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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1712.02965 | Steven Kelk | Janosch D\"ocker, Leo van Iersel, Steven Kelk, Simone Linz | Deciding the existence of a cherry-picking sequence is hard on two trees | Fixed some tiny things. Accepted for journal publication | Discrete Applied Mathematics, 260:131-143, 2019 | 10.1016/j.dam.2019.01.031 | null | q-bio.PE cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here we show that deciding whether two rooted binary phylogenetic trees on
the same set of taxa permit a cherry-picking sequence, a special type of
elimination order on the taxa, is NP-complete. This improves on an earlier
result which proved hardness for eight or more trees. Via a known equivalence
between cherry-picking sequences and temporal phylogenetic networks, our result
proves that it is NP-complete to determine the existence of a temporal
phylogenetic network that contains topological embeddings of both trees. The
hardness result also greatly strengthens previous inapproximability results for
the minimum temporal-hybridization number problem. This is the optimization
version of the problem where we wish to construct a temporal phylogenetic
network that topologically embeds two given rooted binary phylogenetic trees
and that has a minimum number of indegree-2 nodes, which represent events such
as hybridization and horizontal gene transfer. We end on a positive note,
pointing out that fixed parameter tractability results in this area are likely
to ensure the continued relevance of the temporal phylogenetic network model.
| [
{
"created": "Fri, 8 Dec 2017 06:53:22 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Jan 2019 17:02:50 GMT",
"version": "v2"
}
] | 2021-04-13 | [
[
"Döcker",
"Janosch",
""
],
[
"van Iersel",
"Leo",
""
],
[
"Kelk",
"Steven",
""
],
[
"Linz",
"Simone",
""
]
] | Here we show that deciding whether two rooted binary phylogenetic trees on the same set of taxa permit a cherry-picking sequence, a special type of elimination order on the taxa, is NP-complete. This improves on an earlier result which proved hardness for eight or more trees. Via a known equivalence between cherry-picking sequences and temporal phylogenetic networks, our result proves that it is NP-complete to determine the existence of a temporal phylogenetic network that contains topological embeddings of both trees. The hardness result also greatly strengthens previous inapproximability results for the minimum temporal-hybridization number problem. This is the optimization version of the problem where we wish to construct a temporal phylogenetic network that topologically embeds two given rooted binary phylogenetic trees and that has a minimum number of indegree-2 nodes, which represent events such as hybridization and horizontal gene transfer. We end on a positive note, pointing out that fixed parameter tractability results in this area are likely to ensure the continued relevance of the temporal phylogenetic network model. |
2006.16525 | Nishchal Sapkota | Nishchal Sapkota, Rimsha Bhatta, Phillip Dabney, Zhifu Xie | Hunting Co-operation in the Middle Predator in Three Species Food Chain
Model | null | 2019 Proceedings of LA/MS Section of Mathematical Association of
America | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We proposed a three-species food chain model with hunting co-operation among
the middle predator. In this model, third species prey on the middle species
and the middle prey on the first species. The hunting cooperation among the
middle predator affects interestingly on the numbers of both the predators and
the prey. We examined the linear stability of the model theoretically and
numerically. We conducted the two-parameter numerical analysis to check the
long-term behavior and the change in the number of species with respect to
hunting co-operation. Our findings supported the postulates from the two
species food chain model with hunting co-operation.
| [
{
"created": "Tue, 30 Jun 2020 04:42:56 GMT",
"version": "v1"
}
] | 2020-07-01 | [
[
"Sapkota",
"Nishchal",
""
],
[
"Bhatta",
"Rimsha",
""
],
[
"Dabney",
"Phillip",
""
],
[
"Xie",
"Zhifu",
""
]
] | We proposed a three-species food chain model with hunting co-operation among the middle predator. In this model, third species prey on the middle species and the middle prey on the first species. The hunting cooperation among the middle predator affects interestingly on the numbers of both the predators and the prey. We examined the linear stability of the model theoretically and numerically. We conducted the two-parameter numerical analysis to check the long-term behavior and the change in the number of species with respect to hunting co-operation. Our findings supported the postulates from the two species food chain model with hunting co-operation. |
2008.07488 | Rui Wang | Rui Wang, Yuta Hozumi, Yong-Hui Zheng, Changchuan Yin and Guo-Wei Wei | Host immune response driving SARS-CoV-2 evolution | 22 pages, 15 figures | null | null | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The transmission and evolution of severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) are of paramount importance to the controlling and
combating of coronavirus disease 2019 (COVID-19) pandemic. Currently, near
15,000 SARS-CoV-2 single mutations have been recorded, having a great
ramification to the development of diagnostics, vaccines, antibody therapies,
and drugs. However, little is known about SARS-CoV-2 evolutionary
characteristics and general trend. In this work, we present a comprehensive
genotyping analysis of existing SARS-CoV-2 mutations. We reveal that host
immune response via APOBEC and ADAR gene editing gives rise to near 65\% of
recorded mutations. Additionally, we show that children under age five and the
elderly may be at high risk from COVID-19 because of their overreacting to the
viral infection. Moreover, we uncover that populations of Oceania and Africa
react significantly more intensively to SARS-CoV-2 infection than those of
Europe and Asia, which may explain why African Americans were shown to be at
increased risk of dying from COVID-19, in addition to their high risk of
getting sick from COVID-19 caused by systemic health and social inequities.
Finally, our study indicates that for two viral genome sequences of the same
origin, their evolution order may be determined from the ratio of mutation type
C$>$T over T$>$C.
| [
{
"created": "Mon, 17 Aug 2020 17:31:20 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Aug 2020 16:18:58 GMT",
"version": "v2"
}
] | 2020-08-21 | [
[
"Wang",
"Rui",
""
],
[
"Hozumi",
"Yuta",
""
],
[
"Zheng",
"Yong-Hui",
""
],
[
"Yin",
"Changchuan",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | The transmission and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are of paramount importance to the controlling and combating of coronavirus disease 2019 (COVID-19) pandemic. Currently, near 15,000 SARS-CoV-2 single mutations have been recorded, having a great ramification to the development of diagnostics, vaccines, antibody therapies, and drugs. However, little is known about SARS-CoV-2 evolutionary characteristics and general trend. In this work, we present a comprehensive genotyping analysis of existing SARS-CoV-2 mutations. We reveal that host immune response via APOBEC and ADAR gene editing gives rise to near 65\% of recorded mutations. Additionally, we show that children under age five and the elderly may be at high risk from COVID-19 because of their overreacting to the viral infection. Moreover, we uncover that populations of Oceania and Africa react significantly more intensively to SARS-CoV-2 infection than those of Europe and Asia, which may explain why African Americans were shown to be at increased risk of dying from COVID-19, in addition to their high risk of getting sick from COVID-19 caused by systemic health and social inequities. Finally, our study indicates that for two viral genome sequences of the same origin, their evolution order may be determined from the ratio of mutation type C$>$T over T$>$C. |
1202.4647 | Manuela Capello | M. Capello, M. Soria, P. Cotel, G. Potin, L. Dagorn and P. Fr\'eon | The heterogeneous spatial and temporal patterns of behavior of small
pelagic fish in an array of Fish Aggregating Devices (FADs) | 21 pages, 9 figures + 2 supplementary figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying spatial and temporal patterns can reveal the driving factors that
govern the behavior of fish in their environment. In this study, we
characterized the spatial and temporal occupation of 37 acoustically tagged
bigeye scads (Selar Crumenophthalmus) in an array of shallow Fish Aggregating
Devices (FADs) to clarify the mechanism that leads fish to associate with FADs.
A comparison of the number of visits and residence times exhibited by the fish
at different FADs revealed a strong variability over the array of FADS, with
the emergence of a leading FAD, which recorded the majority of visits and
retained the fish for a longer period of time. We found diel variability in the
residence times, with fish associated at daytime and exploring the array of
FADs at nighttime. We demonstrated that this diel temporal pattern was
amplified in the leading FAD. We identified a 24-hour periodicity for a subset
of individuals aggregated to the leading FAD, thus suggesting that those fish
were able to find this FAD after night excursions. The modeling of fish
movements based on a Monte Carlo sampling of inter-FAD transitions revealed
that the observed spatial heterogeneity in the number of visits could not be
explained through simple array-connectivity arguments. Similarly, we
demonstrated that the high residence times recorded at the leading FAD were not
due to the spatial arrangement of individual fish having different associative
characters. We discussed the relationships between these patterns of
association with the FADs, the exploration of the FAD array and the possible
effects of social interactions and environmental factors.
| [
{
"created": "Mon, 20 Feb 2012 10:28:58 GMT",
"version": "v1"
}
] | 2012-02-22 | [
[
"Capello",
"M.",
""
],
[
"Soria",
"M.",
""
],
[
"Cotel",
"P.",
""
],
[
"Potin",
"G.",
""
],
[
"Dagorn",
"L.",
""
],
[
"Fréon",
"P.",
""
]
] | Identifying spatial and temporal patterns can reveal the driving factors that govern the behavior of fish in their environment. In this study, we characterized the spatial and temporal occupation of 37 acoustically tagged bigeye scads (Selar Crumenophthalmus) in an array of shallow Fish Aggregating Devices (FADs) to clarify the mechanism that leads fish to associate with FADs. A comparison of the number of visits and residence times exhibited by the fish at different FADs revealed a strong variability over the array of FADS, with the emergence of a leading FAD, which recorded the majority of visits and retained the fish for a longer period of time. We found diel variability in the residence times, with fish associated at daytime and exploring the array of FADs at nighttime. We demonstrated that this diel temporal pattern was amplified in the leading FAD. We identified a 24-hour periodicity for a subset of individuals aggregated to the leading FAD, thus suggesting that those fish were able to find this FAD after night excursions. The modeling of fish movements based on a Monte Carlo sampling of inter-FAD transitions revealed that the observed spatial heterogeneity in the number of visits could not be explained through simple array-connectivity arguments. Similarly, we demonstrated that the high residence times recorded at the leading FAD were not due to the spatial arrangement of individual fish having different associative characters. We discussed the relationships between these patterns of association with the FADs, the exploration of the FAD array and the possible effects of social interactions and environmental factors. |
1602.05189 | Krzysztof Bartoszek | Krzysztof Bartoszek | A Central Limit Theorem for Punctuated Equilibrium | null | Stochastic Models 36:3, 473-517, 2020 | 10.1080/15326349.2020.1752242 | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current evolutionary biology models usually assume that a phenotype undergoes
gradual change. This is in stark contrast to biological intuition, which
indicates that change can also be punctuated-the phenotype can jump. Such a
jump could especially occur at speciation, i.e. dramatic change occurs that
drives the species apart. Here we derive a Central Limit Theorem for punctuated
equilibrium. We show that, if adaptation is fast, for weak convergence to
normality to hold, the variability in the occurrence of change has to disappear
with time.
| [
{
"created": "Tue, 16 Feb 2016 17:51:50 GMT",
"version": "v1"
},
{
"created": "Wed, 11 May 2016 14:49:33 GMT",
"version": "v2"
},
{
"created": "Thu, 16 Feb 2017 10:48:21 GMT",
"version": "v3"
},
{
"created": "Wed, 2 Aug 2017 11:04:29 GMT",
"version": "v4"
},
{
"created": "Sun, 24 Nov 2019 21:12:43 GMT",
"version": "v5"
},
{
"created": "Thu, 2 Apr 2020 20:50:45 GMT",
"version": "v6"
}
] | 2020-08-07 | [
[
"Bartoszek",
"Krzysztof",
""
]
] | Current evolutionary biology models usually assume that a phenotype undergoes gradual change. This is in stark contrast to biological intuition, which indicates that change can also be punctuated-the phenotype can jump. Such a jump could especially occur at speciation, i.e. dramatic change occurs that drives the species apart. Here we derive a Central Limit Theorem for punctuated equilibrium. We show that, if adaptation is fast, for weak convergence to normality to hold, the variability in the occurrence of change has to disappear with time. |
2307.01289 | Mrinal Pandey | Mrinal Pandey, Young Joon Suh, Minha Kim, Hannah Jane Davis, Jeffrey E
Segall, and Mingming Wu | Mechanical compression regulates tumor spheroid invasion into a 3D
collagen matrix | 10 pages, 5 figure and 3 supplementary figures. Phys. Biol. 2024 | null | 10.1088/1478-3975/ad3ac5 | null | q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Uncontrolled growth of tumor cells in confined spaces leads to the
accumulation of compressive stress within the tumor. Although the effects of
tension within 3D extracellular matrices on tumor growth and invasion are well
established, the role of compression in tumor mechanics and invasion is largely
unexplored. In this study, we modified a Transwell assay such that it provides
constant compressive loads to spheroids embedded within a collagen matrix. We
used microscopic imaging to follow the single cell dynamics of the cells within
the spheroids, as well as invasion into the 3D extracellular matrices (EMCs).
Our experimental results showed that malignant breast tumor (MDA-MB-231) and
non-tumorigenic epithelial (MCF10A) spheroids responded differently to a
constant compression. Cells within the malignant spheroids became more motile
within the spheroids and invaded more into the ECM under compression; whereas
cells within non-tumorigenic MCF10A spheroids became less motile within the
spheroids and did not display apparent detachment from the spheroids under
compression. These findings suggest that compression may play differential
roles in healthy and pathogenic epithelial tissues and highlights the
importance of tumor mechanics and invasion.
| [
{
"created": "Mon, 3 Jul 2023 18:33:28 GMT",
"version": "v1"
}
] | 2024-04-08 | [
[
"Pandey",
"Mrinal",
""
],
[
"Suh",
"Young Joon",
""
],
[
"Kim",
"Minha",
""
],
[
"Davis",
"Hannah Jane",
""
],
[
"Segall",
"Jeffrey E",
""
],
[
"Wu",
"Mingming",
""
]
] | Uncontrolled growth of tumor cells in confined spaces leads to the accumulation of compressive stress within the tumor. Although the effects of tension within 3D extracellular matrices on tumor growth and invasion are well established, the role of compression in tumor mechanics and invasion is largely unexplored. In this study, we modified a Transwell assay such that it provides constant compressive loads to spheroids embedded within a collagen matrix. We used microscopic imaging to follow the single cell dynamics of the cells within the spheroids, as well as invasion into the 3D extracellular matrices (EMCs). Our experimental results showed that malignant breast tumor (MDA-MB-231) and non-tumorigenic epithelial (MCF10A) spheroids responded differently to a constant compression. Cells within the malignant spheroids became more motile within the spheroids and invaded more into the ECM under compression; whereas cells within non-tumorigenic MCF10A spheroids became less motile within the spheroids and did not display apparent detachment from the spheroids under compression. These findings suggest that compression may play differential roles in healthy and pathogenic epithelial tissues and highlights the importance of tumor mechanics and invasion. |
1402.0468 | Michael Deem | Michael W. Deem | Evolution: Life has Evolved to Evolve | 3 pages | Phys. Life Rev. 10 (2013) 333-335 | 10.1016/j.plrev.2013.07.010 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Jim Shapiro synthesizes a great many observations about the mechanisms of
evolution to reach the remarkable conclusion that large-scale modification,
exchange, and rearrangement of the genome are common and should be viewed as
fundamental features of life. In other words, the genome should be viewed not
as mostly read-only with a few rare mutations, but rather as a fully-fledged
read-write library of genetic functions under continuous revision. Revision of
the genome occurs during cellular replication, during multicellular
development, and during evolution of a population of individuals. DNA
formatting controls the timing and location of genetic rearrangements, gene
expression, and genetic repair. Each of these events is under the control of
precise cellular circuits. Shapiro reviews the toolbox of natural genetic
engineering that provides the functionalities necessary for efficient long-term
genome restructuring.
| [
{
"created": "Thu, 30 Jan 2014 03:11:32 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Deem",
"Michael W.",
""
]
] | Jim Shapiro synthesizes a great many observations about the mechanisms of evolution to reach the remarkable conclusion that large-scale modification, exchange, and rearrangement of the genome are common and should be viewed as fundamental features of life. In other words, the genome should be viewed not as mostly read-only with a few rare mutations, but rather as a fully-fledged read-write library of genetic functions under continuous revision. Revision of the genome occurs during cellular replication, during multicellular development, and during evolution of a population of individuals. DNA formatting controls the timing and location of genetic rearrangements, gene expression, and genetic repair. Each of these events is under the control of precise cellular circuits. Shapiro reviews the toolbox of natural genetic engineering that provides the functionalities necessary for efficient long-term genome restructuring. |
2112.03238 | Hins Shaheen | Hina Shaheen, Roderick Melnik | Deep brain stimulation with a computational model for the
cortex-thalamus-basal-ganglia system and network dynamics of neurological
disorders | 15 pages, 7 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep brain stimulation (DBS) can alleviate the movement disorders like
Parkinson's disease (PD). Indeed, it is known that aberrant beta
(13-30Hz)oscillations and the loss of dopaminergic neurons in the basal
ganglia-thalamus (BGTH) and cortex characterize the akinesia symptoms of PD.
However, the relevant biophysical mechanism behind this process still remains
unclear. Based on the prior striatal inhibitory model, we propose an extended
BGTH model incorporating medium spine neurons (MSNs) and fast-spiking
interneurons (FSIs) along with the effect of DBS. We are focusing in this paper
on an open-loop DBS mode, where the stimulation parameters stay constant
independent of variations in the disease state, and modifications of parameters
rely mainly on trial and error of medical experts. Additionally, we propose a
novel combined model of the cerebellar-basal-ganglia thalamocortical network,
MSNs, and FSIs, and show new results that indicate that Parkinsonian
oscillations in the beta-band frequency range emerge from the dynamics of such
a network. Our model predicts that DBS can be used to suppress beta
oscillations in globus pallidus pars interna (GPi) neurons. This research will
help our better understanding of the changes in brain activity caused by DBS,
providing new insight for studying PD in the future.
| [
{
"created": "Mon, 6 Dec 2021 18:46:42 GMT",
"version": "v1"
}
] | 2021-12-07 | [
[
"Shaheen",
"Hina",
""
],
[
"Melnik",
"Roderick",
""
]
] | Deep brain stimulation (DBS) can alleviate the movement disorders like Parkinson's disease (PD). Indeed, it is known that aberrant beta (13-30Hz)oscillations and the loss of dopaminergic neurons in the basal ganglia-thalamus (BGTH) and cortex characterize the akinesia symptoms of PD. However, the relevant biophysical mechanism behind this process still remains unclear. Based on the prior striatal inhibitory model, we propose an extended BGTH model incorporating medium spine neurons (MSNs) and fast-spiking interneurons (FSIs) along with the effect of DBS. We are focusing in this paper on an open-loop DBS mode, where the stimulation parameters stay constant independent of variations in the disease state, and modifications of parameters rely mainly on trial and error of medical experts. Additionally, we propose a novel combined model of the cerebellar-basal-ganglia thalamocortical network, MSNs, and FSIs, and show new results that indicate that Parkinsonian oscillations in the beta-band frequency range emerge from the dynamics of such a network. Our model predicts that DBS can be used to suppress beta oscillations in globus pallidus pars interna (GPi) neurons. This research will help our better understanding of the changes in brain activity caused by DBS, providing new insight for studying PD in the future. |
1208.2238 | Sriram Sankararaman | Sriram Sankararaman, Nick Patterson, Heng Li, Svante P\"a\"abo, David
Reich | The date of interbreeding between Neandertals and modern humans | null | null | null | null | q-bio.PE stat.AP | http://creativecommons.org/licenses/by/3.0/ | Comparisons of DNA sequences between Neandertals and present-day humans have
shown that Neandertals share more genetic variants with non-Africans than with
Africans. This could be due to interbreeding between Neandertals and modern
humans when the two groups met subsequent to the emergence of modern humans
outside Africa. However, it could also be due to population structure that
antedates the origin of Neandertal ancestors in Africa. We measure the extent
of linkage disequilibrium (LD) in the genomes of present-day Europeans and find
that the last gene flow from Neandertals (or their relatives) into Europeans
likely occurred 37,000-86,000 years before the present (BP), and most likely
47,000-65,000 years ago. This supports the recent interbreeding hypothesis, and
suggests that interbreeding may have occurred when modern humans carrying Upper
Paleolithic technologies encountered Neandertals as they expanded out of
Africa.
| [
{
"created": "Fri, 10 Aug 2012 18:15:01 GMT",
"version": "v1"
}
] | 2012-08-13 | [
[
"Sankararaman",
"Sriram",
""
],
[
"Patterson",
"Nick",
""
],
[
"Li",
"Heng",
""
],
[
"Pääbo",
"Svante",
""
],
[
"Reich",
"David",
""
]
] | Comparisons of DNA sequences between Neandertals and present-day humans have shown that Neandertals share more genetic variants with non-Africans than with Africans. This could be due to interbreeding between Neandertals and modern humans when the two groups met subsequent to the emergence of modern humans outside Africa. However, it could also be due to population structure that antedates the origin of Neandertal ancestors in Africa. We measure the extent of linkage disequilibrium (LD) in the genomes of present-day Europeans and find that the last gene flow from Neandertals (or their relatives) into Europeans likely occurred 37,000-86,000 years before the present (BP), and most likely 47,000-65,000 years ago. This supports the recent interbreeding hypothesis, and suggests that interbreeding may have occurred when modern humans carrying Upper Paleolithic technologies encountered Neandertals as they expanded out of Africa. |
1504.00621 | Jinzhi Lei JL | Wenjun Xia, Jinzhi Lei | aa-tRNA competition is crucial for the effective translation efficiency | 8 pages, 10 figures | null | 10.3934/mbe.2018023 | null | q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Translation is a central biological process by which proteins are synthesized
from genetic information contained within mRNAs. Here we study the kinetics of
translation at molecular level through a stochastic simulation model. The model
explicitly include RNA sequences, ribosome dynamics, tRNA pool and biochemical
reactions in the translation elongation. The results show that the translation
efficiency is mainly limited by the available ribosome number, translation
initiation and the translation elongation time. The elongation time is
log-normal distribution with mean and variance determined by both the codon
saturation and the process of aa-tRNA selection at each codon binding.
Moreover, our simulations show that the translation accuracy exponentially
decreases with the sequence length. These results suggest that aa-tRNA
competition is crucial for both translation elongation, translation efficiency
and the accuracy, which in turn determined the effective protein production
rate of correct proteins. Our results improve the dynamical equation of protein
production with a delay differential equation which is dependent on sequence
informations through both the effective production rate and the distribution of
elongation time.
| [
{
"created": "Tue, 31 Mar 2015 01:46:12 GMT",
"version": "v1"
}
] | 2020-02-18 | [
[
"Xia",
"Wenjun",
""
],
[
"Lei",
"Jinzhi",
""
]
] | Translation is a central biological process by which proteins are synthesized from genetic information contained within mRNAs. Here we study the kinetics of translation at molecular level through a stochastic simulation model. The model explicitly include RNA sequences, ribosome dynamics, tRNA pool and biochemical reactions in the translation elongation. The results show that the translation efficiency is mainly limited by the available ribosome number, translation initiation and the translation elongation time. The elongation time is log-normal distribution with mean and variance determined by both the codon saturation and the process of aa-tRNA selection at each codon binding. Moreover, our simulations show that the translation accuracy exponentially decreases with the sequence length. These results suggest that aa-tRNA competition is crucial for both translation elongation, translation efficiency and the accuracy, which in turn determined the effective protein production rate of correct proteins. Our results improve the dynamical equation of protein production with a delay differential equation which is dependent on sequence informations through both the effective production rate and the distribution of elongation time. |
2205.11243 | Zhuoyan Xu | Zhuoyan Xu, Kris Sankaran | Spatial Transcriptomics Dimensionality Reduction using Wavelet Bases | null | null | null | null | q-bio.GN cs.LG stat.AP | http://creativecommons.org/licenses/by/4.0/ | Spatially resolved transcriptomics (ST) measures gene expression along with
the spatial coordinates of the measurements. The analysis of ST data involves
significant computation complexity. In this work, we propose gene expression
dimensionality reduction algorithm that retains spatial structure. We combine
the wavelet transformation with matrix factorization to select
spatially-varying genes. We extract a low-dimensional representation of these
genes. We consider Empirical Bayes setting, imposing regularization through the
prior distribution of factor genes. Additionally, We provide visualization of
extracted representation genes capturing the global spatial pattern. We
illustrate the performance of our methods by spatial structure recovery and
gene expression reconstruction in simulation. In real data experiments, our
method identifies spatial structure of gene factors and outperforms regular
decomposition regarding reconstruction error. We found the connection between
the fluctuation of gene patterns and wavelet technique, providing smoother
visualization. We develop the package and share the workflow generating
reproducible quantitative results and gene visualization. The package is
available at https://github.com/OliverXUZY/waveST.
| [
{
"created": "Thu, 19 May 2022 04:13:51 GMT",
"version": "v1"
}
] | 2022-05-24 | [
[
"Xu",
"Zhuoyan",
""
],
[
"Sankaran",
"Kris",
""
]
] | Spatially resolved transcriptomics (ST) measures gene expression along with the spatial coordinates of the measurements. The analysis of ST data involves significant computation complexity. In this work, we propose gene expression dimensionality reduction algorithm that retains spatial structure. We combine the wavelet transformation with matrix factorization to select spatially-varying genes. We extract a low-dimensional representation of these genes. We consider Empirical Bayes setting, imposing regularization through the prior distribution of factor genes. Additionally, We provide visualization of extracted representation genes capturing the global spatial pattern. We illustrate the performance of our methods by spatial structure recovery and gene expression reconstruction in simulation. In real data experiments, our method identifies spatial structure of gene factors and outperforms regular decomposition regarding reconstruction error. We found the connection between the fluctuation of gene patterns and wavelet technique, providing smoother visualization. We develop the package and share the workflow generating reproducible quantitative results and gene visualization. The package is available at https://github.com/OliverXUZY/waveST. |
0907.5021 | Anatoly Ruvinsky | Anatoly M. Ruvinsky and Ilya A. Vakser | Sequence composition and environment effects on residue fluctuations in
protein structures | 8 pages, 4 figures | null | 10.1063/1.3498743 | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The spectrum and scale of fluctuations in protein structures affect the range
of cell phenomena, including stability of protein structures or their
fragments, allosteric transitions and energy transfer. The study presents a
statistical-thermodynamic analysis of relationship between the sequence
composition and the distribution of residue fluctuations in protein-protein
complexes. A one-node-per residue elastic network model accounting for the
nonhomogeneous protein mass distribution and the inter-atomic interactions
through the renormalized inter-residue potential is developed. Two factors, a
protein mass distribution and a residue environment, were found to determine
the scale of residue fluctuations. Surface residues undergo larger fluctuations
than core residues, showing agreement with experimental observations. Ranking
residues over the normalized scale of fluctuations yields a distinct
classification of amino acids into three groups. The structural instability in
proteins possibly relates to the high content of the highly fluctuating
residues and a deficiency of the weakly fluctuating residues in irregular
secondary structure elements (loops), chameleon sequences and disordered
proteins. Strong correlation between residue fluctuations and the sequence
composition of protein loops supports this hypothesis. Comparing fluctuations
of binding site residues (interface residues) with other surface residues shows
that, on average, the interface is more rigid than the rest of the protein
surface and Gly, Ala, Ser, Cys, Leu and Trp have a propensity to form more
stable docking patches on the interface. The findings have broad implications
for understanding mechanisms of protein association and stability of protein
structures.
| [
{
"created": "Tue, 28 Jul 2009 23:31:35 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Ruvinsky",
"Anatoly M.",
""
],
[
"Vakser",
"Ilya A.",
""
]
] | The spectrum and scale of fluctuations in protein structures affect the range of cell phenomena, including stability of protein structures or their fragments, allosteric transitions and energy transfer. The study presents a statistical-thermodynamic analysis of relationship between the sequence composition and the distribution of residue fluctuations in protein-protein complexes. A one-node-per residue elastic network model accounting for the nonhomogeneous protein mass distribution and the inter-atomic interactions through the renormalized inter-residue potential is developed. Two factors, a protein mass distribution and a residue environment, were found to determine the scale of residue fluctuations. Surface residues undergo larger fluctuations than core residues, showing agreement with experimental observations. Ranking residues over the normalized scale of fluctuations yields a distinct classification of amino acids into three groups. The structural instability in proteins possibly relates to the high content of the highly fluctuating residues and a deficiency of the weakly fluctuating residues in irregular secondary structure elements (loops), chameleon sequences and disordered proteins. Strong correlation between residue fluctuations and the sequence composition of protein loops supports this hypothesis. Comparing fluctuations of binding site residues (interface residues) with other surface residues shows that, on average, the interface is more rigid than the rest of the protein surface and Gly, Ala, Ser, Cys, Leu and Trp have a propensity to form more stable docking patches on the interface. The findings have broad implications for understanding mechanisms of protein association and stability of protein structures. |
2405.20863 | Fr\'ed\'eric Dreyer | Henry Kenlay, Fr\'ed\'eric A. Dreyer, Daniel Cutting, Daniel Nissley,
Charlotte M. Deane | ABodyBuilder3: Improved and scalable antibody structure predictions | 8 pages, 3 figures, 3 tables, code available at
https://github.com/Exscientia/ABodyBuilder3, weights and data available at
https://zenodo.org/records/11354577 | null | null | null | q-bio.BM cs.AI | http://creativecommons.org/licenses/by/4.0/ | Accurate prediction of antibody structure is a central task in the design and
development of monoclonal antibodies, notably to understand both their
developability and their binding properties. In this article, we introduce
ABodyBuilder3, an improved and scalable antibody structure prediction model
based on ImmuneBuilder. We achieve a new state-of-the-art accuracy in the
modelling of CDR loops by leveraging language model embeddings, and show how
predicted structures can be further improved through careful relaxation
strategies. Finally, we incorporate a predicted Local Distance Difference Test
into the model output to allow for a more accurate estimation of uncertainties.
| [
{
"created": "Fri, 31 May 2024 14:45:11 GMT",
"version": "v1"
}
] | 2024-06-03 | [
[
"Kenlay",
"Henry",
""
],
[
"Dreyer",
"Frédéric A.",
""
],
[
"Cutting",
"Daniel",
""
],
[
"Nissley",
"Daniel",
""
],
[
"Deane",
"Charlotte M.",
""
]
] | Accurate prediction of antibody structure is a central task in the design and development of monoclonal antibodies, notably to understand both their developability and their binding properties. In this article, we introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ImmuneBuilder. We achieve a new state-of-the-art accuracy in the modelling of CDR loops by leveraging language model embeddings, and show how predicted structures can be further improved through careful relaxation strategies. Finally, we incorporate a predicted Local Distance Difference Test into the model output to allow for a more accurate estimation of uncertainties. |
1608.00431 | Danielle Bassett | Laura Wiles, Shi Gu, Fabio Pasqualetti, Danielle S. Bassett, David F.
Meaney | Autaptic Connections Shift Network Excitability and Bursting | 31 pages, 6 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network architecture forms a critical constraint on neuronal function. Here
we examine the role of structural autapses, when a neuron synapses onto itself,
in driving network-wide bursting behavior. Using a simple spiking model of
neuronal activity, we study how autaptic connections affect activity patterns,
and evaluate if neuronal degree or controllability are significant factors that
affect changes in bursting from these autaptic connections. We observed that
adding increasing numbers of autaptic connections to excitatory neurons
increased the number of spiking events in the network and the number of
network-wide bursts, particularly in the portion of the phase space in which
excitatory synapses were stronger contributors to bursting behavior than
inhibitory synapses. In comparison, autaptic connections to excitatory neurons
with high average controllability led to higher burst frequencies than adding
the same number of self-looping connections to neurons with high modal
controllability. The number of autaptic connections required to induce bursting
behavior could be lowered by selectively adding autapses to high degree
excitatory neurons. These results suggest a role of autaptic connections in
controlling network-wide bursts in diverse cortical and subcortical regions of
mammalian brain. Moreover, they open up new avenues for the study of dynamic
neurophysiological correlates of structural controllability.
| [
{
"created": "Mon, 1 Aug 2016 13:58:50 GMT",
"version": "v1"
}
] | 2016-08-02 | [
[
"Wiles",
"Laura",
""
],
[
"Gu",
"Shi",
""
],
[
"Pasqualetti",
"Fabio",
""
],
[
"Bassett",
"Danielle S.",
""
],
[
"Meaney",
"David F.",
""
]
] | Network architecture forms a critical constraint on neuronal function. Here we examine the role of structural autapses, when a neuron synapses onto itself, in driving network-wide bursting behavior. Using a simple spiking model of neuronal activity, we study how autaptic connections affect activity patterns, and evaluate if neuronal degree or controllability are significant factors that affect changes in bursting from these autaptic connections. We observed that adding increasing numbers of autaptic connections to excitatory neurons increased the number of spiking events in the network and the number of network-wide bursts, particularly in the portion of the phase space in which excitatory synapses were stronger contributors to bursting behavior than inhibitory synapses. In comparison, autaptic connections to excitatory neurons with high average controllability led to higher burst frequencies than adding the same number of self-looping connections to neurons with high modal controllability. The number of autaptic connections required to induce bursting behavior could be lowered by selectively adding autapses to high degree excitatory neurons. These results suggest a role of autaptic connections in controlling network-wide bursts in diverse cortical and subcortical regions of mammalian brain. Moreover, they open up new avenues for the study of dynamic neurophysiological correlates of structural controllability. |
1608.06471 | Khalil Cherifi | Khalil Cherifi (LBVNR) | Evidence of natural hybridization and introgression between Medicago
ciliaris and Medicago intertexta | null | International Journal of Environmental & Agriculture Research 2
(2016) 129-135 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The present study, investigated some reproductive and fertility parameters in
some wild populations, originating from the North Tunisia (4 populations of
Medicago ciliaris and 3 populations of Medicago intertexta). Previous finding
revealed that these species are genetically distinct and easily recognized by
the number of flowers per inflorescence and pod dimensions. However,
biometrical traits and isozyme patterns intermediacy between these two species
had detected the existence of a potential spontaneous interspecific hybrid
originating from Sedjnane locality in Tunisia. Indeed, the present work has
shown significant decrease of pollen fertility and seed production for this
population when compared to the others(pollen viability 75%, pollen
germinability 8% and pod production=9%). These results suggested a possible
natural interspecific hybrid and confirming introgressive hybridization
possibility between M. intertexta and M. ciliaris.
| [
{
"created": "Tue, 23 Aug 2016 11:34:57 GMT",
"version": "v1"
}
] | 2016-10-05 | [
[
"Cherifi",
"Khalil",
"",
"LBVNR"
]
] | The present study, investigated some reproductive and fertility parameters in some wild populations, originating from the North Tunisia (4 populations of Medicago ciliaris and 3 populations of Medicago intertexta). Previous finding revealed that these species are genetically distinct and easily recognized by the number of flowers per inflorescence and pod dimensions. However, biometrical traits and isozyme patterns intermediacy between these two species had detected the existence of a potential spontaneous interspecific hybrid originating from Sedjnane locality in Tunisia. Indeed, the present work has shown significant decrease of pollen fertility and seed production for this population when compared to the others(pollen viability 75%, pollen germinability 8% and pod production=9%). These results suggested a possible natural interspecific hybrid and confirming introgressive hybridization possibility between M. intertexta and M. ciliaris. |
1404.6520 | Tom Leinster | Richard Reeve, Tom Leinster, Christina A. Cobbold, Jill Thompson, Neil
Brummitt, Sonia N. Mitchell, Louise Matthews | How to partition diversity | null | null | null | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diversity measurement underpins the study of biological systems, but measures
used vary across disciplines. Despite their common use and broad utility, no
unified framework has emerged for measuring, comparing and partitioning
diversity. The introduction of information theory into diversity measurement
has laid the foundations, but the framework is incomplete without the ability
to partition diversity, which is central to fundamental questions across the
life sciences: How do we prioritise communities for conservation? How do we
identify reservoirs and sources of pathogenic organisms? How do we measure
ecological disturbance arising from climate change?
The lack of a common framework means that diversity measures from different
fields have conflicting fundamental properties, allowing conclusions reached to
depend on the measure chosen. This conflict is unnecessary and unhelpful. A
mathematically consistent framework would transform disparate fields by
delivering scientific insights in a common language. It would also allow the
transfer of theoretical and practical developments between fields.
We meet this need, providing a versatile unified framework for partitioning
biological diversity. It encompasses any kind of similarity between
individuals, from functional to genetic, allowing comparisons between
qualitatively different kinds of diversity. Where existing partitioning
measures aggregate information across the whole population, our approach
permits the direct comparison of subcommunities, allowing us to pinpoint
distinct, diverse or representative subcommunities and investigate population
substructure. The framework is provided as a ready-to-use R package to easily
test our approach.
| [
{
"created": "Fri, 25 Apr 2014 19:58:04 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Aug 2014 19:58:38 GMT",
"version": "v2"
},
{
"created": "Thu, 8 Dec 2016 20:38:57 GMT",
"version": "v3"
}
] | 2016-12-09 | [
[
"Reeve",
"Richard",
""
],
[
"Leinster",
"Tom",
""
],
[
"Cobbold",
"Christina A.",
""
],
[
"Thompson",
"Jill",
""
],
[
"Brummitt",
"Neil",
""
],
[
"Mitchell",
"Sonia N.",
""
],
[
"Matthews",
"Louise",
""
]
] | Diversity measurement underpins the study of biological systems, but measures used vary across disciplines. Despite their common use and broad utility, no unified framework has emerged for measuring, comparing and partitioning diversity. The introduction of information theory into diversity measurement has laid the foundations, but the framework is incomplete without the ability to partition diversity, which is central to fundamental questions across the life sciences: How do we prioritise communities for conservation? How do we identify reservoirs and sources of pathogenic organisms? How do we measure ecological disturbance arising from climate change? The lack of a common framework means that diversity measures from different fields have conflicting fundamental properties, allowing conclusions reached to depend on the measure chosen. This conflict is unnecessary and unhelpful. A mathematically consistent framework would transform disparate fields by delivering scientific insights in a common language. It would also allow the transfer of theoretical and practical developments between fields. We meet this need, providing a versatile unified framework for partitioning biological diversity. It encompasses any kind of similarity between individuals, from functional to genetic, allowing comparisons between qualitatively different kinds of diversity. Where existing partitioning measures aggregate information across the whole population, our approach permits the direct comparison of subcommunities, allowing us to pinpoint distinct, diverse or representative subcommunities and investigate population substructure. The framework is provided as a ready-to-use R package to easily test our approach. |
2109.08981 | Gissell Estrada-Rodriguez | Gissell Estrada-Rodriguez and Benoit Perthame | Motility switching and front-back synchronisation in polarized cells | null | null | null | null | q-bio.CB | http://creativecommons.org/publicdomain/zero/1.0/ | The combination of protrusions and retractions in the movement of polarized
cells leads to understand the effect of possible synchronisation between the
two ends of the cells. This synchronisation, in turn, could lead to different
dynamics such as normal and fractional diffusion. Departing from a stochastic
single cell trajectory, where a memory effect induces persistent movement, we
derive a kinetic-renewal system at the mesoscopic scale. We investigate various
scenarios with different levels of complexity, where the two ends of the cell
move either independently or with partial or full synchronisation. We study the
relevant macroscopic limits where we obtain diffusion, drift-diffusion or
fractional diffusion, depending on the initial system. This article clarifies
the form of relevant macroscopic equations that describe the possible effects
of synchronised movement in cells, and sheds light on the switching between
normal and fractional diffusion
| [
{
"created": "Sat, 18 Sep 2021 18:28:52 GMT",
"version": "v1"
}
] | 2021-09-21 | [
[
"Estrada-Rodriguez",
"Gissell",
""
],
[
"Perthame",
"Benoit",
""
]
] | The combination of protrusions and retractions in the movement of polarized cells leads to understand the effect of possible synchronisation between the two ends of the cells. This synchronisation, in turn, could lead to different dynamics such as normal and fractional diffusion. Departing from a stochastic single cell trajectory, where a memory effect induces persistent movement, we derive a kinetic-renewal system at the mesoscopic scale. We investigate various scenarios with different levels of complexity, where the two ends of the cell move either independently or with partial or full synchronisation. We study the relevant macroscopic limits where we obtain diffusion, drift-diffusion or fractional diffusion, depending on the initial system. This article clarifies the form of relevant macroscopic equations that describe the possible effects of synchronised movement in cells, and sheds light on the switching between normal and fractional diffusion |
2101.11277 | Jessie Renton | Jessie Renton and Karen M. Page | Cooperative success in epithelial public goods games | 40 Pages, 15 Figures. Accepted version | Journal of Theoretical Biology, 528:110838, 2021 | 10.1016/j.jtbi.2021.110838 | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Cancer cells obtain mutations which rely on the production of diffusible
growth factors to confer a fitness benefit. These mutations can be considered
cooperative, and studied as public goods games within the framework of
evolutionary game theory. The population structure, benefit function and update
rule all influence the evolutionary success of cooperators. We model the
evolution of cooperation in epithelial cells using the Voronoi tessellation
model. Unlike traditional evolutionary graph theory, this allows us to
implement global updating, for which birth and death events are spatially
decoupled. We compare, for a sigmoid benefit function, the conditions for
cooperation to be favoured and/or beneficial for well mixed and structured
populations. We find that when population structure is combined with global
updating, cooperation is more successful than if there were local updating or
the population were well-mixed. Interestingly, the qualitative behaviour for
the well-mixed population and the Voronoi tessellation model is remarkably
similar, but the latter case requires significantly lower incentives to ensure
cooperation.
| [
{
"created": "Wed, 27 Jan 2021 09:15:48 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Sep 2021 12:51:18 GMT",
"version": "v2"
}
] | 2021-09-06 | [
[
"Renton",
"Jessie",
""
],
[
"Page",
"Karen M.",
""
]
] | Cancer cells obtain mutations which rely on the production of diffusible growth factors to confer a fitness benefit. These mutations can be considered cooperative, and studied as public goods games within the framework of evolutionary game theory. The population structure, benefit function and update rule all influence the evolutionary success of cooperators. We model the evolution of cooperation in epithelial cells using the Voronoi tessellation model. Unlike traditional evolutionary graph theory, this allows us to implement global updating, for which birth and death events are spatially decoupled. We compare, for a sigmoid benefit function, the conditions for cooperation to be favoured and/or beneficial for well mixed and structured populations. We find that when population structure is combined with global updating, cooperation is more successful than if there were local updating or the population were well-mixed. Interestingly, the qualitative behaviour for the well-mixed population and the Voronoi tessellation model is remarkably similar, but the latter case requires significantly lower incentives to ensure cooperation. |
2310.18760 | JunJie Wee | JunJie Wee, Jiahui Chen, Kelin Xia, Guo-Wei Wei | Integration of persistent Laplacian and pre-trained transformer for
protein solubility changes upon mutation | null | null | null | null | q-bio.BM math.AT | http://creativecommons.org/licenses/by/4.0/ | Protein mutations can significantly influence protein solubility, which
results in altered protein functions and leads to various diseases. Despite of
tremendous effort, machine learning prediction of protein solubility changes
upon mutation remains a challenging task as indicated by the poor scores of
normalized Correct Prediction Ratio (CPR). Part of the challenge stems from the
fact that there is no three-dimensional (3D) structures for the wild-type and
mutant proteins. This work integrates persistent Laplacians and pre-trained
Transformer for the task. The Transformer, pretrained with hunderds of millions
of protein sequences, embeds wild-type and mutant sequences, while persistent
Laplacians track the topological invariant change and homotopic shape evolution
induced by mutations in 3D protein structures, which are rendered from
AlphaFold2. The resulting machine learning model was trained on an extensive
data set labeled with three solubility types. Our model outperforms all
existing predictive methods and improves the state-of-the-art up to 15%.
| [
{
"created": "Sat, 28 Oct 2023 17:13:47 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Nov 2023 20:19:28 GMT",
"version": "v2"
}
] | 2023-11-06 | [
[
"Wee",
"JunJie",
""
],
[
"Chen",
"Jiahui",
""
],
[
"Xia",
"Kelin",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR). Part of the challenge stems from the fact that there is no three-dimensional (3D) structures for the wild-type and mutant proteins. This work integrates persistent Laplacians and pre-trained Transformer for the task. The Transformer, pretrained with hunderds of millions of protein sequences, embeds wild-type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2. The resulting machine learning model was trained on an extensive data set labeled with three solubility types. Our model outperforms all existing predictive methods and improves the state-of-the-art up to 15%. |
q-bio/0505008 | Julien Mayor | Julien Mayor and Wulfram Gerstner | Noise-enhanced computation in a model of a cortical column | null | null | null | null | q-bio.NC | null | Varied sensory systems use noise in order to enhance detection of weak
signals. It has been conjectured in the literature that this effect, known as
stochastic resonance, may take place in central cognitive processes such as the
memory retrieval of arithmetical multiplication. We show in a simplified model
of cortical tissue, that complex arithmetical calculations can be carried out
and are enhanced in the presence of a stochastic background. The performance is
shown to be positively correlated to the susceptibility of the network, defined
as its sensitivity to a variation of the mean of its inputs. For nontrivial
arithmetic tasks such as multiplication, stochastic resonance is an emergent
property of the microcircuitry of the model network.
| [
{
"created": "Wed, 4 May 2005 15:54:43 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Mayor",
"Julien",
""
],
[
"Gerstner",
"Wulfram",
""
]
] | Varied sensory systems use noise in order to enhance detection of weak signals. It has been conjectured in the literature that this effect, known as stochastic resonance, may take place in central cognitive processes such as the memory retrieval of arithmetical multiplication. We show in a simplified model of cortical tissue, that complex arithmetical calculations can be carried out and are enhanced in the presence of a stochastic background. The performance is shown to be positively correlated to the susceptibility of the network, defined as its sensitivity to a variation of the mean of its inputs. For nontrivial arithmetic tasks such as multiplication, stochastic resonance is an emergent property of the microcircuitry of the model network. |
1711.08988 | Atsushi Kamimura | Atsushi Kamimura and Kunihiko Kaneko | Exponential growth for self-reproduction in a catalytic reaction
network: relevance of a minority molecular species and crowdedness | 19 pages, submitted for publication | null | 10.1088/1367-2630/aaaf37 | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explanation of exponential growth in self-reproduction is an important step
toward elucidation of the origins of life because optimization of the growth
potential across rounds of selection is necessary for Darwinian evolution. To
produce another copy with approximately the same composition, the exponential
growth rates for all components have to be equal. How such balanced growth is
achieved, however, is not a trivial question, because this kind of growth
requires orchestrated replication of the components in stochastic and nonlinear
catalytic reactions. By considering a mutually catalyzing reaction in two- and
three-dimensional lattices, as represented by a cellular automaton model, we
show that self-reproduction with exponential growth is possible only when the
replication and degradation of one molecular species is much slower than those
of the others, i.e., when there is a minority molecule. Here, the synergetic
effect of molecular discreteness and crowding is necessary to produce the
exponential growth. Otherwise, the growth curves show superexponential growth
because of nonlinearity of the catalytic reactions or subexponential growth due
to replication inhibition by overcrowding of molecules. Our study emphasizes
that the minority molecular species in a catalytic reaction network is
necessary to acquire evolvability at the primitive stage of life.
| [
{
"created": "Fri, 24 Nov 2017 14:36:05 GMT",
"version": "v1"
}
] | 2018-04-18 | [
[
"Kamimura",
"Atsushi",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] | Explanation of exponential growth in self-reproduction is an important step toward elucidation of the origins of life because optimization of the growth potential across rounds of selection is necessary for Darwinian evolution. To produce another copy with approximately the same composition, the exponential growth rates for all components have to be equal. How such balanced growth is achieved, however, is not a trivial question, because this kind of growth requires orchestrated replication of the components in stochastic and nonlinear catalytic reactions. By considering a mutually catalyzing reaction in two- and three-dimensional lattices, as represented by a cellular automaton model, we show that self-reproduction with exponential growth is possible only when the replication and degradation of one molecular species is much slower than those of the others, i.e., when there is a minority molecule. Here, the synergetic effect of molecular discreteness and crowding is necessary to produce the exponential growth. Otherwise, the growth curves show superexponential growth because of nonlinearity of the catalytic reactions or subexponential growth due to replication inhibition by overcrowding of molecules. Our study emphasizes that the minority molecular species in a catalytic reaction network is necessary to acquire evolvability at the primitive stage of life. |
1709.09645 | Moo K. Chung | Moo K. Chung | Statistical Challenges of Big Brain Network Data | 8 pages, 2 figure | null | null | null | q-bio.NC stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore the main characteristics of big brain network data that offer
unique statistical challenges. The brain networks are biologically expected to
be both sparse and hierarchical. Such unique characterizations put specific
topological constraints onto statistical approaches and models we can use
effectively. We explore the limitations of the current models used in the field
and offer alternative approaches and explain new challenges.
| [
{
"created": "Wed, 27 Sep 2017 17:30:10 GMT",
"version": "v1"
},
{
"created": "Sat, 23 Dec 2017 07:31:43 GMT",
"version": "v2"
}
] | 2017-12-27 | [
[
"Chung",
"Moo K.",
""
]
] | We explore the main characteristics of big brain network data that offer unique statistical challenges. The brain networks are biologically expected to be both sparse and hierarchical. Such unique characterizations put specific topological constraints onto statistical approaches and models we can use effectively. We explore the limitations of the current models used in the field and offer alternative approaches and explain new challenges. |
2311.18574 | Jiaxian Yan | Jiaxian Yan, Zaixi Zhang, Kai Zhang, and Qi Liu | Multi-scale Iterative Refinement towards Robust and Versatile Molecular
Docking | 13 pages, 8 figures | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Molecular docking is a key computational tool utilized to predict the binding
conformations of small molecules to protein targets, which is fundamental in
the design of novel drugs. Despite recent advancements in geometric deep
learning-based approaches leading to improvements in blind docking efficiency,
these methods have encountered notable challenges, such as limited
generalization performance on unseen proteins, the inability to concurrently
address the settings of blind docking and site-specific docking, and the
frequent occurrence of physical implausibilities such as inter-molecular steric
clash. In this study, we introduce DeltaDock, a robust and versatile framework
designed for efficient molecular docking to overcome these challenges.
DeltaDock operates in a two-step process: rapid initial complex structures
sampling followed by multi-scale iterative refinement of the initial
structures. In the initial stage, to sample accurate structures with high
efficiency, we develop a ligand-dependent binding site prediction model founded
on large protein models and graph neural networks. This model is then paired
with GPU-accelerated sampling algorithms. The sampled structures are updated
using a multi-scale iterative refinement module that captures both
protein-ligand atom-atom interactions and residue-atom interactions in the
following stage. Distinct from previous geometric deep learning methods that
are conditioned on the blind docking setting, DeltaDock demonstrates superior
performance in both blind docking and site-specific docking settings.
Comprehensive experimental results reveal that DeltaDock consistently surpasses
baseline methods in terms of docking accuracy. Furthermore, it displays
remarkable generalization capabilities and proficiency for predicting
physically valid structures, thereby attesting to its robustness and
reliability in various scenarios.
| [
{
"created": "Thu, 30 Nov 2023 14:09:20 GMT",
"version": "v1"
}
] | 2023-12-01 | [
[
"Yan",
"Jiaxian",
""
],
[
"Zhang",
"Zaixi",
""
],
[
"Zhang",
"Kai",
""
],
[
"Liu",
"Qi",
""
]
] | Molecular docking is a key computational tool utilized to predict the binding conformations of small molecules to protein targets, which is fundamental in the design of novel drugs. Despite recent advancements in geometric deep learning-based approaches leading to improvements in blind docking efficiency, these methods have encountered notable challenges, such as limited generalization performance on unseen proteins, the inability to concurrently address the settings of blind docking and site-specific docking, and the frequent occurrence of physical implausibilities such as inter-molecular steric clash. In this study, we introduce DeltaDock, a robust and versatile framework designed for efficient molecular docking to overcome these challenges. DeltaDock operates in a two-step process: rapid initial complex structures sampling followed by multi-scale iterative refinement of the initial structures. In the initial stage, to sample accurate structures with high efficiency, we develop a ligand-dependent binding site prediction model founded on large protein models and graph neural networks. This model is then paired with GPU-accelerated sampling algorithms. The sampled structures are updated using a multi-scale iterative refinement module that captures both protein-ligand atom-atom interactions and residue-atom interactions in the following stage. Distinct from previous geometric deep learning methods that are conditioned on the blind docking setting, DeltaDock demonstrates superior performance in both blind docking and site-specific docking settings. Comprehensive experimental results reveal that DeltaDock consistently surpasses baseline methods in terms of docking accuracy. Furthermore, it displays remarkable generalization capabilities and proficiency for predicting physically valid structures, thereby attesting to its robustness and reliability in various scenarios. |
1503.05043 | Ricard Sole | Ricard Sol\'e, Salva Duran-Nebreda and Raul Monta\~nez | Synthetic circuit designs for Earth terraformation | 8 pages, 3 figures | null | null | null | q-bio.QM nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mounting evidence indicates that our planet might experience runaway effects
associated to rising temperatures and ecosystem overexploitation, leading to
catastrophic shifts on short time scales. Remediation scenarios capable of
counterbalancing these effects involve geoengineering, sustainable practices
and carbon sequestration, among others. None of these scenarios seems powerful
enough to achieve the desired restoration of safe boundaries. We hypothesise
that synthetic organisms with the appropriate engineering design could be used
to safely prevent declines in some stressed ecosystems and help improving
carbon sequestration. Such schemes would include engineering mutualistic
dependencies preventing undesired evolutionary processes. We hypothesise that
some particular design principles introduce unescapable constraints to the
engineered organisms that act as effective firewalls. Testing this designed
organisms can be achieved by using controlled bioreactor models and accurate
computational models including different scales (from genetic constructs and
metabolic pathways to population dynamics). Our hypothesis heads towards a
future anthropogenic action that should effectively act as Terraforming agents.
It also implies a major challenge in the existing biosafety policies, since we
suggest release of modified organisms as potentially necessary strategy for
success.
| [
{
"created": "Tue, 17 Mar 2015 13:43:21 GMT",
"version": "v1"
}
] | 2015-03-18 | [
[
"Solé",
"Ricard",
""
],
[
"Duran-Nebreda",
"Salva",
""
],
[
"Montañez",
"Raul",
""
]
] | Mounting evidence indicates that our planet might experience runaway effects associated to rising temperatures and ecosystem overexploitation, leading to catastrophic shifts on short time scales. Remediation scenarios capable of counterbalancing these effects involve geoengineering, sustainable practices and carbon sequestration, among others. None of these scenarios seems powerful enough to achieve the desired restoration of safe boundaries. We hypothesise that synthetic organisms with the appropriate engineering design could be used to safely prevent declines in some stressed ecosystems and help improving carbon sequestration. Such schemes would include engineering mutualistic dependencies preventing undesired evolutionary processes. We hypothesise that some particular design principles introduce unescapable constraints to the engineered organisms that act as effective firewalls. Testing this designed organisms can be achieved by using controlled bioreactor models and accurate computational models including different scales (from genetic constructs and metabolic pathways to population dynamics). Our hypothesis heads towards a future anthropogenic action that should effectively act as Terraforming agents. It also implies a major challenge in the existing biosafety policies, since we suggest release of modified organisms as potentially necessary strategy for success. |
q-bio/0503037 | Gianluca Lattanzi | Francesco Pampaloni, Gianluca Lattanzi, Alexandr Jon\'a\v{s}, Thomas
Surrey, Erwin Frey and Ernst-Ludwig Florin | Elastic properties of grafted microtubules | 9 pages, 3 figures | PNAS 103, 10248 (2006) | 10.1073/pnas.0603931103 | LMU-ASC 26/05 | q-bio.BM | null | We use single-particle tracking to study the elastic properties of single
microtubules grafted to a substrate. Thermal fluctuations of the free
microtubule's end are recorded, in order to measure position distribution
functions from which we calculate the persistence length of microtubules with
contour lengths between 2.6 and 48 micrometers. We find the persistence length
to vary by more than a factor of 20 over the total range of contour lengths.
Our results support the hypothesis that shearing between protofilaments
contributes significantly to the mechanics of microtubules.
| [
{
"created": "Thu, 24 Mar 2005 10:47:55 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Pampaloni",
"Francesco",
""
],
[
"Lattanzi",
"Gianluca",
""
],
[
"Jonáš",
"Alexandr",
""
],
[
"Surrey",
"Thomas",
""
],
[
"Frey",
"Erwin",
""
],
[
"Florin",
"Ernst-Ludwig",
""
]
] | We use single-particle tracking to study the elastic properties of single microtubules grafted to a substrate. Thermal fluctuations of the free microtubule's end are recorded, in order to measure position distribution functions from which we calculate the persistence length of microtubules with contour lengths between 2.6 and 48 micrometers. We find the persistence length to vary by more than a factor of 20 over the total range of contour lengths. Our results support the hypothesis that shearing between protofilaments contributes significantly to the mechanics of microtubules. |
2101.11399 | Giacomo Cacciapaglia | Giacomo Cacciapaglia, Corentin Cot, Michele Della Morte, Stefan
Hohenegger, Francesco Sannino, Shahram Vatani | The field theoretical ABC of epidemic dynamics | 57 pages, 40 figures. Article expanded into a review. Prepared for
submission to Physics Reports | null | null | null | q-bio.PE hep-lat hep-th physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | Infectious diseases are a threat for human health with tremendous impact on
our society at large. The recent COVID-19 pandemic, caused by the SARS-CoV-2,
is the latest example of a highly infectious disease ravaging the world, since
late 2019. It is therefore imperative to develop efficient mathematical models,
able to substantially curb the damages of a pandemic by unveiling disease
spreading dynamics and symmetries. This will help inform (non)-pharmaceutical
prevention strategies. For the reasons above we wrote this report that goes at
the heart of mathematical modelling of infectious disease diffusion by
simultaneously investigating the underlying microscopic dynamics in terms of
percolation models, effective description via compartmental models and the
employment of temporal symmetries naturally encoded in the mathematical
language of critical phenomena. Our report reviews these approaches and
determines their common denominators, relevant for theoretical epidemiology and
its link to important concepts in theoretical physics. We show that the
different frameworks exhibit common features such as criticality and
self-similarity under time rescaling. These features are naturally encoded
within the unifying field theoretical approach. The latter leads to an
efficient description of the time evolution of the disease via a framework in
which (near) time-dilation invariance is explicitly realised. As important test
of the relevance of symmetries we show how to mathematically account for
observed phenomena such as multi-wave dynamics. The models presented here are
of immediate relevance for different realms of scientific enquiry from medical
applications to the understanding of human behaviour. Our review offers novel
perspectives on how to model, capture, organise and understand epidemiological
data and disease dynamics for modelling real-world phenomena.
| [
{
"created": "Mon, 25 Jan 2021 15:19:39 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Sep 2021 08:40:33 GMT",
"version": "v2"
}
] | 2021-09-17 | [
[
"Cacciapaglia",
"Giacomo",
""
],
[
"Cot",
"Corentin",
""
],
[
"Della Morte",
"Michele",
""
],
[
"Hohenegger",
"Stefan",
""
],
[
"Sannino",
"Francesco",
""
],
[
"Vatani",
"Shahram",
""
]
] | Infectious diseases are a threat for human health with tremendous impact on our society at large. The recent COVID-19 pandemic, caused by the SARS-CoV-2, is the latest example of a highly infectious disease ravaging the world, since late 2019. It is therefore imperative to develop efficient mathematical models, able to substantially curb the damages of a pandemic by unveiling disease spreading dynamics and symmetries. This will help inform (non)-pharmaceutical prevention strategies. For the reasons above we wrote this report that goes at the heart of mathematical modelling of infectious disease diffusion by simultaneously investigating the underlying microscopic dynamics in terms of percolation models, effective description via compartmental models and the employment of temporal symmetries naturally encoded in the mathematical language of critical phenomena. Our report reviews these approaches and determines their common denominators, relevant for theoretical epidemiology and its link to important concepts in theoretical physics. We show that the different frameworks exhibit common features such as criticality and self-similarity under time rescaling. These features are naturally encoded within the unifying field theoretical approach. The latter leads to an efficient description of the time evolution of the disease via a framework in which (near) time-dilation invariance is explicitly realised. As important test of the relevance of symmetries we show how to mathematically account for observed phenomena such as multi-wave dynamics. The models presented here are of immediate relevance for different realms of scientific enquiry from medical applications to the understanding of human behaviour. Our review offers novel perspectives on how to model, capture, organise and understand epidemiological data and disease dynamics for modelling real-world phenomena. |
1804.10093 | Katherine Medina | Katherine Medina | A GPP algorithm for hippocampal interneuron characterization | 11 pages; 2 figures; 2 tables | null | null | null | q-bio.NC | http://creativecommons.org/publicdomain/zero/1.0/ | Correctly identifying neuronal subsets is critical to multiple downstream
methods in several areas of neuroscience research. The hippocampal interneuron
characterization technology has achieved rapid development in recent years.
However, capturing true neuronal features for accurate interneuron
characterization and segmentation has remained elusive. In the current study, a
novel global preserving estimate algorithm is used to capture the non-linearity
in the features of hippocampal interneurons after factor Algorithm. Our results
provide evidence for the effective integration of the original linear and
nonlinear neuronal features and achieves better characterization performance on
multiple hippocampal interneuron databases through array matching.
| [
{
"created": "Thu, 26 Apr 2018 14:42:03 GMT",
"version": "v1"
}
] | 2018-04-27 | [
[
"Medina",
"Katherine",
""
]
] | Correctly identifying neuronal subsets is critical to multiple downstream methods in several areas of neuroscience research. The hippocampal interneuron characterization technology has achieved rapid development in recent years. However, capturing true neuronal features for accurate interneuron characterization and segmentation has remained elusive. In the current study, a novel global preserving estimate algorithm is used to capture the non-linearity in the features of hippocampal interneurons after factor Algorithm. Our results provide evidence for the effective integration of the original linear and nonlinear neuronal features and achieves better characterization performance on multiple hippocampal interneuron databases through array matching. |
1203.1595 | Pablo Moisset de Espan\'es | Pablo Moisset de Espan\'es and Axel Osses and Iv\'an Rapaport | Fixed-points in Random Boolean Networks: The impact of parallelism in
the scale-free topology case | 13 pages | null | null | null | q-bio.CB q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fixed points are fundamental states in any dynamical system. In the case of
gene regulatory networks (GRNs) they correspond to stable genes profiles
associated to the various cell types. We use Kauffman's approach to model GRNs
with random Boolean networks (RBNs). We start this paper by proving that, if we
fix the values of the source nodes (nodes with in-degree 0), the expected
number of fixed points of any RBN is one (independently of the topology we
choose). For finding such fixed points we use the {\alpha}-asynchronous
dynamics (where every node is updated independently with probability 0 <
{\alpha} < 1). In fact, it is well-known that asynchrony avoids the cycle
attractors into which parallel dynamics tends to fall. We perform simulations
and we show the remarkable property that, if for a given RBN with scale-free
topology and {\alpha}-asynchronous dynamics an initial configuration reaches a
fixed point, then every configuration also reaches a fixed point. By contrast,
in the parallel regime, the percentage of initial configurations reaching a
fixed point (for the same networks) is dramatically smaller. We contrast the
results of the simulations on scale-free networks with the classical
Erdos-Renyi model of random networks. Everything indicates that scale-free
networks are extremely robust. Finally, we study the mean and maximum time/work
needed to reach a fixed point when starting from randomly chosen initial
configurations.
| [
{
"created": "Wed, 7 Mar 2012 20:27:36 GMT",
"version": "v1"
}
] | 2012-03-08 | [
[
"de Espanés",
"Pablo Moisset",
""
],
[
"Osses",
"Axel",
""
],
[
"Rapaport",
"Iván",
""
]
] | Fixed points are fundamental states in any dynamical system. In the case of gene regulatory networks (GRNs) they correspond to stable genes profiles associated to the various cell types. We use Kauffman's approach to model GRNs with random Boolean networks (RBNs). We start this paper by proving that, if we fix the values of the source nodes (nodes with in-degree 0), the expected number of fixed points of any RBN is one (independently of the topology we choose). For finding such fixed points we use the {\alpha}-asynchronous dynamics (where every node is updated independently with probability 0 < {\alpha} < 1). In fact, it is well-known that asynchrony avoids the cycle attractors into which parallel dynamics tends to fall. We perform simulations and we show the remarkable property that, if for a given RBN with scale-free topology and {\alpha}-asynchronous dynamics an initial configuration reaches a fixed point, then every configuration also reaches a fixed point. By contrast, in the parallel regime, the percentage of initial configurations reaching a fixed point (for the same networks) is dramatically smaller. We contrast the results of the simulations on scale-free networks with the classical Erdos-Renyi model of random networks. Everything indicates that scale-free networks are extremely robust. Finally, we study the mean and maximum time/work needed to reach a fixed point when starting from randomly chosen initial configurations. |
2404.00110 | S\'ilvia Sequeira | S\'ilvia O. Sequeira, Ekaterina Pasnak, Carla Viegas, Bianca Gomes,
Marta Dias, Renata Cervantes, Pedro Pena, Magdalena Twaru\.zek, Robert
Kosicki, Susana Viegas, Liliana Aranha Caetano, Maria Jo\~ao Penetra, In\^es
Santos, Ana Teresa Caldeira, Catarina Pinheiro | Microbial assessment in a rare Norwegian book collection: a One Health
approach to cultural heritage | 17 pages, 7 figures, 2 tables | null | 10.3390/microorganisms12061215 | null | q-bio.PE q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Microbial contamination poses a threat to both the preservation of library
and archival collections and the health of staff and users. This study
investigated the microbial communities and potential health risks associated
with the UNESCO-classified Norwegian Sea Trade Archive (NSTA) collection
exhibiting visible microbial colonization and staff health concerns. Dust
samples from book surfaces and the storage environment were analysed using
culturing methods, qPCR, Next Generation Sequencing, and mycotoxin,
cytotoxicity and azole resistance assays. Penicillium sp., Aspergillus sp., and
Cladosporium sp. were the most common fungi identified, with some potentially
toxic species like Stachybotrys sp., Toxicladosporium sp. and Aspergillus
section Fumigati. Fungal resistance to azoles was not detected. Only one
mycotoxin, sterigmatocystin, was found in a heavily contaminated book. Dust
extracts from books exhibited moderate to high cytotoxicity on human lung
cells, suggesting a potential respiratory risk. The collection had higher
contamination levels compared to the storage environment, likely due to
improved storage conditions. Even though, overall low contamination levels were
obtained, which might be underestimated due to the presence of salt (from cod
preservation) that could have interfered with the analyses. This study
underlines the importance of monitoring microbial communities and implementing
proper storage measures to safeguard cultural heritage and staff well-being.
| [
{
"created": "Fri, 29 Mar 2024 18:53:22 GMT",
"version": "v1"
}
] | 2024-06-24 | [
[
"Sequeira",
"Sílvia O.",
""
],
[
"Pasnak",
"Ekaterina",
""
],
[
"Viegas",
"Carla",
""
],
[
"Gomes",
"Bianca",
""
],
[
"Dias",
"Marta",
""
],
[
"Cervantes",
"Renata",
""
],
[
"Pena",
"Pedro",
""
],
[
"Twarużek",
"Magdalena",
""
],
[
"Kosicki",
"Robert",
""
],
[
"Viegas",
"Susana",
""
],
[
"Caetano",
"Liliana Aranha",
""
],
[
"Penetra",
"Maria João",
""
],
[
"Santos",
"Inês",
""
],
[
"Caldeira",
"Ana Teresa",
""
],
[
"Pinheiro",
"Catarina",
""
]
] | Microbial contamination poses a threat to both the preservation of library and archival collections and the health of staff and users. This study investigated the microbial communities and potential health risks associated with the UNESCO-classified Norwegian Sea Trade Archive (NSTA) collection exhibiting visible microbial colonization and staff health concerns. Dust samples from book surfaces and the storage environment were analysed using culturing methods, qPCR, Next Generation Sequencing, and mycotoxin, cytotoxicity and azole resistance assays. Penicillium sp., Aspergillus sp., and Cladosporium sp. were the most common fungi identified, with some potentially toxic species like Stachybotrys sp., Toxicladosporium sp. and Aspergillus section Fumigati. Fungal resistance to azoles was not detected. Only one mycotoxin, sterigmatocystin, was found in a heavily contaminated book. Dust extracts from books exhibited moderate to high cytotoxicity on human lung cells, suggesting a potential respiratory risk. The collection had higher contamination levels compared to the storage environment, likely due to improved storage conditions. Even though, overall low contamination levels were obtained, which might be underestimated due to the presence of salt (from cod preservation) that could have interfered with the analyses. This study underlines the importance of monitoring microbial communities and implementing proper storage measures to safeguard cultural heritage and staff well-being. |
1603.04007 | Bertrand Roehner | Sylvie Berrut, Violette Pouillard, Peter Richmond, Bertrand M. Roehner | Deciphering infant mortality. Part 1: empirical evidence | 46 pages, 14 figures, 4 tables | null | null | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is not (or at least not only) about human infant mortality. In
line with reliability theory, "infant" will refer here to the time interval
following birth during which the mortality (or failure) rate decreases. This
definition provides a systems science perspective in which birth constitutes a
sudden transition which falls within the field of application of the "Transient
Shock" (TS) conjecture put forward in Richmond et al. (2016c). This conjecture
provides predictions about the timing and shape of the death rate peak. (i) It
says that there will be a death rate spike whenever external conditions change
abruptly and drastically. (ii) It predicts that after a steep rising there will
be a much longer hyperbolic relaxation process. These predictions can be tested
by considering living organisms for which birth is a multi-step process. Thus,
for fish there are three states: egg, yolk-sac phase, young adult. The TS
conjecture predicts a mortality spike at the end of the yolk-sac phase, and
this timing is indeed confirmed by observation. Secondly, the hyperbolic nature
of the relaxation process can be tested using high accuracy Swiss statistics
which give postnatal death rates from one hour after birth up to the age of 10
years. It turns out that since the 19th century despite a great overall
reduction in infant mortality, the shape of the age-specific death rate has
remained basically unchanged. This hyperbolic pattern is not specific to
humans. It can also be found in small primates as recorded in the archives of
zoological gardens. Our ultimate objective is to set up a chain of cases which
starts from simple systems and then moves up step by step to more complex
organisms. The cases discussed here can be seen as initial landmarks.
| [
{
"created": "Sun, 13 Mar 2016 08:17:11 GMT",
"version": "v1"
}
] | 2016-03-15 | [
[
"Berrut",
"Sylvie",
""
],
[
"Pouillard",
"Violette",
""
],
[
"Richmond",
"Peter",
""
],
[
"Roehner",
"Bertrand M.",
""
]
] | This paper is not (or at least not only) about human infant mortality. In line with reliability theory, "infant" will refer here to the time interval following birth during which the mortality (or failure) rate decreases. This definition provides a systems science perspective in which birth constitutes a sudden transition which falls within the field of application of the "Transient Shock" (TS) conjecture put forward in Richmond et al. (2016c). This conjecture provides predictions about the timing and shape of the death rate peak. (i) It says that there will be a death rate spike whenever external conditions change abruptly and drastically. (ii) It predicts that after a steep rising there will be a much longer hyperbolic relaxation process. These predictions can be tested by considering living organisms for which birth is a multi-step process. Thus, for fish there are three states: egg, yolk-sac phase, young adult. The TS conjecture predicts a mortality spike at the end of the yolk-sac phase, and this timing is indeed confirmed by observation. Secondly, the hyperbolic nature of the relaxation process can be tested using high accuracy Swiss statistics which give postnatal death rates from one hour after birth up to the age of 10 years. It turns out that since the 19th century despite a great overall reduction in infant mortality, the shape of the age-specific death rate has remained basically unchanged. This hyperbolic pattern is not specific to humans. It can also be found in small primates as recorded in the archives of zoological gardens. Our ultimate objective is to set up a chain of cases which starts from simple systems and then moves up step by step to more complex organisms. The cases discussed here can be seen as initial landmarks. |
2206.00668 | Lovro Vrcek | Lovro Vr\v{c}ek, Xavier Bresson, Thomas Laurent, Martin Schmitz, Mile
\v{S}iki\'c | Learning to Untangle Genome Assembly with Graph Convolutional Networks | null | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A quest to determine the complete sequence of a human DNA from telomere to
telomere started three decades ago and was finally completed in 2021. This
accomplishment was a result of a tremendous effort of numerous experts who
engineered various tools and performed laborious manual inspection to achieve
the first gapless genome sequence. However, such method can hardly be used as a
general approach to assemble different genomes, especially when the assembly
speed is critical given the large amount of data. In this work, we explore a
different approach to the central part of the genome assembly task that
consists of untangling a large assembly graph from which a genomic sequence
needs to be reconstructed. Our main motivation is to reduce human-engineered
heuristics and use deep learning to develop more generalizable reconstruction
techniques. Precisely, we introduce a new learning framework to train a graph
convolutional network to resolve assembly graphs by finding a correct path
through them. The training is supervised with a dataset generated from the
resolved CHM13 human sequence and tested on assembly graphs built using real
human PacBio HiFi reads. Experimental results show that a model, trained on
simulated graphs generated solely from a single chromosome, is able to
remarkably resolve all other chromosomes. Moreover, the model outperforms
hand-crafted heuristics from a state-of-the-art \textit{de novo} assembler on
the same graphs. Reconstructed chromosomes with graph networks are more
accurate on nucleotide level, report lower number of contigs, higher genome
reconstructed fraction and NG50/NGA50 assessment metrics.
| [
{
"created": "Wed, 1 Jun 2022 04:14:25 GMT",
"version": "v1"
}
] | 2022-06-03 | [
[
"Vrček",
"Lovro",
""
],
[
"Bresson",
"Xavier",
""
],
[
"Laurent",
"Thomas",
""
],
[
"Schmitz",
"Martin",
""
],
[
"Šikić",
"Mile",
""
]
] | A quest to determine the complete sequence of a human DNA from telomere to telomere started three decades ago and was finally completed in 2021. This accomplishment was a result of a tremendous effort of numerous experts who engineered various tools and performed laborious manual inspection to achieve the first gapless genome sequence. However, such method can hardly be used as a general approach to assemble different genomes, especially when the assembly speed is critical given the large amount of data. In this work, we explore a different approach to the central part of the genome assembly task that consists of untangling a large assembly graph from which a genomic sequence needs to be reconstructed. Our main motivation is to reduce human-engineered heuristics and use deep learning to develop more generalizable reconstruction techniques. Precisely, we introduce a new learning framework to train a graph convolutional network to resolve assembly graphs by finding a correct path through them. The training is supervised with a dataset generated from the resolved CHM13 human sequence and tested on assembly graphs built using real human PacBio HiFi reads. Experimental results show that a model, trained on simulated graphs generated solely from a single chromosome, is able to remarkably resolve all other chromosomes. Moreover, the model outperforms hand-crafted heuristics from a state-of-the-art \textit{de novo} assembler on the same graphs. Reconstructed chromosomes with graph networks are more accurate on nucleotide level, report lower number of contigs, higher genome reconstructed fraction and NG50/NGA50 assessment metrics. |
1908.00077 | Jennifer Stiso | Jennifer Stiso, Marie-Constance Corsi, Jean M. Vettel, Javier O.
Garcia, Fabio Pasqualetti, Fabrizio De Vico Fallani, Timothy H. Lucas,
Danielle S. Bassett | Learning in brain-computer interface control evidenced by joint
decomposition of brain and behavior | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motor imagery-based brain-computer interfaces (BCIs) use an individuals
ability to volitionally modulate localized brain activity as a therapy for
motor dysfunction or to probe causal relations between brain activity and
behavior. However, many individuals cannot learn to successfully modulate their
brain activity, greatly limiting the efficacy of BCI for therapy and for basic
scientific inquiry. Previous research suggests that coherent activity across
diverse cognitive systems is a hallmark of individuals who can successfully
learn to control the BCI. However, little is known about how these distributed
networks interact through time to support learning. Here, we address this gap
in knowledge by constructing and applying a multimodal network approach to
decipher brain-behavior relations in motor imagery-based brain-computer
interface learning using MEG. Specifically, we employ a minimally constrained
matrix decomposition method (non-negative matrix factorization) to
simultaneously identify regularized, covarying subgraphs of functional
connectivity, to assess their similarity to task performance, and to detect
their time-varying expression. Individuals also displayed marked variation in
the spatial properties of subgraphs such as the connectivity between the
frontal lobe and the rest of the brain, and in the temporal properties of
subgraphs such as the stage of learning at which they reached maximum
expression. From these observations, we posit a conceptual model in which
certain subgraphs support learning by modulating brain activity in regions
important for sustaining attention. To test this model, we use tools that
stipulate regional dynamics on a networked system (network control theory), and
find that good learners display a single subgraph whose temporal expression
tracked performance and whose architecture supports easy modulation of brain
regions important for attention.
| [
{
"created": "Wed, 31 Jul 2019 20:17:07 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Aug 2019 13:42:25 GMT",
"version": "v2"
}
] | 2019-08-05 | [
[
"Stiso",
"Jennifer",
""
],
[
"Corsi",
"Marie-Constance",
""
],
[
"Vettel",
"Jean M.",
""
],
[
"Garcia",
"Javier O.",
""
],
[
"Pasqualetti",
"Fabio",
""
],
[
"Fallani",
"Fabrizio De Vico",
""
],
[
"Lucas",
"Timothy H.",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Motor imagery-based brain-computer interfaces (BCIs) use an individuals ability to volitionally modulate localized brain activity as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Previous research suggests that coherent activity across diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using MEG. Specifically, we employ a minimally constrained matrix decomposition method (non-negative matrix factorization) to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention. |
0804.0216 | Emmanuel Tannenbaum | Amit Kama and Emmanuel Tannenbaum | The effect of the SOS response on the mean fitness of unicellular
populations: A quasispecies approach | 9 pages, 3 figures | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper develops a quasispecies model that incorporates the SOS response.
We consider a unicellular, asexually replicating population of organisms, whose
genomes consist of a single, double-stranded DNA molecule, i.e. one chromosome.
We assume that repair of post-replication mismatched base-pairs occurs with
probability $ \lambda $, and that the SOS response is triggered when the total
number of mismatched base-pairs exceeds $ l_S $. We further assume that the
per-mismatch SOS elimination rate is characterized by a first-order rate
constant $ \kappa_{SOS} $. For a single fitness peak landscape where the master
genome can sustain up to $ l $ mismatches and remain viable, this model is
analytically solvable in the limit of infinite sequence length. The results,
which are confirmed by stochastic simulations, indicate that the SOS response
does indeed confer a fitness advantage to a population, provided that it is
only activated when DNA damage is so extensive that a cell will die if it does
not attempt to repair its DNA.
| [
{
"created": "Tue, 1 Apr 2008 17:53:17 GMT",
"version": "v1"
}
] | 2008-04-02 | [
[
"Kama",
"Amit",
""
],
[
"Tannenbaum",
"Emmanuel",
""
]
] | This paper develops a quasispecies model that incorporates the SOS response. We consider a unicellular, asexually replicating population of organisms, whose genomes consist of a single, double-stranded DNA molecule, i.e. one chromosome. We assume that repair of post-replication mismatched base-pairs occurs with probability $ \lambda $, and that the SOS response is triggered when the total number of mismatched base-pairs exceeds $ l_S $. We further assume that the per-mismatch SOS elimination rate is characterized by a first-order rate constant $ \kappa_{SOS} $. For a single fitness peak landscape where the master genome can sustain up to $ l $ mismatches and remain viable, this model is analytically solvable in the limit of infinite sequence length. The results, which are confirmed by stochastic simulations, indicate that the SOS response does indeed confer a fitness advantage to a population, provided that it is only activated when DNA damage is so extensive that a cell will die if it does not attempt to repair its DNA. |
1611.07801 | Daniel Jacob | Daniel Jacob, Catherine Deborde, Marie Lefebvre, Mickael Maucourt,
Anick Moing | NMRProcFlow: A graphical and interactive tool dedicated to 1D spectra
processing for NMR-based metabolomics | null | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Concerning NMR-based metabolomics, 1D spectra processing often requires an
expert eye for disentangling the intertwined peaks, and so far the best way is
to proceed interactively with a spectra viewer. NMRProcFlow is a graphical and
interactive 1D NMR (1H \& 13C) spectra processing tool dedicated to metabolic
fingerprinting and targeted metabolomic, covering all spectra processing steps
including baseline correction, chemical shift calibration, alignment. It does
not require programming skills. Biologists and NMR spectroscopists can easily
interact and develop synergies by visualizing the NMR spectra along with their
corresponding experimental-factor levels, thus setting a bridge between
experimental design and subsequent statistical analyses.
| [
{
"created": "Wed, 23 Nov 2016 13:59:57 GMT",
"version": "v1"
}
] | 2016-11-24 | [
[
"Jacob",
"Daniel",
""
],
[
"Deborde",
"Catherine",
""
],
[
"Lefebvre",
"Marie",
""
],
[
"Maucourt",
"Mickael",
""
],
[
"Moing",
"Anick",
""
]
] | Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks, and so far the best way is to proceed interactively with a spectra viewer. NMRProcFlow is a graphical and interactive 1D NMR (1H \& 13C) spectra processing tool dedicated to metabolic fingerprinting and targeted metabolomic, covering all spectra processing steps including baseline correction, chemical shift calibration, alignment. It does not require programming skills. Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses. |
2402.17182 | Danny Miller | Miranda PG Zalusky, Danny E Miller | Methylation Operation Wizard (MeOW): Identification of differentially
methylated regions in long-read sequencing data | 7 pages, 1 figure | null | null | null | q-bio.GN q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Long-read sequencing (LRS) is able to simultaneously capture information
about both DNA sequence and modifications, such as CpG methylation in a single
sequencing experiment. Here we present Methylation Operation Wizard (MeOW), a
program to identify and prioritize differentially methylated regions (DMRs)
genome-wide using LRS data. MeOW can be run using either a file containing
counts of per-nucleotide methylated CpG sites or with a bam file containing
modified base tags.
| [
{
"created": "Tue, 27 Feb 2024 03:36:50 GMT",
"version": "v1"
}
] | 2024-02-28 | [
[
"Zalusky",
"Miranda PG",
""
],
[
"Miller",
"Danny E",
""
]
] | Long-read sequencing (LRS) is able to simultaneously capture information about both DNA sequence and modifications, such as CpG methylation in a single sequencing experiment. Here we present Methylation Operation Wizard (MeOW), a program to identify and prioritize differentially methylated regions (DMRs) genome-wide using LRS data. MeOW can be run using either a file containing counts of per-nucleotide methylated CpG sites or with a bam file containing modified base tags. |
1803.07256 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Burst Synchronization in A Scale-Free Neuronal Network with Inhibitory
Spike-Timing-Dependent Plasticity | arXiv admin note: substantial text overlap with arXiv:1708.04543,
arXiv:1801.01385 | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We are concerned about burst synchronization (BS), related to neural
information processes in health and disease, in the Barab\'{a}si-Albert
scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose
neurons. This inhibitory neuronal population has adaptive dynamic synaptic
strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP).
In previous works without considering iSTDP, BS was found to appear in a range
of noise intensities for fixed synaptic inhibition strengths. In contrast, in
our present work, we take into consideration iSTDP and investigate its effect
on BS by varying the noise intensity. Our new main result is to find occurrence
of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via
LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory
synaptic plasticity is in contrast to that in excitatory synaptic plasticity
where good (bad) synchronization gets better (worse) via LTP (LTD). We note
that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic
plasticity are reversed in comparison with those in excitatory synaptic
plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition
strengths are intensively investigated via a microscopic method based on the
distributions of time delays between the pre- and the post-synaptic burst onset
times. Finally, in the presence of iSTDP we investigate the effects of network
architecture on BS by varying the symmetric attachment degree $l^*$ and the
asymmetry parameter $\Delta l$ in the SFN.
| [
{
"created": "Tue, 20 Mar 2018 04:52:01 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Mar 2018 02:16:06 GMT",
"version": "v2"
},
{
"created": "Fri, 6 Apr 2018 05:23:12 GMT",
"version": "v3"
},
{
"created": "Mon, 20 Aug 2018 07:23:05 GMT",
"version": "v4"
}
] | 2018-08-21 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barab\'{a}si-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree $l^*$ and the asymmetry parameter $\Delta l$ in the SFN. |
1805.01608 | Matthieu Vignes | Alex White and Matthieu Vignes | Causal Queries from Observational Data in Biological Systems via
Bayesian Networks: An Empirical Study in Small Networks | This chapter will appear in the forthcoming book "Gene Regulatory
Networks: Methods and Protocols", published by Springer Nature | null | null | null | q-bio.QM q-bio.MN stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological networks are a very convenient modelling and visualisation tool to
discover knowledge from modern high-throughput genomics and postgenomics data
sets. Indeed, biological entities are not isolated, but are components of
complex multi-level systems. We go one step further and advocate for the
consideration of causal representations of the interactions in living
systems.We present the causal formalism and bring it out in the context of
biological networks, when the data is observational. We also discuss its
ability to decipher the causal information flow as observed in gene expression.
We also illustrate our exploration by experiments on small simulated networks
as well as on a real biological data set.
| [
{
"created": "Fri, 4 May 2018 05:09:48 GMT",
"version": "v1"
}
] | 2018-05-07 | [
[
"White",
"Alex",
""
],
[
"Vignes",
"Matthieu",
""
]
] | Biological networks are a very convenient modelling and visualisation tool to discover knowledge from modern high-throughput genomics and postgenomics data sets. Indeed, biological entities are not isolated, but are components of complex multi-level systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems.We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set. |
q-bio/0609048 | Eugene Shakhnovich | D. B. Lukatsky, B. E. Shakhnovich, J. Mintseris, E. I. Shakhnovich | Structural similarity enhances interaction propensity of proteins | null | null | null | null | q-bio.BM | null | We study statistical properties of interacting protein-like surfaces and
predict two strong, related effects: (i) statistically enhanced self-attraction
of proteins; (ii) statistically enhanced attraction of proteins with similar
structures. The effects originate in the fact that the probability to find a
pattern self-match between two identical, even randomly organized interacting
protein surfaces is always higher compared with the probability for a pattern
match between two different, promiscuous protein surfaces. This theoretical
finding explains statistical prevalence of homodimers in protein-protein
interaction networks reported earlier. Further, our findings are confirmed by
the analysis of curated database of protein complexes that showed highly
statistically significant overrepresentation of dimers formed by structurally
similar proteins with highly divergent sequences (superfamily heterodimers). We
predict that significant fraction of heterodimers evolved from homodimers with
the negative design evolutionary pressure applied against promiscuous homodimer
formation. This is achieved through the formation of highly specific contacts
formed by charged residues as demonstrated both in model and real superfamily
heterodimers
| [
{
"created": "Wed, 27 Sep 2006 01:26:50 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Lukatsky",
"D. B.",
""
],
[
"Shakhnovich",
"B. E.",
""
],
[
"Mintseris",
"J.",
""
],
[
"Shakhnovich",
"E. I.",
""
]
] | We study statistical properties of interacting protein-like surfaces and predict two strong, related effects: (i) statistically enhanced self-attraction of proteins; (ii) statistically enhanced attraction of proteins with similar structures. The effects originate in the fact that the probability to find a pattern self-match between two identical, even randomly organized interacting protein surfaces is always higher compared with the probability for a pattern match between two different, promiscuous protein surfaces. This theoretical finding explains statistical prevalence of homodimers in protein-protein interaction networks reported earlier. Further, our findings are confirmed by the analysis of curated database of protein complexes that showed highly statistically significant overrepresentation of dimers formed by structurally similar proteins with highly divergent sequences (superfamily heterodimers). We predict that significant fraction of heterodimers evolved from homodimers with the negative design evolutionary pressure applied against promiscuous homodimer formation. This is achieved through the formation of highly specific contacts formed by charged residues as demonstrated both in model and real superfamily heterodimers |
2309.07146 | Santiago Rosa | Santiago Rosa, Manuel Pulido, Juan Ruiz, Tadeo Cocucci | Transmission matrix parameter estimation of COVID-19 evolution with age
compartments using ensemble-based data assimilation | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The COVID-19 pandemic and its multiple outbreaks have challenged governments
around the world. Much of the epidemiological modeling was based on
pre-pandemic contact information of the population, which changed drastically
due to governmental health measures, so called non-pharmaceutical interventions
made to reduce transmission of the virus, like social distancing and complete
lockdown. In this work, we evaluate an ensemble-based data assimilation
framework applied to a meta-population model to infer the transmission of the
disease between different population agegroups. We perform a set of idealized
twin-experiments to investigate the performance of different possible
parameterizations of the transmission matrix. These experiments show that it is
not possible to unambiguously estimate all the independent parameters of the
transmission matrix. However, under certain parameterizations, the transmission
matrix in an age-compartmental model can be estimated. These estimated
parameters lead to an increase of forecast accuracy in agegroups compartments
assimilating age-dependent accumulated cases and deaths observed in Argentina
compared to a single-compartment model, and reliable estimations of the
effective reproduction number. The age-dependent data assimilation and
forecasting of virus transmission may be important for an accurate prediction
and diagnosis of health care demand.
| [
{
"created": "Wed, 6 Sep 2023 19:42:57 GMT",
"version": "v1"
}
] | 2023-09-15 | [
[
"Rosa",
"Santiago",
""
],
[
"Pulido",
"Manuel",
""
],
[
"Ruiz",
"Juan",
""
],
[
"Cocucci",
"Tadeo",
""
]
] | The COVID-19 pandemic and its multiple outbreaks have challenged governments around the world. Much of the epidemiological modeling was based on pre-pandemic contact information of the population, which changed drastically due to governmental health measures, so called non-pharmaceutical interventions made to reduce transmission of the virus, like social distancing and complete lockdown. In this work, we evaluate an ensemble-based data assimilation framework applied to a meta-population model to infer the transmission of the disease between different population agegroups. We perform a set of idealized twin-experiments to investigate the performance of different possible parameterizations of the transmission matrix. These experiments show that it is not possible to unambiguously estimate all the independent parameters of the transmission matrix. However, under certain parameterizations, the transmission matrix in an age-compartmental model can be estimated. These estimated parameters lead to an increase of forecast accuracy in agegroups compartments assimilating age-dependent accumulated cases and deaths observed in Argentina compared to a single-compartment model, and reliable estimations of the effective reproduction number. The age-dependent data assimilation and forecasting of virus transmission may be important for an accurate prediction and diagnosis of health care demand. |
2103.13464 | Egor Alimpiev | Egor Alimpiev, Noah A Rosenberg | Enumeration of coalescent histories for caterpillar species trees and
$p$-pseudocaterpillar gene trees | null | null | 10.1016/j.aam.2021.102265 | null | q-bio.PE math.CO | http://creativecommons.org/licenses/by/4.0/ | For a fixed set $X$ containing $n$ taxon labels, an ordered pair consisting
of a gene tree topology $G$ and a species tree $S$ bijectively labeled with the
labels of $X$ possesses a set of coalescent histories -- mappings from the set
of internal nodes of $G$ to the set of edges of $S$ describing possible lists
of edges in $S$ on which the coalescences in $G$ take place. Enumerations of
coalescent histories for gene trees and species trees have produced suggestive
results regarding the pairs $(G,S)$ that, for a fixed $n$, have the largest
number of coalescent histories. We define a class of 2-cherry binary tree
topologies that we term $p$-pseudocaterpillars, examining coalescent histories
for non-matching pairs $(G,S)$, in the case in which $S$ has a caterpillar
shape and $G$ has a $p$-pseudocaterpillar shape. Using a construction that
associates coalescent histories for $(G,S)$ with a class of "roadblocked"
monotonic paths, we identify the $p$-pseudocaterpillar labeled gene tree
topology that, for a fixed caterpillar labeled species tree topology, gives
rise to the largest number of coalescent histories. The shape that maximizes
the number of coalescent histories places the "second" cherry of the
$p$-pseudocaterpillar equidistantly from the root of the "first" cherry and
from the tree root. A symmetry in the numbers of coalescent histories for
$p$-pseudocaterpillar gene trees and caterpillar species trees is seen to exist
around the maximizing value of the parameter $p$. The results provide insight
into the factors that influence the number of coalescent histories possible for
a given gene tree and species tree.
| [
{
"created": "Wed, 24 Mar 2021 19:48:36 GMT",
"version": "v1"
}
] | 2022-05-24 | [
[
"Alimpiev",
"Egor",
""
],
[
"Rosenberg",
"Noah A",
""
]
] | For a fixed set $X$ containing $n$ taxon labels, an ordered pair consisting of a gene tree topology $G$ and a species tree $S$ bijectively labeled with the labels of $X$ possesses a set of coalescent histories -- mappings from the set of internal nodes of $G$ to the set of edges of $S$ describing possible lists of edges in $S$ on which the coalescences in $G$ take place. Enumerations of coalescent histories for gene trees and species trees have produced suggestive results regarding the pairs $(G,S)$ that, for a fixed $n$, have the largest number of coalescent histories. We define a class of 2-cherry binary tree topologies that we term $p$-pseudocaterpillars, examining coalescent histories for non-matching pairs $(G,S)$, in the case in which $S$ has a caterpillar shape and $G$ has a $p$-pseudocaterpillar shape. Using a construction that associates coalescent histories for $(G,S)$ with a class of "roadblocked" monotonic paths, we identify the $p$-pseudocaterpillar labeled gene tree topology that, for a fixed caterpillar labeled species tree topology, gives rise to the largest number of coalescent histories. The shape that maximizes the number of coalescent histories places the "second" cherry of the $p$-pseudocaterpillar equidistantly from the root of the "first" cherry and from the tree root. A symmetry in the numbers of coalescent histories for $p$-pseudocaterpillar gene trees and caterpillar species trees is seen to exist around the maximizing value of the parameter $p$. The results provide insight into the factors that influence the number of coalescent histories possible for a given gene tree and species tree. |
1910.04918 | Okyaz Eminaga | Okyaz Eminaga, Yuri Tolkach, Christian Kunder, Mahmood Abbas, Ryan
Han, Rosalie Nolley, Axel Semjonow, Martin Boegemann, Sebastian Huss, Andreas
Loening, Robert West, Geoffrey Sonn, Richard Fan, Olaf Bettendorf, James
Brook and Daniel Rubin | Deep Learning for Prostate Pathology | null | null | null | null | q-bio.TO cs.CV cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The current study detects different morphologies related to prostate
pathology using deep learning models; these models were evaluated on 2,121
hematoxylin and eosin (H&E) stain histology images captured using bright field
microscopy, which spanned a variety of image qualities, origins (whole slide,
tissue micro array, whole mount, Internet), scanning machines, timestamps, H&E
staining protocols, and institutions. For case usage, these models were applied
for the annotation tasks in clinician-oriented pathology reports for
prostatectomy specimens. The true positive rate (TPR) for slides with prostate
cancer was 99.7% by a false positive rate of 0.785%. The F1-scores of Gleason
patterns reported in pathology reports ranged from 0.795 to 1.0 at the case
level. TPR was 93.6% for the cribriform morphology and 72.6% for the ductal
morphology. The correlation between the ground truth and the prediction for the
relative tumor volume was 0.987 n. Our models cover the major components of
prostate pathology and successfully accomplish the annotation tasks.
| [
{
"created": "Fri, 11 Oct 2019 00:10:59 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Oct 2019 07:34:27 GMT",
"version": "v2"
},
{
"created": "Wed, 16 Oct 2019 00:14:28 GMT",
"version": "v3"
}
] | 2019-10-17 | [
[
"Eminaga",
"Okyaz",
""
],
[
"Tolkach",
"Yuri",
""
],
[
"Kunder",
"Christian",
""
],
[
"Abbas",
"Mahmood",
""
],
[
"Han",
"Ryan",
""
],
[
"Nolley",
"Rosalie",
""
],
[
"Semjonow",
"Axel",
""
],
[
"Boegemann",
"Martin",
""
],
[
"Huss",
"Sebastian",
""
],
[
"Loening",
"Andreas",
""
],
[
"West",
"Robert",
""
],
[
"Sonn",
"Geoffrey",
""
],
[
"Fan",
"Richard",
""
],
[
"Bettendorf",
"Olaf",
""
],
[
"Brook",
"James",
""
],
[
"Rubin",
"Daniel",
""
]
] | The current study detects different morphologies related to prostate pathology using deep learning models; these models were evaluated on 2,121 hematoxylin and eosin (H&E) stain histology images captured using bright field microscopy, which spanned a variety of image qualities, origins (whole slide, tissue micro array, whole mount, Internet), scanning machines, timestamps, H&E staining protocols, and institutions. For case usage, these models were applied for the annotation tasks in clinician-oriented pathology reports for prostatectomy specimens. The true positive rate (TPR) for slides with prostate cancer was 99.7% by a false positive rate of 0.785%. The F1-scores of Gleason patterns reported in pathology reports ranged from 0.795 to 1.0 at the case level. TPR was 93.6% for the cribriform morphology and 72.6% for the ductal morphology. The correlation between the ground truth and the prediction for the relative tumor volume was 0.987 n. Our models cover the major components of prostate pathology and successfully accomplish the annotation tasks. |
q-bio/0511048 | Jonathan Coe | J. B. Coe and Y. Mao | Gompertz mortality law and scaling behaviour of the Penna model | 5 pages, 3 figures | Physical Review E 72, 051925, (2005) | 10.1103/PhysRevE.72.051925 | null | q-bio.PE | null | The Penna model is a model of evolutionary ageing through mutation
accumulation where traditionally time and the age of an organism are treated as
discrete variables and an organism's genome by a binary bit string. We
reformulate the asexual Penna model and show that, a universal scale invariance
emerges as we increase the number of discrete genome bits to the limit of a
continuum. The continuum model, introduced by Almeida and Thomas in
[Int.J.Mod.Phys.C, 11, 1209 (2000)] can be recovered from the discrete model in
the limit of infinite bits coupled with a vanishing mutation rate per bit.
Finally, we show that scale invariant properties may lead to the ubiquitous
Gompertz Law for mortality rates for early ages, which is generally regarded as
being empirical.
| [
{
"created": "Tue, 29 Nov 2005 14:13:53 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Coe",
"J. B.",
""
],
[
"Mao",
"Y.",
""
]
] | The Penna model is a model of evolutionary ageing through mutation accumulation where traditionally time and the age of an organism are treated as discrete variables and an organism's genome by a binary bit string. We reformulate the asexual Penna model and show that, a universal scale invariance emerges as we increase the number of discrete genome bits to the limit of a continuum. The continuum model, introduced by Almeida and Thomas in [Int.J.Mod.Phys.C, 11, 1209 (2000)] can be recovered from the discrete model in the limit of infinite bits coupled with a vanishing mutation rate per bit. Finally, we show that scale invariant properties may lead to the ubiquitous Gompertz Law for mortality rates for early ages, which is generally regarded as being empirical. |
2311.18219 | Yuan Liu | Yuan Liu and Hong-Bin Shen | FoldExplorer: Fast and Accurate Protein Structure Search with
Sequence-Enhanced Graph Embedding | 14 pages, 8 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of highly accurate protein structure prediction methods has fueled
an exponential expansion of the protein structure database. Consequently, there
is a rising demand for rapid and precise structural homolog search. Traditional
alignment-based methods are dedicated to precise comparisons between pairs,
exhibiting high accuracy. However, their sluggish processing speed is no longer
adequate for managing the current massive volume of data. In response to this
challenge, we propose a novel deep-learning approach FoldExplorer. It harnesses
the powerful capabilities of graph attention neural networks and protein large
language models for protein structures and sequences data processing to
generate embeddings for protein structures. The structural embeddings can be
used for fast and accurate protein search. The embeddings also provide insights
into the protein space. FoldExplorer demonstrates a substantial performance
improvement of 5% to 8% over the current state-of-the-art algorithm on the
benchmark datasets. Meanwhile, FoldExplorer does not compromise on search speed
and excels particularly in searching on a large-scale dataset.
| [
{
"created": "Thu, 30 Nov 2023 03:29:20 GMT",
"version": "v1"
}
] | 2023-12-01 | [
[
"Liu",
"Yuan",
""
],
[
"Shen",
"Hong-Bin",
""
]
] | The advent of highly accurate protein structure prediction methods has fueled an exponential expansion of the protein structure database. Consequently, there is a rising demand for rapid and precise structural homolog search. Traditional alignment-based methods are dedicated to precise comparisons between pairs, exhibiting high accuracy. However, their sluggish processing speed is no longer adequate for managing the current massive volume of data. In response to this challenge, we propose a novel deep-learning approach FoldExplorer. It harnesses the powerful capabilities of graph attention neural networks and protein large language models for protein structures and sequences data processing to generate embeddings for protein structures. The structural embeddings can be used for fast and accurate protein search. The embeddings also provide insights into the protein space. FoldExplorer demonstrates a substantial performance improvement of 5% to 8% over the current state-of-the-art algorithm on the benchmark datasets. Meanwhile, FoldExplorer does not compromise on search speed and excels particularly in searching on a large-scale dataset. |
1306.5261 | Kirk Lohmueller | Kirk E. Lohmueller | The impact of population demography and selection on the genetic
architecture of complex traits | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Population genetic studies have found evidence for dramatic population growth
in recent human history. It is unclear how this recent population growth,
combined with the effects of negative natural selection, has affected patterns
of deleterious variation, as well as the number, frequencies, and effect sizes
of mutations that contribute risk to complex traits. Here I use simulations
under population genetic models where a proportion of the heritability of the
trait is accounted for by mutations in a subset of the exome. I show that
recent population growth increases the proportion of nonsynonymous variants
segregating in the population, but does not affect the genetic load relative to
that in a population that did not expand. Under a model where a mutation's
effect on a trait is correlated with its effect on fitness, rare variants
explain a greater portion of the additive genetic variance of the trait in a
population that has recently expanded than in a population that did not
recently expand. Further, when using a single-marker test, for a given
false-positive rate and sample size, recent population growth decreases the
expected number of significant association with the trait relative to the
number detected in a population that did not expand. However, in a model where
there is no correlation between a mutation's effect on fitness and the effect
on the trait, common variants account for much of the additive genetic
variance, regardless of demography. Moreover, here demography does not affect
the number of significant association detected. These finding suggest recent
population history may be an important factor influencing the power of
association tests in accounting for the missing heritability of certain complex
traits.
| [
{
"created": "Fri, 21 Jun 2013 21:53:31 GMT",
"version": "v1"
},
{
"created": "Sun, 9 Feb 2014 06:29:25 GMT",
"version": "v2"
}
] | 2014-02-11 | [
[
"Lohmueller",
"Kirk E.",
""
]
] | Population genetic studies have found evidence for dramatic population growth in recent human history. It is unclear how this recent population growth, combined with the effects of negative natural selection, has affected patterns of deleterious variation, as well as the number, frequencies, and effect sizes of mutations that contribute risk to complex traits. Here I use simulations under population genetic models where a proportion of the heritability of the trait is accounted for by mutations in a subset of the exome. I show that recent population growth increases the proportion of nonsynonymous variants segregating in the population, but does not affect the genetic load relative to that in a population that did not expand. Under a model where a mutation's effect on a trait is correlated with its effect on fitness, rare variants explain a greater portion of the additive genetic variance of the trait in a population that has recently expanded than in a population that did not recently expand. Further, when using a single-marker test, for a given false-positive rate and sample size, recent population growth decreases the expected number of significant association with the trait relative to the number detected in a population that did not expand. However, in a model where there is no correlation between a mutation's effect on fitness and the effect on the trait, common variants account for much of the additive genetic variance, regardless of demography. Moreover, here demography does not affect the number of significant association detected. These finding suggest recent population history may be an important factor influencing the power of association tests in accounting for the missing heritability of certain complex traits. |
1401.2331 | Helene Loevenbruck | Lucile Rapin (GIPSA-lab), Marion Dohen (GIPSA-lab), Mircea Polosan
(GIN), Pascal Perrier (GIPSA-lab), H\'el\`ene Loevenbruck (GIPSA-lab, LPNC) | An EMG study of the lip muscles during covert auditory verbal
hallucinations in schizophrenia | null | Journal of Speech, Language, and Hearing Research 56 (2013)
S1882-S1893 | 10.1044/1092-4388(2013/12-0210) | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: Auditory verbal hallucinations (AVHs) are speech perceptions in the
absence of a external stimulation. An influential theoretical account of AVHs
in schizophrenia claims that a deficit in inner speech monitoring would cause
the verbal thoughts of the patient to be perceived as external voices. The
account is based on a predictive control model, in which verbal self-monitoring
is implemented. The aim of this study was to examine lip muscle activity during
AVHs in schizophrenia patients, in order to check whether inner speech
occurred. Methods: Lip muscle activity was recorded during covert AVHs (without
articulation) and rest. Surface electromyography (EMG) was used on eleven
schizophrenia patients. Results: Our results show an increase in EMG activity
in the orbicularis oris inferior muscle, during covert AVHs relative to rest.
This increase is not due to general muscular tension since there was no
increase of muscular activity in the forearm muscle. Conclusion: This evidence
that AVHs might be self-generated inner speech is discussed in the framework of
a predictive control model. Further work is needed to better describe how the
inner speech monitoring dysfunction occurs and how inner speech is controlled
and monitored. This will help better understanding how AVHs occur.
| [
{
"created": "Fri, 10 Jan 2014 14:01:56 GMT",
"version": "v1"
}
] | 2014-01-13 | [
[
"Rapin",
"Lucile",
"",
"GIPSA-lab"
],
[
"Dohen",
"Marion",
"",
"GIPSA-lab"
],
[
"Polosan",
"Mircea",
"",
"GIN"
],
[
"Perrier",
"Pascal",
"",
"GIPSA-lab"
],
[
"Loevenbruck",
"Hélène",
"",
"GIPSA-lab, LPNC"
]
] | Purpose: Auditory verbal hallucinations (AVHs) are speech perceptions in the absence of a external stimulation. An influential theoretical account of AVHs in schizophrenia claims that a deficit in inner speech monitoring would cause the verbal thoughts of the patient to be perceived as external voices. The account is based on a predictive control model, in which verbal self-monitoring is implemented. The aim of this study was to examine lip muscle activity during AVHs in schizophrenia patients, in order to check whether inner speech occurred. Methods: Lip muscle activity was recorded during covert AVHs (without articulation) and rest. Surface electromyography (EMG) was used on eleven schizophrenia patients. Results: Our results show an increase in EMG activity in the orbicularis oris inferior muscle, during covert AVHs relative to rest. This increase is not due to general muscular tension since there was no increase of muscular activity in the forearm muscle. Conclusion: This evidence that AVHs might be self-generated inner speech is discussed in the framework of a predictive control model. Further work is needed to better describe how the inner speech monitoring dysfunction occurs and how inner speech is controlled and monitored. This will help better understanding how AVHs occur. |
1604.04913 | Kevin Leder | Qie He, Junfeng Zhu, David Dingli, Jasmine Foo, Kevin Leder | Optimized Treatment Schedules for Chronic Myeloid Leukemia | 26 pages, 7 figures | null | 10.1371/journal.pcbi.1005129 | null | q-bio.TO math.OC q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over the past decade, several targeted therapies (e.g. imatinib, dasatinib,
nilotinib) have been developed to treat Chronic Myeloid Leukemia (CML). Despite
an initial response to therapy, drug resistance remains a problem for some CML
patients. Recent studies have shown that resistance mutations that preexist
treatment can be detected in a substan- tial number of patients, and that this
may be associated with eventual treatment failure. One proposed method to
extend treatment efficacy is to use a combination of multiple targeted
therapies. However, the design of such combination therapies (timing, sequence,
etc.) remains an open challenge. In this work we mathematically model the
dynamics of CML response to combination therapy and analyze the impact of
combination treatment schedules on treatment efficacy in patients with
preexisting resistance. We then propose an optimization problem to find the
best schedule of multiple therapies based on the evolution of CML according to
our ordinary differential equation model. This resulting optimiza- tion problem
is nontrivial due to the presence of ordinary different equation constraints
and integer variables. Our model also incorporates realistic drug toxicity
constraints by tracking the dynamics of patient neutrophil counts in response
to therapy. Using realis- tic parameter estimates, we determine optimal
combination strategies that maximize time until treatment failure.
| [
{
"created": "Sun, 17 Apr 2016 19:16:17 GMT",
"version": "v1"
}
] | 2017-02-08 | [
[
"He",
"Qie",
""
],
[
"Zhu",
"Junfeng",
""
],
[
"Dingli",
"David",
""
],
[
"Foo",
"Jasmine",
""
],
[
"Leder",
"Kevin",
""
]
] | Over the past decade, several targeted therapies (e.g. imatinib, dasatinib, nilotinib) have been developed to treat Chronic Myeloid Leukemia (CML). Despite an initial response to therapy, drug resistance remains a problem for some CML patients. Recent studies have shown that resistance mutations that preexist treatment can be detected in a substan- tial number of patients, and that this may be associated with eventual treatment failure. One proposed method to extend treatment efficacy is to use a combination of multiple targeted therapies. However, the design of such combination therapies (timing, sequence, etc.) remains an open challenge. In this work we mathematically model the dynamics of CML response to combination therapy and analyze the impact of combination treatment schedules on treatment efficacy in patients with preexisting resistance. We then propose an optimization problem to find the best schedule of multiple therapies based on the evolution of CML according to our ordinary differential equation model. This resulting optimiza- tion problem is nontrivial due to the presence of ordinary different equation constraints and integer variables. Our model also incorporates realistic drug toxicity constraints by tracking the dynamics of patient neutrophil counts in response to therapy. Using realis- tic parameter estimates, we determine optimal combination strategies that maximize time until treatment failure. |
1504.03940 | Sergei Kozyrev | S.V. Kozyrev | Model of protein fragments and statistical potentials | 17 pages, some discussion is added or improved | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss a model of protein conformations where the conformations are
combinations of short fragments from some small set. For these fragments we
consider a distribution of frequencies of occurrence of pairs (sequence of
amino acids, conformation), averaged over some balls in the spaces of sequences
and conformations. These frequencies can be estimated due to smallness of
epsilon-entropy of the set of conformations of protein fragments.
We consider statistical potentials for protein fragments which describe the
mentioned frequencies of occurrence and discuss model of free energy of a
protein where the free energy is equal to a sum of statistical potentials of
the fragments.
We also consider contribution of contacts of fragments to the energy of
protein conformation, and contribution from statistical potentials of some
hierarchical set of larger protein fragments. This set of fragments is
constructed using the distribution of frequencies of occurrence of short
fragments.
We discuss applications of this model to problem of prediction of the native
conformation of a protein from its primary structure and to description of
dynamics of a protein. Modification of structural alignment taking into account
statistical potentials for protein fragments is considered and application to
threading procedure for proteins is discussed.
| [
{
"created": "Wed, 15 Apr 2015 15:16:49 GMT",
"version": "v1"
},
{
"created": "Sat, 29 Aug 2015 11:41:21 GMT",
"version": "v2"
},
{
"created": "Tue, 5 Jan 2016 09:28:32 GMT",
"version": "v3"
},
{
"created": "Sun, 3 Jul 2016 18:05:52 GMT",
"version": "v4"
}
] | 2016-07-05 | [
[
"Kozyrev",
"S. V.",
""
]
] | We discuss a model of protein conformations where the conformations are combinations of short fragments from some small set. For these fragments we consider a distribution of frequencies of occurrence of pairs (sequence of amino acids, conformation), averaged over some balls in the spaces of sequences and conformations. These frequencies can be estimated due to smallness of epsilon-entropy of the set of conformations of protein fragments. We consider statistical potentials for protein fragments which describe the mentioned frequencies of occurrence and discuss model of free energy of a protein where the free energy is equal to a sum of statistical potentials of the fragments. We also consider contribution of contacts of fragments to the energy of protein conformation, and contribution from statistical potentials of some hierarchical set of larger protein fragments. This set of fragments is constructed using the distribution of frequencies of occurrence of short fragments. We discuss applications of this model to problem of prediction of the native conformation of a protein from its primary structure and to description of dynamics of a protein. Modification of structural alignment taking into account statistical potentials for protein fragments is considered and application to threading procedure for proteins is discussed. |
1812.09137 | Florian Hartig | Florian Hartig | Simulation Modeling | Chapter for Oxford Bibliographies in Ecology, Editor David Gibson | Oxford Bibliographies in Ecology, 2017 | 10.1093/OBO/9780199830060-0189 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rise of computers, simulation models have emerged beside the more
traditional statistical and mathematical models as a third pillar for
ecological analysis. Broadly speaking, a simulation model is an algorithm,
typically implemented as a computer program, which propagates the states of a
system forward. Unlike in a mathematical model, however, this propagation does
not employ the methods of calculus but rather a set of rules or formulae that
directly prescribe the next state. Such an algorithmic model specification is
particularly suited for describing systems that are difficult to capture or
analyze with differential equations such as: (a) systems that are highly
nonlinear or chaotic; (b) discrete systems, for example networks or groups of
distinct individuals; (c) systems that are stochastic; and (d) systems that are
too complex to be successfully treated with classical calculus. As these
situations are frequently encountered in ecology, simulation models are now
widely applied across the discipline. They have been instrumental in developing
new insights into classical questions of species' coexistence, community
assembly, population dynamics, biogeography, and many more. The methods for
this relatively young field are still being actively developed, and practical
work with simulation models requires ecologists to learn new skills such as
coding, sensitivity analysis, calibration, validation, and forecasting
uncertainties. Moreover, scientific inquiry with complex systems has led to
subtle changes to the philosophical and epistemological views regarding
simplicity, reductionism, and the relationship between prediction and
understanding.
| [
{
"created": "Fri, 21 Dec 2018 14:17:52 GMT",
"version": "v1"
}
] | 2018-12-24 | [
[
"Hartig",
"Florian",
""
]
] | With the rise of computers, simulation models have emerged beside the more traditional statistical and mathematical models as a third pillar for ecological analysis. Broadly speaking, a simulation model is an algorithm, typically implemented as a computer program, which propagates the states of a system forward. Unlike in a mathematical model, however, this propagation does not employ the methods of calculus but rather a set of rules or formulae that directly prescribe the next state. Such an algorithmic model specification is particularly suited for describing systems that are difficult to capture or analyze with differential equations such as: (a) systems that are highly nonlinear or chaotic; (b) discrete systems, for example networks or groups of distinct individuals; (c) systems that are stochastic; and (d) systems that are too complex to be successfully treated with classical calculus. As these situations are frequently encountered in ecology, simulation models are now widely applied across the discipline. They have been instrumental in developing new insights into classical questions of species' coexistence, community assembly, population dynamics, biogeography, and many more. The methods for this relatively young field are still being actively developed, and practical work with simulation models requires ecologists to learn new skills such as coding, sensitivity analysis, calibration, validation, and forecasting uncertainties. Moreover, scientific inquiry with complex systems has led to subtle changes to the philosophical and epistemological views regarding simplicity, reductionism, and the relationship between prediction and understanding. |
1609.02959 | Steven Frank | Steven A. Frank | Puzzles in modern biology. II. Language, cancer and the recursive
processes of evolutionary innovation | null | F1000Research 5:2089 (2016) | 10.12688/f1000research.9568.1 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Human language emerged abruptly. Diverse body forms evolved suddenly.
Seed-bearing plants spread rapidly. How do complex evolutionary innovations
arise so quickly? Resolving alternative claims remains difficult. The great
events of the past happened a long time ago. Cancer provides a model to study
evolutionary innovation. A tumor must evolve many novel traits to become an
aggressive cancer. I use what we know or could study about cancer to describe
the key processes of innovation. In general, evolutionary systems form a
hierarchy of recursive processes. Those recursive processes determine the rates
at which innovations are generated, spread and transmitted. I relate the
recursive processes to abrupt evolutionary innovation.
| [
{
"created": "Fri, 9 Sep 2016 22:16:22 GMT",
"version": "v1"
}
] | 2016-09-13 | [
[
"Frank",
"Steven A.",
""
]
] | Human language emerged abruptly. Diverse body forms evolved suddenly. Seed-bearing plants spread rapidly. How do complex evolutionary innovations arise so quickly? Resolving alternative claims remains difficult. The great events of the past happened a long time ago. Cancer provides a model to study evolutionary innovation. A tumor must evolve many novel traits to become an aggressive cancer. I use what we know or could study about cancer to describe the key processes of innovation. In general, evolutionary systems form a hierarchy of recursive processes. Those recursive processes determine the rates at which innovations are generated, spread and transmitted. I relate the recursive processes to abrupt evolutionary innovation. |
1910.04932 | Ali Sadeghian | Hyun Choi, Ali Sadeghian, Sergio Marconi, Ethan White, Daisy Zhe Wang | Measuring Impact of Climate Change on Tree Species: analysis of JSDM on
FIA data | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the first beings affected by changes in the climate are trees, one of
our most vital resources. In this study tree species interaction and the
response to climate in different ecological environments is observed by
applying a joint species distribution model to different ecological domains in
the United States. Joint species distribution models are useful to learn
inter-species relationships and species response to the environment. The
climates' impact on the tree species is measured through species abundance in
an area. We compare the model's performance across all ecological domains and
study the sensitivity of the climate variables. With the prediction of
abundances, tree species populations can be predicted in the future and measure
the impact of climate change on tree populations.
| [
{
"created": "Fri, 11 Oct 2019 01:35:13 GMT",
"version": "v1"
}
] | 2019-10-14 | [
[
"Choi",
"Hyun",
""
],
[
"Sadeghian",
"Ali",
""
],
[
"Marconi",
"Sergio",
""
],
[
"White",
"Ethan",
""
],
[
"Wang",
"Daisy Zhe",
""
]
] | One of the first beings affected by changes in the climate are trees, one of our most vital resources. In this study tree species interaction and the response to climate in different ecological environments is observed by applying a joint species distribution model to different ecological domains in the United States. Joint species distribution models are useful to learn inter-species relationships and species response to the environment. The climates' impact on the tree species is measured through species abundance in an area. We compare the model's performance across all ecological domains and study the sensitivity of the climate variables. With the prediction of abundances, tree species populations can be predicted in the future and measure the impact of climate change on tree populations. |
q-bio/0508034 | B. J. Powell | Paul Meredith, B. J. Powell, Jennifer Riesz, Stephen
Nighswander-Rempel, Mark R. Pederson, and Evan Moore | Towards Structure-Property-Function Relationships for Eumelanin | 19 pages, 8 figures, Invited highlight article for Soft Matter | Soft Matter, 2006, 2(1), 37 - 44 | 10.1039/b511922g | null | q-bio.BM q-bio.TO | null | We discuss recent progress towards the establishment of important
structure-property-function relationships in eumelanins - key functional
bio-macromolecular systems responsible for photo-protection and immune response
in humans, and implicated in the development of melanoma skin cancer. We focus
on the link between eumelanin's secondary structure and optical properties such
as broad band UV-visible absorption and strong non-radiative relaxation; both
key features of the photo-protective function. We emphasise the insights gained
through a holistic approach combining optical spectroscopy with first
principles quantum chemical calculations, and advance the hypothesis that the
robust functionality characteristic of eumelanin is related to extreme chemical
and structural disorder at the secondary level. This inherent disorder is a low
cost natural resource, and it is interesting to speculate as to whether it may
play a role in other functional bio-macromolecular systems.
| [
{
"created": "Wed, 24 Aug 2005 02:26:02 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Meredith",
"Paul",
""
],
[
"Powell",
"B. J.",
""
],
[
"Riesz",
"Jennifer",
""
],
[
"Nighswander-Rempel",
"Stephen",
""
],
[
"Pederson",
"Mark R.",
""
],
[
"Moore",
"Evan",
""
]
] | We discuss recent progress towards the establishment of important structure-property-function relationships in eumelanins - key functional bio-macromolecular systems responsible for photo-protection and immune response in humans, and implicated in the development of melanoma skin cancer. We focus on the link between eumelanin's secondary structure and optical properties such as broad band UV-visible absorption and strong non-radiative relaxation; both key features of the photo-protective function. We emphasise the insights gained through a holistic approach combining optical spectroscopy with first principles quantum chemical calculations, and advance the hypothesis that the robust functionality characteristic of eumelanin is related to extreme chemical and structural disorder at the secondary level. This inherent disorder is a low cost natural resource, and it is interesting to speculate as to whether it may play a role in other functional bio-macromolecular systems. |
1507.02562 | Thomas R. Sokolowski | Thomas R. Sokolowski, Aleksandra M. Walczak, William Bialek and
Ga\v{s}per Tka\v{c}ik | Extending the dynamic range of transcription factor action by
translational regulation | 14 pages, 5 figures | Phys. Rev. E 93, 022404 (2016) | 10.1103/PhysRevE.93.022404 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A crucial step in the regulation of gene expression is binding of
transcription factor (TF) proteins to regulatory sites along the DNA. But
transcription factors act at nanomolar concentrations, and noise due to random
arrival of these molecules at their binding sites can severely limit the
precision of regulation. Recent work on the optimization of information flow
through regulatory networks indicates that the lower end of the dynamic range
of concentrations is simply inaccessible, overwhelmed by the impact of this
noise. Motivated by the behavior of homeodomain proteins, such as the maternal
morphogen Bicoid in the fruit fly embryo, we suggest a scheme in which
transcription factors also act as indirect translational regulators, binding to
the mRNA of other transcription factors. Intuitively, each mRNA molecule acts
as an independent sensor of the TF concentration, and averaging over these
multiple sensors reduces the noise. We analyze information flow through this
new scheme and identify conditions under which it outperforms direct
transcriptional regulation. Our results suggest that the dual role of
homeodomain proteins is not just a historical accident, but a solution to a
crucial physics problem in the regulation of gene expression.
| [
{
"created": "Thu, 9 Jul 2015 15:37:13 GMT",
"version": "v1"
}
] | 2016-02-10 | [
[
"Sokolowski",
"Thomas R.",
""
],
[
"Walczak",
"Aleksandra M.",
""
],
[
"Bialek",
"William",
""
],
[
"Tkačik",
"Gašper",
""
]
] | A crucial step in the regulation of gene expression is binding of transcription factor (TF) proteins to regulatory sites along the DNA. But transcription factors act at nanomolar concentrations, and noise due to random arrival of these molecules at their binding sites can severely limit the precision of regulation. Recent work on the optimization of information flow through regulatory networks indicates that the lower end of the dynamic range of concentrations is simply inaccessible, overwhelmed by the impact of this noise. Motivated by the behavior of homeodomain proteins, such as the maternal morphogen Bicoid in the fruit fly embryo, we suggest a scheme in which transcription factors also act as indirect translational regulators, binding to the mRNA of other transcription factors. Intuitively, each mRNA molecule acts as an independent sensor of the TF concentration, and averaging over these multiple sensors reduces the noise. We analyze information flow through this new scheme and identify conditions under which it outperforms direct transcriptional regulation. Our results suggest that the dual role of homeodomain proteins is not just a historical accident, but a solution to a crucial physics problem in the regulation of gene expression. |
1411.7916 | Thomas Pfeil | Thomas Pfeil, Jakob Jordan, Tom Tetzlaff, Andreas Gr\"ubl, Johannes
Schemmel, Markus Diesmann, Karlheinz Meier | The effect of heterogeneity on decorrelation mechanisms in spiking
neural networks: a neuromorphic-hardware study | 20 pages, 10 figures, supplements | Phys. Rev. X 6, 021023 (2016) | 10.1103/PhysRevX.6.021023 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High-level brain function such as memory, classification or reasoning can be
realized by means of recurrent networks of simplified model neurons. Analog
neuromorphic hardware constitutes a fast and energy efficient substrate for the
implementation of such neural computing architectures in technical applications
and neuroscientific research. The functional performance of neural networks is
often critically dependent on the level of correlations in the neural activity.
In finite networks, correlations are typically inevitable due to shared
presynaptic input. Recent theoretical studies have shown that inhibitory
feedback, abundant in biological neural networks, can actively suppress these
shared-input correlations and thereby enable neurons to fire nearly
independently. For networks of spiking neurons, the decorrelating effect of
inhibitory feedback has so far been explicitly demonstrated only for
homogeneous networks of neurons with linear sub-threshold dynamics. Theory,
however, suggests that the effect is a general phenomenon, present in any
system with sufficient inhibitory feedback, irrespective of the details of the
network structure or the neuronal and synaptic properties. Here, we investigate
the effect of network heterogeneity on correlations in sparse, random networks
of inhibitory neurons with non-linear, conductance-based synapses. Emulations
of these networks on the analog neuromorphic hardware system Spikey allow us to
test the efficiency of decorrelation by inhibitory feedback in the presence of
hardware-specific heterogeneities. The configurability of the hardware
substrate enables us to modulate the extent of heterogeneity in a systematic
manner. We selectively study the effects of shared input and recurrent
connections on correlations in membrane potentials and spike trains. Our
results confirm ...
| [
{
"created": "Fri, 28 Nov 2014 15:51:59 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Jul 2015 12:38:05 GMT",
"version": "v2"
},
{
"created": "Fri, 19 Feb 2016 20:30:02 GMT",
"version": "v3"
},
{
"created": "Thu, 9 Jun 2016 14:23:52 GMT",
"version": "v4"
}
] | 2016-06-10 | [
[
"Pfeil",
"Thomas",
""
],
[
"Jordan",
"Jakob",
""
],
[
"Tetzlaff",
"Tom",
""
],
[
"Grübl",
"Andreas",
""
],
[
"Schemmel",
"Johannes",
""
],
[
"Diesmann",
"Markus",
""
],
[
"Meier",
"Karlheinz",
""
]
] | High-level brain function such as memory, classification or reasoning can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear sub-threshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with non-linear, conductance-based synapses. Emulations of these networks on the analog neuromorphic hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm ... |
0706.0117 | Toby Johnson | Toby Johnson | Reciprocal best hits are not a logically sufficient condition for
orthology | null | null | null | null | q-bio.GN | null | It is common to use reciprocal best hits, also known as a boomerang
criterion, for determining orthology between sequences. The best hits may be
found by blast, or by other more recently developed algorithms. Previous work
seems to have assumed that reciprocal best hits is a sufficient but not
necessary condition for orthology. In this article, I explain why reciprocal
best hits cannot logically be a sufficient condition for orthology. If
reciprocal best hits is neither sufficient nor necessary for orthology, it
would seem worthwhile to examine further the logical foundations of some
unsupervised algorithms that are used to identify orthologs.
| [
{
"created": "Fri, 1 Jun 2007 10:19:11 GMT",
"version": "v1"
}
] | 2007-06-04 | [
[
"Johnson",
"Toby",
""
]
] | It is common to use reciprocal best hits, also known as a boomerang criterion, for determining orthology between sequences. The best hits may be found by blast, or by other more recently developed algorithms. Previous work seems to have assumed that reciprocal best hits is a sufficient but not necessary condition for orthology. In this article, I explain why reciprocal best hits cannot logically be a sufficient condition for orthology. If reciprocal best hits is neither sufficient nor necessary for orthology, it would seem worthwhile to examine further the logical foundations of some unsupervised algorithms that are used to identify orthologs. |
0710.0383 | Mauro Mobilia Dr | Tobias Reichenbach, Mauro Mobilia, Erwin Frey | Noise and Correlations in a Spatial Population Model with Cyclic
Competition | 4 pages of main text, 3 color figures + 2 pages of supplementary
material (EPAPS Document). Final version for Physical Review Letters | Phys. Rev. Lett. 99, 238105 (2007) | 10.1103/PhysRevLett.99.238105 | LMU-ASC 66/07 | q-bio.PE cond-mat.stat-mech physics.bio-ph q-bio.QM | null | Noise and spatial degrees of freedom characterize most ecosystems. Some
aspects of their influence on the coevolution of populations with cyclic
interspecies competition have been demonstrated in recent experiments [e.g. B.
Kerr et al., Nature {\bf 418}, 171 (2002)]. To reach a better theoretical
understanding of these phenomena, we consider a paradigmatic spatial model
where three species exhibit cyclic dominance. Using an individual-based
description, as well as stochastic partial differential and deterministic
reaction-diffusion equations, we account for stochastic fluctuations and
spatial diffusion at different levels, and show how fascinating patterns of
entangled spirals emerge. We rationalize our analysis by computing the
spatio-temporal correlation functions and provide analytical expressions for
the front velocity and the wavelength of the propagating spiral waves.
| [
{
"created": "Tue, 2 Oct 2007 12:43:41 GMT",
"version": "v1"
},
{
"created": "Sat, 8 Dec 2007 18:41:20 GMT",
"version": "v2"
}
] | 2007-12-08 | [
[
"Reichenbach",
"Tobias",
""
],
[
"Mobilia",
"Mauro",
""
],
[
"Frey",
"Erwin",
""
]
] | Noise and spatial degrees of freedom characterize most ecosystems. Some aspects of their influence on the coevolution of populations with cyclic interspecies competition have been demonstrated in recent experiments [e.g. B. Kerr et al., Nature {\bf 418}, 171 (2002)]. To reach a better theoretical understanding of these phenomena, we consider a paradigmatic spatial model where three species exhibit cyclic dominance. Using an individual-based description, as well as stochastic partial differential and deterministic reaction-diffusion equations, we account for stochastic fluctuations and spatial diffusion at different levels, and show how fascinating patterns of entangled spirals emerge. We rationalize our analysis by computing the spatio-temporal correlation functions and provide analytical expressions for the front velocity and the wavelength of the propagating spiral waves. |
1012.3437 | Raul Isea | Gustavo Rivera, Fernando Gonz\'alez-Nilo, Tom\'as Perez-Acle, Raul
Isea and David S. Holmes | The Virtual Institute for Integrative Biology (VIIB) | 10 pages, ISBN 978-84-7834-565-6 | Proceedings of the Third Conference of the EELA Project (2007). R.
Gavela, B. Marechal, R. Barbera et al. (Eds.) pp. 111-120 | null | null | q-bio.OT | http://creativecommons.org/licenses/by-nc-sa/3.0/ | The Virtual Institute for Integrative Biology (VIIB) is a Latin American
initiative for achieving global collaborative e-Science in the areas of
bioinformatics, genome biology, systems biology, metagenomics, medical
applications and nanobiotechnolgy. The scientific agenda of VIIB includes:
construction of databases for comparative genomics, the AlterORF database for
alternate open reading frames discovery in genomes, bioinformatics services and
protein simulations for biotechnological and medical applications. Human
resource development has been promoted through co-sponsored students and shared
teaching and seminars via video conferencing. E-Science challenges include:
interoperability and connectivity concerns, high performance computing
limitations, and the development of customized computational frameworks and
flexible workflows to efficiently exploit shared resources without causing
impediments to the user. Outreach programs include training workshops and
classes for high school teachers and students and the new Adopt-a-Gene
initiative. The VIIB has proved an effective way for small teams to transcend
the critical mass problem, to overcome geographic limitations, to harness the
power of large scale, collaborative science and improve the visibility of Latin
American science It may provide a useful paradigm for developing further
e-Science initiatives in Latin America and other emerging regions.
| [
{
"created": "Wed, 15 Dec 2010 19:41:49 GMT",
"version": "v1"
}
] | 2010-12-16 | [
[
"Rivera",
"Gustavo",
""
],
[
"González-Nilo",
"Fernando",
""
],
[
"Perez-Acle",
"Tomás",
""
],
[
"Isea",
"Raul",
""
],
[
"Holmes",
"David S.",
""
]
] | The Virtual Institute for Integrative Biology (VIIB) is a Latin American initiative for achieving global collaborative e-Science in the areas of bioinformatics, genome biology, systems biology, metagenomics, medical applications and nanobiotechnolgy. The scientific agenda of VIIB includes: construction of databases for comparative genomics, the AlterORF database for alternate open reading frames discovery in genomes, bioinformatics services and protein simulations for biotechnological and medical applications. Human resource development has been promoted through co-sponsored students and shared teaching and seminars via video conferencing. E-Science challenges include: interoperability and connectivity concerns, high performance computing limitations, and the development of customized computational frameworks and flexible workflows to efficiently exploit shared resources without causing impediments to the user. Outreach programs include training workshops and classes for high school teachers and students and the new Adopt-a-Gene initiative. The VIIB has proved an effective way for small teams to transcend the critical mass problem, to overcome geographic limitations, to harness the power of large scale, collaborative science and improve the visibility of Latin American science It may provide a useful paradigm for developing further e-Science initiatives in Latin America and other emerging regions. |
1605.03005 | Yoram Burak | Neta Ravid Tannenbaum and Yoram Burak | Shaping neural circuits by high order synaptic interactions | Version 2 contains minor revisions. 33 pages, 10 figures, and 5
supporting figures. Accepted to PLoS Computational Biology; An earlier
version of this work appeared in abstract form (Program No. 260.23. 2015
Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2015.
Online.) | (2016). PLoS Comput Biol 12(8): e1005056 | 10.1371/journal.pcbi.1005056 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Spike timing dependent plasticity (STDP) is believed to play an important
role in shaping the structure of neural circuits. Here we show that STDP
generates effective interactions between synapses of different neurons, which
were neglected in previous theoretical treatments, and can be described as a
sum over contributions from structural motifs. These interactions can have a
pivotal influence on the connectivity patterns that emerge under the influence
of STDP. In particular, we consider two highly ordered forms of structure: wide
synfire chains, in which groups of neurons project to each other sequentially,
and self connected assemblies. We show that high order synaptic interactions
can enable the formation of both structures, depending on the form of the STDP
function and the time course of synaptic currents. Furthermore, within a
certain regime of biophysical parameters, emergence of the ordered connectivity
occurs robustly and autonomously in a stochastic network of spiking neurons,
without a need to expose the neural network to structured inputs during
learning.
| [
{
"created": "Tue, 10 May 2016 13:31:58 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Jul 2016 08:26:55 GMT",
"version": "v2"
}
] | 2016-08-25 | [
[
"Tannenbaum",
"Neta Ravid",
""
],
[
"Burak",
"Yoram",
""
]
] | Spike timing dependent plasticity (STDP) is believed to play an important role in shaping the structure of neural circuits. Here we show that STDP generates effective interactions between synapses of different neurons, which were neglected in previous theoretical treatments, and can be described as a sum over contributions from structural motifs. These interactions can have a pivotal influence on the connectivity patterns that emerge under the influence of STDP. In particular, we consider two highly ordered forms of structure: wide synfire chains, in which groups of neurons project to each other sequentially, and self connected assemblies. We show that high order synaptic interactions can enable the formation of both structures, depending on the form of the STDP function and the time course of synaptic currents. Furthermore, within a certain regime of biophysical parameters, emergence of the ordered connectivity occurs robustly and autonomously in a stochastic network of spiking neurons, without a need to expose the neural network to structured inputs during learning. |
2105.13570 | Jiayu Shang | Jiayu Shang and Yanni Sun | Predicting the hosts of prokaryotic viruses using GCN-based
semi-supervised learning | 16 pages, 14 figures | BMC Biol 19, 250 (2021) | 10.1186/s12915-021-01180-4 | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Background: Prokaryotic viruses, which infect bacteria and archaea, are the
most abundant and diverse biological entities in the biosphere. To understand
their regulatory roles in various ecosystems and to harness the potential of
bacteriophages for use in therapy, more knowledge of viral-host relationships
is required. High-throughput sequencing and its application to the microbiome
have offered new opportunities for computational approaches for predicting
which hosts particular viruses can infect. However, there are two main
challenges for computational host prediction. First, the empirically known
virus-host relationships are very limited. Second, although sequence similarity
between viruses and their prokaryote hosts have been used as a major feature
for host prediction, the alignment is either missing or ambiguous in many
cases. Thus, there is still a need to improve the accuracy of host prediction.
Results: In this work, we present a semi-supervised learning model, named
HostG, to conduct host prediction for novel viruses. We construct a knowledge
graph by utilizing both virus-virus protein similarity and virus-host DNA
sequence similarity. Then graph convolutional network (GCN) is adopted to
exploit viruses with or without known hosts in training to enhance the learning
ability. During the GCN training, we minimize the expected calibrated error
(ECE) to ensure the confidence of the predictions. We tested HostG on both
simulated and real sequencing data and compared its performance with other
state-of-the-art methods specifcally designed for virus host classification
(VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class). Conclusion: HostG
outperforms other popular methods, demonstrating the efficacy of using a
GCN-based semi-supervised learning approach. A particular advantage of HostG is
its ability to predict hosts from new taxa.
| [
{
"created": "Fri, 28 May 2021 03:29:31 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Nov 2021 04:13:28 GMT",
"version": "v2"
},
{
"created": "Thu, 2 Dec 2021 15:28:01 GMT",
"version": "v3"
}
] | 2021-12-03 | [
[
"Shang",
"Jiayu",
""
],
[
"Sun",
"Yanni",
""
]
] | Background: Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction. Results: In this work, we present a semi-supervised learning model, named HostG, to conduct host prediction for novel viruses. We construct a knowledge graph by utilizing both virus-virus protein similarity and virus-host DNA sequence similarity. Then graph convolutional network (GCN) is adopted to exploit viruses with or without known hosts in training to enhance the learning ability. During the GCN training, we minimize the expected calibrated error (ECE) to ensure the confidence of the predictions. We tested HostG on both simulated and real sequencing data and compared its performance with other state-of-the-art methods specifcally designed for virus host classification (VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class). Conclusion: HostG outperforms other popular methods, demonstrating the efficacy of using a GCN-based semi-supervised learning approach. A particular advantage of HostG is its ability to predict hosts from new taxa. |
1608.08314 | Sebastian Schreiber | Robert Stephen Cantrell and Chris Cosner and Yuan Lou and Sebastian J.
Schreiber | Evolution of natal dispersal in spatially heterogenous environments | Revision correcting several minor errors | Mathematical biosciences 283 (2017): 136-144 | 10.1016/j.mbs.2016.11.003 | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the evolution of dispersal is an important issue in
evolutionary ecology. For continuous time models in which individuals disperse
throughout their lifetime, it has been shown that a balanced dispersal
strategy, which results in an ideal free distribution, is evolutionary stable
in spatially varying but temporally constant environments. Many species,
however, primarily disperse prior to reproduction (natal dispersal) and less
commonly between reproductive events (breeding dispersal). As demographic and
dispersal terms combine in a multiplicative way for models of natal dispersal,
rather than the additive way for the previously studied models, we develop new
mathematical methods to study the evolution of natal dispersal for
continuous-time and discrete-time models. A fundamental ecological dichotomy is
identified for the non-trivial equilibrium of these models: (i) the per-capita
growth rates for individuals in all patches is equal to zero, or (ii)
individuals in some patches experience negative per-capita growth rates, while
individuals in other patches experience positive per-capita growth rates. The
first possibility corresponds to an ideal-free distribution, while the second
possibility corresponds to a "source-sink" spatial structure. We prove that
populations with a dispersal strategy leading to an ideal-free distribution
displace populations with dispersal strategy leading to a source-sink spatial
structure. When there are patches which can not sustain a population,
ideal-free strategies can be achieved by sedentary populations, and we show
that these populations can displace populations with any irreducible dispersal
strategy. Collectively, these results support that evolution selects for natal
or breeding dispersal strategies which lead to ideal-free distributions in
spatially heterogenous, but temporally homogenous, environments.
| [
{
"created": "Tue, 30 Aug 2016 03:36:48 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Nov 2016 20:52:56 GMT",
"version": "v2"
}
] | 2019-02-12 | [
[
"Cantrell",
"Robert Stephen",
""
],
[
"Cosner",
"Chris",
""
],
[
"Lou",
"Yuan",
""
],
[
"Schreiber",
"Sebastian J.",
""
]
] | Understanding the evolution of dispersal is an important issue in evolutionary ecology. For continuous time models in which individuals disperse throughout their lifetime, it has been shown that a balanced dispersal strategy, which results in an ideal free distribution, is evolutionary stable in spatially varying but temporally constant environments. Many species, however, primarily disperse prior to reproduction (natal dispersal) and less commonly between reproductive events (breeding dispersal). As demographic and dispersal terms combine in a multiplicative way for models of natal dispersal, rather than the additive way for the previously studied models, we develop new mathematical methods to study the evolution of natal dispersal for continuous-time and discrete-time models. A fundamental ecological dichotomy is identified for the non-trivial equilibrium of these models: (i) the per-capita growth rates for individuals in all patches is equal to zero, or (ii) individuals in some patches experience negative per-capita growth rates, while individuals in other patches experience positive per-capita growth rates. The first possibility corresponds to an ideal-free distribution, while the second possibility corresponds to a "source-sink" spatial structure. We prove that populations with a dispersal strategy leading to an ideal-free distribution displace populations with dispersal strategy leading to a source-sink spatial structure. When there are patches which can not sustain a population, ideal-free strategies can be achieved by sedentary populations, and we show that these populations can displace populations with any irreducible dispersal strategy. Collectively, these results support that evolution selects for natal or breeding dispersal strategies which lead to ideal-free distributions in spatially heterogenous, but temporally homogenous, environments. |
0908.2885 | Mike Steel Prof. | Andreas Dress, Vincent Moulton, Mike Steel, Taoyang Wu | Species, Clusters and the 'Tree of Life': A graph-theoretic perspective | 19 pages, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A hierarchical structure describing the inter-relationships of species has
long been a fundamental concept in systematic biology, from Linnean
classification through to the more recent quest for a 'Tree of Life.' In this
paper we use an approach based on discrete mathematics to address a basic
question: Could one delineate this hierarchical structure in nature purely by
reference to the 'genealogy' of present-day individuals, which describes how
they are related with one another by ancestry through a continuous line of
descent? We describe several mathematically precise ways by which one can
naturally define collections of subsets of present day individuals so that
these subsets are nested (and so form a tree) based purely on the directed
graph that describes the ancestry of these individuals. We also explore the
relationship between these and related clustering constructions.
| [
{
"created": "Thu, 20 Aug 2009 09:26:30 GMT",
"version": "v1"
}
] | 2009-08-21 | [
[
"Dress",
"Andreas",
""
],
[
"Moulton",
"Vincent",
""
],
[
"Steel",
"Mike",
""
],
[
"Wu",
"Taoyang",
""
]
] | A hierarchical structure describing the inter-relationships of species has long been a fundamental concept in systematic biology, from Linnean classification through to the more recent quest for a 'Tree of Life.' In this paper we use an approach based on discrete mathematics to address a basic question: Could one delineate this hierarchical structure in nature purely by reference to the 'genealogy' of present-day individuals, which describes how they are related with one another by ancestry through a continuous line of descent? We describe several mathematically precise ways by which one can naturally define collections of subsets of present day individuals so that these subsets are nested (and so form a tree) based purely on the directed graph that describes the ancestry of these individuals. We also explore the relationship between these and related clustering constructions. |
0810.2358 | Kevin E. Cahill | Kevin Cahill | Molecular Electroporation and the Transduction of Oligoarginines | 15 pages, 5 figures | Phys. Biol. 7 (2010) 016001 (14pp) | 10.1088/1478-3975/7/1/016001 | null | q-bio.BM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Certain short polycations, such as TAT and polyarginine, rapidly pass through
the plasma membranes of mammalian cells by an unknown mechanism called
transduction as well as by endocytosis and macropinocytosis. These
cell-penetrating peptides (CPPs) promise to be medically useful when fused to
biologically active peptides. I offer a simple model in which one or more CPPs
and the phosphatidylserines of the inner leaflet form a kind of capacitor with
a voltage in excess of 180 mV, high enough to create a molecular electropore.
The model is consistent with an empirical upper limit on the cargo peptide of
40--60 amino acids and with experimental data on how the transduction of a
polyarginine-fluorophore into mouse C2C12 myoblasts depends on the number of
arginines in the CPP and on the CPP concentration. The model makes three
testable predictions.
| [
{
"created": "Tue, 14 Oct 2008 04:56:23 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Nov 2008 05:02:35 GMT",
"version": "v2"
},
{
"created": "Fri, 30 Oct 2009 00:37:23 GMT",
"version": "v3"
}
] | 2010-09-21 | [
[
"Cahill",
"Kevin",
""
]
] | Certain short polycations, such as TAT and polyarginine, rapidly pass through the plasma membranes of mammalian cells by an unknown mechanism called transduction as well as by endocytosis and macropinocytosis. These cell-penetrating peptides (CPPs) promise to be medically useful when fused to biologically active peptides. I offer a simple model in which one or more CPPs and the phosphatidylserines of the inner leaflet form a kind of capacitor with a voltage in excess of 180 mV, high enough to create a molecular electropore. The model is consistent with an empirical upper limit on the cargo peptide of 40--60 amino acids and with experimental data on how the transduction of a polyarginine-fluorophore into mouse C2C12 myoblasts depends on the number of arginines in the CPP and on the CPP concentration. The model makes three testable predictions. |
2210.16993 | Chunyu Liu | Chunyu Liu and Jiacai Zhang | STN: a new tensor network method to identify stimulus category from
brain activity pattern | 12 pages | null | null | EFI-94-11 | q-bio.NC cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural decoding is still a challenge and hot topic in neurocomputing science.
Recently, many studies have shown that brain network patterns containing rich
spatial and temporal structure information, which represents the activation
information of brain under external stimuli. %Therefore, the research of
decoding stimuli from brain network received extensive more attention. The
traditional method extracts brain network features directly from the common
machine learning method, then puts these features into the classifier, and
realizes to decode external stimuli. However, this method cannot effectively
extract the multi-dimensional structural information, which is hidden in the
brain network. The tensor researchers show that the tensor decomposition model
can fully mine unique spatio-temporal structure characteristics in
multi-dimensional structure data. This research proposed a stimulus constrained
tensor brain model(STN)which involves the tensor decomposition idea and
stimulus category constraint information. The model was verified on the real
neuroimaging data sets (MEG and fMRI). The experimental results show that the
STN model achieves more than 11.06% and 18.46% on accuracy matrix compared with
others methods on two modal data sets. These results imply the superiority of
extracting discriminative characteristics about STN model, especially for
decoding object stimuli with semantic information.
| [
{
"created": "Mon, 31 Oct 2022 00:42:48 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Nov 2022 01:35:43 GMT",
"version": "v2"
},
{
"created": "Wed, 23 Nov 2022 00:18:32 GMT",
"version": "v3"
}
] | 2022-11-24 | [
[
"Liu",
"Chunyu",
""
],
[
"Zhang",
"Jiacai",
""
]
] | Neural decoding is still a challenge and hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatial and temporal structure information, which represents the activation information of brain under external stimuli. %Therefore, the research of decoding stimuli from brain network received extensive more attention. The traditional method extracts brain network features directly from the common machine learning method, then puts these features into the classifier, and realizes to decode external stimuli. However, this method cannot effectively extract the multi-dimensional structural information, which is hidden in the brain network. The tensor researchers show that the tensor decomposition model can fully mine unique spatio-temporal structure characteristics in multi-dimensional structure data. This research proposed a stimulus constrained tensor brain model(STN)which involves the tensor decomposition idea and stimulus category constraint information. The model was verified on the real neuroimaging data sets (MEG and fMRI). The experimental results show that the STN model achieves more than 11.06% and 18.46% on accuracy matrix compared with others methods on two modal data sets. These results imply the superiority of extracting discriminative characteristics about STN model, especially for decoding object stimuli with semantic information. |
2202.02245 | Zijin Gu | Zijin Gu, Keith Jamison, Mert Sabuncu, and Amy Kuceyeski | Personalized visual encoding model construction with small data | null | null | null | null | q-bio.QM cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Encoding models that predict brain response patterns to stimuli are one way
to capture this relationship between variability in bottom-up neural systems
and individual's behavior or pathological state. However, they generally need a
large amount of training data to achieve optimal accuracy. Here, we propose and
test an alternative personalized ensemble encoding model approach to utilize
existing encoding models, to create encoding models for novel individuals with
relatively little stimuli-response data. We show that these personalized
ensemble encoding models trained with small amounts of data for a specific
individual, i.e. ~300 image-response pairs, achieve accuracy not different from
models trained on ~20,000 image-response pairs for the same individual.
Importantly, the personalized ensemble encoding models preserve patterns of
inter-individual variability in the image-response relationship. Additionally,
we show the proposed approach is robust against domain shift by validating on a
prospectively collected set of image-response data in novel individuals with a
different scanner and experimental setup. Finally, we use our personalized
ensemble encoding model within the recently developed NeuroGen framework to
generate optimal stimuli designed to maximize specific regions' activations for
a specific individual. We show that the inter-individual differences in face
areas responses to images of animal vs human faces observed previously is
replicated using NeuroGen with the ensemble encoding model. Our approach shows
the potential to use previously collected, deeply sampled data to efficiently
create accurate, personalized encoding models and, subsequently, personalized
optimal synthetic images for new individuals scanned under different
experimental conditions.
| [
{
"created": "Fri, 4 Feb 2022 17:24:50 GMT",
"version": "v1"
},
{
"created": "Sun, 15 May 2022 02:52:47 GMT",
"version": "v2"
}
] | 2022-05-17 | [
[
"Gu",
"Zijin",
""
],
[
"Jamison",
"Keith",
""
],
[
"Sabuncu",
"Mert",
""
],
[
"Kuceyeski",
"Amy",
""
]
] | Encoding models that predict brain response patterns to stimuli are one way to capture this relationship between variability in bottom-up neural systems and individual's behavior or pathological state. However, they generally need a large amount of training data to achieve optimal accuracy. Here, we propose and test an alternative personalized ensemble encoding model approach to utilize existing encoding models, to create encoding models for novel individuals with relatively little stimuli-response data. We show that these personalized ensemble encoding models trained with small amounts of data for a specific individual, i.e. ~300 image-response pairs, achieve accuracy not different from models trained on ~20,000 image-response pairs for the same individual. Importantly, the personalized ensemble encoding models preserve patterns of inter-individual variability in the image-response relationship. Additionally, we show the proposed approach is robust against domain shift by validating on a prospectively collected set of image-response data in novel individuals with a different scanner and experimental setup. Finally, we use our personalized ensemble encoding model within the recently developed NeuroGen framework to generate optimal stimuli designed to maximize specific regions' activations for a specific individual. We show that the inter-individual differences in face areas responses to images of animal vs human faces observed previously is replicated using NeuroGen with the ensemble encoding model. Our approach shows the potential to use previously collected, deeply sampled data to efficiently create accurate, personalized encoding models and, subsequently, personalized optimal synthetic images for new individuals scanned under different experimental conditions. |
1308.0317 | William Bialek | Mikhail Tikhonov and William Bialek | Complexity in genetic networks: topology vs. strength of interactions | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genetic regulatory networks are defined by their topology and by a multitude
of continuously adjustable parameters. Here we present a class of simple models
within which the relative importance of topology vs. interaction strengths
becomes a well-posed problem. We find that complexity - the ability of the
network to adopt multiple stable states - is dominated by the adjustable
parameters. We comment on the implications for real networks and their
evolution.
| [
{
"created": "Thu, 1 Aug 2013 19:48:34 GMT",
"version": "v1"
}
] | 2013-08-02 | [
[
"Tikhonov",
"Mikhail",
""
],
[
"Bialek",
"William",
""
]
] | Genetic regulatory networks are defined by their topology and by a multitude of continuously adjustable parameters. Here we present a class of simple models within which the relative importance of topology vs. interaction strengths becomes a well-posed problem. We find that complexity - the ability of the network to adopt multiple stable states - is dominated by the adjustable parameters. We comment on the implications for real networks and their evolution. |
2207.12173 | Arunabha Majumdar | Arunabha Majumdar and Bogdan Pasaniuc | A Bayesian method for estimating gene-level polygenicity under the
framework of transcriptome-wide association study | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Polygnicity refers to the phenomenon that multiple genetic variants have a
non-zero effect on a complex trait. It is defined as the proportion of genetic
variants that have a nonzero effect on the trait. Evaluation of polygenicity
can provide valuable insights into the genetic architecture of the trait.
Several recent works have attempted to estimate polygenicity at the SNP level.
However, evaluating polygenicity at the gene level can be biologically more
meaningful. We propose the notion of gene-level polygenicity, defined as the
proportion of genes having a non-zero effect on the trait under the framework
of transcriptome-wide association study. We introduce a Bayesian approach
polygene to estimate this quantity for a trait. The method is based on spike
and slab prior and simultaneously provides an optimal subset of non-null genes.
Our simulation study shows that polygene efficiently estimates gene-level
polygenicity. The method produces downward bias for small choices of trait
heritability due to a non-null gene, which diminishes rapidly with an increase
in the GWAS sample size. While identifying the optimal subset of non-null
genes, polygene offers a high level of specificity and an overall good level of
sensitivity -- the sensitivity increases as the sample size of the reference
panel expression and GWAS data increase. We applied the method to seven
phenotypes in the UK Biobank, integrating expression data. We find height to be
most polygenic and asthma to be the least polygenic. Our analysis suggests that
both HDL and triglycerides are more polygenic than LDL.
| [
{
"created": "Mon, 25 Jul 2022 13:13:29 GMT",
"version": "v1"
}
] | 2022-07-26 | [
[
"Majumdar",
"Arunabha",
""
],
[
"Pasaniuc",
"Bogdan",
""
]
] | Polygnicity refers to the phenomenon that multiple genetic variants have a non-zero effect on a complex trait. It is defined as the proportion of genetic variants that have a nonzero effect on the trait. Evaluation of polygenicity can provide valuable insights into the genetic architecture of the trait. Several recent works have attempted to estimate polygenicity at the SNP level. However, evaluating polygenicity at the gene level can be biologically more meaningful. We propose the notion of gene-level polygenicity, defined as the proportion of genes having a non-zero effect on the trait under the framework of transcriptome-wide association study. We introduce a Bayesian approach polygene to estimate this quantity for a trait. The method is based on spike and slab prior and simultaneously provides an optimal subset of non-null genes. Our simulation study shows that polygene efficiently estimates gene-level polygenicity. The method produces downward bias for small choices of trait heritability due to a non-null gene, which diminishes rapidly with an increase in the GWAS sample size. While identifying the optimal subset of non-null genes, polygene offers a high level of specificity and an overall good level of sensitivity -- the sensitivity increases as the sample size of the reference panel expression and GWAS data increase. We applied the method to seven phenotypes in the UK Biobank, integrating expression data. We find height to be most polygenic and asthma to be the least polygenic. Our analysis suggests that both HDL and triglycerides are more polygenic than LDL. |
2110.14232 | Shixiang Wang | Shixiang Wang, Xue-Song Liu, Jianfeng Li and Qi Zhao | ezcox: An R/CRAN Package for Cox Model Batch Processing and
Visualization | 6 pages, 3 figures | null | null | null | q-bio.QM q-bio.GN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Cox analysis is a common clinical data analysis technique to link valuable
variables to clinical outcomes including dead and relapse. In the omics era,
Cox model batch processing is a basic strategy for screening clinically
relevant variables, biomarker discovery and gene signature identification.
However, all such analyses have been implemented with homebrew code in research
community, thus lack of transparency and reproducibility. Here, we present
ezcox, the first R/CRAN package for Cox model batch processing and
visualization. ezcox is an open source R package under GPL-3 license and it is
free available at https://github.com/ShixiangWang/ezcox and
https://cran.r-project.org/package=ezcox.
| [
{
"created": "Wed, 27 Oct 2021 07:35:53 GMT",
"version": "v1"
}
] | 2021-10-28 | [
[
"Wang",
"Shixiang",
""
],
[
"Liu",
"Xue-Song",
""
],
[
"Li",
"Jianfeng",
""
],
[
"Zhao",
"Qi",
""
]
] | Cox analysis is a common clinical data analysis technique to link valuable variables to clinical outcomes including dead and relapse. In the omics era, Cox model batch processing is a basic strategy for screening clinically relevant variables, biomarker discovery and gene signature identification. However, all such analyses have been implemented with homebrew code in research community, thus lack of transparency and reproducibility. Here, we present ezcox, the first R/CRAN package for Cox model batch processing and visualization. ezcox is an open source R package under GPL-3 license and it is free available at https://github.com/ShixiangWang/ezcox and https://cran.r-project.org/package=ezcox. |
1311.7328 | Weiqun Peng | Ji-Eun Lee, Chaochen Wang, Shiliyang Xu, Young-Wook Cho, Lifeng Wang,
Xuesong Feng, Vittorio Sartorelli, Anne Baldridge, Weiqun Peng, and Kai Ge | H3K4 mono- and di-methyltransferase MLL4 is required for enhancer
activation during cell differentiation | eLife 2013 | null | null | null | q-bio.GN | http://creativecommons.org/licenses/publicdomain/ | Enhancers play a central role in cell-type-specific gene expression and are
marked by H3K4me1/2. Active enhancers are further marked by H3K27ac. However,
the methyltransferases responsible for H3K4me1/2 on enhancers remain elusive.
Furthermore, how these enzymes function on enhancers to regulate
cell-type-specific gene expression is unclear. Here we identify MLL4 (KMT2D) as
a major mammalian H3K4 mono- and di-methyltransferase with partial functional
redundancy with MLL3 (KMT2C). Using adipogenesis and myogenesis as model
systems, we show that MLL4 exhibits cell-type- and
differentiation-stage-specific genomic binding and is predominantly localized
on enhancers. MLL4 co-localizes with lineage-determining transcription factors
(TFs) on active enhancers during differentiation. Deletion of MLL4 markedly
decreases H3K4me1/2, H3K27ac, Polymerase II and Mediator levels on enhancers
and leads to severe defects in cell-type-specific gene expression and cell
differentiation. Together, these findings identify MLL4 as a major mammalian
H3K4 mono- and di-methyltransferase essential for enhancer activation during
cell differentiation.
| [
{
"created": "Thu, 28 Nov 2013 14:25:50 GMT",
"version": "v1"
}
] | 2013-12-02 | [
[
"Lee",
"Ji-Eun",
""
],
[
"Wang",
"Chaochen",
""
],
[
"Xu",
"Shiliyang",
""
],
[
"Cho",
"Young-Wook",
""
],
[
"Wang",
"Lifeng",
""
],
[
"Feng",
"Xuesong",
""
],
[
"Sartorelli",
"Vittorio",
""
],
[
"Baldridge",
"Anne",
""
],
[
"Peng",
"Weiqun",
""
],
[
"Ge",
"Kai",
""
]
] | Enhancers play a central role in cell-type-specific gene expression and are marked by H3K4me1/2. Active enhancers are further marked by H3K27ac. However, the methyltransferases responsible for H3K4me1/2 on enhancers remain elusive. Furthermore, how these enzymes function on enhancers to regulate cell-type-specific gene expression is unclear. Here we identify MLL4 (KMT2D) as a major mammalian H3K4 mono- and di-methyltransferase with partial functional redundancy with MLL3 (KMT2C). Using adipogenesis and myogenesis as model systems, we show that MLL4 exhibits cell-type- and differentiation-stage-specific genomic binding and is predominantly localized on enhancers. MLL4 co-localizes with lineage-determining transcription factors (TFs) on active enhancers during differentiation. Deletion of MLL4 markedly decreases H3K4me1/2, H3K27ac, Polymerase II and Mediator levels on enhancers and leads to severe defects in cell-type-specific gene expression and cell differentiation. Together, these findings identify MLL4 as a major mammalian H3K4 mono- and di-methyltransferase essential for enhancer activation during cell differentiation. |
1505.06249 | Alexandre Drouin | Alexandre Drouin, S\'ebastien Gigu\`ere, Maxime D\'eraspe,
Fran\c{c}ois Laviolette, Mario Marchand, Jacques Corbeil | Greedy Biomarker Discovery in the Genome with Applications to
Antimicrobial Resistance | Peer-reviewed and accepted for an oral presentation in the Greed is
Great workshop at the International Conference on Machine Learning, Lille,
France, 2015 | null | null | null | q-bio.GN cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Set Covering Machine (SCM) is a greedy learning algorithm that produces
sparse classifiers. We extend the SCM for datasets that contain a huge number
of features. The whole genetic material of living organisms is an example of
such a case, where the number of feature exceeds 10^7. Three human pathogens
were used to evaluate the performance of the SCM at predicting antimicrobial
resistance. Our results show that the SCM compares favorably in terms of
sparsity and accuracy against L1 and L2 regularized Support Vector Machines and
CART decision trees. Moreover, the SCM was the only algorithm that could
consider the full feature space. For all other algorithms, the latter had to be
filtered as a preprocessing step.
| [
{
"created": "Fri, 22 May 2015 23:29:40 GMT",
"version": "v1"
}
] | 2015-05-26 | [
[
"Drouin",
"Alexandre",
""
],
[
"Giguère",
"Sébastien",
""
],
[
"Déraspe",
"Maxime",
""
],
[
"Laviolette",
"François",
""
],
[
"Marchand",
"Mario",
""
],
[
"Corbeil",
"Jacques",
""
]
] | The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers. We extend the SCM for datasets that contain a huge number of features. The whole genetic material of living organisms is an example of such a case, where the number of feature exceeds 10^7. Three human pathogens were used to evaluate the performance of the SCM at predicting antimicrobial resistance. Our results show that the SCM compares favorably in terms of sparsity and accuracy against L1 and L2 regularized Support Vector Machines and CART decision trees. Moreover, the SCM was the only algorithm that could consider the full feature space. For all other algorithms, the latter had to be filtered as a preprocessing step. |
2111.09414 | Elcin Huseyn | Elcin Huseyn | Developmental Status and Perspectives for Tissue Engineering in Urology | null | null | null | null | q-bio.TO physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Tissue engineering technology and tissue cell-based stem cell research have
made great strides in treating tissue and organ damage, correcting tissue and
organ dysfunction, and reducing surgical complications. In the past,
traditional methods have used biological substitutes for tissue repair
materials, while tissue engineering technology has focused on merging sperm
cells with biological materials to form biological tissues with the same
structure and function as their own tissues. The advantage is that tissue
engineering technology can overcome donors. Material procurement restrictions
can effectively reduce complications. The aim of studying tissue engineering
technology is to find sperm cells and suitable biological materials to replace
the original biological functions of tissues and to establish a suitable in
vivo microenvironment. This article mainly describes the current developments
of tissue engineering in various fields of urology and discusses the future
trends of tissue engineering technology in the treatment of complex diseases of
the urinary system. The results of the research in this article indicate that
while the current clinical studies are relatively few, the good results from
existing animal model studies indicate good prospects of tissue engineering
technology for the treatment of various urinary tract diseases in the future.
| [
{
"created": "Sun, 14 Nov 2021 16:02:02 GMT",
"version": "v1"
}
] | 2021-11-19 | [
[
"Huseyn",
"Elcin",
""
]
] | Tissue engineering technology and tissue cell-based stem cell research have made great strides in treating tissue and organ damage, correcting tissue and organ dysfunction, and reducing surgical complications. In the past, traditional methods have used biological substitutes for tissue repair materials, while tissue engineering technology has focused on merging sperm cells with biological materials to form biological tissues with the same structure and function as their own tissues. The advantage is that tissue engineering technology can overcome donors. Material procurement restrictions can effectively reduce complications. The aim of studying tissue engineering technology is to find sperm cells and suitable biological materials to replace the original biological functions of tissues and to establish a suitable in vivo microenvironment. This article mainly describes the current developments of tissue engineering in various fields of urology and discusses the future trends of tissue engineering technology in the treatment of complex diseases of the urinary system. The results of the research in this article indicate that while the current clinical studies are relatively few, the good results from existing animal model studies indicate good prospects of tissue engineering technology for the treatment of various urinary tract diseases in the future. |
q-bio/0506035 | Sreepurna Malakar | Sukanto Bhattacharya and Sreepurna Malakar | Monte Carlo modeling of the effect of extreme events on the extinction
dynamics of animal species with 2-year life cycles | 12 pages, 6 tables, 6 figures | null | null | null | q-bio.QM q-bio.PE | null | Our paper computationally explores the extinction dynamics of an animal
species effected by a sudden spike in mortality due to an extreme event. In our
study, the animal species has a 2-year life cycle and is endowed with a high
survival probability under normal circumstances. Our proposed approach does not
involve any restraining assumptions concerning environmental variables or
predator-prey relationships. Rather it is based on the simple premise that if
observed on an year-to-year basis, the population size will be noted to either
have gone up or come down as compared to last year. The conceptualization is
borrowed from the theory of asset pricing in stochastic finance. Our results
indicate that an extreme event with a maximum shock size (i.e. the maximum
number of immediate mortalities that may be caused by an extreme event)
exceeding two-thirds the size of the pristine population can potentially drive
any animal species with a 2-year life cycle to extinction for any fecundity
level.
| [
{
"created": "Wed, 22 Jun 2005 23:42:29 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bhattacharya",
"Sukanto",
""
],
[
"Malakar",
"Sreepurna",
""
]
] | Our paper computationally explores the extinction dynamics of an animal species effected by a sudden spike in mortality due to an extreme event. In our study, the animal species has a 2-year life cycle and is endowed with a high survival probability under normal circumstances. Our proposed approach does not involve any restraining assumptions concerning environmental variables or predator-prey relationships. Rather it is based on the simple premise that if observed on an year-to-year basis, the population size will be noted to either have gone up or come down as compared to last year. The conceptualization is borrowed from the theory of asset pricing in stochastic finance. Our results indicate that an extreme event with a maximum shock size (i.e. the maximum number of immediate mortalities that may be caused by an extreme event) exceeding two-thirds the size of the pristine population can potentially drive any animal species with a 2-year life cycle to extinction for any fecundity level. |
2407.09564 | Maxime Lenormand | Marie Soret, Sylvain Moulherat, Maxime Lenormand, Sandra Luque | Implication of modelling choices on connectivity estimation: A
comparative analysis | 34 pages, 9 figures + Appendix | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We focus on connectivity methods used to understand and predict how
landscapes and habitats facilitate or impede the movement and dispersal of
species. Our objective is to compare the implication of methodological choices
at three stages of the modelling framework: landscape characterisation,
connectivity estimation, and connectivity assessment. What are the convergences
and divergences of different modelling approaches? What are the implications of
their combined results for landscape planning? We implemented two landscape
characterisation approaches: expert opinion and species distribution model
(SDM); four connectivity estimation models: Euclidean distance, least-cost
paths (LCP), circuit theory, and stochastic movement simulation (SMS); and two
connectivity indices: flux and area-weighted flux (dPCflux). We compared
outcomes such as movement maps and habitat prioritisation for a rural landscape
in southwestern France. Landscape characterisation is the main factor
influencing connectivity assessment. The movement maps reflect the models'
assumptions: LCP produced narrow beams reflecting the optimal pathways; whereas
circuit theory and SMS produced wider estimation reflecting movement
stochasticity, with SMS integrating behavioural drivers. The indices
highlighted different aspects: dPCflux the surface of suitable habitats and
flux their proximity. We recommend focusing on landscape characterisation
before engaging further in the modelling framework. We emphasise the importance
of stochasticity and behavioural drivers in connectivity, which can be
reflected using circuit theory, SMS or other stochastic individual-based
models. We stress the importance of using multiple indices to capture the
multi-factorial aspect of connectivity.
| [
{
"created": "Fri, 5 Jul 2024 06:01:51 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Soret",
"Marie",
""
],
[
"Moulherat",
"Sylvain",
""
],
[
"Lenormand",
"Maxime",
""
],
[
"Luque",
"Sandra",
""
]
] | We focus on connectivity methods used to understand and predict how landscapes and habitats facilitate or impede the movement and dispersal of species. Our objective is to compare the implication of methodological choices at three stages of the modelling framework: landscape characterisation, connectivity estimation, and connectivity assessment. What are the convergences and divergences of different modelling approaches? What are the implications of their combined results for landscape planning? We implemented two landscape characterisation approaches: expert opinion and species distribution model (SDM); four connectivity estimation models: Euclidean distance, least-cost paths (LCP), circuit theory, and stochastic movement simulation (SMS); and two connectivity indices: flux and area-weighted flux (dPCflux). We compared outcomes such as movement maps and habitat prioritisation for a rural landscape in southwestern France. Landscape characterisation is the main factor influencing connectivity assessment. The movement maps reflect the models' assumptions: LCP produced narrow beams reflecting the optimal pathways; whereas circuit theory and SMS produced wider estimation reflecting movement stochasticity, with SMS integrating behavioural drivers. The indices highlighted different aspects: dPCflux the surface of suitable habitats and flux their proximity. We recommend focusing on landscape characterisation before engaging further in the modelling framework. We emphasise the importance of stochasticity and behavioural drivers in connectivity, which can be reflected using circuit theory, SMS or other stochastic individual-based models. We stress the importance of using multiple indices to capture the multi-factorial aspect of connectivity. |
1403.1011 | Petter Holme | Petter Holme | Model versions and fast algorithms for network epidemiology | This write-up covers some details about simulating epidemiological
models on networks. Feedback is appreciated | Journal of Logistical Engineering University 30, 1-7 (2014) | 10.3969/j.issn.1672-7843.2014.03.001 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network epidemiology has become a core framework for investigating the role
of human contact patterns in the spreading of infectious diseases. In network
epidemiology represents the contact structure as a network of nodes
(individuals) connected by links (sometimes as a temporal network where the
links are not continuously active) and the disease as a compartmental model
(where individuals are assigned states with respect to the disease and follow
certain transition rules between the states). In this paper, we discuss fast
algorithms for such simulations and also compare two commonly used versions -
one where there is a constant recovery rate (the number of individuals that
stop being infectious per time is proportional to the number of such people),
the other where the duration of the disease is constant. We find that, for most
practical purposes, these versions are qualitatively the same.
| [
{
"created": "Wed, 5 Mar 2014 05:52:09 GMT",
"version": "v1"
}
] | 2014-06-10 | [
[
"Holme",
"Petter",
""
]
] | Network epidemiology has become a core framework for investigating the role of human contact patterns in the spreading of infectious diseases. In network epidemiology represents the contact structure as a network of nodes (individuals) connected by links (sometimes as a temporal network where the links are not continuously active) and the disease as a compartmental model (where individuals are assigned states with respect to the disease and follow certain transition rules between the states). In this paper, we discuss fast algorithms for such simulations and also compare two commonly used versions - one where there is a constant recovery rate (the number of individuals that stop being infectious per time is proportional to the number of such people), the other where the duration of the disease is constant. We find that, for most practical purposes, these versions are qualitatively the same. |
1210.2993 | Elisenda Feliu | Heather A. Harrington, Elisenda Feliu, Carsten Wiuf, Michael M. P.
Stumpf | Cellular compartments cause multistability in biochemical reaction
networks and allow cells to process more information | null | null | null | null | q-bio.MN q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many biological, physical, and social interactions have a particular
dependence on where they take place. In living cells, protein movement between
the nucleus and cytoplasm affects cellular response (i.e., proteins must be
present in the nucleus to regulate their target genes). Here we use recent
developments from dynamical systems and chemical reaction network theory to
identify and characterize the key-role of the spatial organization of
eukaryotic cells in cellular information processing. In particular the
existence of distinct compartments plays a pivotal role in whether a system is
capable of multistationarity (multiple response states), and is thus directly
linked to the amount of information that the signaling molecules can represent
in the nucleus. Multistationarity provides a mechanism for switching between
different response states in cell signaling systems and enables multiple
outcomes for cellular-decision making. We find that introducing species
localization can alter the capacity for multistationarity and mathematically
demonstrate that shuttling confers flexibility for and greater control of the
emergence of an all-or-none response.
| [
{
"created": "Wed, 10 Oct 2012 17:53:30 GMT",
"version": "v1"
}
] | 2012-10-11 | [
[
"Harrington",
"Heather A.",
""
],
[
"Feliu",
"Elisenda",
""
],
[
"Wiuf",
"Carsten",
""
],
[
"Stumpf",
"Michael M. P.",
""
]
] | Many biological, physical, and social interactions have a particular dependence on where they take place. In living cells, protein movement between the nucleus and cytoplasm affects cellular response (i.e., proteins must be present in the nucleus to regulate their target genes). Here we use recent developments from dynamical systems and chemical reaction network theory to identify and characterize the key-role of the spatial organization of eukaryotic cells in cellular information processing. In particular the existence of distinct compartments plays a pivotal role in whether a system is capable of multistationarity (multiple response states), and is thus directly linked to the amount of information that the signaling molecules can represent in the nucleus. Multistationarity provides a mechanism for switching between different response states in cell signaling systems and enables multiple outcomes for cellular-decision making. We find that introducing species localization can alter the capacity for multistationarity and mathematically demonstrate that shuttling confers flexibility for and greater control of the emergence of an all-or-none response. |
1606.08585 | Abhay Sharma | Abhay Sharma | Reanalyzing variable directionality of gene expression in
transgenerational epigenetic inheritance | 12 pages, 1 figure, 1 table | null | null | null | q-bio.GN | http://creativecommons.org/publicdomain/zero/1.0/ | A previous report claimed no evidence of transgenerational epigenetic
inheritance in a mouse model of in utero environmental exposure, based on the
observation that gene expression changes observed in the germ cells of G1 and
G2 male fetus were not in the same direction. A subsequent data reanalysis
however showed a statistically significant overlap between G1 and G2 genes
irrespective of direction, leading to the suggestion that, as phenotypic
variability in epigenetic transmission has been observed in several other
examples also, the above report provided evidence in favor of, not against,
transgenerational inheritance. This criticism has recently been questioned.
Here, it is shown that the questions raised are based not only on incorrect
statistical calculations but also on wrong premise that gene expression changes
do not constitute a phenotype.
| [
{
"created": "Tue, 28 Jun 2016 07:30:01 GMT",
"version": "v1"
}
] | 2016-06-29 | [
[
"Sharma",
"Abhay",
""
]
] | A previous report claimed no evidence of transgenerational epigenetic inheritance in a mouse model of in utero environmental exposure, based on the observation that gene expression changes observed in the germ cells of G1 and G2 male fetus were not in the same direction. A subsequent data reanalysis however showed a statistically significant overlap between G1 and G2 genes irrespective of direction, leading to the suggestion that, as phenotypic variability in epigenetic transmission has been observed in several other examples also, the above report provided evidence in favor of, not against, transgenerational inheritance. This criticism has recently been questioned. Here, it is shown that the questions raised are based not only on incorrect statistical calculations but also on wrong premise that gene expression changes do not constitute a phenotype. |
1805.07768 | Jorge Fernandez-de-Cossio-Diaz | Jorge Fernandez-de-Cossio-Diaz, Roberto Mulet, Alexei Vazquez | Cell population heterogeneity driven by stochastic partition and growth
optimality | null | null | 10.1038/s41598-019-45882-w | null | q-bio.MN cond-mat.stat-mech physics.bio-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A fundamental question in biology is how cell populations evolve into
different subtypes based on homogeneous processes at the single cell level.
Here we show that population bimodality can emerge even when biological
processes are homogenous at the cell level and the environment is kept
constant. Our model is based on the stochastic partitioning of a cell component
with an optimal copy number. We show that the existence of unimodal or bimodal
distributions depends on the variance of partition errors and the growth rate
tolerance around the optimal copy number. In particular, our theory provides a
consistent explanation for the maintenance of aneuploid states in a population.
The proposed model can also be relevant for other cell components such as
mitochondria and plasmids, whose abundances affect the growth rate and are
subject to stochastic partition at cell division.
| [
{
"created": "Sun, 20 May 2018 14:16:20 GMT",
"version": "v1"
},
{
"created": "Sun, 29 Jul 2018 10:14:42 GMT",
"version": "v2"
},
{
"created": "Wed, 19 Jun 2019 13:04:34 GMT",
"version": "v3"
}
] | 2019-07-01 | [
[
"Fernandez-de-Cossio-Diaz",
"Jorge",
""
],
[
"Mulet",
"Roberto",
""
],
[
"Vazquez",
"Alexei",
""
]
] | A fundamental question in biology is how cell populations evolve into different subtypes based on homogeneous processes at the single cell level. Here we show that population bimodality can emerge even when biological processes are homogenous at the cell level and the environment is kept constant. Our model is based on the stochastic partitioning of a cell component with an optimal copy number. We show that the existence of unimodal or bimodal distributions depends on the variance of partition errors and the growth rate tolerance around the optimal copy number. In particular, our theory provides a consistent explanation for the maintenance of aneuploid states in a population. The proposed model can also be relevant for other cell components such as mitochondria and plasmids, whose abundances affect the growth rate and are subject to stochastic partition at cell division. |
1809.09798 | E. Song | Euijun Song | Energy landscape analysis of cardiac fibrillation wave dynamics using
pairwise maximum entropy model | 11 pages, 3 figures, Presented at the 62nd Biophysical Society Annual
Meeting, San Francisco, CA, 2018 | null | null | null | q-bio.TO nlin.PS physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Cardiac fibrillation is characterized by chaotic and disintegrated spiral
wave dynamics patterns, whereas sinus rhythm shows synchronized excitation
patterns. To determine functional correlations among cardiomyocytes during
complex fibrillation states, we applied a pairwise maximum entropy model (MEM)
to the 2D numerical simulation data of human atrial fibrillation. We then
constructed an energy landscape and estimated a hierarchical structure among
the different local minima (attractors) to explain the dynamic properties of
cardiac fibrillation. The MEM could describe the wave dynamics of sinus rhythm,
single stable rotor, and single rotor with wavebreak (both accuracy and
reliability>0.9), but not the multiple random wavelet case. The energy
landscapes exhibited unique profiles of local minima and energy barriers,
characterizing the spatiotemporal patterns of cardiac fibrillation dynamics.
The pairwise MEM-based energy landscape analysis provides reliable explanations
of complex nonlinear dynamics of cardiac fibrillation, which might be
determined by the presence of a 'driver' such as a sinus node or rotor.
| [
{
"created": "Wed, 26 Sep 2018 04:19:12 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Oct 2018 05:10:41 GMT",
"version": "v2"
},
{
"created": "Tue, 9 Aug 2022 03:48:23 GMT",
"version": "v3"
},
{
"created": "Wed, 19 Oct 2022 12:37:11 GMT",
"version": "v4"
}
] | 2022-10-20 | [
[
"Song",
"Euijun",
""
]
] | Cardiac fibrillation is characterized by chaotic and disintegrated spiral wave dynamics patterns, whereas sinus rhythm shows synchronized excitation patterns. To determine functional correlations among cardiomyocytes during complex fibrillation states, we applied a pairwise maximum entropy model (MEM) to the 2D numerical simulation data of human atrial fibrillation. We then constructed an energy landscape and estimated a hierarchical structure among the different local minima (attractors) to explain the dynamic properties of cardiac fibrillation. The MEM could describe the wave dynamics of sinus rhythm, single stable rotor, and single rotor with wavebreak (both accuracy and reliability>0.9), but not the multiple random wavelet case. The energy landscapes exhibited unique profiles of local minima and energy barriers, characterizing the spatiotemporal patterns of cardiac fibrillation dynamics. The pairwise MEM-based energy landscape analysis provides reliable explanations of complex nonlinear dynamics of cardiac fibrillation, which might be determined by the presence of a 'driver' such as a sinus node or rotor. |
1601.03559 | Michele Monti | Michele Monti and Pieter Rein ten Wolde | The accuracy of telling time via oscillatory signals | null | null | 10.1088/1478-3975/13/3/035005 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Circadian clocks are the central timekeepers of life, allowing cells to
anticipate changes between day and night. Experiments in recent years have
revealed that circadian clocks can be highly stable, raising the question how
reliably they can be read out. Here, we combine mathematical modeling with
information theory to address the question how accurately a cell can infer the
time from an ensemble of protein oscillations, which are driven by a circadian
clock. We show that the precision increases with the number of oscillations and
their amplitude relative to their noise. Our analysis also reveals that their
exists an optimal phase relation that minimizes the error in the estimate of
time, which depends on the relative noise levels of the protein oscillations.
Lastly, our work shows that cross-correlations in the noise of the protein
oscillations can enhance the mutual information, which suggests that
cross-regulatory interactions between the proteins that read out the clock can
be beneficial for temporal information transmission.
| [
{
"created": "Thu, 14 Jan 2016 11:32:50 GMT",
"version": "v1"
}
] | 2016-06-22 | [
[
"Monti",
"Michele",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | Circadian clocks are the central timekeepers of life, allowing cells to anticipate changes between day and night. Experiments in recent years have revealed that circadian clocks can be highly stable, raising the question how reliably they can be read out. Here, we combine mathematical modeling with information theory to address the question how accurately a cell can infer the time from an ensemble of protein oscillations, which are driven by a circadian clock. We show that the precision increases with the number of oscillations and their amplitude relative to their noise. Our analysis also reveals that their exists an optimal phase relation that minimizes the error in the estimate of time, which depends on the relative noise levels of the protein oscillations. Lastly, our work shows that cross-correlations in the noise of the protein oscillations can enhance the mutual information, which suggests that cross-regulatory interactions between the proteins that read out the clock can be beneficial for temporal information transmission. |
2103.09150 | Margaret Cheung | Andrei G. Gasic, Atrayee Sarkar, and Margaret S. Cheung | Understanding Protein-Complex Assembly through Grand Canonical Maximum
Entropy Modeling | 8 figures | Phys. Rev. Research 3, 033220 (2021) | 10.1103/PhysRevResearch.3.033220 | null | q-bio.BM q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded
systems where the abundance of these proteins vary with cell types. While some
protein complexes form putative structures that can be visualized with imaging,
there are far more protein complexes that are yet to be solved because of their
dynamic associations with one another. Yet, it is possible to infer these
protein complexes through a physical model. However, it is often not clear to
physicists what kind of data from biology is necessary for such a modeling
endeavor. Here, we aim to model these clusters of coarse-grained protein
assemblies from multiple subunits through the constraints of interactions among
the subunits and the chemical potential of each subunit. We obtained the
constraints on the interactions among subunits from the known protein
structures. We inferred the chemical potential, that dictates the particle
number distribution of each protein subunit, from the knowledge of protein
abundance from experimental data. Guided by the maximum entropy principle, we
formulate an inverse statistical mechanical method to infer the distribution of
particle numbers from the data of protein abundance as chemical potentials for
a grand canonical multi-component mixture. Using grand canonical Monte Carlo
simulations, we captured a distribution of high-order clusters in a protein
complex of Succinate Dehydrogenase (SDH) with four known subunits. The
complexity of hierarchical clusters varies with the relative protein abundance
of each subunit in distinctive cell types such as lung, heart, and brain. When
the crowding content increases, we observed that crowding stabilizes emergent
clusters that do not exist in dilute conditions. We, therefore, proposed a
testable hypothesis that the hierarchical complexity of protein clusters on a
molecular scale is a plausible biomarker of predicting the phenotypes of a
cell.
| [
{
"created": "Tue, 16 Mar 2021 15:47:30 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Mar 2021 03:40:00 GMT",
"version": "v2"
},
{
"created": "Tue, 7 Sep 2021 17:48:40 GMT",
"version": "v3"
}
] | 2021-09-15 | [
[
"Gasic",
"Andrei G.",
""
],
[
"Sarkar",
"Atrayee",
""
],
[
"Cheung",
"Margaret S.",
""
]
] | Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Yet, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-grained protein assemblies from multiple subunits through the constraints of interactions among the subunits and the chemical potential of each subunit. We obtained the constraints on the interactions among subunits from the known protein structures. We inferred the chemical potential, that dictates the particle number distribution of each protein subunit, from the knowledge of protein abundance from experimental data. Guided by the maximum entropy principle, we formulate an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multi-component mixture. Using grand canonical Monte Carlo simulations, we captured a distribution of high-order clusters in a protein complex of Succinate Dehydrogenase (SDH) with four known subunits. The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain. When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions. We, therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell. |
2012.02089 | George Lamb | George Lamb, Brooks Paige | Bayesian Graph Neural Networks for Molecular Property Prediction | Presented at NeurIPS 2020 Machine Learning for Molecules workshop | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph neural networks for molecular property prediction are frequently
underspecified by data and fail to generalise to new scaffolds at test time. A
potential solution is Bayesian learning, which can capture our uncertainty in
the model parameters. This study benchmarks a set of Bayesian methods applied
to a directed MPNN, using the QM9 regression dataset. We find that capturing
uncertainty in both readout and message passing parameters yields enhanced
predictive accuracy, calibration, and performance on a downstream molecular
search task.
| [
{
"created": "Wed, 25 Nov 2020 22:32:54 GMT",
"version": "v1"
}
] | 2020-12-04 | [
[
"Lamb",
"George",
""
],
[
"Paige",
"Brooks",
""
]
] | Graph neural networks for molecular property prediction are frequently underspecified by data and fail to generalise to new scaffolds at test time. A potential solution is Bayesian learning, which can capture our uncertainty in the model parameters. This study benchmarks a set of Bayesian methods applied to a directed MPNN, using the QM9 regression dataset. We find that capturing uncertainty in both readout and message passing parameters yields enhanced predictive accuracy, calibration, and performance on a downstream molecular search task. |
1311.1992 | Daniela Delneri | Sarah K. Hewitt, Ian Donaldson, Simon C. Lovell and Daniela Delneri | Sequencing and characterisation of rearrangements in three S.
pastorianus strains reveals the presence of chimeric genes and gives evidence
of breakpoint reuse | null | null | 10.1371/journal.pone.0092203 | null | q-bio.GN | http://creativecommons.org/licenses/by/3.0/ | Gross chromosomal rearrangements have the potential to be evolutionarily
advantageous to an adapting organism. The generation of a hybrid species
increases opportunity for recombination by bringing together two homologous
genomes. We sought to define the location of genomic rearrangements in three
strains of Saccharomyces pastorianus, a natural lager-brewing yeast hybrid of
Saccharomyces cerevisiae and Saccharomyces eubayanus, using whole genome
shotgun sequencing. Each strain of S. pastorianus has lost species-specific
portions of its genome and has undergone extensive recombination, producing
chimeric chromosomes. We predicted 30 breakpoints that we confirmed at the
single nucleotide level by designing species-specific primers that flank each
breakpoint, and then sequencing the PCR product. These rearrangements are the
result of recombination between areas of homology between the two subgenomes,
rather than repetitive elements such as transposons or tRNAs. Interestingly,
28/30 S. cerevisiae- S. eubayanus recombination breakpoints are located within
genic regions, generating chimeric genes. Furthermore we show evidence for the
reuse of two breakpoints, located in HSP82 and KEM1, in strains of proposed
independent origin.
| [
{
"created": "Fri, 8 Nov 2013 14:54:07 GMT",
"version": "v1"
}
] | 2015-06-17 | [
[
"Hewitt",
"Sarah K.",
""
],
[
"Donaldson",
"Ian",
""
],
[
"Lovell",
"Simon C.",
""
],
[
"Delneri",
"Daniela",
""
]
] | Gross chromosomal rearrangements have the potential to be evolutionarily advantageous to an adapting organism. The generation of a hybrid species increases opportunity for recombination by bringing together two homologous genomes. We sought to define the location of genomic rearrangements in three strains of Saccharomyces pastorianus, a natural lager-brewing yeast hybrid of Saccharomyces cerevisiae and Saccharomyces eubayanus, using whole genome shotgun sequencing. Each strain of S. pastorianus has lost species-specific portions of its genome and has undergone extensive recombination, producing chimeric chromosomes. We predicted 30 breakpoints that we confirmed at the single nucleotide level by designing species-specific primers that flank each breakpoint, and then sequencing the PCR product. These rearrangements are the result of recombination between areas of homology between the two subgenomes, rather than repetitive elements such as transposons or tRNAs. Interestingly, 28/30 S. cerevisiae- S. eubayanus recombination breakpoints are located within genic regions, generating chimeric genes. Furthermore we show evidence for the reuse of two breakpoints, located in HSP82 and KEM1, in strains of proposed independent origin. |
1105.5014 | Franz Baumdicker | Franz Baumdicker and Peter Pfaffelhuber | Evolution of bacterial genomes under horizontal gene transfer | Published at the 58th World Statistics Congress of the International
Statistical Institute (ISI) in Dublin | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unraveling the evolutionary forces shaping bacterial diversity can today be
tackled using a growing amount of genomic data. While the genome of eukaryotes
is highly stable, bacterial genomes from cells of the same species highly vary
in gene content. This huge variation in gene content led to the concepts of the
distributed genome of bacteria and their pangenome (Tettelin et al.,2005;
Ehrlich et al.,2005). We present a population genetic model for gene content
evolution which accounts for several mechanisms. Gene uptake from the
environment is modeled by events of gene gain along the genealogical tree
relating the population. Pseudogenization may lead to deletion of genes and is
incoporated by gene loss. These two mechanisms were studied by Huson and Steel
(2004) using a fixed phylogenetic tree. Taking the random genealogy given by
the coalescent (Kingman, 1982; Hudson, 1983), we studied the resulting genomic
diversity already in Baumdicker et al. (2010). In the present paper, we extend
the model in order to incorporate events of interspecies horizontal gene
transfer. Within this model, we derive expectations for the gene frequency
spectrum and other quantities of interest.
| [
{
"created": "Wed, 25 May 2011 13:11:56 GMT",
"version": "v1"
}
] | 2015-03-19 | [
[
"Baumdicker",
"Franz",
""
],
[
"Pfaffelhuber",
"Peter",
""
]
] | Unraveling the evolutionary forces shaping bacterial diversity can today be tackled using a growing amount of genomic data. While the genome of eukaryotes is highly stable, bacterial genomes from cells of the same species highly vary in gene content. This huge variation in gene content led to the concepts of the distributed genome of bacteria and their pangenome (Tettelin et al.,2005; Ehrlich et al.,2005). We present a population genetic model for gene content evolution which accounts for several mechanisms. Gene uptake from the environment is modeled by events of gene gain along the genealogical tree relating the population. Pseudogenization may lead to deletion of genes and is incoporated by gene loss. These two mechanisms were studied by Huson and Steel (2004) using a fixed phylogenetic tree. Taking the random genealogy given by the coalescent (Kingman, 1982; Hudson, 1983), we studied the resulting genomic diversity already in Baumdicker et al. (2010). In the present paper, we extend the model in order to incorporate events of interspecies horizontal gene transfer. Within this model, we derive expectations for the gene frequency spectrum and other quantities of interest. |
0712.0391 | Jose Albornoz | Jos\'e M. Albornoz, Antonio Parravano | Modeling a simple enzyme reaction with delay and discretization | 13 pages, 7 figures. Submitted to Journal of Theoretical Biology | null | null | null | q-bio.MN q-bio.QM | null | A comparison is made between conventional Michaelis-Menten kinetics and two
models that take into account the duration of the conformational changes that
take place at the molecular level during the catalytic cycle of a monomer. The
models consider the time that elapses from the moment an enzyme-substrate
complex forms until the moment a product molecule is released, as well as the
recovery time needed to reset the conformational change that took place. In the
first model the dynamics is described by a set of delayed differential
equations, instead of the ordinary differential equations associated to
Michaelis-Menten kinetics. In the second model the delay, the discretization
inherent to enzyme reactions and the stochastic binding of substrates to
enzimes at the molecular level is considered. All three models agree at
equilibrium, as expected; however, out-of-equilibrium dynamics can differ
substantially. In particular, both delayed models show oscillations at low
values of the Michaelis constant which are not reproduced by the
Michaelis-Menten model. Additionally, in certain cases, the dynamics shown by
the continuous delayed model differs from the dynamics of the discrete delayed
model when some reactant become scarce.
| [
{
"created": "Mon, 3 Dec 2007 21:15:01 GMT",
"version": "v1"
}
] | 2007-12-05 | [
[
"Albornoz",
"José M.",
""
],
[
"Parravano",
"Antonio",
""
]
] | A comparison is made between conventional Michaelis-Menten kinetics and two models that take into account the duration of the conformational changes that take place at the molecular level during the catalytic cycle of a monomer. The models consider the time that elapses from the moment an enzyme-substrate complex forms until the moment a product molecule is released, as well as the recovery time needed to reset the conformational change that took place. In the first model the dynamics is described by a set of delayed differential equations, instead of the ordinary differential equations associated to Michaelis-Menten kinetics. In the second model the delay, the discretization inherent to enzyme reactions and the stochastic binding of substrates to enzimes at the molecular level is considered. All three models agree at equilibrium, as expected; however, out-of-equilibrium dynamics can differ substantially. In particular, both delayed models show oscillations at low values of the Michaelis constant which are not reproduced by the Michaelis-Menten model. Additionally, in certain cases, the dynamics shown by the continuous delayed model differs from the dynamics of the discrete delayed model when some reactant become scarce. |
2201.02990 | Ana Carpio | Ana Carpio, Rafael Gonzalez-Albaladejo | Immersed boundary approach to biofilm spread on surfaces | null | Commun. Comput. Phys. Vol. 31, No. 1, pp. 257-292, 2022 | 10.4208/cicp.OA-2021-0039 | null | q-bio.CB physics.bio-ph physics.comp-ph physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a computational framework to study the growth and spread of
bacterial biofilms on interfaces, as well as the action of antibiotics on them.
Bacterial membranes are represented by boundaries immersed in a fluid matrix
and subject to interaction forces. Growth, division and death of bacterial
cells follow dynamic energy budget rules, in response to variations in
environmental concentrations of nutrients, toxicants and substances released by
the cells. In this way, we create, destroy and enlarge boundaries, either
spherical or rod-like. Appropriate forces represent details of the interaction
between cells, and the interaction with the environment. Numerical simulations
illustrate the evolution of top views and diametral slices of small biofilm
seeds, as well as the action of antibiotics. We show that cocktails of
antibiotics targeting active and dormant cells can entirely eradicate a
biofilm.
| [
{
"created": "Sun, 9 Jan 2022 11:47:25 GMT",
"version": "v1"
}
] | 2022-01-11 | [
[
"Carpio",
"Ana",
""
],
[
"Gonzalez-Albaladejo",
"Rafael",
""
]
] | We propose a computational framework to study the growth and spread of bacterial biofilms on interfaces, as well as the action of antibiotics on them. Bacterial membranes are represented by boundaries immersed in a fluid matrix and subject to interaction forces. Growth, division and death of bacterial cells follow dynamic energy budget rules, in response to variations in environmental concentrations of nutrients, toxicants and substances released by the cells. In this way, we create, destroy and enlarge boundaries, either spherical or rod-like. Appropriate forces represent details of the interaction between cells, and the interaction with the environment. Numerical simulations illustrate the evolution of top views and diametral slices of small biofilm seeds, as well as the action of antibiotics. We show that cocktails of antibiotics targeting active and dormant cells can entirely eradicate a biofilm. |
1409.5062 | Keri Ighwela | Keri Alhadi Ighwela, Aziz Bin Ahmad and A.B. Abol-Munafi | Water Stability and Nutrient Leaching of Different Levels of Maltose
Formulated Fish Pellets | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/3.0/ | The effects of different levels of maltose on feed pellet water stability and
nutrient leaching were studied. Five treatments, including control with three
replicates with setup (0.0, 20, 25, 30 and 35%). Pellet leaching rates were
used to indicate pellet water stability. The results show that the presence of
maltose in the diets significantly improved pellet water stability (p<0.05),
but the leaching rates of the feed (35% maltose) observed higher than other
feeds. Increased maltose resulted in the corresponding decrease in pellet
stability. The protein leaching rate of control feed and feed (20% maltose) was
significantly (p < 0.05) lower than the rates of other diets The lipid leaching
rate of control feed was lower than the rates of other diets, while the feed
(35% maltose) was more leaching rate. It improved feeds water stability is one
important reason why maltose enhances fish growth.
| [
{
"created": "Wed, 17 Sep 2014 16:45:00 GMT",
"version": "v1"
}
] | 2014-09-18 | [
[
"Ighwela",
"Keri Alhadi",
""
],
[
"Ahmad",
"Aziz Bin",
""
],
[
"Abol-Munafi",
"A. B.",
""
]
] | The effects of different levels of maltose on feed pellet water stability and nutrient leaching were studied. Five treatments, including control with three replicates with setup (0.0, 20, 25, 30 and 35%). Pellet leaching rates were used to indicate pellet water stability. The results show that the presence of maltose in the diets significantly improved pellet water stability (p<0.05), but the leaching rates of the feed (35% maltose) observed higher than other feeds. Increased maltose resulted in the corresponding decrease in pellet stability. The protein leaching rate of control feed and feed (20% maltose) was significantly (p < 0.05) lower than the rates of other diets The lipid leaching rate of control feed was lower than the rates of other diets, while the feed (35% maltose) was more leaching rate. It improved feeds water stability is one important reason why maltose enhances fish growth. |
1605.01213 | Alexander L\"uck | Alexander L\"uck, Verena Wolf | Generalized Method of Moments for Estimating Parameters of Stochastic
Reaction Networks | 18 pages, 5 figures, minor changes concerning measurement errors | null | 10.1186/s12918-016-0342-8 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Discrete-state stochastic models have become a well-established approach to
describe biochemical reaction networks that are influenced by the inherent
randomness of cellular events. In the last years severalmethods for accurately
approximating the statistical moments of such models have become very popular
since they allow an efficient analysis of complex networks. We propose a
generalized method of moments approach for inferring the parameters of reaction
networks based on a sophisticated matching of the statistical moments of the
corresponding stochastic model and the sample moments of population snapshot
data. The proposed parameter estimation method exploits recently developed
moment-based approximations and provides estimators with desirable statistical
properties when a large number of samples is available. We demonstrate the
usefulness and efficiency of the inference method on two case studies. The
generalized method of moments provides accurate and fast estimations of unknown
parameters of reaction networks. The accuracy increases when also moments of
order higher than two are considered. In addition, the variance of the
estimator decreases, when more samples are given or when higher order moments
are included.
| [
{
"created": "Wed, 4 May 2016 10:44:13 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Aug 2016 08:39:56 GMT",
"version": "v2"
},
{
"created": "Fri, 7 Oct 2016 11:51:53 GMT",
"version": "v3"
}
] | 2017-07-03 | [
[
"Lück",
"Alexander",
""
],
[
"Wolf",
"Verena",
""
]
] | Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years severalmethods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks. We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies. The generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included. |
1907.08659 | Walter Veit | Walter Veit | Modeling Morality | Preprint: Model-Based Reasoning in Science and Technology | null | null | null | q-bio.PE econ.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unlike any other field, the science of morality has drawn attention from an
extraordinarily diverse set of disciplines. An interdisciplinary research
program has formed in which economists, biologists, neuroscientists,
psychologists, and even philosophers have been eager to provide answers to
puzzling questions raised by the existence of human morality. Models and
simulations, for a variety of reasons, have played various important roles in
this endeavor. Their use, however, has sometimes been deemed as useless,
trivial and inadequate. The role of models in the science of morality has been
vastly underappreciated. This omission shall be remedied here, offering a much
more positive picture on the contributions modelers made to our understanding
of morality.
| [
{
"created": "Fri, 19 Jul 2019 19:29:12 GMT",
"version": "v1"
}
] | 2019-07-23 | [
[
"Veit",
"Walter",
""
]
] | Unlike any other field, the science of morality has drawn attention from an extraordinarily diverse set of disciplines. An interdisciplinary research program has formed in which economists, biologists, neuroscientists, psychologists, and even philosophers have been eager to provide answers to puzzling questions raised by the existence of human morality. Models and simulations, for a variety of reasons, have played various important roles in this endeavor. Their use, however, has sometimes been deemed as useless, trivial and inadequate. The role of models in the science of morality has been vastly underappreciated. This omission shall be remedied here, offering a much more positive picture on the contributions modelers made to our understanding of morality. |
2005.02809 | Adejoke Obajuluwa Dr | Adejoke Olukayode Obajuluwa, Pius Abimbola Okiki, Tiwalola Madoc
Obajuluwa, Olakunle Bamikole Afolabi | In-silico nucleotide and protein analyses of S-gene region in selected
zoonotic coronaviruses reveal conserved domains and evolutionary emergence
with trajectory course of viral entry from SARS-CoV2 genomic data | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | The recent zoonotic coronavirus virus outbreak of a novel type [COVID 19] has
necessitated the adequate understanding of the evolutionary pathway of zoonotic
viruses which adversely affects human populations for therapeutic constructs to
combat the pandemic now and in the future. We analyzed conserved domains of the
severe acute respiratory coronavirus 2 [SARS-CoV2] for possible targets of
viral entry inhibition in host cells, evolutionary relationship of human
coronavirus [229E] and zoonotic coronaviruses with SAR-CoV2 as well as
evolutionary relationship between selected SARS-CoV 2 genomic data. Conserved
domains with antagonistic action on host innate antiviral cellular mechanisms
in SARS-CoV 2 include nsp 11, nsp 13 etc. Also, multiple sequence alignments of
the spike [S] gene protein of selected candidate zoonotic coronaviruses
alongside the S gene protein of the SARs-CoV2 revealed closest evolutionary
relationship [95.6%] with pangolin coronaviruses [S] gene. Clades formed
between Wuhan SARS-CoV2 phylogeny data and five others suggests viral entry
trajectory while revealing genomic and protein SARS CoV 2 data from Philippines
as early ancestors. Therefore, phylogeny of SARS-CoV 2 genomic data suggests
profiling in diverse populations with and without the outbreak alongside
migration history and racial background for mutation tracking and dating of
viral subtype divergence which is essential for effective management of present
and future zoonotic coronavirus outbreaks.
| [
{
"created": "Wed, 6 May 2020 13:32:55 GMT",
"version": "v1"
}
] | 2020-05-07 | [
[
"Obajuluwa",
"Adejoke Olukayode",
""
],
[
"Okiki",
"Pius Abimbola",
""
],
[
"Obajuluwa",
"Tiwalola Madoc",
""
],
[
"Afolabi",
"Olakunle Bamikole",
""
]
] | The recent zoonotic coronavirus virus outbreak of a novel type [COVID 19] has necessitated the adequate understanding of the evolutionary pathway of zoonotic viruses which adversely affects human populations for therapeutic constructs to combat the pandemic now and in the future. We analyzed conserved domains of the severe acute respiratory coronavirus 2 [SARS-CoV2] for possible targets of viral entry inhibition in host cells, evolutionary relationship of human coronavirus [229E] and zoonotic coronaviruses with SAR-CoV2 as well as evolutionary relationship between selected SARS-CoV 2 genomic data. Conserved domains with antagonistic action on host innate antiviral cellular mechanisms in SARS-CoV 2 include nsp 11, nsp 13 etc. Also, multiple sequence alignments of the spike [S] gene protein of selected candidate zoonotic coronaviruses alongside the S gene protein of the SARs-CoV2 revealed closest evolutionary relationship [95.6%] with pangolin coronaviruses [S] gene. Clades formed between Wuhan SARS-CoV2 phylogeny data and five others suggests viral entry trajectory while revealing genomic and protein SARS CoV 2 data from Philippines as early ancestors. Therefore, phylogeny of SARS-CoV 2 genomic data suggests profiling in diverse populations with and without the outbreak alongside migration history and racial background for mutation tracking and dating of viral subtype divergence which is essential for effective management of present and future zoonotic coronavirus outbreaks. |
1904.06326 | Lucas Barberis | L. Ben\'itez, L. Barberis, C. A. Condat | Modeling tumorspheres reveals cancer stem cell niche building and
plasticity | 18 pg | null | 10.1016/j.physa.2019.121906 | null | q-bio.CB cond-mat.other | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cancer stem cells have been shown to be critical to the development of a
variety of solid cancers. The precise interplay mechanisms between cancer stem
cells and the rest of a tissue are still not elucidated. To shed light on the
interactions between stem and non-stem cancer cell populations we develop a
two-population mathematical model, which is suitable to describe tumorsphere
growth. Both interspecific and intraspecific interactions, mediated by the
microenvironment, are included. We show that there is a tipping point,
characterized by a transcritical bifurcation, where a purely non-stem cell
attractor is replaced by a new attractor that contains both stem and
differentiated cancer cells. The model is then applied to describe the outcome
of a recent experiment. This description reveals that, while the intraspecific
interactions are inhibitory, the interspecific interactions stimulate growth.
This can be understood in terms of stem cells needing differentiated cells to
reinforce their niches, and phenotypic plasticity favoring the
de-differentiation of differentiated cells into cancer stem cells. We posit
that this is a consequence of the deregulation of the quorum sensing that
maintains homeostasis in healthy tissues.
| [
{
"created": "Tue, 9 Apr 2019 19:13:15 GMT",
"version": "v1"
},
{
"created": "Tue, 14 May 2019 16:55:56 GMT",
"version": "v2"
}
] | 2019-09-04 | [
[
"Benítez",
"L.",
""
],
[
"Barberis",
"L.",
""
],
[
"Condat",
"C. A.",
""
]
] | Cancer stem cells have been shown to be critical to the development of a variety of solid cancers. The precise interplay mechanisms between cancer stem cells and the rest of a tissue are still not elucidated. To shed light on the interactions between stem and non-stem cancer cell populations we develop a two-population mathematical model, which is suitable to describe tumorsphere growth. Both interspecific and intraspecific interactions, mediated by the microenvironment, are included. We show that there is a tipping point, characterized by a transcritical bifurcation, where a purely non-stem cell attractor is replaced by a new attractor that contains both stem and differentiated cancer cells. The model is then applied to describe the outcome of a recent experiment. This description reveals that, while the intraspecific interactions are inhibitory, the interspecific interactions stimulate growth. This can be understood in terms of stem cells needing differentiated cells to reinforce their niches, and phenotypic plasticity favoring the de-differentiation of differentiated cells into cancer stem cells. We posit that this is a consequence of the deregulation of the quorum sensing that maintains homeostasis in healthy tissues. |
1211.0413 | Sudip Kundu | Saurav Mallik and Sudip Kundu | The Lipid-RNA World | no figures and 7 pages | null | null | null | q-bio.BM physics.bio-ph q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The simplest possible beginning of abiogenesis has been a riddle from the
last century, which is most successfully solved by the Lipid World hypothesis.
However, origin of the next stages of evolution starting form lipids is still
in dark. We propose a 'Lipid-RNA World Scenario' based on the assumption that
modern stable lipid-RNA interactions are molecular fossils of an ancient stage
of evolution when RNA World originated from Lipid World. In accordance to the
faint young sun conditions, we present an 'ice-covered hydrothermal vent' model
of Hadean Ocean. Our hypothetical model suggests that faint young sun condition
probably provided susceptible physical conditions for an evolutionary route
from Lipid-World to Protein-RNA World, through an intermediate Lipid-RNA World.
Ancient ribozymes were 'protected' by lipids assuring their survival in
prebiotic ocean. The origin of natural selection ensures transition of
Lipid-RNA World to Protein-RNA World after the origin of ribosome. Assuming the
modern peptidyltransferase as the proto-ribosome structure, we have presented a
hypothetical translation mechanism: proto-ribosome randomly polymerized amino
acids being attached to the inner layer of a lipid-vesicle, using only physical
energies available from our Hadean Ocean model. In accordance to the strategy
of chemical evolution, we also have described the possible evolutionary
behavior of this proto-ribosome, which explains the contemporary
three-dimensional structure of 50S subunit and supports the predictions
regarding the ancient regions of it. It also explains the origin of
membrane-free 'minimal ribosome' in the time of LUCA.
| [
{
"created": "Fri, 2 Nov 2012 10:37:48 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Feb 2013 10:23:35 GMT",
"version": "v2"
}
] | 2013-02-26 | [
[
"Mallik",
"Saurav",
""
],
[
"Kundu",
"Sudip",
""
]
] | The simplest possible beginning of abiogenesis has been a riddle from the last century, which is most successfully solved by the Lipid World hypothesis. However, origin of the next stages of evolution starting form lipids is still in dark. We propose a 'Lipid-RNA World Scenario' based on the assumption that modern stable lipid-RNA interactions are molecular fossils of an ancient stage of evolution when RNA World originated from Lipid World. In accordance to the faint young sun conditions, we present an 'ice-covered hydrothermal vent' model of Hadean Ocean. Our hypothetical model suggests that faint young sun condition probably provided susceptible physical conditions for an evolutionary route from Lipid-World to Protein-RNA World, through an intermediate Lipid-RNA World. Ancient ribozymes were 'protected' by lipids assuring their survival in prebiotic ocean. The origin of natural selection ensures transition of Lipid-RNA World to Protein-RNA World after the origin of ribosome. Assuming the modern peptidyltransferase as the proto-ribosome structure, we have presented a hypothetical translation mechanism: proto-ribosome randomly polymerized amino acids being attached to the inner layer of a lipid-vesicle, using only physical energies available from our Hadean Ocean model. In accordance to the strategy of chemical evolution, we also have described the possible evolutionary behavior of this proto-ribosome, which explains the contemporary three-dimensional structure of 50S subunit and supports the predictions regarding the ancient regions of it. It also explains the origin of membrane-free 'minimal ribosome' in the time of LUCA. |
0912.4726 | Eugene Shakhnovich | Muyoung Heo and Eugene Shakhnovich | Interplay between pleiotropy and secondary selection determines rise and
fall of mutators in stress response | null | null | 10.1371/journal.pcbi.1000710 | null | q-bio.BM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dramatic rise of mutators has been found to accompany adaptation of bacteria
in response to many kinds of stress. Two views on the evolutionary origin of
this phenomenon emerged: the pleiotropic hypothesis positing that it is a
byproduct of environmental stress or other specific stress response mechanisms
and the second order selection which states that mutators hitchhike to fixation
with unrelated beneficial alleles. Conventional population genetics models
could not fully resolve this controversy because they are based on certain
assumptions about fitness landscape. Here we address this problem using a
microscopic multiscale model, which couples physically realistic molecular
descriptions of proteins and their interactions with population genetics of
carrier organisms without assuming any a priori fitness landscape. We found
that both pleiotropy and second order selection play a crucial role at
different stages of adaptation: the supply of mutators is provided through
destabilization of error correction complexes or fluctuations of production
levels of prototypic mismatch repair proteins (pleiotropic effects), while rise
and fixation of mutators occur when there is a sufficient supply of beneficial
mutations in replication-controlling genes. This general mechanism assures a
robust and reliable adaptation of organisms to unforeseen challenges. This
study highlights physical principles underlying physical biological mechanisms
of stress response and adaptation.
| [
{
"created": "Wed, 23 Dec 2009 20:43:19 GMT",
"version": "v1"
}
] | 2015-05-14 | [
[
"Heo",
"Muyoung",
""
],
[
"Shakhnovich",
"Eugene",
""
]
] | Dramatic rise of mutators has been found to accompany adaptation of bacteria in response to many kinds of stress. Two views on the evolutionary origin of this phenomenon emerged: the pleiotropic hypothesis positing that it is a byproduct of environmental stress or other specific stress response mechanisms and the second order selection which states that mutators hitchhike to fixation with unrelated beneficial alleles. Conventional population genetics models could not fully resolve this controversy because they are based on certain assumptions about fitness landscape. Here we address this problem using a microscopic multiscale model, which couples physically realistic molecular descriptions of proteins and their interactions with population genetics of carrier organisms without assuming any a priori fitness landscape. We found that both pleiotropy and second order selection play a crucial role at different stages of adaptation: the supply of mutators is provided through destabilization of error correction complexes or fluctuations of production levels of prototypic mismatch repair proteins (pleiotropic effects), while rise and fixation of mutators occur when there is a sufficient supply of beneficial mutations in replication-controlling genes. This general mechanism assures a robust and reliable adaptation of organisms to unforeseen challenges. This study highlights physical principles underlying physical biological mechanisms of stress response and adaptation. |
1508.06579 | Yuri A. Dabaghian | Yuri Dabaghian | Geometry of Spatial Memory Replay | 15 pages, 5 figures, Neural Computation, 2016 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Place cells in the rat hippocampus play a key role in creating the animal's
internal representation of the world. During active navigation, these cells
spike only in discrete locations, together encoding a map of the environment.
Electrophysiological recordings have shown that the animal can revisit this map
mentally, during both sleep and awake states, reactivating the place cells that
fired during its exploration in the same sequence they were originally
activated. Although consistency of place cell activity during active navigation
is arguably enforced by sensory and proprioceptive inputs, it remains unclear
how a consistent representation of space can be maintained during spontaneous
replay. We propose a model that can account for this phenomenon and suggests
that a spatially consistent replay requires a number of constraints on the
hippocampal network that affect its synaptic architecture and the statistics of
synaptic connection strengths.
| [
{
"created": "Wed, 26 Aug 2015 17:28:48 GMT",
"version": "v1"
},
{
"created": "Sun, 20 Mar 2016 18:43:16 GMT",
"version": "v2"
}
] | 2016-03-22 | [
[
"Dabaghian",
"Yuri",
""
]
] | Place cells in the rat hippocampus play a key role in creating the animal's internal representation of the world. During active navigation, these cells spike only in discrete locations, together encoding a map of the environment. Electrophysiological recordings have shown that the animal can revisit this map mentally, during both sleep and awake states, reactivating the place cells that fired during its exploration in the same sequence they were originally activated. Although consistency of place cell activity during active navigation is arguably enforced by sensory and proprioceptive inputs, it remains unclear how a consistent representation of space can be maintained during spontaneous replay. We propose a model that can account for this phenomenon and suggests that a spatially consistent replay requires a number of constraints on the hippocampal network that affect its synaptic architecture and the statistics of synaptic connection strengths. |
q-bio/0507033 | Dietrich Stauffer | Dietrich Stauffer and Klaus Rohde | Simulation of Rapoport's rule for latitudinal species spread | 14 pages including 6 figures | null | null | null | q-bio.PE | null | Rapoport's rule claims that latitudinal ranges of plant and animal species
are generally smaller at low than at high latitudes. However, doubts as to the
generality of the rule have been expressed, because studies providing evidence
against the rule are more numerous than those in support of it. In groups for
which support has been provided, the trend of increasing latitudinal ranges
with latitude is restricted to or at least most distinct at high latitudes,
suggesting that the effect may be a local phenomenon, for example the result of
glaciations. Here we test the rule using two models, a simple one-dimensional
one with a fixed number of animals expanding in a northern or southerly
direction only, and the evolutionary/ecological Chowdhury model using birth,
ageing, death, mutation, speciation, prey-predator relations and food levels.
Simulations with both models gave results contradicting Rapoport's rule. In the
first, latitudinal ranges were roughly independent of latitude, in the second,
latitudinal ranges were greatest at low latitudes, as also shown empirically
for some well studied groups of animals.
| [
{
"created": "Thu, 21 Jul 2005 14:49:33 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Stauffer",
"Dietrich",
""
],
[
"Rohde",
"Klaus",
""
]
] | Rapoport's rule claims that latitudinal ranges of plant and animal species are generally smaller at low than at high latitudes. However, doubts as to the generality of the rule have been expressed, because studies providing evidence against the rule are more numerous than those in support of it. In groups for which support has been provided, the trend of increasing latitudinal ranges with latitude is restricted to or at least most distinct at high latitudes, suggesting that the effect may be a local phenomenon, for example the result of glaciations. Here we test the rule using two models, a simple one-dimensional one with a fixed number of animals expanding in a northern or southerly direction only, and the evolutionary/ecological Chowdhury model using birth, ageing, death, mutation, speciation, prey-predator relations and food levels. Simulations with both models gave results contradicting Rapoport's rule. In the first, latitudinal ranges were roughly independent of latitude, in the second, latitudinal ranges were greatest at low latitudes, as also shown empirically for some well studied groups of animals. |
2101.10582 | Sourav Kumar Sasmal | Sourav Kumar Sasmal and Yasuhiro Takeuchi | Modeling the Allee effects induced by cost of predation fear and its
carry-over effects | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predation driven Allee effects play an important role in the dynamics of
small population, however, such predation-driven Allee effects cannot occur for
the model with type I functional response. It generally occurs when a
generalist predator targets some specific prey. However, apart from the lethal
effects of predation, there are some non-lethal effects in the presence of
predator. Due to the fear of predation, positive density dependence growth may
be observed at low population density, because of reduced foraging activities.
Moreover, this non-lethal effect can be carried over generations. In the
present manuscript, we investigate the role of predation fear and its
carry-over effects in prey-predator model. First, we study the single species
model in global perspective. We have shown that depending on the birth rate,
our single species model describes three types of growth dynamics, namely,
strong Allee dynamics, weak Allee dynamics and logistic dynamics. Then we
consider the explicit dynamics of predator, with type I functional response.
Basic dynamical properties, as well as global stability of each equilibria have
been discussed. From our analysis, we can observe that both the fear and its
carry-over effects have significant role in the stability of the coexistence
equilibrium, even if for the model with type I functional response. The
phenomenon paradox of enrichment can be observed in our model, which cannot be
observed in the classical prey-predator model with type I functional response.
However, we can see that such phenomenon can be ruled out by choosing suitable
non-lethal effect parameters. Therefore, our study shows how non-lethal effects
change the dynamics of a competition model, and has important biological
insights, specially for the understanding of the dynamics of small populations.
| [
{
"created": "Tue, 26 Jan 2021 06:21:22 GMT",
"version": "v1"
}
] | 2021-01-27 | [
[
"Sasmal",
"Sourav Kumar",
""
],
[
"Takeuchi",
"Yasuhiro",
""
]
] | Predation driven Allee effects play an important role in the dynamics of small population, however, such predation-driven Allee effects cannot occur for the model with type I functional response. It generally occurs when a generalist predator targets some specific prey. However, apart from the lethal effects of predation, there are some non-lethal effects in the presence of predator. Due to the fear of predation, positive density dependence growth may be observed at low population density, because of reduced foraging activities. Moreover, this non-lethal effect can be carried over generations. In the present manuscript, we investigate the role of predation fear and its carry-over effects in prey-predator model. First, we study the single species model in global perspective. We have shown that depending on the birth rate, our single species model describes three types of growth dynamics, namely, strong Allee dynamics, weak Allee dynamics and logistic dynamics. Then we consider the explicit dynamics of predator, with type I functional response. Basic dynamical properties, as well as global stability of each equilibria have been discussed. From our analysis, we can observe that both the fear and its carry-over effects have significant role in the stability of the coexistence equilibrium, even if for the model with type I functional response. The phenomenon paradox of enrichment can be observed in our model, which cannot be observed in the classical prey-predator model with type I functional response. However, we can see that such phenomenon can be ruled out by choosing suitable non-lethal effect parameters. Therefore, our study shows how non-lethal effects change the dynamics of a competition model, and has important biological insights, specially for the understanding of the dynamics of small populations. |
2106.02441 | Francesco Zamponi | Matteo Bisardi, Juan Rodriguez-Rivas, Francesco Zamponi, Martin Weigt | Modeling sequence-space exploration and emergence of epistatic signals
in protein evolution | 16 pages, 14 figures | Molecular Biology and Evolution 39, msab321 (2022) | 10.1093/molbev/msab321 | null | q-bio.BM cond-mat.dis-nn q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During their evolution, proteins explore sequence space via an interplay
between random mutations and phenotypic selection. Here we build upon recent
progress in reconstructing data-driven fitness landscapes for families of
homologous proteins, to propose stochastic models of experimental protein
evolution. These models predict quantitatively important features of
experimentally evolved sequence libraries, like fitness distributions and
position-specific mutational spectra. They also allow us to efficiently
simulate sequence libraries for a vast array of combinations of experimental
parameters like sequence divergence, selection strength and library size. We
showcase the potential of the approach in re-analyzing two recent experiments
to determine protein structure from signals of epistasis emerging in
experimental sequence libraries. To be detectable, these signals require
sufficiently large and sufficiently diverged libraries. Our modeling framework
offers a quantitative explanation for the variable success of recently
published experiments. Furthermore, we can forecast the outcome of time- and
resource-intensive evolution experiments, opening thereby a way to
computationally optimize experimental protocols.
| [
{
"created": "Fri, 4 Jun 2021 12:39:52 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Jan 2022 10:55:14 GMT",
"version": "v2"
}
] | 2022-01-28 | [
[
"Bisardi",
"Matteo",
""
],
[
"Rodriguez-Rivas",
"Juan",
""
],
[
"Zamponi",
"Francesco",
""
],
[
"Weigt",
"Martin",
""
]
] | During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection. Here we build upon recent progress in reconstructing data-driven fitness landscapes for families of homologous proteins, to propose stochastic models of experimental protein evolution. These models predict quantitatively important features of experimentally evolved sequence libraries, like fitness distributions and position-specific mutational spectra. They also allow us to efficiently simulate sequence libraries for a vast array of combinations of experimental parameters like sequence divergence, selection strength and library size. We showcase the potential of the approach in re-analyzing two recent experiments to determine protein structure from signals of epistasis emerging in experimental sequence libraries. To be detectable, these signals require sufficiently large and sufficiently diverged libraries. Our modeling framework offers a quantitative explanation for the variable success of recently published experiments. Furthermore, we can forecast the outcome of time- and resource-intensive evolution experiments, opening thereby a way to computationally optimize experimental protocols. |
1309.2588 | Kyung Hyuk Kim | Kyung Hyuk Kim, Hong Qian, Herbert M. Sauro | Nonlinear Biochemical Signal Processing via Noise Propagation | 23 pages, 5 figures, Accepted by Journal of Chemical Physics | null | 10.1063/1.4822103 | null | q-bio.QM q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single-cell studies often show significant phenotypic variability due to the
stochastic nature of intra-cellular biochemical reactions. When the numbers of
molecules, e.g., transcription factors and regulatory enzymes, are in low
abundance, fluctuations in biochemical activities become significant and such
"noise" can propagate through regulatory cascades in terms of biochemical
reaction networks. Here we develop an intuitive, yet fully quantitative method
for analyzing how noise affects cellular phenotypes based on identifying a
system's nonlinearities and noise propagations. We observe that such noise can
simultaneously enhance sensitivities in one behavioral region while reducing
sensitivities in another. Employing this novel phenomenon we designed three
biochemical signal processing modules: (a) A gene regulatory network that acts
as a concentration detector with both enhanced amplitude and sensitivity. (b) A
non-cooperative positive feedback system, with a graded dose-response in the
deterministic case, that serves as a bistable switch due to noise-induced
bimodality. (c) A noise-induced linear amplifier for gene regulation that
requires no feedback. The methods developed in the present work allow one to
understand and engineer nonlinear biochemical signal processors based on
fluctuation-induced phenotypes.
| [
{
"created": "Tue, 10 Sep 2013 17:46:40 GMT",
"version": "v1"
}
] | 2015-06-17 | [
[
"Kim",
"Kyung Hyuk",
""
],
[
"Qian",
"Hong",
""
],
[
"Sauro",
"Herbert M.",
""
]
] | Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networks. Here we develop an intuitive, yet fully quantitative method for analyzing how noise affects cellular phenotypes based on identifying a system's nonlinearities and noise propagations. We observe that such noise can simultaneously enhance sensitivities in one behavioral region while reducing sensitivities in another. Employing this novel phenomenon we designed three biochemical signal processing modules: (a) A gene regulatory network that acts as a concentration detector with both enhanced amplitude and sensitivity. (b) A non-cooperative positive feedback system, with a graded dose-response in the deterministic case, that serves as a bistable switch due to noise-induced bimodality. (c) A noise-induced linear amplifier for gene regulation that requires no feedback. The methods developed in the present work allow one to understand and engineer nonlinear biochemical signal processors based on fluctuation-induced phenotypes. |
1608.08166 | Thiparat Chotibut | Thiparat Chotibut, David R. Nelson | Population Genetics with Fluctuating Population Sizes | Submitted to Journal of Statistical Physics, Special Issue: Dedicated
to the Memory of Leo Kadanoff. arXiv admin note: text overlap with
arXiv:1412.6688 | null | 10.1007/s10955-017-1741-y | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Standard neutral population genetics theory with a strictly fixed population
size has important limitations. An alternative model that allows independently
fluctuating population sizes and reproduces the standard neutral evolution is
reviewed. We then study a situation such that the competing species are neutral
at the equilibrium population size but population size fluctuations
nevertheless favor fixation of one species over the other. In this case, a
separation of timescales emerges naturally and allows adiabatic elimination of
a fast population size variable to deduce the fluctuations-induced selection
dynamics near the equilibrium population size. The results highlight the
incompleteness of the standard population genetics with a strictly fixed
population size.
| [
{
"created": "Mon, 29 Aug 2016 18:19:55 GMT",
"version": "v1"
}
] | 2017-03-08 | [
[
"Chotibut",
"Thiparat",
""
],
[
"Nelson",
"David R.",
""
]
] | Standard neutral population genetics theory with a strictly fixed population size has important limitations. An alternative model that allows independently fluctuating population sizes and reproduces the standard neutral evolution is reviewed. We then study a situation such that the competing species are neutral at the equilibrium population size but population size fluctuations nevertheless favor fixation of one species over the other. In this case, a separation of timescales emerges naturally and allows adiabatic elimination of a fast population size variable to deduce the fluctuations-induced selection dynamics near the equilibrium population size. The results highlight the incompleteness of the standard population genetics with a strictly fixed population size. |
1801.04008 | Eslam Abbas | Eslam Abbas | Comorbid CAD and Ventricular Hypertrophy Compromise The Perfusion of
Myocardial Tissue at Subcritical Stenosis of Epicardial Coronaries | 10 pages and 2 figures | null | 10.1186/s43044-019-0003-5 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | BACKGROUND: Most studies of CAD revascularization have been based on and
reported according to angiographic criteria which don't consider the relation
between the resulting effective flow distal to the stenosis and the demand of a
hypertrophied myocardial tissue.
MODEL: Mathematical model of the myocardial perfusion in comorbid CAD and
ventricular hypertrophy using Poiseuille's law. The analysis yields that the
curve, which represents the relation between the perfusion and the severity of
CAD depending on angiographic and/or angiophysiologic criteria, is shifted to
the right by the effect of myocardial tissue hypertrophy. The right shift of
said curve, which is directly proportional to the degree of ventricular
hypertrophy, indicates that the perfusion of the corresponding myocardial
tissue is compromised at angiographically and/or angiophysiologically
subsignificant stenosis of the supplying epicardial vessel.
RESULTS: Patients with comorbid CAD and left ventricular hypertrophy are more
sensitive to CAD-related hemodynamic changes. They are more prone to develop
ischemic complications, than their peers with isolated CAD regarding the same
degree of coronary stenosis.
CONCLUSION: Patients with comorbid CAD and ventricular hypertrophy suffer
from myocardial hypoperfusion at angiographically and/or angiophysiologically
subcritical epicardial stenosis. Accordingly; the comorbidity of both diseases
should be considered upon designing of the treatment regime.
| [
{
"created": "Thu, 11 Jan 2018 22:46:55 GMT",
"version": "v1"
}
] | 2019-11-01 | [
[
"Abbas",
"Eslam",
""
]
] | BACKGROUND: Most studies of CAD revascularization have been based on and reported according to angiographic criteria which don't consider the relation between the resulting effective flow distal to the stenosis and the demand of a hypertrophied myocardial tissue. MODEL: Mathematical model of the myocardial perfusion in comorbid CAD and ventricular hypertrophy using Poiseuille's law. The analysis yields that the curve, which represents the relation between the perfusion and the severity of CAD depending on angiographic and/or angiophysiologic criteria, is shifted to the right by the effect of myocardial tissue hypertrophy. The right shift of said curve, which is directly proportional to the degree of ventricular hypertrophy, indicates that the perfusion of the corresponding myocardial tissue is compromised at angiographically and/or angiophysiologically subsignificant stenosis of the supplying epicardial vessel. RESULTS: Patients with comorbid CAD and left ventricular hypertrophy are more sensitive to CAD-related hemodynamic changes. They are more prone to develop ischemic complications, than their peers with isolated CAD regarding the same degree of coronary stenosis. CONCLUSION: Patients with comorbid CAD and ventricular hypertrophy suffer from myocardial hypoperfusion at angiographically and/or angiophysiologically subcritical epicardial stenosis. Accordingly; the comorbidity of both diseases should be considered upon designing of the treatment regime. |
2101.00819 | Babak Nouri-Moghaddam | Babak Nouri-Moghaddam, Mehdi Ghazanfari, Mohammad Fathian | A Novel Bio-Inspired Hybrid Multi-Filter Wrapper Gene Selection Method
with Ensemble Classifier for Microarray Data | 22 pages, 10 figures | null | null | null | q-bio.QM cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Microarray technology is known as one of the most important tools for
collecting DNA expression data. This technology allows researchers to
investigate and examine types of diseases and their origins. However,
microarray data are often associated with challenges such as small sample size,
a significant number of genes, imbalanced data, etc. that make classification
models inefficient. Thus, a new hybrid solution based on multi-filter and
adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is
presented to solve the gene selection problem and construct the Ensemble
Classifier. In the proposed solution, to reduce the dataset's dimensions, a
multi-filter model uses a combination of five filter methods to remove
redundant and irrelevant genes. Then, an AC-MOFOA based on the concepts of
non-dominated sorting, crowding distance, chaos theory, and adaptive operators
is presented. AC-MOFOA as a wrapper method aimed at reducing dataset
dimensions, optimizing KELM, and increasing the accuracy of the classification,
simultaneously. Next, in this method, an ensemble classifier model is presented
using AC-MOFOA results to classify microarray data. The performance of the
proposed algorithm was evaluated on nine public microarray datasets, and its
results were compared in terms of the number of selected genes, classification
efficiency, execution time, time complexity, and hypervolume indicator
criterion with five hybrid multi-objective methods. According to the results,
the proposed hybrid method could increase the accuracy of the KELM in most
datasets by reducing the dataset's dimensions and achieve similar or superior
performance compared to other multi-objective methods. Furthermore, the
proposed Ensemble Classifier model could provide better classification accuracy
and generalizability in microarray data compared to conventional ensemble
methods.
| [
{
"created": "Mon, 4 Jan 2021 07:57:35 GMT",
"version": "v1"
}
] | 2021-01-05 | [
[
"Nouri-Moghaddam",
"Babak",
""
],
[
"Ghazanfari",
"Mehdi",
""
],
[
"Fathian",
"Mohammad",
""
]
] | Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with challenges such as small sample size, a significant number of genes, imbalanced data, etc. that make classification models inefficient. Thus, a new hybrid solution based on multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, to reduce the dataset's dimensions, a multi-filter model uses a combination of five filter methods to remove redundant and irrelevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, and hypervolume indicator criterion with five hybrid multi-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in microarray data compared to conventional ensemble methods. |
2206.06951 | Alfonso Nieto-Castanon | Alfonso Nieto-Castanon | Brain-wide connectome inferences using functional connectivity
MultiVariate Pattern Analyses (fc-MVPA) | null | null | 10.1371/journal.pcbi.1010634 | null | q-bio.QM q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Current functional Magnetic Resonance Imaging technology is able to resolve
billions of individual functional connections characterizing the human
connectome. Classical statistical inferential procedures attempting to make
valid inferences across this many measures from a reduced set of observations
and from a limited number of subjects can be severely underpowered for any but
the largest effect sizes. This manuscript discusses fc-MVPA (functional
connectivity Multivariate Pattern Analysis), a novel application of
multivariate pattern analysis techniques in the context of brain-wide
connectome inferences. The theory behind fc-MVPA is presented, and several of
its key concepts are illustrated through examples from a publicly available
resting state dataset, including an example analysis evaluating gender
differences across the entire functional connectome. Last, Monte Carlo
simulations are used to demonstrated this method's validity and sensitivity. In
addition to offering powerful whole-brain inferences, fc-MVPA also provides a
meaningful characterization of the heterogeneity in functional connectivity
across subjects.
| [
{
"created": "Tue, 14 Jun 2022 16:14:11 GMT",
"version": "v1"
}
] | 2023-01-11 | [
[
"Nieto-Castanon",
"Alfonso",
""
]
] | Current functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations and from a limited number of subjects can be severely underpowered for any but the largest effect sizes. This manuscript discusses fc-MVPA (functional connectivity Multivariate Pattern Analysis), a novel application of multivariate pattern analysis techniques in the context of brain-wide connectome inferences. The theory behind fc-MVPA is presented, and several of its key concepts are illustrated through examples from a publicly available resting state dataset, including an example analysis evaluating gender differences across the entire functional connectome. Last, Monte Carlo simulations are used to demonstrated this method's validity and sensitivity. In addition to offering powerful whole-brain inferences, fc-MVPA also provides a meaningful characterization of the heterogeneity in functional connectivity across subjects. |
1406.3016 | Chandrajit Basu | Devesh Singh, Chandrajit Basu, Merve Meinhardt-Wollweber, and Bernhard
Roth | LEDs for Energy Efficient Greenhouse Lighting | 22 pages, 7 figures | null | null | DS_CB_June2014 | q-bio.OT physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Light energy is an important factor for plant growth. In regions where the
natural light source, i.e. solar radiation, is not sufficient for growth
optimization, additional light sources are being used. Traditional light
sources such as high pressure sodium lamps and other metal halide lamps are not
very efficient and generate high radiant heat. Therefore, new sustainable
solutions should be developed for energy efficient greenhouse lighting. Recent
developments in the field of light source technologies have opened up new
perspectives for sustainable and highly efficient light sources in the form of
light-emitting diodes, i.e. LEDs, for greenhouse lighting. This review focuses
on the potential of LEDs to replace traditional light sources in the
greenhouse. In a comparative economic analysis of traditional vs. LED lighting,
we show that the introduction of LEDs allows reduction of the production cost
of vegetables in the long-run of several years, due to the high energy
efficiency, low maintenance cost and longevity of LEDs. In order to evaluate
LEDs as a true alternative to current lighting sources, species specific plant
response to different wavelengths is discussed in a comparative study. However,
more detailed scientific studies are necessary to understand the effect of
different LED spectra on plants physiology. Technical innovations are required
to design and realize an energy efficient light source with a spectrum tailored
for optimal plant growth in specific plant species.
| [
{
"created": "Wed, 11 Jun 2014 09:33:13 GMT",
"version": "v1"
}
] | 2014-06-13 | [
[
"Singh",
"Devesh",
""
],
[
"Basu",
"Chandrajit",
""
],
[
"Meinhardt-Wollweber",
"Merve",
""
],
[
"Roth",
"Bernhard",
""
]
] | Light energy is an important factor for plant growth. In regions where the natural light source, i.e. solar radiation, is not sufficient for growth optimization, additional light sources are being used. Traditional light sources such as high pressure sodium lamps and other metal halide lamps are not very efficient and generate high radiant heat. Therefore, new sustainable solutions should be developed for energy efficient greenhouse lighting. Recent developments in the field of light source technologies have opened up new perspectives for sustainable and highly efficient light sources in the form of light-emitting diodes, i.e. LEDs, for greenhouse lighting. This review focuses on the potential of LEDs to replace traditional light sources in the greenhouse. In a comparative economic analysis of traditional vs. LED lighting, we show that the introduction of LEDs allows reduction of the production cost of vegetables in the long-run of several years, due to the high energy efficiency, low maintenance cost and longevity of LEDs. In order to evaluate LEDs as a true alternative to current lighting sources, species specific plant response to different wavelengths is discussed in a comparative study. However, more detailed scientific studies are necessary to understand the effect of different LED spectra on plants physiology. Technical innovations are required to design and realize an energy efficient light source with a spectrum tailored for optimal plant growth in specific plant species. |
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