs reward and motion execution. Mathematically, we use methods from ordinary differential equations and continuous time dynamical systems. We use classical analytical methods as well as computational approaches to study phenomena in the phase plane (e.g., behavior of the system's solutions when given certain initial conditions) and in the parameter space (e.g., sensitive dependence of initial conditions). We find that different obsessive-compulsive subtypes may correspond to different abnormalities in the network connectivity profiles. We suggest that it is combinations of parameters (connectivity strengths between regions), rather the than the value of any one parameter taken independently, that provides the best basis for predicting behavior, and for understanding the heterogeneity of the illness. |
2110.04986 | Seyednami Niyakan | Seyednami Niyakan and Xiaoning Qian | COVID-Datathon: Biomarker identification for COVID-19 severity based on
BALF scRNA-seq data | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergence
began in late 2019 and has since spread rapidly worldwide. The characteristics
of respiratory immune response to this emerging virus is not clear. Recently,
Single-cell RNA sequencing (scRNA-seq) transcriptome profiling of
Bronchoalveolar lavage fluid (BALF) cells has been done to elucidate the
potential mechanisms underlying in COVID-19. With the aim of better utilizing
this atlas of BALF cells in response to the virus, here we propose a
bioinformatics pipeline to identify candidate biomarkers of COVID-19 severity,
which may help characterize BALF cells to have better mechanistic understanding
of SARS-CoV-2 infection. The proposed pipeline is implemented in R and is
available at https://github.com/namini94/scBALF_Hackathon.
| [
{
"created": "Mon, 11 Oct 2021 03:58:29 GMT",
"version": "v1"
}
] | 2021-10-12 | [
[
"Niyakan",
"Seyednami",
""
],
[
"Qian",
"Xiaoning",
""
]
] | The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergence began in late 2019 and has since spread rapidly worldwide. The characteristics of respiratory immune response to this emerging virus is not clear. Recently, Single-cell RNA sequencing (scRNA-seq) transcriptome profiling of Bronchoalveolar lavage fluid (BALF) cells has been done to elucidate the potential mechanisms underlying in COVID-19. With the aim of better utilizing this atlas of BALF cells in response to the virus, here we propose a bioinformatics pipeline to identify candidate biomarkers of COVID-19 severity, which may help characterize BALF cells to have better mechanistic understanding of SARS-CoV-2 infection. The proposed pipeline is implemented in R and is available at https://github.com/namini94/scBALF_Hackathon. |
1802.02930 | Liane Gabora | Liane Gabora | Evolution of the Science Fiction Writer's Capacity to Imagine the Future | 6 pages; 1 figure. In Proceedings of International Science Fiction
Prototyping Conference (SCI-FI'18). Ostend, Belgium: EUROSIS (a division of
the European Technology Institute). (Held April 18-19 in Bruges, Belgium.)
arXiv admin note: substantial text overlap with arXiv:1704.05056 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Drawing upon a body of research on the evolution of creativity, this paper
proposes a theory of how, when, and why the forward-thinking story-telling
abilities of humans evolved, culminating in the visionary abilities of science
fiction writers. The ability to recursively chain thoughts together evolved
approximately two million years ago. Language abilities, and the ability to
shift between different modes of thought, evolved approximately 100,000 years
ago. Science fiction dates to at least the second Century AD. It is suggested
that well before this time, but after 100,000 years ago, and concurrent with
the evolution of a division of labour between creators and imitators there
arose a division of labour between past, present, and future thinkers.
Agent-based model research suggests there are social benefits to the evolution
of individual differences in creativity such that there is a balance between
novelty-generating creators and continuity-perpetuating imitators. A balance
between individuals focused on the past, present, and future would be expected
to yield similar adaptive benefits.
| [
{
"created": "Wed, 7 Feb 2018 00:02:19 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Mar 2019 22:02:15 GMT",
"version": "v2"
}
] | 2019-03-15 | [
[
"Gabora",
"Liane",
""
]
] | Drawing upon a body of research on the evolution of creativity, this paper proposes a theory of how, when, and why the forward-thinking story-telling abilities of humans evolved, culminating in the visionary abilities of science fiction writers. The ability to recursively chain thoughts together evolved approximately two million years ago. Language abilities, and the ability to shift between different modes of thought, evolved approximately 100,000 years ago. Science fiction dates to at least the second Century AD. It is suggested that well before this time, but after 100,000 years ago, and concurrent with the evolution of a division of labour between creators and imitators there arose a division of labour between past, present, and future thinkers. Agent-based model research suggests there are social benefits to the evolution of individual differences in creativity such that there is a balance between novelty-generating creators and continuity-perpetuating imitators. A balance between individuals focused on the past, present, and future would be expected to yield similar adaptive benefits. |
1502.05592 | Istv\'an Kolossv\'ary | Istv\'an Kolossv\'ary | Conceptual framework for performing simultaneous fold and sequence
optimization in multi-scale protein modeling | 16 pages, 5 figures, 3 tables, and supplementary material. arXiv
admin note: substantial text overlap with arXiv:1205.4705 | null | null | null | q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a dual optimization concept of predicting optimal sequences as
well as optimal folds of off-lattice protein models in the context of
multi-scale modeling. We validate the utility of the recently introduced
hidden-force Monte Carlo optimization algorithm by finding significantly lower
energy folds for minimalist and detailed protein models than previously
reported. Further, we also find the protein sequence that yields the lowest
energy fold amongst all sequences for a given chain length and residue mixture.
In particular, for protein models with a binary sequence, we show that the
sequence-optimized folds form more compact cores than the lowest energy folds
of the historically fixed, Fibonacci-series sequences of chain lengths of 13,
21, 34, 55, and 89. We then extend our search algorithm to use UNRES, one of
the leading united-residue protein force fields. Our combined fold and sequence
optimization on three test proteins reveal an inherent bias in UNRES favoring
alpha helical structures even when secondary structure prediction clearly
suggests only beta sheets besides random coil, and virtually no helices. One
test in particular, a triple-stranded antiparallel beta-sheet protein domain,
demonstrates that by permutations of its sequence UNRES re-folds this structure
into a perfect alpha helix but, in fact, the helix is just an artefact of the
force field, the structure quickly unfolds in all-atom state-of-the-art
molecular dynamics simulation.
| [
{
"created": "Sun, 21 Dec 2014 01:37:53 GMT",
"version": "v1"
}
] | 2015-02-20 | [
[
"Kolossváry",
"István",
""
]
] | We present a dual optimization concept of predicting optimal sequences as well as optimal folds of off-lattice protein models in the context of multi-scale modeling. We validate the utility of the recently introduced hidden-force Monte Carlo optimization algorithm by finding significantly lower energy folds for minimalist and detailed protein models than previously reported. Further, we also find the protein sequence that yields the lowest energy fold amongst all sequences for a given chain length and residue mixture. In particular, for protein models with a binary sequence, we show that the sequence-optimized folds form more compact cores than the lowest energy folds of the historically fixed, Fibonacci-series sequences of chain lengths of 13, 21, 34, 55, and 89. We then extend our search algorithm to use UNRES, one of the leading united-residue protein force fields. Our combined fold and sequence optimization on three test proteins reveal an inherent bias in UNRES favoring alpha helical structures even when secondary structure prediction clearly suggests only beta sheets besides random coil, and virtually no helices. One test in particular, a triple-stranded antiparallel beta-sheet protein domain, demonstrates that by permutations of its sequence UNRES re-folds this structure into a perfect alpha helix but, in fact, the helix is just an artefact of the force field, the structure quickly unfolds in all-atom state-of-the-art molecular dynamics simulation. |
1407.3668 | Roman Zubarev A | Alexey Chernobrovkin (1), Consuelo Marin Vicente (1,2), Neus Visa (2)
and Roman A. Zubarev (1) ((1) Karolinska Institutet, (2) Stockholm
University) | Expression proteomics reveals protein targets and highlights mechanisms
of action of small molecule drugs | 26 pages, 3 figures, 3 supplementary figures. Raw mass-spectrometry
files were deposited to chorusproject.org. Supplementary tables and figures
available upon request | null | 10.1038/srep11176 | null | q-bio.MN q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phenomenological screening of small molecule libraries for anticancer
activity yields potentially interesting candidate molecules, with a bottleneck
in the determination of drug targets and the mechanism of anticancer action. A
novel approach to drug target deconvolution compares the abundance profiles of
proteins expressed in a panel of cells treated with different drugs, and
identifies proteins with cell-type independent and drug-specific regulation
that is exceptionally strong in relation to the other proteins. Mapping top
candidates on known protein networks reveals the mechanism of drug action,
while abundant proteins provide a signature of cellular death/survival
pathways. The above approach can significantly shorten drug target
identification, and thus facilitate the emergence of novel anticancer
treatments.
| [
{
"created": "Mon, 14 Jul 2014 14:26:19 GMT",
"version": "v1"
}
] | 2015-06-11 | [
[
"Chernobrovkin",
"Alexey",
""
],
[
"Vicente",
"Consuelo Marin",
""
],
[
"Visa",
"Neus",
""
],
[
"Zubarev",
"Roman A.",
""
]
] | Phenomenological screening of small molecule libraries for anticancer activity yields potentially interesting candidate molecules, with a bottleneck in the determination of drug targets and the mechanism of anticancer action. A novel approach to drug target deconvolution compares the abundance profiles of proteins expressed in a panel of cells treated with different drugs, and identifies proteins with cell-type independent and drug-specific regulation that is exceptionally strong in relation to the other proteins. Mapping top candidates on known protein networks reveals the mechanism of drug action, while abundant proteins provide a signature of cellular death/survival pathways. The above approach can significantly shorten drug target identification, and thus facilitate the emergence of novel anticancer treatments. |
1507.05050 | Stuart Borrett Stuart Borrett | Stuart R. Borrett, Montgomery Carter, David E. Hines | Six General Ecosystem Properties are more Intense in Biogeochemical
Cycling Networks than Food Webs | 28 pages, 6 figures, 3 tables, 2 Supplementary Tables | null | 10.1093/comnet/cnw001 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network analysis has revealed whole-network properties hypothesized to be
general characteristics of ecosystems including pathway proliferation, and
network non-locality, homogenization, amplification, mutualism, and synergism.
Collectively these properties characterize the impact of indirect interactions
among ecosystem elements. While ecosystem networks generally trace a
thermodynamically conserved unit through the system, there appear to be several
model classes. For example, trophic (TRO) networks are built upon a food web,
usually follow energy or carbon, and are the most abundant in the literature.
Biogeochemical cycling (BGC) networks trace nutrients like nitrogen or
phosphorus and tend to have more recycling than TRO. We tested (1) the
hypothesized generality of the properties in BGC networks and (2) that they
tend to be more strongly expressed in BGC networks than in the TRO networks due
to increased recycling. We compared the properties in 22 BGC and 57 TRO
ecosystem networks from the literature using enaR. The results generally
support the hypotheses. First, five of the properties occurred in all 22 BGC
models, while network mutualism occurred in 86% of the models. Further, these
results were generally robust to a $\pm$50% uncertainty in the model
parameters. Second, the mean network statistics for the six properties were
statistically significantly greater in the BGC models than the TRO models.
These results (1) confirm the general presence of these properties in ecosystem
networks, (2) highlight the significance of model types in determining property
intensities, (3) reinforce the importance of recycling, and (4) provide a set
of indicator benchmarks for future systems comparisons. Further, this work
highlights how indirect effects distributed by network connectivity can
transform whole-ecosystem functioning, and adds to the growing domain of
network ecology.
| [
{
"created": "Tue, 14 Jul 2015 21:37:09 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Aug 2015 13:43:46 GMT",
"version": "v2"
}
] | 2016-04-13 | [
[
"Borrett",
"Stuart R.",
""
],
[
"Carter",
"Montgomery",
""
],
[
"Hines",
"David E.",
""
]
] | Network analysis has revealed whole-network properties hypothesized to be general characteristics of ecosystems including pathway proliferation, and network non-locality, homogenization, amplification, mutualism, and synergism. Collectively these properties characterize the impact of indirect interactions among ecosystem elements. While ecosystem networks generally trace a thermodynamically conserved unit through the system, there appear to be several model classes. For example, trophic (TRO) networks are built upon a food web, usually follow energy or carbon, and are the most abundant in the literature. Biogeochemical cycling (BGC) networks trace nutrients like nitrogen or phosphorus and tend to have more recycling than TRO. We tested (1) the hypothesized generality of the properties in BGC networks and (2) that they tend to be more strongly expressed in BGC networks than in the TRO networks due to increased recycling. We compared the properties in 22 BGC and 57 TRO ecosystem networks from the literature using enaR. The results generally support the hypotheses. First, five of the properties occurred in all 22 BGC models, while network mutualism occurred in 86% of the models. Further, these results were generally robust to a $\pm$50% uncertainty in the model parameters. Second, the mean network statistics for the six properties were statistically significantly greater in the BGC models than the TRO models. These results (1) confirm the general presence of these properties in ecosystem networks, (2) highlight the significance of model types in determining property intensities, (3) reinforce the importance of recycling, and (4) provide a set of indicator benchmarks for future systems comparisons. Further, this work highlights how indirect effects distributed by network connectivity can transform whole-ecosystem functioning, and adds to the growing domain of network ecology. |
1906.08481 | Claus Metzner | Claus Metzner | Detecting long-range attraction between migrating cells based on p-value
distributions | null | null | null | null | q-bio.QM q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Immune cells have evolved to recognize and eliminate pathogens, and the
efficiency of this process can be measured in a Petri dish. Yet, even if the
cells are time-lapse recorded and tracked with high resolution, it is difficult
to judge whether the immune cells find their targets by mere chance, or if they
approach them in a goal-directed way, perhaps using remote sensing mechanisms
such as chemotaxis. To answer this question, we assign to each step of an
immune cell a 'p-value', the probability that a move, at least as
target-directed as observed, can be explained with target-independent migration
behavior. The resulting distribution of p-values is compared to the
distribution of a reference system with randomized target positions. By using
simulated data, based on various chemotactic search mechanisms, we demonstrate
that our method can reliably distinguish between blind migration and
target-directed 'hunting' behavior.
| [
{
"created": "Thu, 20 Jun 2019 07:49:22 GMT",
"version": "v1"
}
] | 2019-06-21 | [
[
"Metzner",
"Claus",
""
]
] | Immune cells have evolved to recognize and eliminate pathogens, and the efficiency of this process can be measured in a Petri dish. Yet, even if the cells are time-lapse recorded and tracked with high resolution, it is difficult to judge whether the immune cells find their targets by mere chance, or if they approach them in a goal-directed way, perhaps using remote sensing mechanisms such as chemotaxis. To answer this question, we assign to each step of an immune cell a 'p-value', the probability that a move, at least as target-directed as observed, can be explained with target-independent migration behavior. The resulting distribution of p-values is compared to the distribution of a reference system with randomized target positions. By using simulated data, based on various chemotactic search mechanisms, we demonstrate that our method can reliably distinguish between blind migration and target-directed 'hunting' behavior. |
1212.4270 | Steffen Waldherr | Steffen Waldherr and Frank Allg\"ower | Network-level dynamics of diffusively coupled cells | null | Proceedings of the 51st IEEE Conference on Decision and Control
(CDC), 2012, pp. 5517-5522. (C) IEEE | null | null | q-bio.CB math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study molecular dynamics within populations of diffusively coupled cells
under the assumption of fast diffusive exchange. As a technical tool, we
propose conditions on boundedness and ultimate boundedness for systems with a
singular perturbation, which extend the classical asymptotic stability results
for singularly perturbed systems. Based on these results, we show that with
common models of intracellular dynamics, the cell population is coordinated in
the sense that all cells converge close to a common equilibrium point. We then
study a more specific example of coupled cells which behave as bistable
switches, where the intracellular dynamics are such that cells may be in one of
two equilibrium points. Here, we find that the whole population is bistable in
the sense that it converges to a population state where either all cells are
close to the one equilibrium point, or all cells are close to the other
equilibrium point. Finally, we discuss applications of these results for the
robustness of cellular decision making in coupled populations.
| [
{
"created": "Tue, 18 Dec 2012 08:47:44 GMT",
"version": "v1"
}
] | 2012-12-19 | [
[
"Waldherr",
"Steffen",
""
],
[
"Allgöwer",
"Frank",
""
]
] | We study molecular dynamics within populations of diffusively coupled cells under the assumption of fast diffusive exchange. As a technical tool, we propose conditions on boundedness and ultimate boundedness for systems with a singular perturbation, which extend the classical asymptotic stability results for singularly perturbed systems. Based on these results, we show that with common models of intracellular dynamics, the cell population is coordinated in the sense that all cells converge close to a common equilibrium point. We then study a more specific example of coupled cells which behave as bistable switches, where the intracellular dynamics are such that cells may be in one of two equilibrium points. Here, we find that the whole population is bistable in the sense that it converges to a population state where either all cells are close to the one equilibrium point, or all cells are close to the other equilibrium point. Finally, we discuss applications of these results for the robustness of cellular decision making in coupled populations. |
q-bio/0309027 | Didier A. Depireux | Didier A. Depireux, Jonathan Z. Simon and Shihab A. Shamma | Measuring the dynamics of neural responses in primary auditory cortex | 27 pages, 17 figures, straight LaTeX +psfig. Originally submitted to
the neuro-sys archive which was never publicly announced (was 9804002) | null | null | null | q-bio.NC | null | We review recent developments in the measurement of the dynamics of the
response properties of auditory cortical neurons to broadband sounds, which is
closely related to the perception of timbre. The emphasis is on a method that
characterizes the spectro-temporal properties of single neurons to dynamic,
broadband sounds, akin to the drifting gratings used in vision. The method
treats the spectral and temporal aspects of the response on an equal footing.
| [
{
"created": "Thu, 16 Apr 1998 19:23:54 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Depireux",
"Didier A.",
""
],
[
"Simon",
"Jonathan Z.",
""
],
[
"Shamma",
"Shihab A.",
""
]
] | We review recent developments in the measurement of the dynamics of the response properties of auditory cortical neurons to broadband sounds, which is closely related to the perception of timbre. The emphasis is on a method that characterizes the spectro-temporal properties of single neurons to dynamic, broadband sounds, akin to the drifting gratings used in vision. The method treats the spectral and temporal aspects of the response on an equal footing. |
1407.3215 | Jean Carlson | Edwin C. Yuan, David L. Alderson, Sean Stromberg, and Jean M. Carlson | Optimal vaccination in a stochastic epidemic model of two
non-interacting populations | 21 pages, 7 figures | null | 10.1371/journal.pone.0115826 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Developing robust, quantitative methods to optimize resource allocations in
response to epidemics has the potential to save lives and minimize health care
costs. In this paper, we develop and apply a computationally efficient
algorithm that enables us to calculate the complete probability distribution
for the final epidemic size in a stochastic Susceptible-Infected-Recovered
(SIR) model. Based on these results, we determine the optimal allocations of a
limited quantity of vaccine between two non-interacting populations. We compare
the stochastic solution to results obtained for the traditional, deterministic
SIR model. For intermediate quantities of vaccine, the deterministic model is a
poor estimate of the optimal strategy for the more realistic, stochastic case.
| [
{
"created": "Thu, 10 Jul 2014 05:12:24 GMT",
"version": "v1"
}
] | 2015-06-22 | [
[
"Yuan",
"Edwin C.",
""
],
[
"Alderson",
"David L.",
""
],
[
"Stromberg",
"Sean",
""
],
[
"Carlson",
"Jean M.",
""
]
] | Developing robust, quantitative methods to optimize resource allocations in response to epidemics has the potential to save lives and minimize health care costs. In this paper, we develop and apply a computationally efficient algorithm that enables us to calculate the complete probability distribution for the final epidemic size in a stochastic Susceptible-Infected-Recovered (SIR) model. Based on these results, we determine the optimal allocations of a limited quantity of vaccine between two non-interacting populations. We compare the stochastic solution to results obtained for the traditional, deterministic SIR model. For intermediate quantities of vaccine, the deterministic model is a poor estimate of the optimal strategy for the more realistic, stochastic case. |
2204.07547 | Robert Haase | Robert Haase, Elnaz Fazeli, David Legland, Michael Doube, Si\^an
Culley, Ilya Belevich, Eija Jokitalo, Martin Schorb, Anna Klemm, Christian
Tischer | A Hitchhiker`s Guide through the Bio-image Analysis Software Universe | null | null | null | null | q-bio.QM eess.IV | http://creativecommons.org/licenses/by/4.0/ | Modern research in the life sciences is unthinkable without computational
methods for extracting, quantifying and visualizing information derived from
biological microscopy imaging data. In the past decade, we observed a dramatic
increase in available software packages for these purposes. As it is
increasingly difficult to keep track of the number of available image analysis
platforms, tool collections, components and emerging technologies, we provide a
conservative overview of software we use in daily routine and give insights
into emerging new tools. We give guidance on which aspects to consider when
choosing the right platform, including aspects such as image data type, skills
of the team, infrastructure and community at the institute and availability of
time and budget.
| [
{
"created": "Fri, 15 Apr 2022 17:10:09 GMT",
"version": "v1"
}
] | 2022-04-18 | [
[
"Haase",
"Robert",
""
],
[
"Fazeli",
"Elnaz",
""
],
[
"Legland",
"David",
""
],
[
"Doube",
"Michael",
""
],
[
"Culley",
"Siân",
""
],
[
"Belevich",
"Ilya",
""
],
[
"Jokitalo",
"Eija",
""
],
[
"Schorb",
"Martin",
""
],
[
"Klemm",
"Anna",
""
],
[
"Tischer",
"Christian",
""
]
] | Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualizing information derived from biological microscopy imaging data. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the right platform, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget. |
1804.10508 | Robert Pepperell | Robert Pepperell | Consciousness as a physical process caused by the organization of energy
in the brain | 22 pages, 1 figure | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To explain consciousness as a physical process we must acknowledge the role
of energy in the brain. Energetic activity is fundamental to all physical
processes and causally drives biological behaviour. Recent neuroscientific
evidence can be interpreted in a way that suggests consciousness is a product
of the organization of energetic activity in the brain. The nature of energy
itself, though, remains largely mysterious, and we do not fully understand how
it contributes to brain function or consciousness. According to the principle
outlined here, energy, along with forces and work, can be described as
actualized differences of motion and tension. By observing physical systems, we
can infer there is something it is like to undergo actualized difference from
the intrinsic perspective of the system. Consciousness occurs because there is
something it is like, intrinsically, to undergo a certain organization of
actualized differences in the brain.
| [
{
"created": "Fri, 27 Apr 2018 13:53:59 GMT",
"version": "v1"
},
{
"created": "Fri, 4 May 2018 14:18:48 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Oct 2018 17:37:19 GMT",
"version": "v3"
},
{
"created": "Thu, 4 Oct 2018 06:08:02 GMT",
"version": "v4"
},
{
"created": "Wed, 10 Oct 2018 17:18:02 GMT",
"version": "v5"
},
{
"created": "Fri, 12 Oct 2018 13:41:12 GMT",
"version": "v6"
}
] | 2018-10-15 | [
[
"Pepperell",
"Robert",
""
]
] | To explain consciousness as a physical process we must acknowledge the role of energy in the brain. Energetic activity is fundamental to all physical processes and causally drives biological behaviour. Recent neuroscientific evidence can be interpreted in a way that suggests consciousness is a product of the organization of energetic activity in the brain. The nature of energy itself, though, remains largely mysterious, and we do not fully understand how it contributes to brain function or consciousness. According to the principle outlined here, energy, along with forces and work, can be described as actualized differences of motion and tension. By observing physical systems, we can infer there is something it is like to undergo actualized difference from the intrinsic perspective of the system. Consciousness occurs because there is something it is like, intrinsically, to undergo a certain organization of actualized differences in the brain. |
2001.06754 | Shaoli Wang | Shaoli Wang, Xiyan Bai | Psychological effect can lead to bistability in epidemics | 8 pages, 3 figures. arXiv admin note: substantial text overlap with
arXiv:1911.13002 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study the psychological effect in a SIS epidemic model. The
basic reproduction number is obtained. However, the disease free equilibrium is
always asymptotically stable, which doesn't depends on the basic reproduction
number. The system has a saddle-node bifurcation appear and displays bistable
behavior, which is a new phenomenon in epidemic dynamics and different from the
backward bifurcation behavior.
| [
{
"created": "Sun, 19 Jan 2020 02:22:31 GMT",
"version": "v1"
}
] | 2020-01-22 | [
[
"Wang",
"Shaoli",
""
],
[
"Bai",
"Xiyan",
""
]
] | In this paper, we study the psychological effect in a SIS epidemic model. The basic reproduction number is obtained. However, the disease free equilibrium is always asymptotically stable, which doesn't depends on the basic reproduction number. The system has a saddle-node bifurcation appear and displays bistable behavior, which is a new phenomenon in epidemic dynamics and different from the backward bifurcation behavior. |
1001.2879 | Sheng Wang | Sheng Wang | Hydropathy Conformational Letter and its Substitution Matrix HP-CLESUM:
an Application to Protein Structural Alignment | 8 pages, 5 figures | null | null | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Protein sequence world is discrete as 20 amino acids (AA) while
its structure world is continuous, though can be discretized into structural
alphabets (SA). In order to reveal the relationship between sequence and
structure, it is interesting to consider both AA and SA in a joint space.
However, such space has too many parameters, so the reduction of AA is
necessary to bring down the parameter numbers.
Result: We've developed a simple but effective approach called entropic
clustering based on selecting the best mutual information between a given
reduction of AAs and SAs. The optimized reduction of AA into two groups leads
to hydrophobic and hydrophilic. Combined with our SA, namely conformational
letter (CL) of 17 alphabets, we get a joint alphabet called hydropathy
conformational letter (hp-CL). A joint substitution matrix with (17*2)*(17*2)
indices is derived from FSSP. Moreover, we check the three coding systems, say
AA, CL and hp-CL against a large database consisting proteins from family to
fold, with their performance on the TopK accuracy of both similar fragment pair
(SFP) and the neighbor of aligned fragment pair (AFP). The TopK selection is
according to the score calculated by the coding system's substitution matrix.
Finally, embedding hp-CL in a pairwise alignment algorithm, say CLeFAPS, to
replace the original CL, will get an improvement on the HOMSTRAD benchmark.
| [
{
"created": "Mon, 18 Jan 2010 20:58:25 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Jan 2010 22:19:11 GMT",
"version": "v2"
}
] | 2010-01-19 | [
[
"Wang",
"Sheng",
""
]
] | Motivation: Protein sequence world is discrete as 20 amino acids (AA) while its structure world is continuous, though can be discretized into structural alphabets (SA). In order to reveal the relationship between sequence and structure, it is interesting to consider both AA and SA in a joint space. However, such space has too many parameters, so the reduction of AA is necessary to bring down the parameter numbers. Result: We've developed a simple but effective approach called entropic clustering based on selecting the best mutual information between a given reduction of AAs and SAs. The optimized reduction of AA into two groups leads to hydrophobic and hydrophilic. Combined with our SA, namely conformational letter (CL) of 17 alphabets, we get a joint alphabet called hydropathy conformational letter (hp-CL). A joint substitution matrix with (17*2)*(17*2) indices is derived from FSSP. Moreover, we check the three coding systems, say AA, CL and hp-CL against a large database consisting proteins from family to fold, with their performance on the TopK accuracy of both similar fragment pair (SFP) and the neighbor of aligned fragment pair (AFP). The TopK selection is according to the score calculated by the coding system's substitution matrix. Finally, embedding hp-CL in a pairwise alignment algorithm, say CLeFAPS, to replace the original CL, will get an improvement on the HOMSTRAD benchmark. |
1205.3748 | Sandip Ghosal | Zhen Chen, Sandip Ghosal | The nonlinear electromigration of analytes into confined spaces | 14 pages, 5 Figures, 1 Appendix | null | 10.1098/rspa.2012.0221 | null | q-bio.QM physics.bio-ph physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of electromigration of a sample ion (analyte) within
a uniform background electrolyte when the confining channel undergoes a sudden
contraction. One example of such a situation arises in microfluidics in the
electrokinetic injection of the analyte into a micro-capillary from a reservoir
of much larger size. Here the sample concentration propagates as a wave driven
by the electric field. The dynamics is governed by the Nerst-Planck-Poisson
system of equations for ionic transport.A reduced one dimensional nonlinear
equation describing the evolution of the sample concentration is derived.We
integrate this equation numerically to obtain the evolution of the wave shape
and determine how the the injected mass depends on the sample concentration in
the reservoir.It is shown that due to the nonlinear coupling of the ionic
concentrations and the electric field, the concentration of the injected sample
could be substantially less than the concentration of the sample in the
reservoir.
| [
{
"created": "Wed, 16 May 2012 17:53:06 GMT",
"version": "v1"
}
] | 2015-06-05 | [
[
"Chen",
"Zhen",
""
],
[
"Ghosal",
"Sandip",
""
]
] | We consider the problem of electromigration of a sample ion (analyte) within a uniform background electrolyte when the confining channel undergoes a sudden contraction. One example of such a situation arises in microfluidics in the electrokinetic injection of the analyte into a micro-capillary from a reservoir of much larger size. Here the sample concentration propagates as a wave driven by the electric field. The dynamics is governed by the Nerst-Planck-Poisson system of equations for ionic transport.A reduced one dimensional nonlinear equation describing the evolution of the sample concentration is derived.We integrate this equation numerically to obtain the evolution of the wave shape and determine how the the injected mass depends on the sample concentration in the reservoir.It is shown that due to the nonlinear coupling of the ionic concentrations and the electric field, the concentration of the injected sample could be substantially less than the concentration of the sample in the reservoir. |
1811.05205 | Thu Thuy Nguyen | C. Burdet, J. Guegan, X. Duval, M. Le Tyrant, H. Bergeron (ISMO), C.
Manuguerra, J. Raude, C. Leport (IAME), P. Zylberman | The need for an integrative thinking to fight against emerging
infectious diseases | null | Epidemiology and Public Health / Revue d'Epid{\'e}miologie et de
Sant{\'e} Publique, Elsevier Masson, 2018, 66 (1), pp.81 - 90 | 10.1016/j.respe.2017.08.001 | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present here the proceedings of the 5th seminar on emerging infectious
diseases (EIDs), held in Paris on March 22nd, 2016, with seven priority
proposals that can be outlined as follows:$\bullet$Encourage research on the
prediction, screening and early detection of new risks of
infection$\bullet$Develop research and surveillance concerning transmission of
pathogens between animals and humans, with their reinforcement in particular in
intertropical areas (`hot-spots') thanks to public support$\bullet$Pursue aid
development and support in these areas of prevention and training for local
health personnel, and to foster risk awareness in the population$\bullet$Ensure
adapted patient care in order to promote adherence to treatment and to epidemic
propagation reduction measures$\bullet$Develop greater sensitization and
training among politicians and healthcare providers, in order to better prepare
them to respond to new types of crises$\bullet$Modify the logic of governance,
drawing from all available modes of communication and incorporating new
information-sharing tools$\bullet$Develop economic research on the fight
against EIDs, taking into account specific driving factors in order to create a
balance between preventive and treatment approaches.
| [
{
"created": "Tue, 13 Nov 2018 10:49:05 GMT",
"version": "v1"
}
] | 2018-11-14 | [
[
"Burdet",
"C.",
"",
"ISMO"
],
[
"Guegan",
"J.",
"",
"ISMO"
],
[
"Duval",
"X.",
"",
"ISMO"
],
[
"Tyrant",
"M. Le",
"",
"ISMO"
],
[
"Bergeron",
"H.",
"",
"ISMO"
],
[
"Manuguerra",
"C.",
"",
"IAME"
],
[
"Raude",
"J.",
"",
"IAME"
],
[
"Leport",
"C.",
"",
"IAME"
],
[
"Zylberman",
"P.",
""
]
] | We present here the proceedings of the 5th seminar on emerging infectious diseases (EIDs), held in Paris on March 22nd, 2016, with seven priority proposals that can be outlined as follows:$\bullet$Encourage research on the prediction, screening and early detection of new risks of infection$\bullet$Develop research and surveillance concerning transmission of pathogens between animals and humans, with their reinforcement in particular in intertropical areas (`hot-spots') thanks to public support$\bullet$Pursue aid development and support in these areas of prevention and training for local health personnel, and to foster risk awareness in the population$\bullet$Ensure adapted patient care in order to promote adherence to treatment and to epidemic propagation reduction measures$\bullet$Develop greater sensitization and training among politicians and healthcare providers, in order to better prepare them to respond to new types of crises$\bullet$Modify the logic of governance, drawing from all available modes of communication and incorporating new information-sharing tools$\bullet$Develop economic research on the fight against EIDs, taking into account specific driving factors in order to create a balance between preventive and treatment approaches. |
1904.12798 | Diego Fasoli | Diego Fasoli and Stefano Panzeri | Mathematical studies of the dynamics of finite-size binary neural
networks: A review of recent progress | 27 pages, 7 figures | null | null | null | q-bio.NC math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional mathematical approaches to studying analytically the dynamics of
neural networks rely on the mean-field approximation, which is rigorously
applicable only to networks of infinite size. However, all existing real
biological networks have finite size, and many of them, such as microscopic
circuits in invertebrates, are composed only of a few tens of neurons. Thus, it
is important to be able to extend to small-size networks our ability to study
analytically neural dynamics. Analytical solutions of the dynamics of
finite-size neural networks have remained elusive for many decades, because the
powerful methods of statistical analysis, such as the central limit theorem and
the law of large numbers, do not apply to small networks. In this article, we
critically review recent progress on the study of the dynamics of small
networks composed of binary neurons. In particular, we review the mathematical
techniques we developed for studying the bifurcations of the network dynamics,
the dualism between neural activity and membrane potentials, cross-neuron
correlations, and pattern storage in stochastic networks. Finally, we highlight
key challenges that remain open, future directions for further progress, and
possible implications of our results for neuroscience.
| [
{
"created": "Mon, 29 Apr 2019 16:21:39 GMT",
"version": "v1"
}
] | 2019-04-30 | [
[
"Fasoli",
"Diego",
""
],
[
"Panzeri",
"Stefano",
""
]
] | Traditional mathematical approaches to studying analytically the dynamics of neural networks rely on the mean-field approximation, which is rigorously applicable only to networks of infinite size. However, all existing real biological networks have finite size, and many of them, such as microscopic circuits in invertebrates, are composed only of a few tens of neurons. Thus, it is important to be able to extend to small-size networks our ability to study analytically neural dynamics. Analytical solutions of the dynamics of finite-size neural networks have remained elusive for many decades, because the powerful methods of statistical analysis, such as the central limit theorem and the law of large numbers, do not apply to small networks. In this article, we critically review recent progress on the study of the dynamics of small networks composed of binary neurons. In particular, we review the mathematical techniques we developed for studying the bifurcations of the network dynamics, the dualism between neural activity and membrane potentials, cross-neuron correlations, and pattern storage in stochastic networks. Finally, we highlight key challenges that remain open, future directions for further progress, and possible implications of our results for neuroscience. |
1802.07834 | El Mahdi El Mhamdi | El Mahdi El Mhamdi, Rachid Guerraoui, Alexandre Maurer, Vladislav
Tempez | Learning to Gather without Communication | Preliminary version, presented at the 5th Biological Distributed
Algorithms Workshop. Washington D.C, July 28th, 2017 | null | null | null | q-bio.PE cs.DC cs.LG cs.MA stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A standard belief on emerging collective behavior is that it emerges from
simple individual rules. Most of the mathematical research on such collective
behavior starts from imperative individual rules, like always go to the center.
But how could an (optimal) individual rule emerge during a short period within
the group lifetime, especially if communication is not available. We argue that
such rules can actually emerge in a group in a short span of time via
collective (multi-agent) reinforcement learning, i.e learning via rewards and
punishments. We consider the gathering problem: several agents (social animals,
swarming robots...) must gather around a same position, which is not determined
in advance. They must do so without communication on their planned decision,
just by looking at the position of other agents. We present the first
experimental evidence that a gathering behavior can be learned without
communication in a partially observable environment. The learned behavior has
the same properties as a self-stabilizing distributed algorithm, as processes
can gather from any initial state (and thus tolerate any transient failure).
Besides, we show that it is possible to tolerate the brutal loss of up to 90\%
of agents without significant impact on the behavior.
| [
{
"created": "Wed, 21 Feb 2018 22:26:21 GMT",
"version": "v1"
}
] | 2018-02-23 | [
[
"Mhamdi",
"El Mahdi El",
""
],
[
"Guerraoui",
"Rachid",
""
],
[
"Maurer",
"Alexandre",
""
],
[
"Tempez",
"Vladislav",
""
]
] | A standard belief on emerging collective behavior is that it emerges from simple individual rules. Most of the mathematical research on such collective behavior starts from imperative individual rules, like always go to the center. But how could an (optimal) individual rule emerge during a short period within the group lifetime, especially if communication is not available. We argue that such rules can actually emerge in a group in a short span of time via collective (multi-agent) reinforcement learning, i.e learning via rewards and punishments. We consider the gathering problem: several agents (social animals, swarming robots...) must gather around a same position, which is not determined in advance. They must do so without communication on their planned decision, just by looking at the position of other agents. We present the first experimental evidence that a gathering behavior can be learned without communication in a partially observable environment. The learned behavior has the same properties as a self-stabilizing distributed algorithm, as processes can gather from any initial state (and thus tolerate any transient failure). Besides, we show that it is possible to tolerate the brutal loss of up to 90\% of agents without significant impact on the behavior. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.