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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2307.07794 | Anna Levina | Sahel Azizpour, Viola Priesemann, Johannes Zierenberg, Anna Levina | Available observation time regulates optimal balance between sensitivity
and confidence | 6 pages, 3 figures; methods: 4 pages, 1 figure; supplementary.
Comments and suggestions are welcome | null | null | null | q-bio.NC cond-mat.dis-nn | http://creativecommons.org/licenses/by/4.0/ | Tasks that require information about the world imply a trade-off between the
time spent on observation and the variance of the response. In particular, fast
decisions need to rely on uncertain information. However, standard estimates of
information processing capabilities, such as the dynamic range, are defined
based on mean values that assume infinite observation times. Here, we show that
limiting the observation time results in distributions of responses whose
variance increases with the temporal correlations in a system and, importantly,
affects a system's confidence in distinguishing inputs and thereby making
decisions. To quantify the ability to distinguish features of an input, we
propose several measures and demonstrate them on the prime example of a
recurrent neural network that represents an input rate by a response firing
averaged over a finite observation time. We show analytically and in
simulations that the optimal tuning of the network depends on the available
observation time, implying that tasks require a ``useful'' rather than maximal
sensitivity. Interestingly, this shifts the optimal dynamic regime from
critical to subcritical for finite observation times and highlights the
importance of incorporating the finite observation times concept in future
studies of information processing capabilities in a principled manner.
| [
{
"created": "Sat, 15 Jul 2023 12:52:27 GMT",
"version": "v1"
}
] | 2023-07-18 | [
[
"Azizpour",
"Sahel",
""
],
[
"Priesemann",
"Viola",
""
],
[
"Zierenberg",
"Johannes",
""
],
[
"Levina",
"Anna",
""
]
] | Tasks that require information about the world imply a trade-off between the time spent on observation and the variance of the response. In particular, fast decisions need to rely on uncertain information. However, standard estimates of information processing capabilities, such as the dynamic range, are defined based on mean values that assume infinite observation times. Here, we show that limiting the observation time results in distributions of responses whose variance increases with the temporal correlations in a system and, importantly, affects a system's confidence in distinguishing inputs and thereby making decisions. To quantify the ability to distinguish features of an input, we propose several measures and demonstrate them on the prime example of a recurrent neural network that represents an input rate by a response firing averaged over a finite observation time. We show analytically and in simulations that the optimal tuning of the network depends on the available observation time, implying that tasks require a ``useful'' rather than maximal sensitivity. Interestingly, this shifts the optimal dynamic regime from critical to subcritical for finite observation times and highlights the importance of incorporating the finite observation times concept in future studies of information processing capabilities in a principled manner. |
1804.06835 | Jose A. Cuesta | Jacobo Aguirre, Pablo Catal\'an, Jos\'e A. Cuesta, and Susanna
Manrubia | On the networked architecture of genotype spaces and its critical
effects on molecular evolution | 48 pages, 4 figures | Open Biology 8, 180069 (2018) | 10.1098/rsob.180069 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolutionary dynamics is often viewed as a subtle process of change
accumulation that causes a divergence among organisms and their genomes.
However, this interpretation is an inheritance of a gradualistic view that has
been challenged at the macroevolutionary, ecological, and molecular level.
Actually, when the complex architecture of genotype spaces is taken into
account, the evolutionary dynamics of molecular populations becomes
intrinsically non-uniform, sharing deep qualitative and quantitative
similarities with slowly driven physical systems: non-linear responses
analogous to critical transitions, sudden state changes, or hysteresis, among
others. Furthermore, the phenotypic plasticity inherent to genotypes transforms
classical fitness landscapes into multiscapes where adaptation in response to
an environmental change may be very fast. The quantitative nature of adaptive
molecular processes is deeply dependent on a networks-of-networks multilayered
structure of the map from genotype to function that we begin to unveil.
| [
{
"created": "Wed, 18 Apr 2018 17:49:47 GMT",
"version": "v1"
}
] | 2018-07-27 | [
[
"Aguirre",
"Jacobo",
""
],
[
"Catalán",
"Pablo",
""
],
[
"Cuesta",
"José A.",
""
],
[
"Manrubia",
"Susanna",
""
]
] | Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological, and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, sharing deep qualitative and quantitative similarities with slowly driven physical systems: non-linear responses analogous to critical transitions, sudden state changes, or hysteresis, among others. Furthermore, the phenotypic plasticity inherent to genotypes transforms classical fitness landscapes into multiscapes where adaptation in response to an environmental change may be very fast. The quantitative nature of adaptive molecular processes is deeply dependent on a networks-of-networks multilayered structure of the map from genotype to function that we begin to unveil. |
1809.05336 | Richa Tripathi | Richa Tripathi, Dyutiman Mukhopadhyay, Chakresh Kumar Singh, Krishna
Prasad Miyapuram and Shivakumar Jolad | Characterizing functional brain networks and emotional centers based on
Rasa theory of Indian aesthetics | 13 pages, 7 figures | null | 10.1007/978-3-030-36683-4_68 | Complex Networks and Their Applications VIII pp 854-867 | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | In Indian history of arts, Rasas are the aesthetics associated with any
auditory, visual, literary or musical piece of art that evokes highly
orchestrated emotional states. In this work, we study the functional response
of the brain to movie clippings meant to evoke the Rasas through network
analysis. We extract functional brain networks using coherence measures on EEG
recordings of film clips from popular Indian Bollywood movies representing nine
Rasas in the Indian Natyasastra. Structural and functional network measures
were computed for these brain networks, averaging over a range of significant
edge weights, in different brainwave frequency bands. We identify segregation
of neuronal wiring in the brain into modules using a community detection
algorithm. Further, using mutual information measure, we compare and contrast
the modular organizations of brain network corresponding to different Rasas.
Hubs identified using centrality measure reveal the network nodes that are
central to information propagation across all Rasas. We also observe that the
functional connectivity is suppressed when high-frequency waves such as beta
and gamma are dominant in the brain. The significant links causing differences
between the Rasa pairs are extracted statistically.
| [
{
"created": "Fri, 14 Sep 2018 10:15:54 GMT",
"version": "v1"
}
] | 2020-05-05 | [
[
"Tripathi",
"Richa",
""
],
[
"Mukhopadhyay",
"Dyutiman",
""
],
[
"Singh",
"Chakresh Kumar",
""
],
[
"Miyapuram",
"Krishna Prasad",
""
],
[
"Jolad",
"Shivakumar",
""
]
] | In Indian history of arts, Rasas are the aesthetics associated with any auditory, visual, literary or musical piece of art that evokes highly orchestrated emotional states. In this work, we study the functional response of the brain to movie clippings meant to evoke the Rasas through network analysis. We extract functional brain networks using coherence measures on EEG recordings of film clips from popular Indian Bollywood movies representing nine Rasas in the Indian Natyasastra. Structural and functional network measures were computed for these brain networks, averaging over a range of significant edge weights, in different brainwave frequency bands. We identify segregation of neuronal wiring in the brain into modules using a community detection algorithm. Further, using mutual information measure, we compare and contrast the modular organizations of brain network corresponding to different Rasas. Hubs identified using centrality measure reveal the network nodes that are central to information propagation across all Rasas. We also observe that the functional connectivity is suppressed when high-frequency waves such as beta and gamma are dominant in the brain. The significant links causing differences between the Rasa pairs are extracted statistically. |
1011.0244 | Ascelin Gordon Dr | Ascelin Gordon, William T. Langford, Matt D. White, James A. Todd,
Lucy Bastin | Modelling trade offs between public and private conservation policies | 20 pages, 5 figures | null | 10.1016/j.biocon.2010.10.011 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To reduce global biodiversity loss, there is an urgent need to determine the
most efficient allocation of conservation resources. Recently, there has been a
growing trend for many governments to supplement public ownership and
management of reserves with incentive programs for conservation on private
land. At the same time, policies to promote conservation on private land are
rarely evaluated in terms of their ecological consequences. This raises
important questions, such as the extent to which private land conservation can
improve conservation outcomes, and how it should be mixed with more traditional
public land conservation. We address these questions, using a general framework
for modelling environmental policies and a case study examining the
conservation of endangered native grasslands to the west of Melbourne,
Australia. Specifically, we examine three policies that involve: i) spending
all resources on creating public conservation areas; ii) spending all resources
on an ongoing incentive program where private landholders are paid to manage
vegetation on their property with 5-year contracts; and iii) splitting
resources between these two approaches. The performance of each strategy is
quantified with a vegetation condition change model that predicts future
changes in grassland quality. Of the policies tested, no one policy was always
best and policy performance depended on the objectives of those enacting the
policy. This work demonstrates a general method for evaluating environmental
policies and highlights the utility of a model which combines ecological and
socioeconomic processes.
| [
{
"created": "Mon, 1 Nov 2010 04:04:47 GMT",
"version": "v1"
}
] | 2010-11-02 | [
[
"Gordon",
"Ascelin",
""
],
[
"Langford",
"William T.",
""
],
[
"White",
"Matt D.",
""
],
[
"Todd",
"James A.",
""
],
[
"Bastin",
"Lucy",
""
]
] | To reduce global biodiversity loss, there is an urgent need to determine the most efficient allocation of conservation resources. Recently, there has been a growing trend for many governments to supplement public ownership and management of reserves with incentive programs for conservation on private land. At the same time, policies to promote conservation on private land are rarely evaluated in terms of their ecological consequences. This raises important questions, such as the extent to which private land conservation can improve conservation outcomes, and how it should be mixed with more traditional public land conservation. We address these questions, using a general framework for modelling environmental policies and a case study examining the conservation of endangered native grasslands to the west of Melbourne, Australia. Specifically, we examine three policies that involve: i) spending all resources on creating public conservation areas; ii) spending all resources on an ongoing incentive program where private landholders are paid to manage vegetation on their property with 5-year contracts; and iii) splitting resources between these two approaches. The performance of each strategy is quantified with a vegetation condition change model that predicts future changes in grassland quality. Of the policies tested, no one policy was always best and policy performance depended on the objectives of those enacting the policy. This work demonstrates a general method for evaluating environmental policies and highlights the utility of a model which combines ecological and socioeconomic processes. |
2007.01436 | Vikram Sundar | Vikram Sundar (1) and Lucy Colwell (1 and 2) ((1) Google Research, (2)
Department of Chemistry, University of Cambridge) | Attribution Methods Reveal Flaws in Fingerprint-Based Virtual Screening | 4 pages, 5 figures. In proceedings for the 2020 ICML workshop on
Machine Learning Interpretability for Scientific Discovery | null | null | null | q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fingerprint-based models for protein-ligand binding have demonstrated
outstanding success on benchmark datasets; however, these models may not learn
the correct binding rules. To assess this concern, we use in silico datasets
with known binding rules to develop a general framework for evaluating model
attribution. This framework identifies fragments that a model considers
necessary to achieve a particular score, sidestepping the need for a model to
be differentiable. Our results confirm that high-performing models may not
learn the correct binding rule, and suggest concrete steps that can remedy this
situation. We show that adding fragment-matched inactive molecules (decoys) to
the data reduces attribution false negatives, while attribution false positives
largely arise from the background correlation structure of molecular data.
Normalizing for these background correlations helps to reveal the true binding
logic. Our work highlights the danger of trusting attributions from
high-performing models and suggests that a closer examination of fingerprint
correlation structure and better decoy selection may help reduce
misattributions.
| [
{
"created": "Thu, 2 Jul 2020 23:23:47 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Jul 2020 22:34:00 GMT",
"version": "v2"
}
] | 2020-07-10 | [
[
"Sundar",
"Vikram",
"",
"1 and 2"
],
[
"Colwell",
"Lucy",
"",
"1 and 2"
]
] | Fingerprint-based models for protein-ligand binding have demonstrated outstanding success on benchmark datasets; however, these models may not learn the correct binding rules. To assess this concern, we use in silico datasets with known binding rules to develop a general framework for evaluating model attribution. This framework identifies fragments that a model considers necessary to achieve a particular score, sidestepping the need for a model to be differentiable. Our results confirm that high-performing models may not learn the correct binding rule, and suggest concrete steps that can remedy this situation. We show that adding fragment-matched inactive molecules (decoys) to the data reduces attribution false negatives, while attribution false positives largely arise from the background correlation structure of molecular data. Normalizing for these background correlations helps to reveal the true binding logic. Our work highlights the danger of trusting attributions from high-performing models and suggests that a closer examination of fingerprint correlation structure and better decoy selection may help reduce misattributions. |
1612.09330 | Jason Merritt | Jason Merritt and Seppe Kuehn | Frequency and amplitude dependent population dynamics during cycles of
feast and famine | New sets of experiments; substantially revised model. Supplemental
Material included in ancillary files | Phys. Rev. Lett. 121, 098101 (2018) | 10.1103/PhysRevLett.121.098101 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In nature microbial populations are subject to fluctuating nutrient levels.
Nutrient fluctuations are important for evolutionary and ecological dynamics in
microbial communities since they impact growth rates, population sizes and
biofilm formation. Here we use automated continuous-culture devices and
high-throughput imaging to show that when populations of Escherichia coli are
subjected to cycles of nutrient excess (feasts) and scarcity (famine) their
abundance dynamics during famines depend on the frequency and amplitude of
feasts. We show that frequency and amplitude dependent dynamics in planktonic
populations arise from nutrient and history dependent rates of aggregation and
dispersal. A phenomenological model recapitulates our experimental
observations. Our results show that the statistical properties of environmental
fluctuations have substantial impacts on spatial structure in bacterial
populations driving large changes in abundance dynamics.
| [
{
"created": "Thu, 29 Dec 2016 22:05:43 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Feb 2018 05:43:16 GMT",
"version": "v2"
}
] | 2018-09-12 | [
[
"Merritt",
"Jason",
""
],
[
"Kuehn",
"Seppe",
""
]
] | In nature microbial populations are subject to fluctuating nutrient levels. Nutrient fluctuations are important for evolutionary and ecological dynamics in microbial communities since they impact growth rates, population sizes and biofilm formation. Here we use automated continuous-culture devices and high-throughput imaging to show that when populations of Escherichia coli are subjected to cycles of nutrient excess (feasts) and scarcity (famine) their abundance dynamics during famines depend on the frequency and amplitude of feasts. We show that frequency and amplitude dependent dynamics in planktonic populations arise from nutrient and history dependent rates of aggregation and dispersal. A phenomenological model recapitulates our experimental observations. Our results show that the statistical properties of environmental fluctuations have substantial impacts on spatial structure in bacterial populations driving large changes in abundance dynamics. |
2306.05555 | Tung D. Nguyen | Tung D. Nguyen and Yixiang Wu and Tingting Tang and Amy Veprauskas and
Ying Zhou and Behzad Djafari Rouhani and Zhisheng Shuai | Impact of resource distributions on the competition of species in stream
environment | 32 pages | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our earlier work in \cite{nguyen2022population} shows that concentrating the
resources on the upstream end tends to maximize the total biomass in a
metapopulation model for a stream species. In this paper, we continue our
research direction by further considering a Lotka-Voletrra competition patch
model for two stream species. We show that the species whose resource
allocations maximize the total biomass has competitive advantage.
| [
{
"created": "Thu, 8 Jun 2023 20:54:31 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Jul 2023 18:25:12 GMT",
"version": "v2"
}
] | 2023-07-31 | [
[
"Nguyen",
"Tung D.",
""
],
[
"Wu",
"Yixiang",
""
],
[
"Tang",
"Tingting",
""
],
[
"Veprauskas",
"Amy",
""
],
[
"Zhou",
"Ying",
""
],
[
"Rouhani",
"Behzad Djafari",
""
],
[
"Shuai",
"Zhisheng",
""
]
] | Our earlier work in \cite{nguyen2022population} shows that concentrating the resources on the upstream end tends to maximize the total biomass in a metapopulation model for a stream species. In this paper, we continue our research direction by further considering a Lotka-Voletrra competition patch model for two stream species. We show that the species whose resource allocations maximize the total biomass has competitive advantage. |
1803.01639 | Tuomas Rajala | T. Rajala and S. Olhede and D.J. Murrell | When do we have the power to detect biological interactions in spatial
point patterns? | Main text 18 pages on 12pt font, 4 figures. Appendix 7 pages | null | null | null | q-bio.PE stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Determining the relative importance of environmental factors, biotic
interactions and stochasticity in assembling and maintaining species-rich
communities remains a major challenge in ecology. In plant communities,
interactions between individuals of different species are expected to leave a
spatial signature in the form of positive or negative spatial correlations over
distances relating to the spatial scale of interaction. Most studies using
spatial point process tools have found relatively little evidence for
interactions between pairs of species. More interactions tend to be detected in
communities with fewer species. However, there is currently no understanding of
how the power to detect spatial interactions may change with sample size, or
the scale and intensity of interactions.
We use a simple 2-species model where the scale and intensity of interactions
are controlled to simulate point pattern data. In combination with an
approximation to the variance of the spatial summary statistics that we sample,
we investigate the power of current spatial point pattern methods to correctly
reject the null model of bivariate species independence.
We show that the power to detect interactions is positively related to the
abundances of the species tested, and the intensity and scale of interactions.
Increasing imbalance in abundances has a negative effect on the power to detect
interactions. At population sizes typically found in currently available
datasets for species-rich plant communities we find only a very low power to
detect interactions. Differences in power may explain the increased frequency
of interactions in communities with fewer species. Furthermore, the
community-wide frequency of detected interactions is very sensitive to a
minimum abundance criterion for including species in the analyses.
| [
{
"created": "Mon, 5 Mar 2018 12:50:19 GMT",
"version": "v1"
},
{
"created": "Sat, 17 Mar 2018 10:35:54 GMT",
"version": "v2"
}
] | 2018-03-20 | [
[
"Rajala",
"T.",
""
],
[
"Olhede",
"S.",
""
],
[
"Murrell",
"D. J.",
""
]
] | Determining the relative importance of environmental factors, biotic interactions and stochasticity in assembling and maintaining species-rich communities remains a major challenge in ecology. In plant communities, interactions between individuals of different species are expected to leave a spatial signature in the form of positive or negative spatial correlations over distances relating to the spatial scale of interaction. Most studies using spatial point process tools have found relatively little evidence for interactions between pairs of species. More interactions tend to be detected in communities with fewer species. However, there is currently no understanding of how the power to detect spatial interactions may change with sample size, or the scale and intensity of interactions. We use a simple 2-species model where the scale and intensity of interactions are controlled to simulate point pattern data. In combination with an approximation to the variance of the spatial summary statistics that we sample, we investigate the power of current spatial point pattern methods to correctly reject the null model of bivariate species independence. We show that the power to detect interactions is positively related to the abundances of the species tested, and the intensity and scale of interactions. Increasing imbalance in abundances has a negative effect on the power to detect interactions. At population sizes typically found in currently available datasets for species-rich plant communities we find only a very low power to detect interactions. Differences in power may explain the increased frequency of interactions in communities with fewer species. Furthermore, the community-wide frequency of detected interactions is very sensitive to a minimum abundance criterion for including species in the analyses. |
1611.05741 | Shivakumar Jolad | Murali Krishna Enduri and Shivakumar Jolad | Estimation of reproduction number and non stationary spectral analysis
of Dengue epidemic | 15 pages, 7 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we analyze the post monsoon Dengue outbreaks by analyzing the
transient and long term dynamics of Dengue incidences and its environmental
correlates in Ahmedabad city in western India from 2005-2012. We calculate the
reproduction number $R_p$ using the growth rate of post monsoon Dengue
outbreaks and biological parameters like host and vector incubation periods and
vector mortality rate, and its uncertainties are estimated through Monte-Carlo
simulations by sampling parameters from their respective probability
distributions. Reduction in Female Aedes mosquito density required for an
effective prevention of Dengue outbreaks is also calculated. The non stationary
pattern of Dengue incidences and its climatic correlates like rainfall
temperature is analyzed through Wavelet based methods. We find that the mean
time lag between peak of monsoon and Dengue is 9 weeks. Monsoon and Dengue
cases are phase locked from 2008-2012 in the 16-32 weeks band. The duration of
post monsoon outbreak has been increasing every year, especially post 2008,
even though the intensity and duration of monsoon has been decreasing.
Temperature and Dengue incidences show correlations in the same band, but phase
lock is not stationary.
| [
{
"created": "Wed, 16 Nov 2016 18:00:26 GMT",
"version": "v1"
}
] | 2016-11-18 | [
[
"Enduri",
"Murali Krishna",
""
],
[
"Jolad",
"Shivakumar",
""
]
] | In this work we analyze the post monsoon Dengue outbreaks by analyzing the transient and long term dynamics of Dengue incidences and its environmental correlates in Ahmedabad city in western India from 2005-2012. We calculate the reproduction number $R_p$ using the growth rate of post monsoon Dengue outbreaks and biological parameters like host and vector incubation periods and vector mortality rate, and its uncertainties are estimated through Monte-Carlo simulations by sampling parameters from their respective probability distributions. Reduction in Female Aedes mosquito density required for an effective prevention of Dengue outbreaks is also calculated. The non stationary pattern of Dengue incidences and its climatic correlates like rainfall temperature is analyzed through Wavelet based methods. We find that the mean time lag between peak of monsoon and Dengue is 9 weeks. Monsoon and Dengue cases are phase locked from 2008-2012 in the 16-32 weeks band. The duration of post monsoon outbreak has been increasing every year, especially post 2008, even though the intensity and duration of monsoon has been decreasing. Temperature and Dengue incidences show correlations in the same band, but phase lock is not stationary. |
2301.02149 | Wilfred Ndifon | Abdoelnaser M Degoot and Wilfred Ndifon | Stochastics of DNA Quantification | 49 pages, 4 figures | null | null | null | q-bio.QM math.CO stat.ME | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A common approach to quantifying DNA involves repeated cycles of DNA
amplification. This approach, employed by the polymerase chain reaction (PCR),
produces outputs that are corrupted by amplification noise, making it
challenging to accurately back-calculate the amount of input DNA. Standard
mathematical solutions to this back-calculation problem do not take adequate
account of such noise and are error-prone. Here, we develop a parsimonious
mathematical model of the stochastic mapping of input DNA onto experimental
outputs that accounts, in a natural way, for amplification noise. We use the
model to derive the probability density of the quantification cycle, a
frequently reported experimental output, which can be fit to data to estimate
input DNA. Strikingly, the model predicts that a sample with only one input DNA
molecule has a $<$4% chance of testing positive, which is $>$25-fold lower than
assumed by a standard method of interpreting PCR data. We provide formulae for
calculating both the limit of detection and the limit of quantification, two
important operating characteristics of DNA quantification methods that are
frequently assessed by using ad-hoc mathematical techniques. Our results
provide a mathematical foundation for the rigorous analysis of DNA
quantification.
| [
{
"created": "Thu, 5 Jan 2023 16:49:43 GMT",
"version": "v1"
}
] | 2023-01-06 | [
[
"Degoot",
"Abdoelnaser M",
""
],
[
"Ndifon",
"Wilfred",
""
]
] | A common approach to quantifying DNA involves repeated cycles of DNA amplification. This approach, employed by the polymerase chain reaction (PCR), produces outputs that are corrupted by amplification noise, making it challenging to accurately back-calculate the amount of input DNA. Standard mathematical solutions to this back-calculation problem do not take adequate account of such noise and are error-prone. Here, we develop a parsimonious mathematical model of the stochastic mapping of input DNA onto experimental outputs that accounts, in a natural way, for amplification noise. We use the model to derive the probability density of the quantification cycle, a frequently reported experimental output, which can be fit to data to estimate input DNA. Strikingly, the model predicts that a sample with only one input DNA molecule has a $<$4% chance of testing positive, which is $>$25-fold lower than assumed by a standard method of interpreting PCR data. We provide formulae for calculating both the limit of detection and the limit of quantification, two important operating characteristics of DNA quantification methods that are frequently assessed by using ad-hoc mathematical techniques. Our results provide a mathematical foundation for the rigorous analysis of DNA quantification. |
0707.2011 | Negadi Tidjani none | Tidjani Negadi | The genetic code multiplet structure, in one number | 9 pages | Symmetry: Culture and Science, Volume 18, Numbers 2-3, pages
149-160 (2007) | null | null | q-bio.OT | null | The standard genetic code multiplet structure as well as the correct
degeneracies, class by class, are all extracted from the (unique) number 23,
the order of the permutation group of 23 objects.
| [
{
"created": "Fri, 13 Jul 2007 13:58:01 GMT",
"version": "v1"
}
] | 2008-09-01 | [
[
"Negadi",
"Tidjani",
""
]
] | The standard genetic code multiplet structure as well as the correct degeneracies, class by class, are all extracted from the (unique) number 23, the order of the permutation group of 23 objects. |
q-bio/0506005 | Ana Carpio | A. Carpio | Asymptotic construction of pulses in the Hodgkin Huxley model for
myelinated nerves | to appear in Phys. Rev. E | null | 10.1103/PhysRevE.72.011905 | null | q-bio.NC q-bio.QM | null | A quantitative description of pulses and wave trains in the spatially
discrete Hodgkin-Huxley model for myelinated nerves is given. Predictions of
the shape and speed of the waves and the thresholds for propagation failure are
obtained. Our asymptotic predictions agree quite well with numerical solutions
of the model and describe wave patterns generated by repeated firing at a
boundary.
| [
{
"created": "Tue, 7 Jun 2005 13:53:06 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Carpio",
"A.",
""
]
] | A quantitative description of pulses and wave trains in the spatially discrete Hodgkin-Huxley model for myelinated nerves is given. Predictions of the shape and speed of the waves and the thresholds for propagation failure are obtained. Our asymptotic predictions agree quite well with numerical solutions of the model and describe wave patterns generated by repeated firing at a boundary. |
1909.12138 | Noemi Castelletti | Noemi Castelletti, Maria Vittoria Barbarossa | A mathematical view on head lice infestations | null | null | null | null | q-bio.PE math.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Commonly known as head lice, Pediculus humanus capitis are human
ectoparasites which cause infestations in children worldwide. Understanding the
life cycle of head lice is an important step in knowing how to treat lice
infestations, as the parasite behavior depends considerably on its age and
gender. In this work we propose a mathematical model for head lice population
dynamics in hosts who could be or not quarantined and treated. Considering a
lice population structured by age and gender we formulate the model as a system
of hyperbolic PDEs, which can be reduced to compartmental systems of delay or
ordinary differential equations. Besides studying fundamental properties of the
model, such as existence, uniqueness and nonnegativity of solutions, we show
the existence of (in certain cases multiple) equilibria at which the
infestation persists on the host's head. Aiming to assess the performance of
treatments against head lice infestations, by mean of computer experiments and
numerical simulations we investigate four possible treatment strategies. Our
main results can be summarized as follows: (i) early detection is crucial for
quick and efficient eradication of lice infestations; (ii) dimeticone-based
products applied every 4 days effectively remove lice in at most three
applications even in case of severe infestations and (iii) minimization of the
reinfection risk, e.g. by mean of synchronized treatments in
families/classrooms is recommended.
| [
{
"created": "Thu, 26 Sep 2019 14:20:10 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Sep 2019 12:46:30 GMT",
"version": "v2"
}
] | 2019-09-30 | [
[
"Castelletti",
"Noemi",
""
],
[
"Barbarossa",
"Maria Vittoria",
""
]
] | Commonly known as head lice, Pediculus humanus capitis are human ectoparasites which cause infestations in children worldwide. Understanding the life cycle of head lice is an important step in knowing how to treat lice infestations, as the parasite behavior depends considerably on its age and gender. In this work we propose a mathematical model for head lice population dynamics in hosts who could be or not quarantined and treated. Considering a lice population structured by age and gender we formulate the model as a system of hyperbolic PDEs, which can be reduced to compartmental systems of delay or ordinary differential equations. Besides studying fundamental properties of the model, such as existence, uniqueness and nonnegativity of solutions, we show the existence of (in certain cases multiple) equilibria at which the infestation persists on the host's head. Aiming to assess the performance of treatments against head lice infestations, by mean of computer experiments and numerical simulations we investigate four possible treatment strategies. Our main results can be summarized as follows: (i) early detection is crucial for quick and efficient eradication of lice infestations; (ii) dimeticone-based products applied every 4 days effectively remove lice in at most three applications even in case of severe infestations and (iii) minimization of the reinfection risk, e.g. by mean of synchronized treatments in families/classrooms is recommended. |
1509.06810 | Thorsten Pr\"ustel | Thorsten Pr\"ustel and Martin Meier-Schellersheim | Exact propagation without analytical solutions | 14 pages, 3 figures | null | null | null | q-bio.QM | http://creativecommons.org/publicdomain/zero/1.0/ | We present a simulation algorithm that accurately propagates a molecule pair
using large time steps without the need to invoke the full exact analytical
solutions of the Smoluchowski diffusion equation. Because the proposed method
only uses uniform and Gaussian random numbers, it allows for position updates
that are two to three orders of magnitude faster than those of a corresponding
scheme based on full solutions, while mantaining the same degree of accuracy.
Neither simplifying nor ad hoc assumptions that are foreign to the underlying
Smoluchowski theory are employed, instead, the algorithm faithfully
incorporates the individual elements of the theoretical model. The method is
flexible and applicable in 1, 2 and 3 dimensions, suggesting that it may find
broad usage in various stochastic simulation algorithms. We demonstrate the
algorithm for the case of a non-reactive, irreversible and reversible reacting
molecule pair.
| [
{
"created": "Tue, 22 Sep 2015 23:28:16 GMT",
"version": "v1"
}
] | 2015-09-24 | [
[
"Prüstel",
"Thorsten",
""
],
[
"Meier-Schellersheim",
"Martin",
""
]
] | We present a simulation algorithm that accurately propagates a molecule pair using large time steps without the need to invoke the full exact analytical solutions of the Smoluchowski diffusion equation. Because the proposed method only uses uniform and Gaussian random numbers, it allows for position updates that are two to three orders of magnitude faster than those of a corresponding scheme based on full solutions, while mantaining the same degree of accuracy. Neither simplifying nor ad hoc assumptions that are foreign to the underlying Smoluchowski theory are employed, instead, the algorithm faithfully incorporates the individual elements of the theoretical model. The method is flexible and applicable in 1, 2 and 3 dimensions, suggesting that it may find broad usage in various stochastic simulation algorithms. We demonstrate the algorithm for the case of a non-reactive, irreversible and reversible reacting molecule pair. |
1411.2847 | Benjamin Amor | B. Amor, S. N. Yaliraki, R. Woscholski, and M. Barahona | Uncovering allosteric pathways in caspase-1 with Markov transient
analysis and multiscale community detection | 14 pages, 8 figures | Mol Biosyst. 2014 Aug;10(8):2247-58 | 10.1039/c4mb00088a | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Allosteric regulation at distant sites is central to many cellular processes.
In particular, allosteric sites in proteins are a major target to increase the
range and selectivity of new drugs, and there is a need for methods capable of
identifying intra-molecular signalling pathways leading to allosteric effects.
Here, we use an atomistic graph-theoretical approach that exploits Markov
transients to extract such pathways and exemplify our results in an important
allosteric protein, caspase-1. Firstly, we use Markov Stability community
detection to perform a multiscale analysis of the structure of caspase-1 which
reveals that the active conformation has a weaker, less compartmentalised
large-scale structure as compared to the inactive conformation, resulting in
greater intra-protein coherence and signal propagation. We also carry out a
full computational point mutagenesis and identify that only a few residues are
critical to such structural coherence. Secondly, we characterise explicitly the
transients of random walks originating at the active site and predict the
location of a known allosteric site in this protein quantifying the
contribution of individual bonds to the communication pathway between the
active and allosteric sites. Several of the bonds we find have been shown
experimentally to be functionally critical, but we also predict a number of as
yet unidentified bonds which may contribute to the pathway. Our approach offers
a computationally inexpensive method for the identification of allosteric sites
and communication pathways in proteins using a fully atomistic description.
| [
{
"created": "Tue, 11 Nov 2014 15:14:35 GMT",
"version": "v1"
}
] | 2014-11-12 | [
[
"Amor",
"B.",
""
],
[
"Yaliraki",
"S. N.",
""
],
[
"Woscholski",
"R.",
""
],
[
"Barahona",
"M.",
""
]
] | Allosteric regulation at distant sites is central to many cellular processes. In particular, allosteric sites in proteins are a major target to increase the range and selectivity of new drugs, and there is a need for methods capable of identifying intra-molecular signalling pathways leading to allosteric effects. Here, we use an atomistic graph-theoretical approach that exploits Markov transients to extract such pathways and exemplify our results in an important allosteric protein, caspase-1. Firstly, we use Markov Stability community detection to perform a multiscale analysis of the structure of caspase-1 which reveals that the active conformation has a weaker, less compartmentalised large-scale structure as compared to the inactive conformation, resulting in greater intra-protein coherence and signal propagation. We also carry out a full computational point mutagenesis and identify that only a few residues are critical to such structural coherence. Secondly, we characterise explicitly the transients of random walks originating at the active site and predict the location of a known allosteric site in this protein quantifying the contribution of individual bonds to the communication pathway between the active and allosteric sites. Several of the bonds we find have been shown experimentally to be functionally critical, but we also predict a number of as yet unidentified bonds which may contribute to the pathway. Our approach offers a computationally inexpensive method for the identification of allosteric sites and communication pathways in proteins using a fully atomistic description. |
2205.07045 | Yuhao Huang | Yuhao Huang, Corey Keller | How can I investigate causal brain networks with iEEG? | Forthcoming chapter in "Intracranial EEG for Cognitive Neuroscience" | null | null | null | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While many human imaging methodologies probe the structural and functional
connectivity of the brain, techniques to investigate cortical networks in a
causal and directional manner are critical but limited. The use of iEEG enables
several approaches to directly characterize brain regions that are functionally
connected and in some cases also establish directionality of these connections.
In this chapter we focus on the basis, method and application of the
cortico-cortical evoked potential (CCEP), whereby electrical pulses applied to
one set of intracranial electrodes yields an electrically-induced brain
response at local and remote regions. In this chapter, CCEPs are first
contextualized within common brain connectivity methods used to define cortical
networks and how CCEP adds unique information. Second, the practical and
analytical considerations when using CCEP are discussed. Third, we review the
neurophysiology underlying CCEPs and the applications of CCEPs including
exploring functional and pathological brain networks and probing brain
plasticity. Finally, we end with a discussion of limitations, caveats, and
directions to improve CCEP utilization in the future.
| [
{
"created": "Sat, 14 May 2022 12:01:35 GMT",
"version": "v1"
}
] | 2022-05-17 | [
[
"Huang",
"Yuhao",
""
],
[
"Keller",
"Corey",
""
]
] | While many human imaging methodologies probe the structural and functional connectivity of the brain, techniques to investigate cortical networks in a causal and directional manner are critical but limited. The use of iEEG enables several approaches to directly characterize brain regions that are functionally connected and in some cases also establish directionality of these connections. In this chapter we focus on the basis, method and application of the cortico-cortical evoked potential (CCEP), whereby electrical pulses applied to one set of intracranial electrodes yields an electrically-induced brain response at local and remote regions. In this chapter, CCEPs are first contextualized within common brain connectivity methods used to define cortical networks and how CCEP adds unique information. Second, the practical and analytical considerations when using CCEP are discussed. Third, we review the neurophysiology underlying CCEPs and the applications of CCEPs including exploring functional and pathological brain networks and probing brain plasticity. Finally, we end with a discussion of limitations, caveats, and directions to improve CCEP utilization in the future. |
0710.3258 | Steven Kelk | Jaroslaw Byrka, Pawel Gawrychowski, Katharina T. Huber, Steven Kelk | Worst-case optimal approximation algorithms for maximizing triplet
consistency within phylogenetic networks | A new version with heavily optimized derandomization running time.
And a very fast triplet-consistency checking algorithm as subroutine | null | null | null | q-bio.PE | null | This article concerns the following question arising in computational
evolutionary biology. For a given subclass of phylogenetic networks, what is
the maximum value of 0 <= p <= 1 such that for every input set T of rooted
triplets, there exists some network N(T) from the subclass such that at least
p|T| of the triplets are consistent with N(T)? Here we prove that the set
containing all triplets (the full triplet set) in some sense defines p, and
moreover that any network N achieving fraction p' for the full triplet set can
be converted in polynomial time into an isomorphic network N'(T) achieving >=
p' for an arbitrary triplet set T. We demonstrate the power of this result for
the field of phylogenetics by giving worst-case optimal algorithms for level-1
phylogenetic networks (a much-studied extension of phylogenetic trees),
improving considerably upon the 5/12 fraction obtained recently by Jansson,
Nguyen and Sung. For level-2 phylogenetic networks we show that p >= 0.61. We
note that all the results in this article also apply to weighted triplet sets.
| [
{
"created": "Wed, 17 Oct 2007 10:17:17 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Nov 2007 13:33:28 GMT",
"version": "v2"
},
{
"created": "Wed, 13 Feb 2008 10:30:19 GMT",
"version": "v3"
}
] | 2008-02-13 | [
[
"Byrka",
"Jaroslaw",
""
],
[
"Gawrychowski",
"Pawel",
""
],
[
"Huber",
"Katharina T.",
""
],
[
"Kelk",
"Steven",
""
]
] | This article concerns the following question arising in computational evolutionary biology. For a given subclass of phylogenetic networks, what is the maximum value of 0 <= p <= 1 such that for every input set T of rooted triplets, there exists some network N(T) from the subclass such that at least p|T| of the triplets are consistent with N(T)? Here we prove that the set containing all triplets (the full triplet set) in some sense defines p, and moreover that any network N achieving fraction p' for the full triplet set can be converted in polynomial time into an isomorphic network N'(T) achieving >= p' for an arbitrary triplet set T. We demonstrate the power of this result for the field of phylogenetics by giving worst-case optimal algorithms for level-1 phylogenetic networks (a much-studied extension of phylogenetic trees), improving considerably upon the 5/12 fraction obtained recently by Jansson, Nguyen and Sung. For level-2 phylogenetic networks we show that p >= 0.61. We note that all the results in this article also apply to weighted triplet sets. |
2004.01248 | Samuel Heroy | Samuel Heroy | Metropolitan-scale COVID-19 outbreaks: how similar are they? | null | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study, we use US county-level COVID-19 case data from January
21-March 25, 2020 to study the exponential behavior of case growth at the
metropolitan scale. In particular, we assume that all localized outbreaks are
in an early stage (either undergoing exponential growth in the number of cases,
or are effectively contained) and compare the explanatory performance of
different simple exponential and linear growth models for different
metropolitan areas. While we find no relationship between city size and
exponential growth rate (directly related to $R0$, which denotes average the
number of cases an infected individual infects), we do find that larger cities
seem to begin exponential spreading earlier and are thus in a more advanced
stage of the pandemic at the time of submission. We also use more recent data
to compute prediction errors given our models, and find that in many cities,
exponential growth models trained on data before March 26 are poor predictors
for case numbers in this more recent period (March 26-30), likely indicating a
reduction in the number of new cases facilitated through social distancing.
| [
{
"created": "Thu, 2 Apr 2020 20:25:56 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Apr 2020 09:23:41 GMT",
"version": "v2"
}
] | 2020-04-07 | [
[
"Heroy",
"Samuel",
""
]
] | In this study, we use US county-level COVID-19 case data from January 21-March 25, 2020 to study the exponential behavior of case growth at the metropolitan scale. In particular, we assume that all localized outbreaks are in an early stage (either undergoing exponential growth in the number of cases, or are effectively contained) and compare the explanatory performance of different simple exponential and linear growth models for different metropolitan areas. While we find no relationship between city size and exponential growth rate (directly related to $R0$, which denotes average the number of cases an infected individual infects), we do find that larger cities seem to begin exponential spreading earlier and are thus in a more advanced stage of the pandemic at the time of submission. We also use more recent data to compute prediction errors given our models, and find that in many cities, exponential growth models trained on data before March 26 are poor predictors for case numbers in this more recent period (March 26-30), likely indicating a reduction in the number of new cases facilitated through social distancing. |
1304.3266 | Jes\'us Requena Carri\'on | Jes\'us Requena-Carri\'on, Ferney A. Beltr\'an-Molina, Antonio G.
Marques | Relating the spectrum of cardiac signals to the spatiotemporal dynamics
of cardiac sources | 28 pages, 3 figures | null | null | null | q-bio.QM physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An increasing number of studies use the spectrum of cardiac signals for
analyzing the spatiotemporal dynamics of complex cardiac arrhythmias. However,
the relationship between the spectrum of cardiac signals and the spatiotemporal
dynamics of the underlying cardiac sources remains to date unclear. In this
paper, we derive a mathematical expression relating the spectrum of cardiac
signals to the spatiotemporal dynamics of cardiac sources and the measurement
characteristics of the lead systems. Then, by using analytical methods and
computer simulations we analyze the spectrum of cardiac signals measured by
idealized lead systems during correlated and uncorrelated spatiotemporal
dynamics. Our results show that lead systems can have distorting effects on the
spectral envelope of cardiac signals, which depend on the spatial resolution of
the lead systems and on the degree of spatiotemporal correlation of the
underlying cardiac sources. In addition to this, our results indicate that the
spectral features that do not depend on the spectral envelope, such as the
dominant frequency, behave robustly against different choices of lead systems.
| [
{
"created": "Thu, 11 Apr 2013 11:58:50 GMT",
"version": "v1"
}
] | 2013-04-12 | [
[
"Requena-Carrión",
"Jesús",
""
],
[
"Beltrán-Molina",
"Ferney A.",
""
],
[
"Marques",
"Antonio G.",
""
]
] | An increasing number of studies use the spectrum of cardiac signals for analyzing the spatiotemporal dynamics of complex cardiac arrhythmias. However, the relationship between the spectrum of cardiac signals and the spatiotemporal dynamics of the underlying cardiac sources remains to date unclear. In this paper, we derive a mathematical expression relating the spectrum of cardiac signals to the spatiotemporal dynamics of cardiac sources and the measurement characteristics of the lead systems. Then, by using analytical methods and computer simulations we analyze the spectrum of cardiac signals measured by idealized lead systems during correlated and uncorrelated spatiotemporal dynamics. Our results show that lead systems can have distorting effects on the spectral envelope of cardiac signals, which depend on the spatial resolution of the lead systems and on the degree of spatiotemporal correlation of the underlying cardiac sources. In addition to this, our results indicate that the spectral features that do not depend on the spectral envelope, such as the dominant frequency, behave robustly against different choices of lead systems. |
2309.04741 | Roozbeh H. Pazuki | Roozbeh H. Pazuki, Robert G. Endres | Upper limits on the robustness of Turing models and other
multiparametric dynamical systems | null | null | null | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional linear stability analysis based on matrix diagonalization is a
computationally intensive $O(n^3)$ process for $n$-dimensional systems of
differential equations, posing substantial limitations for the exploration of
Turing systems of pattern formation where an additional wave-number parameter
needs to be investigated. In this study, we introduce an efficient $O(n)$
technique that leverages Gershgorin's theorem to determine upper limits on
regions of parameter space and the wave number beyond which Turing
instabilities cannot occur. This method offers a streamlined avenue for
exploring the phase diagrams of other complex multiparametric models, such as
those found in systems biology.
| [
{
"created": "Sat, 9 Sep 2023 09:59:33 GMT",
"version": "v1"
}
] | 2023-09-12 | [
[
"Pazuki",
"Roozbeh H.",
""
],
[
"Endres",
"Robert G.",
""
]
] | Traditional linear stability analysis based on matrix diagonalization is a computationally intensive $O(n^3)$ process for $n$-dimensional systems of differential equations, posing substantial limitations for the exploration of Turing systems of pattern formation where an additional wave-number parameter needs to be investigated. In this study, we introduce an efficient $O(n)$ technique that leverages Gershgorin's theorem to determine upper limits on regions of parameter space and the wave number beyond which Turing instabilities cannot occur. This method offers a streamlined avenue for exploring the phase diagrams of other complex multiparametric models, such as those found in systems biology. |
1508.04174 | Hyunju Kim | Hyunju Kim and Paul Davies and Sara Imari Walker | New Scaling Relation for Information Transfer in Biological Networks | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Living systems are often described utilizing informational analogies. An
important open question is whether information is merely a useful conceptual
metaphor, or intrinsic to the operation of biological systems. To address this
question, we provide a rigorous case study of the informational architecture of
two representative biological networks: the Boolean network model for the
cell-cycle regulatory network of the fission yeast S. pombe and that of the
budding yeast S. cerevisiae. We compare our results for these biological
networks to the same analysis performed on ensembles of two different types of
random networks. We show that both biological networks share features in common
that are not shared by either ensemble. In particular, the biological networks
in our study, on average, process more information than the random networks.
They also exhibit a scaling relation in information transferred between nodes
that distinguishes them from either ensemble: even when compared to the
ensemble of random networks that shares important topological properties, such
as a scale-free structure. We show that the most biologically distinct regime
of this scaling relation is associated with the dynamics and function of the
biological networks. Information processing in biological networks is therefore
interpreted as an emergent property of topology (causal structure) and dynamics
(function). These results demonstrate quantitatively how the informational
architecture of biologically evolved networks can distinguish them from other
classes of network architecture that do not share the same informational
properties.
| [
{
"created": "Mon, 17 Aug 2015 23:06:43 GMT",
"version": "v1"
}
] | 2015-08-19 | [
[
"Kim",
"Hyunju",
""
],
[
"Davies",
"Paul",
""
],
[
"Walker",
"Sara Imari",
""
]
] | Living systems are often described utilizing informational analogies. An important open question is whether information is merely a useful conceptual metaphor, or intrinsic to the operation of biological systems. To address this question, we provide a rigorous case study of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast S. pombe and that of the budding yeast S. cerevisiae. We compare our results for these biological networks to the same analysis performed on ensembles of two different types of random networks. We show that both biological networks share features in common that are not shared by either ensemble. In particular, the biological networks in our study, on average, process more information than the random networks. They also exhibit a scaling relation in information transferred between nodes that distinguishes them from either ensemble: even when compared to the ensemble of random networks that shares important topological properties, such as a scale-free structure. We show that the most biologically distinct regime of this scaling relation is associated with the dynamics and function of the biological networks. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). These results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. |
1705.02867 | Alberto Sorrentino | Alberto Sorrentino and Michele Piana | Inverse Modeling for MEG/EEG data | 15 pages, 1 figure | null | null | null | q-bio.QM math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide an overview of the state-of-the-art for mathematical methods that
are used to reconstruct brain activity from neurophysiological data. After a
brief introduction on the mathematics of the forward problem, we discuss
standard and recently proposed regularization methods, as well as Monte Carlo
techniques for Bayesian inference. We classify the inverse methods based on the
underlying source model, and discuss advantages and disadvantages. Finally we
describe an application to the pre-surgical evaluation of epileptic patients.
| [
{
"created": "Mon, 8 May 2017 13:37:23 GMT",
"version": "v1"
}
] | 2017-05-09 | [
[
"Sorrentino",
"Alberto",
""
],
[
"Piana",
"Michele",
""
]
] | We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients. |
1301.5527 | David A. Kessler | Shlomit Weisman, David A. Kessler | Coexistence in an inhomogeneous environment | null | null | 10.1371/journal.pone.0062699 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the two-dimensional extension of the model of Kessler and Sander
of competition between two species identical except for dispersion rates. In
this class of models, the spatial inhomogeneity of reproduction rates gives
rise to an implicit cost of dispersal, due to the tendency to leave favorable
locations. Then, as in the Hamilton-May model with its explicit dispersal cost,
the tradeoff between dispersal case and the beneficial role of dispersal in
limiting fluctuations, leads to an advantage of one dispersal rate over
another, and the eventual extinction of the disadvantaged species. In two
dimensions we find that while the competition leads to the elimination of one
species at high and low population density, at intermediate densities the two
species can coexist essentially indefinitely. This is a new phenomenon not
present in either the one-dimensional form of the Kessler-Sander model nor in
the totally connected Hamilton-May model, and points to the importance of
geometry in the question of dispersal.
| [
{
"created": "Wed, 23 Jan 2013 15:09:33 GMT",
"version": "v1"
}
] | 2015-06-12 | [
[
"Weisman",
"Shlomit",
""
],
[
"Kessler",
"David A.",
""
]
] | We examine the two-dimensional extension of the model of Kessler and Sander of competition between two species identical except for dispersion rates. In this class of models, the spatial inhomogeneity of reproduction rates gives rise to an implicit cost of dispersal, due to the tendency to leave favorable locations. Then, as in the Hamilton-May model with its explicit dispersal cost, the tradeoff between dispersal case and the beneficial role of dispersal in limiting fluctuations, leads to an advantage of one dispersal rate over another, and the eventual extinction of the disadvantaged species. In two dimensions we find that while the competition leads to the elimination of one species at high and low population density, at intermediate densities the two species can coexist essentially indefinitely. This is a new phenomenon not present in either the one-dimensional form of the Kessler-Sander model nor in the totally connected Hamilton-May model, and points to the importance of geometry in the question of dispersal. |
2306.05984 | Anyou Wang | Anyou Wang | Noncoding RNAs evolutionarily extend animal lifespan | 13 pages and 4 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The mechanisms underlying lifespan evolution in organisms have long been
mysterious. However, recent studies have demonstrated that organisms
evolutionarily gain noncoding RNAs (ncRNAs) that carry endogenous profound
functions in higher organisms, including lifespan. This study unveils ncRNAs as
crucial drivers driving animal lifespan evolution. Species in the animal
kingdom evolutionarily increase their ncRNA length in their genomes, coinciding
with trimming mitochondrial genome length. This leads to lower energy
consumption and ultimately lifespan extension. Notably, during lifespan
extension, species exhibit a gradual acquisition of long-life ncRNA motifs
while concurrently losing short-life motifs. These longevity-associated ncRNA
motifs, such as GGTGCG, are particularly active in key tissues, including the
endometrium, ovary, testis, and cerebral cortex. The activation of ncRNAs in
the ovary and endometrium offers insights into why women generally exhibit
longer lifespans than men. This groundbreaking discovery reveals the pivotal
role of ncRNAs in driving lifespan evolution and provides a fundamental
foundation for the study of longevity and aging.
| [
{
"created": "Fri, 9 Jun 2023 15:52:45 GMT",
"version": "v1"
}
] | 2023-06-12 | [
[
"Wang",
"Anyou",
""
]
] | The mechanisms underlying lifespan evolution in organisms have long been mysterious. However, recent studies have demonstrated that organisms evolutionarily gain noncoding RNAs (ncRNAs) that carry endogenous profound functions in higher organisms, including lifespan. This study unveils ncRNAs as crucial drivers driving animal lifespan evolution. Species in the animal kingdom evolutionarily increase their ncRNA length in their genomes, coinciding with trimming mitochondrial genome length. This leads to lower energy consumption and ultimately lifespan extension. Notably, during lifespan extension, species exhibit a gradual acquisition of long-life ncRNA motifs while concurrently losing short-life motifs. These longevity-associated ncRNA motifs, such as GGTGCG, are particularly active in key tissues, including the endometrium, ovary, testis, and cerebral cortex. The activation of ncRNAs in the ovary and endometrium offers insights into why women generally exhibit longer lifespans than men. This groundbreaking discovery reveals the pivotal role of ncRNAs in driving lifespan evolution and provides a fundamental foundation for the study of longevity and aging. |
0905.2843 | Wolfgang Keil | Wolfgang Keil, Karl-Friedrich Schmidt, Siegrid Loewel, Matthias
Kaschube | Reorganization of columnar architecture in the growing visual cortex | 8+13 pages, 4+8 figures, paper + supplementary material | PNAS July 6, 2010 vol. 107 no. 27 12293-12298 | 10.1073/pnas.0913020107 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many cortical areas increase in size considerably during postnatal
development, progressively displacing neuronal cell bodies from each other. At
present, little is known about how cortical growth affects the development of
neuronal circuits. Here, in acute and chronic experiments, we study the layout
of ocular dominance (OD) columns in cat primary visual cortex (V1) during a
period of substantial postnatal growth. We find that despite a considerable
size increase of V1, the spacing between columns is largely preserved. In
contrast, their spatial arrangement changes systematically over this period.
While in young animals columns are more band-like, layouts become more
isotropic in mature animals. We propose a novel mechanism of growth-induced
reorganization that is based on the `zigzag instability', a dynamical
instability observed in several inanimate pattern forming systems. We argue
that this mechanism is inherent to a wide class of models for the
activity-dependent formation of OD columns. Analyzing one member of this class,
the Elastic Network model, we show that this mechanism can account for the
preservation of column spacing and the specific mode of reorganization of OD
columns that we observe. We conclude that neurons systematically shift their
selectivities during normal development and that this reorganization is induced
by the cortical expansion during growth. Our work suggests that cortical
circuits remain plastic for an extended period in development in order to
facilitate the modification of neuronal circuits to adjust for cortical growth.
| [
{
"created": "Mon, 18 May 2009 11:51:34 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Apr 2011 15:04:59 GMT",
"version": "v2"
}
] | 2011-04-12 | [
[
"Keil",
"Wolfgang",
""
],
[
"Schmidt",
"Karl-Friedrich",
""
],
[
"Loewel",
"Siegrid",
""
],
[
"Kaschube",
"Matthias",
""
]
] | Many cortical areas increase in size considerably during postnatal development, progressively displacing neuronal cell bodies from each other. At present, little is known about how cortical growth affects the development of neuronal circuits. Here, in acute and chronic experiments, we study the layout of ocular dominance (OD) columns in cat primary visual cortex (V1) during a period of substantial postnatal growth. We find that despite a considerable size increase of V1, the spacing between columns is largely preserved. In contrast, their spatial arrangement changes systematically over this period. While in young animals columns are more band-like, layouts become more isotropic in mature animals. We propose a novel mechanism of growth-induced reorganization that is based on the `zigzag instability', a dynamical instability observed in several inanimate pattern forming systems. We argue that this mechanism is inherent to a wide class of models for the activity-dependent formation of OD columns. Analyzing one member of this class, the Elastic Network model, we show that this mechanism can account for the preservation of column spacing and the specific mode of reorganization of OD columns that we observe. We conclude that neurons systematically shift their selectivities during normal development and that this reorganization is induced by the cortical expansion during growth. Our work suggests that cortical circuits remain plastic for an extended period in development in order to facilitate the modification of neuronal circuits to adjust for cortical growth. |
1303.6993 | Alfred Bennun | Alfred Bennun | The coupling of thermodynamics with the organizational water-protein
intra-dynamics driven by the H-bonds dissipative potential of cluster water | 9 pages, 2 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Red cell-Hb-CSF functions as a sensor adapting response to Hb
heterotropic equilibriums. At the lungs O2 and Mg2+, each one increasing
affinity for the other stabilize the relax (R) form [(O2)4Hb(Mg)2].(H2O)R. At
tissue level, the inclusion of H+ and 2,3-DPG excludes O2 and Mg2+ to stabilize
the tense (T) form 2,3-DPG-deoxyHb-(H2O)T. Both senses are integrated into a
cycle T into R and R into T, without involving a direct reversal. The
dissipative potential of water cluster (H2O)n interacts with the hydrophilic
asymmetries of Hb, to restrict randomness of the kinetic sense implicated in a
single peak for activation energy (Ea). The hydration shells could sequence an
enhanced Ea into several peaks, to sequentially activate transitions states.
Hence, changes in dipole state, sliding, pKa, n-H-bonds, etc., could became
concatenated for vectoriality. (H2O)n by the loss of H-bonds couple with to the
hydration turnover of proteins and ions to result in incomplete water cluster
(H2O)n*, with a lower-n. (H2O)n* became a carrier of heat/entropy into the
cerebrospinal fluid (CSF) which has to be replaced 3.7 times per day. OxyHb
formation involves sliding-down of alpha vs beta chains, to shift alpha1 and
alpha2 Pro 44 into allowing the entrance of a fully hydrated
[Mg.(H2O)6](H2O)12-14(2+) (or Zn2+) into the hydrophilic beta2-alpha1 and
beta1-alpha2 interfaces. OxyHb pKa of 6.4 leads to H+-dissociation increasing
negative charge of R-groups. This at beta2-alpha1 sequence two tetradentate
chelates, first an Mg2+, bonding with beta2 His 92 and a second Mg2+ with
alpha1 His 87, to cooperatively release hindrance. The interconversion of
oxy-to-deoxyHb, pKa=8, leads to the amphoteric imidazole to became positively
charged and proximal histidines return into hindrance position, releasing the
incomplete hydrated Mg.(H2O)inc(2+) and O2 into CSF.
| [
{
"created": "Wed, 27 Mar 2013 22:14:45 GMT",
"version": "v1"
}
] | 2013-03-29 | [
[
"Bennun",
"Alfred",
""
]
] | The Red cell-Hb-CSF functions as a sensor adapting response to Hb heterotropic equilibriums. At the lungs O2 and Mg2+, each one increasing affinity for the other stabilize the relax (R) form [(O2)4Hb(Mg)2].(H2O)R. At tissue level, the inclusion of H+ and 2,3-DPG excludes O2 and Mg2+ to stabilize the tense (T) form 2,3-DPG-deoxyHb-(H2O)T. Both senses are integrated into a cycle T into R and R into T, without involving a direct reversal. The dissipative potential of water cluster (H2O)n interacts with the hydrophilic asymmetries of Hb, to restrict randomness of the kinetic sense implicated in a single peak for activation energy (Ea). The hydration shells could sequence an enhanced Ea into several peaks, to sequentially activate transitions states. Hence, changes in dipole state, sliding, pKa, n-H-bonds, etc., could became concatenated for vectoriality. (H2O)n by the loss of H-bonds couple with to the hydration turnover of proteins and ions to result in incomplete water cluster (H2O)n*, with a lower-n. (H2O)n* became a carrier of heat/entropy into the cerebrospinal fluid (CSF) which has to be replaced 3.7 times per day. OxyHb formation involves sliding-down of alpha vs beta chains, to shift alpha1 and alpha2 Pro 44 into allowing the entrance of a fully hydrated [Mg.(H2O)6](H2O)12-14(2+) (or Zn2+) into the hydrophilic beta2-alpha1 and beta1-alpha2 interfaces. OxyHb pKa of 6.4 leads to H+-dissociation increasing negative charge of R-groups. This at beta2-alpha1 sequence two tetradentate chelates, first an Mg2+, bonding with beta2 His 92 and a second Mg2+ with alpha1 His 87, to cooperatively release hindrance. The interconversion of oxy-to-deoxyHb, pKa=8, leads to the amphoteric imidazole to became positively charged and proximal histidines return into hindrance position, releasing the incomplete hydrated Mg.(H2O)inc(2+) and O2 into CSF. |
2111.14421 | Ivana Pajic-Lijakovic Dr. | Ivana Pajic-Lijakovic and MIlan Milivojevic | Marangoni effect and cell spreading | 7837 words, 3 figures, 1 table, 64 references | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Cells are very sensitive to the shear stress (SS). However, undesirable SS is
generated during physiological process such as collective cell migration (CCM)
and influences the biological processes such as morphogenesis, wound healing
and cancer invasion. Despite extensive research devoted to study the stress
generation caused by CCM, we still do not fully understand the main cause of SS
generation. An attempt is made here to offer some answers to these questions by
considering the rearrangement of cell monolayers. The SS generation represents
a consequence of natural and forced convection. While forced convection is
dependent on cell speed, the natural convection is induced by the gradient of
tissue surface tension. The phenomenon is known as the Marangoni effect. The
gradient of tissue surface tension induces directed cell spreading from the
regions of lower tissue surface tension to the regions of higher tissue surface
tension. This directed cell migration is described by the Marangoni flux. The
phenomenon has been recognized during the rearrangement of (1) epithelial cell
monolayers and (2) mixed cell monolayers made by epithelial and mesenchymal
cells. The consequence of the Marangoni effect is an intensive spreading of
cancer cells through an epithelium. In this work, a review of existing
literature about SS generation caused by CCM is given along with the assortment
of published experimental findings, in order to invite experimentalists to test
given theoretical considerations in multicellular systems.
| [
{
"created": "Mon, 29 Nov 2021 10:06:05 GMT",
"version": "v1"
}
] | 2021-11-30 | [
[
"Pajic-Lijakovic",
"Ivana",
""
],
[
"Milivojevic",
"MIlan",
""
]
] | Cells are very sensitive to the shear stress (SS). However, undesirable SS is generated during physiological process such as collective cell migration (CCM) and influences the biological processes such as morphogenesis, wound healing and cancer invasion. Despite extensive research devoted to study the stress generation caused by CCM, we still do not fully understand the main cause of SS generation. An attempt is made here to offer some answers to these questions by considering the rearrangement of cell monolayers. The SS generation represents a consequence of natural and forced convection. While forced convection is dependent on cell speed, the natural convection is induced by the gradient of tissue surface tension. The phenomenon is known as the Marangoni effect. The gradient of tissue surface tension induces directed cell spreading from the regions of lower tissue surface tension to the regions of higher tissue surface tension. This directed cell migration is described by the Marangoni flux. The phenomenon has been recognized during the rearrangement of (1) epithelial cell monolayers and (2) mixed cell monolayers made by epithelial and mesenchymal cells. The consequence of the Marangoni effect is an intensive spreading of cancer cells through an epithelium. In this work, a review of existing literature about SS generation caused by CCM is given along with the assortment of published experimental findings, in order to invite experimentalists to test given theoretical considerations in multicellular systems. |
2108.08358 | Sean Lawley | Elijah D Counterman, Sean D Lawley | Designing drug regimens that mitigate nonadherence | 38 pages, 7 figures | null | null | null | q-bio.QM math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medication adherence is a well-known problem for pharmaceutical treatment of
chronic diseases. Understanding how nonadherence affects treatment efficacy is
made difficult by the ethics of clinical trials that force patients to skip
doses of the medication being tested, the unpredictable timing of missed doses
by actual patients, and the many competing variables that can either mitigate
or magnify the deleterious effects of nonadherence, such as pharmacokinetic
absorption and elimination rates, dosing intervals, dose sizes, adherence
rates, etc. In this paper, we formulate and analyze a mathematical model of the
drug concentration in an imperfectly adherent patient. Our model takes the form
of the standard single compartment pharmacokinetic model with first order
absorption and elimination, except that the patient takes medication only at a
given proportion of the prescribed dosing times. Doses are missed randomly, and
we use stochastic analysis to study the resulting random drug level in the
body. We then use our mathematical results to propose principles for designing
drug regimens that are robust to nonadherence. In particular, we quantify the
resilience of extended release drugs to nonadherence, which is quite
significant in some circumstances, and we show the benefit of taking a double
dose following a missed dose if the drug absorption or elimination rate is slow
compared to the dosing interval. We further use our results to compare some
antiepileptic and antipsychotic drug regimens.
| [
{
"created": "Wed, 18 Aug 2021 19:27:31 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Dec 2021 22:20:31 GMT",
"version": "v2"
}
] | 2021-12-30 | [
[
"Counterman",
"Elijah D",
""
],
[
"Lawley",
"Sean D",
""
]
] | Medication adherence is a well-known problem for pharmaceutical treatment of chronic diseases. Understanding how nonadherence affects treatment efficacy is made difficult by the ethics of clinical trials that force patients to skip doses of the medication being tested, the unpredictable timing of missed doses by actual patients, and the many competing variables that can either mitigate or magnify the deleterious effects of nonadherence, such as pharmacokinetic absorption and elimination rates, dosing intervals, dose sizes, adherence rates, etc. In this paper, we formulate and analyze a mathematical model of the drug concentration in an imperfectly adherent patient. Our model takes the form of the standard single compartment pharmacokinetic model with first order absorption and elimination, except that the patient takes medication only at a given proportion of the prescribed dosing times. Doses are missed randomly, and we use stochastic analysis to study the resulting random drug level in the body. We then use our mathematical results to propose principles for designing drug regimens that are robust to nonadherence. In particular, we quantify the resilience of extended release drugs to nonadherence, which is quite significant in some circumstances, and we show the benefit of taking a double dose following a missed dose if the drug absorption or elimination rate is slow compared to the dosing interval. We further use our results to compare some antiepileptic and antipsychotic drug regimens. |
1308.1985 | Hunter Fraser | Hunter B. Fraser | Cell-cycle regulated transcription associates with DNA replication
timing in yeast and human | null | null | null | null | q-bio.GN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Eukaryotic DNA replication follows a specific temporal program, with some
genomic regions consistently replicating earlier than others, yet what
determines this program is largely unknown. Highly transcribed regions have
been observed to replicate in early S-phase in all plant and animal species
studied to date, but this relationship is thought to be absent from both
budding yeast and fission yeast. No association between cell-cycle regulated
transcription and replication timing has been reported for any species. Here I
show that in budding yeast, fission yeast, and human, the genes most highly
transcribed during S-phase replicate early, whereas those repressed in S-phase
replicate late. Transcription during other cell-cycle phases shows either the
opposite correlation with replication timing, or no relation. The relationship
is strongest near late-firing origins of replication, which is not consistent
with a previously proposed model -- that replication timing may affect
transcription -- and instead suggests a potential mechanism involving the
recruitment of limiting replication initiation factors during S-phase. These
results suggest that S-phase transcription may be an important determinant of
DNA replication timing across eukaryotes, which may explain the
well-established association between transcription and replication timing.
| [
{
"created": "Thu, 8 Aug 2013 21:43:33 GMT",
"version": "v1"
}
] | 2013-08-12 | [
[
"Fraser",
"Hunter B.",
""
]
] | Eukaryotic DNA replication follows a specific temporal program, with some genomic regions consistently replicating earlier than others, yet what determines this program is largely unknown. Highly transcribed regions have been observed to replicate in early S-phase in all plant and animal species studied to date, but this relationship is thought to be absent from both budding yeast and fission yeast. No association between cell-cycle regulated transcription and replication timing has been reported for any species. Here I show that in budding yeast, fission yeast, and human, the genes most highly transcribed during S-phase replicate early, whereas those repressed in S-phase replicate late. Transcription during other cell-cycle phases shows either the opposite correlation with replication timing, or no relation. The relationship is strongest near late-firing origins of replication, which is not consistent with a previously proposed model -- that replication timing may affect transcription -- and instead suggests a potential mechanism involving the recruitment of limiting replication initiation factors during S-phase. These results suggest that S-phase transcription may be an important determinant of DNA replication timing across eukaryotes, which may explain the well-established association between transcription and replication timing. |
2107.11770 | Niklas Kolbe | Niklas Kolbe, Lorenz Hexemer, Lukas-Malte Bammert, Alexander Loewer,
M\'aria Luk\'a\v{c}ov\'a-Medvi\v{d}ov\'a and Stefan Legewie | Data-based stochastic modeling reveals sources of activity bursts in
single-cell TGF-$\beta$ signaling | 27 pages, 7 figures, 5 tables | null | 10.1371/journal.pcbi.1010266 | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cells sense their surrounding by employing intracellular signaling pathways
that transmit hormonal signals from the cell membrane to the nucleus.
TGF-$\beta$/SMAD signaling encodes various cell fates, controls tissue
homeostasis and is deregulated in diseases such as cancer. The pathway shows
strong heterogeneity at the single-cell level, but quantitative insights into
mechanisms underlying fluctuations at various time scales are still missing,
partly due to inefficiency in the calibration of stochastic models that
mechanistically describe signaling processes. In this work we analyze
single-cell TGF-$\beta$/SMAD signaling and show that it exhibits temporal
stochastic bursts which are dose-dependent and whose number and magnitude
correlate with cell migration. We propose a stochastic modeling approach to
mechanistically describe these pathway fluctuations with high computational
efficiency. Employing high-order numerical integration and fitting to burst
statistics we enable efficient quantitative parameter estimation and
discriminate models that assume noise in different reactions at the receptor
level. This modeling approach suggests that stochasticity in the
internalization of TGF-$\beta$ receptors into endosomes plays a key role in the
observed temporal bursting. Further, the model predicts the single-cell
dynamics of TGF-$\beta$/SMAD signaling in untested conditions, e.g.,
successfully reflects memory effects of signaling noise and cellular
sensitivity towards repeated stimulation. Taken together, our computational
framework based on burst analysis, noise modeling and path computation scheme
is a suitable tool for the data-based modeling of complex signaling pathways,
capable of identifying the source of temporal noise.
| [
{
"created": "Sun, 25 Jul 2021 09:49:42 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Jan 2022 21:13:21 GMT",
"version": "v2"
}
] | 2022-10-12 | [
[
"Kolbe",
"Niklas",
""
],
[
"Hexemer",
"Lorenz",
""
],
[
"Bammert",
"Lukas-Malte",
""
],
[
"Loewer",
"Alexander",
""
],
[
"Lukáčová-Medviďová",
"Mária",
""
],
[
"Legewie",
"Stefan",
""
]
] | Cells sense their surrounding by employing intracellular signaling pathways that transmit hormonal signals from the cell membrane to the nucleus. TGF-$\beta$/SMAD signaling encodes various cell fates, controls tissue homeostasis and is deregulated in diseases such as cancer. The pathway shows strong heterogeneity at the single-cell level, but quantitative insights into mechanisms underlying fluctuations at various time scales are still missing, partly due to inefficiency in the calibration of stochastic models that mechanistically describe signaling processes. In this work we analyze single-cell TGF-$\beta$/SMAD signaling and show that it exhibits temporal stochastic bursts which are dose-dependent and whose number and magnitude correlate with cell migration. We propose a stochastic modeling approach to mechanistically describe these pathway fluctuations with high computational efficiency. Employing high-order numerical integration and fitting to burst statistics we enable efficient quantitative parameter estimation and discriminate models that assume noise in different reactions at the receptor level. This modeling approach suggests that stochasticity in the internalization of TGF-$\beta$ receptors into endosomes plays a key role in the observed temporal bursting. Further, the model predicts the single-cell dynamics of TGF-$\beta$/SMAD signaling in untested conditions, e.g., successfully reflects memory effects of signaling noise and cellular sensitivity towards repeated stimulation. Taken together, our computational framework based on burst analysis, noise modeling and path computation scheme is a suitable tool for the data-based modeling of complex signaling pathways, capable of identifying the source of temporal noise. |
1807.11935 | Danielle Bassett | Danielle S. Bassett, Perry Zurn, Joshua I. Gold | Network models in neuroscience | Under consideration as a book chapter in Cerebral Cortex 3.0, MIT
Press | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | From interacting cellular components to networks of neurons and neural
systems, interconnected units comprise a fundamental organizing principle of
the nervous system. Understanding how their patterns of connections and
interactions give rise to the many functions of the nervous system is a primary
goal of neuroscience. Recently, this pursuit has begun to benefit from the
development of new mathematical tools that can relate a system's architecture
to its dynamics and function. These tools, which are known collectively as
network science, have been used with increasing success to build models of
neural systems across spatial scales and species. Here we discuss the nature of
network models in neuroscience. We begin with a review of model theory from a
philosophical perspective to inform our view of networks as models of complex
systems in general, and of the brain in particular. We then summarize the types
of models that are frequently studied in network neuroscience along three
primary dimensions: from data representations to first-principles theory, from
biophysical realism to functional phenomenology, and from elementary
descriptions to coarse-grained approximations. We then consider ways to
validate these models, focusing on approaches that perturb a system to probe
its function. We close with a description of important frontiers in the
construction of network models and their relevance for understanding
increasingly complex functions of neural systems.
| [
{
"created": "Tue, 31 Jul 2018 17:51:16 GMT",
"version": "v1"
}
] | 2018-08-01 | [
[
"Bassett",
"Danielle S.",
""
],
[
"Zurn",
"Perry",
""
],
[
"Gold",
"Joshua I.",
""
]
] | From interacting cellular components to networks of neurons and neural systems, interconnected units comprise a fundamental organizing principle of the nervous system. Understanding how their patterns of connections and interactions give rise to the many functions of the nervous system is a primary goal of neuroscience. Recently, this pursuit has begun to benefit from the development of new mathematical tools that can relate a system's architecture to its dynamics and function. These tools, which are known collectively as network science, have been used with increasing success to build models of neural systems across spatial scales and species. Here we discuss the nature of network models in neuroscience. We begin with a review of model theory from a philosophical perspective to inform our view of networks as models of complex systems in general, and of the brain in particular. We then summarize the types of models that are frequently studied in network neuroscience along three primary dimensions: from data representations to first-principles theory, from biophysical realism to functional phenomenology, and from elementary descriptions to coarse-grained approximations. We then consider ways to validate these models, focusing on approaches that perturb a system to probe its function. We close with a description of important frontiers in the construction of network models and their relevance for understanding increasingly complex functions of neural systems. |
2108.04240 | Charalambos Chrysostomou | Charalambos Chrysostomou, Floris Alexandrou, Mihalis A. Nicolaou and
Huseyin Seker | Classification of Influenza Hemagglutinin Protein Sequences using
Convolutional Neural Networks | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | The Influenza virus can be considered as one of the most severe viruses that
can infect multiple species with often fatal consequences to the hosts. The
Hemagglutinin (HA) gene of the virus can be a target for antiviral drug
development realised through accurate identification of its sub-types and
possible the targeted hosts. This paper focuses on accurately predicting if an
Influenza type A virus can infect specific hosts, and more specifically, Human,
Avian and Swine hosts, using only the protein sequence of the HA gene. In more
detail, we propose encoding the protein sequences into numerical signals using
the Hydrophobicity Index and subsequently utilising a Convolutional Neural
Network-based predictive model. The Influenza HA protein sequences used in the
proposed work are obtained from the Influenza Research Database (IRD).
Specifically, complete and unique HA protein sequences were used for avian,
human and swine hosts. The data obtained for this work was 17999 human-host
proteins, 17667 avian-host proteins and 9278 swine-host proteins. Given this
set of collected proteins, the proposed method yields as much as 10% higher
accuracy for an individual class (namely, Avian) and 5% higher overall accuracy
than in an earlier study. It is also observed that the accuracy for each class
in this work is more balanced than what was presented in this earlier study. As
the results show, the proposed model can distinguish HA protein sequences with
high accuracy whenever the virus under investigation can infect Human, Avian or
Swine hosts.
| [
{
"created": "Mon, 9 Aug 2021 10:42:26 GMT",
"version": "v1"
}
] | 2021-08-11 | [
[
"Chrysostomou",
"Charalambos",
""
],
[
"Alexandrou",
"Floris",
""
],
[
"Nicolaou",
"Mihalis A.",
""
],
[
"Seker",
"Huseyin",
""
]
] | The Influenza virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Hemagglutinin (HA) gene of the virus can be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. This paper focuses on accurately predicting if an Influenza type A virus can infect specific hosts, and more specifically, Human, Avian and Swine hosts, using only the protein sequence of the HA gene. In more detail, we propose encoding the protein sequences into numerical signals using the Hydrophobicity Index and subsequently utilising a Convolutional Neural Network-based predictive model. The Influenza HA protein sequences used in the proposed work are obtained from the Influenza Research Database (IRD). Specifically, complete and unique HA protein sequences were used for avian, human and swine hosts. The data obtained for this work was 17999 human-host proteins, 17667 avian-host proteins and 9278 swine-host proteins. Given this set of collected proteins, the proposed method yields as much as 10% higher accuracy for an individual class (namely, Avian) and 5% higher overall accuracy than in an earlier study. It is also observed that the accuracy for each class in this work is more balanced than what was presented in this earlier study. As the results show, the proposed model can distinguish HA protein sequences with high accuracy whenever the virus under investigation can infect Human, Avian or Swine hosts. |
2309.03928 | Mandana Mirbakhsh | Mandana Mirbakhsh, Zahra Zahed | Enhancing Phosphorus Uptake in Sugarcane: A Critical Evaluation of Humic
Acid and Phosphorus Fertilizers Effectiveness | null | null | null | null | q-bio.QM physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Our research conducted in an area characterized by alkaline, lime-abundant
soils investigated the potential of utilizing phosphorus fertilizer and humic
acid to enhance phosphorus absorption in sugarcane crops. The results indicated
that the application of phosphorus fertilizer significantly increased the total
and bioavailable phosphorus in the rhizospheric soil, despite observing a
decrease in phosphatase enzyme activity. An important observation was the
considerable growth of active carbon, a crucial soil health indicator, under
the influence of humic acid treatments. The findings also demonstrated an
enhancement in phosphorus absorption by sugarcane due to the synergistic
application of humic acid and phosphorus fertilizer at both harvest periods.
Interestingly, humic acid treatments, when applied through immersion, were
found to be more effective than soil applications, implying a greater impact on
root absorption processes. The findings underline the potential of integrating
humic acid into sugarcane cultivation for better phosphorus absorption. Our
study offers valuable insights for improved soil management strategies, and
could potentially pave the way towards more sustainable agricultural practices.
However, we also recommend further investigation into alternative methods of
humic acid application and its usage at different stages of plant growth. Such
exploration could provide a comprehensive understanding of the potential
benefits and most effective utilization of humic acid in agriculture,
especially in regions with similar soil characteristics as West Azarbaijan,
Iran
| [
{
"created": "Thu, 7 Sep 2023 12:57:09 GMT",
"version": "v1"
}
] | 2023-09-11 | [
[
"Mirbakhsh",
"Mandana",
""
],
[
"Zahed",
"Zahra",
""
]
] | Our research conducted in an area characterized by alkaline, lime-abundant soils investigated the potential of utilizing phosphorus fertilizer and humic acid to enhance phosphorus absorption in sugarcane crops. The results indicated that the application of phosphorus fertilizer significantly increased the total and bioavailable phosphorus in the rhizospheric soil, despite observing a decrease in phosphatase enzyme activity. An important observation was the considerable growth of active carbon, a crucial soil health indicator, under the influence of humic acid treatments. The findings also demonstrated an enhancement in phosphorus absorption by sugarcane due to the synergistic application of humic acid and phosphorus fertilizer at both harvest periods. Interestingly, humic acid treatments, when applied through immersion, were found to be more effective than soil applications, implying a greater impact on root absorption processes. The findings underline the potential of integrating humic acid into sugarcane cultivation for better phosphorus absorption. Our study offers valuable insights for improved soil management strategies, and could potentially pave the way towards more sustainable agricultural practices. However, we also recommend further investigation into alternative methods of humic acid application and its usage at different stages of plant growth. Such exploration could provide a comprehensive understanding of the potential benefits and most effective utilization of humic acid in agriculture, especially in regions with similar soil characteristics as West Azarbaijan, Iran |
1512.00695 | Marta Tyran-Kaminska | Michael C. Mackey, Marta Tyran-Kaminska | The Limiting Dynamics of a Bistable Molecular Switch With and Without
Noise | 27 pages, 10 figures | Journal of Mathematical Biology 73 (2016), 367-395 | 10.1007/s00285-015-0949-1 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the dynamics of a population of organisms containing two mutually
inhibitory gene regulatory networks, that can result in a bistable switch-like
behaviour. We completely characterize their local and global dynamics in the
absence of any noise, and then go on to consider the effects of either noise
coming from bursting (transcription or translation), or Gaussian noise in
molecular degradation rates when there is a dominant slow variable in the
system. We show analytically how the steady state distribution in the
population can range from a single unimodal distribution through a bimodal
distribution and give the explicit analytic form for the invariant stationary
density which is globally asymptotically stable. Rather remarkably, the
behaviour of the stationary density with respect to the parameters
characterizing the molecular behaviour of the bistable switch is qualitatively
identical in the presence of noise coming from bursting as well as in the
presence of Gaussian noise in the degradation rate. This implies that one
cannot distinguish between either the dominant source or nature of noise based
on the stationary molecular distribution in a population of cells. We finally
show that the switch model with bursting but two dominant slow genes has an
asymptotically stable stationary density.
| [
{
"created": "Wed, 2 Dec 2015 14:01:54 GMT",
"version": "v1"
}
] | 2016-10-07 | [
[
"Mackey",
"Michael C.",
""
],
[
"Tyran-Kaminska",
"Marta",
""
]
] | We consider the dynamics of a population of organisms containing two mutually inhibitory gene regulatory networks, that can result in a bistable switch-like behaviour. We completely characterize their local and global dynamics in the absence of any noise, and then go on to consider the effects of either noise coming from bursting (transcription or translation), or Gaussian noise in molecular degradation rates when there is a dominant slow variable in the system. We show analytically how the steady state distribution in the population can range from a single unimodal distribution through a bimodal distribution and give the explicit analytic form for the invariant stationary density which is globally asymptotically stable. Rather remarkably, the behaviour of the stationary density with respect to the parameters characterizing the molecular behaviour of the bistable switch is qualitatively identical in the presence of noise coming from bursting as well as in the presence of Gaussian noise in the degradation rate. This implies that one cannot distinguish between either the dominant source or nature of noise based on the stationary molecular distribution in a population of cells. We finally show that the switch model with bursting but two dominant slow genes has an asymptotically stable stationary density. |
q-bio/0611035 | Sylvain Hanneton | Sylvain Hanneton (NPSM), Claudia Munoz (NPSM) | Action for perception : influence of handedness in visuo-auditory
sensory substitution | null | Enactive 2006 : Enaction & Complexity, France (20/11/2006) 73-74 | null | null | q-bio.NC | null | In this preliminary study we address the question of the influence of
handedness on the localization of targets perceived through a visuo-auditory
substitution device. Participants hold the device in one hand in order to
explore the environment and to perceive the target. They point to the estimated
location of the target with the other hand. This preliminary results support
our hypothesis that pointing is more accurate when the device is held in the
right dominant hand. Dexterity has to be attributed to the active part of the
perceptive system. This study has obviously to be completed but it shows how
the concept of enaction is important and how it can be experimentaly addressed
in the field of sensory substitution.
| [
{
"created": "Thu, 9 Nov 2006 13:27:54 GMT",
"version": "v1"
}
] | 2019-04-22 | [
[
"Hanneton",
"Sylvain",
"",
"NPSM"
],
[
"Munoz",
"Claudia",
"",
"NPSM"
]
] | In this preliminary study we address the question of the influence of handedness on the localization of targets perceived through a visuo-auditory substitution device. Participants hold the device in one hand in order to explore the environment and to perceive the target. They point to the estimated location of the target with the other hand. This preliminary results support our hypothesis that pointing is more accurate when the device is held in the right dominant hand. Dexterity has to be attributed to the active part of the perceptive system. This study has obviously to be completed but it shows how the concept of enaction is important and how it can be experimentaly addressed in the field of sensory substitution. |
q-bio/0602027 | Charles Epstein | Charles L. Epstein | Anderson Localization, Non-linearity and Stable Genetic Diversity | 25 pages, 8 Figures | null | 10.1007/s10955-006-9149-0 | null | q-bio.PE cond-mat.stat-mech math.DS math.SP nlin.AO q-bio.QM | null | In many models of genotypic evolution, the vector of genotype populations
satisfies a system of linear ordinary differential equations. This system of
equations models a competition between differential replication rates (fitness)
and mutation. Mutation operates as a generalized diffusion process on genotype
space. In the large time asymptotics, the replication term tends to produce a
single dominant quasispecies, unless the mutation rate is too high, in which
case the populations of different genotypes becomes de-localized. We introduce
a more macroscopic picture of genotypic evolution wherein a random replication
term in the linear model displays features analogous to Anderson localization.
When coupled with non-linearities that limit the population of any given
genotype, we obtain a model whose large time asymptotics display stable
genotypic diversity
| [
{
"created": "Tue, 28 Feb 2006 14:52:25 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Epstein",
"Charles L.",
""
]
] | In many models of genotypic evolution, the vector of genotype populations satisfies a system of linear ordinary differential equations. This system of equations models a competition between differential replication rates (fitness) and mutation. Mutation operates as a generalized diffusion process on genotype space. In the large time asymptotics, the replication term tends to produce a single dominant quasispecies, unless the mutation rate is too high, in which case the populations of different genotypes becomes de-localized. We introduce a more macroscopic picture of genotypic evolution wherein a random replication term in the linear model displays features analogous to Anderson localization. When coupled with non-linearities that limit the population of any given genotype, we obtain a model whose large time asymptotics display stable genotypic diversity |
q-bio/0508020 | Michael Deem | Jun Sun, David J. Earl, and Michael W. Deem | Glassy Dynamics in the Adaptive Immune Response Prevents Autoimmune
Disease | 5 pages, 3 figures, to appear in Phys. Rev. Lett | null | 10.1103/PhysRevLett.95.148104 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | null | The immune system normally protects the human host against death by
infection. However, when an immune response is mistakenly directed at self
antigens, autoimmune disease can occur. We describe a model of protein
evolution to simulate the dynamics of the adaptive immune response to antigens.
Computer simulations of the dynamics of antibody evolution show that different
evolutionary mechanisms, namely gene segment swapping and point mutation, lead
to different evolved antibody binding affinities. Although a combination of
gene segment swapping and point mutation can yield a greater affinity to a
specific antigen than point mutation alone, the antibodies so evolved are
highly cross-reactive and would cause autoimmune disease, and this is not the
chosen dynamics of the immune system. We suggest that in the immune system a
balance has evolved between binding affinity and specificity in the mechanism
for searching the amino acid sequence space of antibodies.
| [
{
"created": "Wed, 17 Aug 2005 08:08:26 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Sun",
"Jun",
""
],
[
"Earl",
"David J.",
""
],
[
"Deem",
"Michael W.",
""
]
] | The immune system normally protects the human host against death by infection. However, when an immune response is mistakenly directed at self antigens, autoimmune disease can occur. We describe a model of protein evolution to simulate the dynamics of the adaptive immune response to antigens. Computer simulations of the dynamics of antibody evolution show that different evolutionary mechanisms, namely gene segment swapping and point mutation, lead to different evolved antibody binding affinities. Although a combination of gene segment swapping and point mutation can yield a greater affinity to a specific antigen than point mutation alone, the antibodies so evolved are highly cross-reactive and would cause autoimmune disease, and this is not the chosen dynamics of the immune system. We suggest that in the immune system a balance has evolved between binding affinity and specificity in the mechanism for searching the amino acid sequence space of antibodies. |
2404.05865 | Qi Dai | Qi Dai, Ryan Davis, Houlin Hong, and Ying Gu | Effectiveness of Self-Assessment Software to Evaluate Preclinical
Operative Procedures | null | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objectives: To assess the effectiveness of digital scanning techniques for
self-assessment and of preparations and restorations in preclinical dental
education when compared to traditional faculty grading. Methods: Forty-four
separate Class I (#30-O), Class II (#30-MO) preparations, and class II amalgam
restorations (#31-MO) were generated respectively under preclinical assessment
setting. Calibrated faculty evaluated the preparations and restorations using a
standard rubric from preclinical operative class. The same teeth were scanned
using Planmeca PlanScan intraoral scanner and graded using the Romexis E4D
Compare Software. Each tooth was compared against a corresponding gold standard
tooth with tolerance intervals ranging from 100{\mu}m to 500{\mu}m. These
scores were compared to traditional faculty grades using a linear mixed model
to estimate the mean differences at 95% confidence interval for each tolerance
level. Results: The average Compare Software grade of Class I preparation at
300{\mu}m tolerance had the smallest mean difference of 1.64 points on a 100
points scale compared to the average faculty grade. Class II preparation at
400{\mu}m tolerance had the smallest mean difference of 0.41 points. Finally,
Class II Restoration at 300{\mu}m tolerance had the smallest mean difference at
0.20 points. Conclusion: In this study, tolerance levels that best correlated
the Compare Software grades with the faculty grades were determined for three
operative procedures: class I preparation, class II preparation and class II
restoration. This Compare Software can be used as a useful adjunct method for
more objective grading. It also can be used by students as a great
self-assessment tool.
| [
{
"created": "Mon, 8 Apr 2024 20:54:58 GMT",
"version": "v1"
}
] | 2024-04-10 | [
[
"Dai",
"Qi",
""
],
[
"Davis",
"Ryan",
""
],
[
"Hong",
"Houlin",
""
],
[
"Gu",
"Ying",
""
]
] | Objectives: To assess the effectiveness of digital scanning techniques for self-assessment and of preparations and restorations in preclinical dental education when compared to traditional faculty grading. Methods: Forty-four separate Class I (#30-O), Class II (#30-MO) preparations, and class II amalgam restorations (#31-MO) were generated respectively under preclinical assessment setting. Calibrated faculty evaluated the preparations and restorations using a standard rubric from preclinical operative class. The same teeth were scanned using Planmeca PlanScan intraoral scanner and graded using the Romexis E4D Compare Software. Each tooth was compared against a corresponding gold standard tooth with tolerance intervals ranging from 100{\mu}m to 500{\mu}m. These scores were compared to traditional faculty grades using a linear mixed model to estimate the mean differences at 95% confidence interval for each tolerance level. Results: The average Compare Software grade of Class I preparation at 300{\mu}m tolerance had the smallest mean difference of 1.64 points on a 100 points scale compared to the average faculty grade. Class II preparation at 400{\mu}m tolerance had the smallest mean difference of 0.41 points. Finally, Class II Restoration at 300{\mu}m tolerance had the smallest mean difference at 0.20 points. Conclusion: In this study, tolerance levels that best correlated the Compare Software grades with the faculty grades were determined for three operative procedures: class I preparation, class II preparation and class II restoration. This Compare Software can be used as a useful adjunct method for more objective grading. It also can be used by students as a great self-assessment tool. |
2010.10129 | Thomas Sturm | Niclas Kruff, Christoph L\"uders, Ovidiu Radulescu, Thomas Sturm,
Sebastian Walcher | Algorithmic Reduction of Biological Networks With Multiple Time Scales | null | null | null | null | q-bio.MN cs.LO cs.SC math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a symbolic algorithmic approach that allows to compute invariant
manifolds and corresponding reduced systems for differential equations modeling
biological networks which comprise chemical reaction networks for cellular
biochemistry, and compartmental models for pharmacology, epidemiology and
ecology. Multiple time scales of a given network are obtained by scaling, based
on tropical geometry. Our reduction is mathematically justified within a
singular perturbation setting. The existence of invariant manifolds is subject
to hyperbolicity conditions, for which we propose an algorithmic test based on
Hurwitz criteria. We finally obtain a sequence of nested invariant manifolds
and respective reduced systems on those manifolds. Our theoretical results are
generally accompanied by rigorous algorithmic descriptions suitable for direct
implementation based on existing off-the-shelf software systems, specifically
symbolic computation libraries and Satisfiability Modulo Theories solvers. We
present computational examples taken from the well-known BioModels database
using our own prototypical implementations.
| [
{
"created": "Tue, 20 Oct 2020 08:48:09 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Mar 2021 17:47:20 GMT",
"version": "v2"
}
] | 2021-03-03 | [
[
"Kruff",
"Niclas",
""
],
[
"Lüders",
"Christoph",
""
],
[
"Radulescu",
"Ovidiu",
""
],
[
"Sturm",
"Thomas",
""
],
[
"Walcher",
"Sebastian",
""
]
] | We present a symbolic algorithmic approach that allows to compute invariant manifolds and corresponding reduced systems for differential equations modeling biological networks which comprise chemical reaction networks for cellular biochemistry, and compartmental models for pharmacology, epidemiology and ecology. Multiple time scales of a given network are obtained by scaling, based on tropical geometry. Our reduction is mathematically justified within a singular perturbation setting. The existence of invariant manifolds is subject to hyperbolicity conditions, for which we propose an algorithmic test based on Hurwitz criteria. We finally obtain a sequence of nested invariant manifolds and respective reduced systems on those manifolds. Our theoretical results are generally accompanied by rigorous algorithmic descriptions suitable for direct implementation based on existing off-the-shelf software systems, specifically symbolic computation libraries and Satisfiability Modulo Theories solvers. We present computational examples taken from the well-known BioModels database using our own prototypical implementations. |
1911.07711 | Cole Butler | Cole Butler, Jinjin Cheng, Lorena Correa, Maria Preciado-Rivas, Andres
Rios-Gutierrez, Cesar Montalvo, and Christopher Kribs | Comparison of screening for methicillin-resistant Staphylococcus aureus
(MRSA) at hospital admission and discharge | 20 pages, 7 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Methicillin-resistant Staphylococcus aureus (MRSA) is a significant
contributor to the growing concern of antibiotic resistant bacteria, especially
given its stubborn persistence in hospitals and other health care facility
settings. In combination with this characteristic of S. aureus (colloquially
referred to as staph), MRSA presents an additional barrier to treatment and is
now believed to have colonized two of every 100 people worldwide. According to
the CDC, MRSA prevalence sits as high as 25-50% in countries such as the United
Kingdom and the United States. Given the resistant nature of staph as well as
its capability of evolving to compensate antibiotic treatment, controlling MRSA
levels is more a matter of precautionary and defensive measures. This study
examines the method of "search and isolation," which seeks to isolate MRSA
positive patients in a hospital so as to decrease infection potential. Although
this strategy is straightforward, the question of just whom to screen is of
practical importance. We compare screening at admission to screening at
discharge. To do this, we develop a mathematical model and use simulations to
determine MRSA endemic levels in a hospital with either control measure
implemented. We found that screening at discharge was the more effective method
in controlling MRSA endemicity, but at the cost of a greater number of isolated
patients.
| [
{
"created": "Mon, 18 Nov 2019 15:40:59 GMT",
"version": "v1"
}
] | 2019-11-19 | [
[
"Butler",
"Cole",
""
],
[
"Cheng",
"Jinjin",
""
],
[
"Correa",
"Lorena",
""
],
[
"Preciado-Rivas",
"Maria",
""
],
[
"Rios-Gutierrez",
"Andres",
""
],
[
"Montalvo",
"Cesar",
""
],
[
"Kribs",
"Christopher",
""
]
] | Methicillin-resistant Staphylococcus aureus (MRSA) is a significant contributor to the growing concern of antibiotic resistant bacteria, especially given its stubborn persistence in hospitals and other health care facility settings. In combination with this characteristic of S. aureus (colloquially referred to as staph), MRSA presents an additional barrier to treatment and is now believed to have colonized two of every 100 people worldwide. According to the CDC, MRSA prevalence sits as high as 25-50% in countries such as the United Kingdom and the United States. Given the resistant nature of staph as well as its capability of evolving to compensate antibiotic treatment, controlling MRSA levels is more a matter of precautionary and defensive measures. This study examines the method of "search and isolation," which seeks to isolate MRSA positive patients in a hospital so as to decrease infection potential. Although this strategy is straightforward, the question of just whom to screen is of practical importance. We compare screening at admission to screening at discharge. To do this, we develop a mathematical model and use simulations to determine MRSA endemic levels in a hospital with either control measure implemented. We found that screening at discharge was the more effective method in controlling MRSA endemicity, but at the cost of a greater number of isolated patients. |
1602.01889 | Furong Huang | Furong Huang, Animashree Anandkumar, Christian Borgs, Jennifer Chayes,
Ernest Fraenkel, Michael Hawrylycz, Ed Lein, Alessandro Ingrosso, Srinivas
Turaga | Discovering Neuronal Cell Types and Their Gene Expression Profiles Using
a Spatial Point Process Mixture Model | null | null | null | null | q-bio.NC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cataloging the neuronal cell types that comprise circuitry of individual
brain regions is a major goal of modern neuroscience and the BRAIN initiative.
Single-cell RNA sequencing can now be used to measure the gene expression
profiles of individual neurons and to categorize neurons based on their gene
expression profiles. While the single-cell techniques are extremely powerful
and hold great promise, they are currently still labor intensive, have a high
cost per cell, and, most importantly, do not provide information on spatial
distribution of cell types in specific regions of the brain. We propose a
complementary approach that uses computational methods to infer the cell types
and their gene expression profiles through analysis of brain-wide single-cell
resolution in situ hybridization (ISH) imagery contained in the Allen Brain
Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH
image for each gene and model it as a spatial point process mixture, whose
mixture weights are given by the cell types which express that gene. By fitting
a point process mixture model jointly to the ISH images, we infer both the
spatial point process distribution for each cell type and their gene expression
profile. We validate our predictions of cell type-specific gene expression
profiles using single cell RNA sequencing data, recently published for the
mouse somatosensory cortex. Jointly with the gene expression profiles, cell
features such as cell size, orientation, intensity and local density level are
inferred per cell type.
| [
{
"created": "Thu, 4 Feb 2016 23:52:18 GMT",
"version": "v1"
},
{
"created": "Sat, 11 Jun 2016 01:45:12 GMT",
"version": "v2"
}
] | 2016-06-14 | [
[
"Huang",
"Furong",
""
],
[
"Anandkumar",
"Animashree",
""
],
[
"Borgs",
"Christian",
""
],
[
"Chayes",
"Jennifer",
""
],
[
"Fraenkel",
"Ernest",
""
],
[
"Hawrylycz",
"Michael",
""
],
[
"Lein",
"Ed",
""
],
[
"Ingrosso",
"Alessandro",
""
],
[
"Turaga",
"Srinivas",
""
]
] | Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles. While the single-cell techniques are extremely powerful and hold great promise, they are currently still labor intensive, have a high cost per cell, and, most importantly, do not provide information on spatial distribution of cell types in specific regions of the brain. We propose a complementary approach that uses computational methods to infer the cell types and their gene expression profiles through analysis of brain-wide single-cell resolution in situ hybridization (ISH) imagery contained in the Allen Brain Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH image for each gene and model it as a spatial point process mixture, whose mixture weights are given by the cell types which express that gene. By fitting a point process mixture model jointly to the ISH images, we infer both the spatial point process distribution for each cell type and their gene expression profile. We validate our predictions of cell type-specific gene expression profiles using single cell RNA sequencing data, recently published for the mouse somatosensory cortex. Jointly with the gene expression profiles, cell features such as cell size, orientation, intensity and local density level are inferred per cell type. |
0904.1063 | Miguel Navascues | M. Navascu\'es (BIO), B. C. Emerson (BIO) | Chloroplast microsatellites: measures of genetic diversity and the
effect of homoplasy | null | Molecular Ecology 14, 5 (2005) 1333-41 | 10.1111/j.1365-294X.2005.02504.x | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chloroplast microsatellites have been widely used in population genetic
studies of conifers in recent years. However, their haplotype configurations
suggest that they could have high levels of homoplasy, thus limiting the power
of these molecular markers. A coalescent-based computer simulation was used to
explore the influence of homoplasy on measures of genetic diversity based on
chloroplast microsatellites. The conditions of the simulation were defined to
fit isolated populations originating from the colonization of one single
haplotype into an area left available after a glacial retreat. Simulated data
were compared with empirical data available from the literature for a species
of Pinus that has expanded north after the Last Glacial Maximum. In the
evaluation of genetic diversity, homoplasy was found to have little influence
on Nei's unbiased haplotype diversity (H(E)) while Goldstein's genetic distance
estimates (D2sh) were much more affected. The effect of the number of
chloroplast microsatellite loci for evaluation of genetic diversity is also
discussed.
| [
{
"created": "Tue, 7 Apr 2009 06:46:24 GMT",
"version": "v1"
}
] | 2009-04-08 | [
[
"Navascués",
"M.",
"",
"BIO"
],
[
"Emerson",
"B. C.",
"",
"BIO"
]
] | Chloroplast microsatellites have been widely used in population genetic studies of conifers in recent years. However, their haplotype configurations suggest that they could have high levels of homoplasy, thus limiting the power of these molecular markers. A coalescent-based computer simulation was used to explore the influence of homoplasy on measures of genetic diversity based on chloroplast microsatellites. The conditions of the simulation were defined to fit isolated populations originating from the colonization of one single haplotype into an area left available after a glacial retreat. Simulated data were compared with empirical data available from the literature for a species of Pinus that has expanded north after the Last Glacial Maximum. In the evaluation of genetic diversity, homoplasy was found to have little influence on Nei's unbiased haplotype diversity (H(E)) while Goldstein's genetic distance estimates (D2sh) were much more affected. The effect of the number of chloroplast microsatellite loci for evaluation of genetic diversity is also discussed. |
2403.03688 | Valerie Carabetta | Liya Popova and Valerie J. Carabetta | The use of next-generation sequencing in personalized medicine | 37 pages, 3 figures, 1 table | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | The revolutionary progress in development of next-generation sequencing (NGS)
technologies has made it possible to deliver accurate genomic information in a
timely manner. Over the past several years, NGS has transformed biomedical and
clinical research and found its application in the field of personalized
medicine. Here we discuss the rise of personalized medicine and the history of
NGS. We discuss current applications and uses of NGS in medicine, including
infectious diseases, oncology, genomic medicine, and dermatology. We provide a
brief discussion of selected studies where NGS was used to respond to wide
variety of questions in biomedical research and clinical medicine. Finally, we
discuss the challenges of implementing NGS into routine clinical use.
| [
{
"created": "Wed, 6 Mar 2024 13:14:25 GMT",
"version": "v1"
}
] | 2024-03-07 | [
[
"Popova",
"Liya",
""
],
[
"Carabetta",
"Valerie J.",
""
]
] | The revolutionary progress in development of next-generation sequencing (NGS) technologies has made it possible to deliver accurate genomic information in a timely manner. Over the past several years, NGS has transformed biomedical and clinical research and found its application in the field of personalized medicine. Here we discuss the rise of personalized medicine and the history of NGS. We discuss current applications and uses of NGS in medicine, including infectious diseases, oncology, genomic medicine, and dermatology. We provide a brief discussion of selected studies where NGS was used to respond to wide variety of questions in biomedical research and clinical medicine. Finally, we discuss the challenges of implementing NGS into routine clinical use. |
1906.10819 | David Murrugarra | Devin Willmott and David Murrugarra and Qiang Ye | Improving RNA secondary structure prediction via state inference with
deep recurrent neural networks | 15 pages, 3 figures, and 5 tables | Computational and Mathematical Biophysics, 8(1), 36-50, 2020 | 10.1515/cmb-2020-0002 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of determining which nucleotides of an RNA sequence are paired or
unpaired in the secondary structure of an RNA, which we call RNA state
inference, can be studied by different machine learning techniques. Successful
state inference of RNA sequences can be used to generate auxiliary information
for data-directed RNA secondary structure prediction. Bidirectional long
short-term memory (LSTM) neural networks have emerged as a powerful tool that
can model global nonlinear sequence dependencies and have achieved
state-of-the-art performances on many different classification problems. This
paper presents a practical approach to RNA secondary structure inference
centered around a deep learning method for state inference. State predictions
from a deep bidirectional LSTM are used to generate synthetic SHAPE data that
can be incorporated into RNA secondary structure prediction via the Nearest
Neighbor Thermodynamic Model (NNTM). This method produces predicted secondary
structures for a diverse test set of 16S ribosomal RNA that are, on average, 25
percentage points more accurate than undirected MFE structures. These
improvements range from several percentage points for some sequences to nearly
50 percentage points for others. Accuracy is highly dependent on the success of
our state inference method, and investigating the global features of our state
predictions reveals that accuracy of both our state inference and structure
inference methods are highly dependent on the similarity of the sequence to the
dataset. This paper presents a deep learning state inference tool, trained and
tested on 16S ribosomal RNA. Converting these state predictions into synthetic
SHAPE data with which to direct NNTM can result in large improvements in
secondary structure prediction accuracy, as shown on a test set of 16S rRNA.
| [
{
"created": "Wed, 26 Jun 2019 02:47:36 GMT",
"version": "v1"
},
{
"created": "Sun, 23 Feb 2020 15:40:21 GMT",
"version": "v2"
}
] | 2024-07-09 | [
[
"Willmott",
"Devin",
""
],
[
"Murrugarra",
"David",
""
],
[
"Ye",
"Qiang",
""
]
] | The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by different machine learning techniques. Successful state inference of RNA sequences can be used to generate auxiliary information for data-directed RNA secondary structure prediction. Bidirectional long short-term memory (LSTM) neural networks have emerged as a powerful tool that can model global nonlinear sequence dependencies and have achieved state-of-the-art performances on many different classification problems. This paper presents a practical approach to RNA secondary structure inference centered around a deep learning method for state inference. State predictions from a deep bidirectional LSTM are used to generate synthetic SHAPE data that can be incorporated into RNA secondary structure prediction via the Nearest Neighbor Thermodynamic Model (NNTM). This method produces predicted secondary structures for a diverse test set of 16S ribosomal RNA that are, on average, 25 percentage points more accurate than undirected MFE structures. These improvements range from several percentage points for some sequences to nearly 50 percentage points for others. Accuracy is highly dependent on the success of our state inference method, and investigating the global features of our state predictions reveals that accuracy of both our state inference and structure inference methods are highly dependent on the similarity of the sequence to the dataset. This paper presents a deep learning state inference tool, trained and tested on 16S ribosomal RNA. Converting these state predictions into synthetic SHAPE data with which to direct NNTM can result in large improvements in secondary structure prediction accuracy, as shown on a test set of 16S rRNA. |
2101.02557 | Julia Ann Jose | Julia Ann Jose, Trae Waggoner, Sudarsan Manikandan | Continuous Glucose Monitoring Prediction | null | null | null | null | q-bio.OT cs.LG | http://creativecommons.org/licenses/by/4.0/ | Diabetes is one of the deadliest diseases in the world and affects nearly 10
percent of the global adult population. Fortunately, powerful new technologies
allow for a consistent and reliable treatment plan for people with diabetes.
One major development is a system called continuous blood glucose monitoring
(CGM). In this review, we look at three different continuous meal detection
algorithms that were developed using given CGM data from patients with
diabetes. From this analysis, an initial meal prediction algorithm was also
developed utilizing these methods.
| [
{
"created": "Mon, 4 Jan 2021 21:32:20 GMT",
"version": "v1"
}
] | 2021-01-08 | [
[
"Jose",
"Julia Ann",
""
],
[
"Waggoner",
"Trae",
""
],
[
"Manikandan",
"Sudarsan",
""
]
] | Diabetes is one of the deadliest diseases in the world and affects nearly 10 percent of the global adult population. Fortunately, powerful new technologies allow for a consistent and reliable treatment plan for people with diabetes. One major development is a system called continuous blood glucose monitoring (CGM). In this review, we look at three different continuous meal detection algorithms that were developed using given CGM data from patients with diabetes. From this analysis, an initial meal prediction algorithm was also developed utilizing these methods. |
2401.05282 | Ricardo Henriques Prof | Leonor Morgado, Estibaliz G\'omez-de-Mariscal, Hannah S. Heil and
Ricardo Henriques | The Rise of Data-Driven Microscopy powered by Machine Learning | 7 pages, 4 figures, review | null | 10.1111/jmi.13282 | null | q-bio.QM physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Optical microscopy is an indispensable tool in life sciences research, but
conventional techniques require compromises between imaging parameters like
speed, resolution, field-of-view, and phototoxicity. To overcome these
limitations, data-driven microscopes incorporate feedback loops between data
acquisition and analysis. This review overviews how machine learning enables
automated image analysis to optimise microscopy in real-time. We first
introduce key data-driven microscopy concepts and machine learning methods
relevant to microscopy image analysis. Subsequently, we highlight pioneering
works and recent advances in integrating machine learning into microscopy
acquisition workflows, including optimising illumination, switching modalities
and acquisition rates, and triggering targeted experiments. We then discuss the
remaining challenges and future outlook. Overall, intelligent microscopes that
can sense, analyse, and adapt promise to transform optical imaging by opening
new experimental possibilities.
| [
{
"created": "Wed, 10 Jan 2024 17:28:17 GMT",
"version": "v1"
}
] | 2024-04-01 | [
[
"Morgado",
"Leonor",
""
],
[
"Gómez-de-Mariscal",
"Estibaliz",
""
],
[
"Heil",
"Hannah S.",
""
],
[
"Henriques",
"Ricardo",
""
]
] | Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field-of-view, and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real-time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse, and adapt promise to transform optical imaging by opening new experimental possibilities. |
1501.01677 | Norichika Ogata | Norichika Ogata, Toshinori Kozaki, Takeshi Yokoyama, Tamako Hata and
Kikuo Iwabuchi | Comparison between the amount of environmental change and the amount of
transcriptome change | null | null | 10.1371/journal.pone.0144822 | null | q-bio.CB q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cells must coordinate adjustments in genome expression to accommodate changes
in their environment. We hypothesized that the amount of transcriptome change
is proportional to the amount of environmental change. To capture the effects
of environmental changes on the transcriptome, we compared transcriptome
diversities (defined as the Shannon entropy of frequency distribution) of
silkworm fat-body tissues cultured with several concentrations of
phenobarbital. Although there was no proportional relationship, we did identify
a drug concentration tipping point between 0.25 and 1.0 mM. Cells cultured in
media containing lower drug concentrations than the tipping point showed
uniformly high transcriptome diversities, while those cultured at higher drug
concentrations than the tipping point showed uniformly low transcriptome
diversities. The plasticity of transcriptome diversity was corroborated by
cultivations of fat bodies in MGM-450 insect medium without phenobarbital and
in 0.25 mM phenobarbital-supplemented MGM-450 insect medium after previous
cultivation (cultivation for 80 hours in MGM-450 insect medium without
phenobarbital, followed by cultivation for 10 hours in 1.0 mM
phenobarbital-supplemented MGM-450 insect medium). Interestingly, the
transcriptome diversities of cells cultured in media containing 0.25 mM
phenobarbital after previous cultivation (cultivation for 80 hours in MGM-450
insect medium without phenobarbital, followed by cultivation for 10 hours in
1.0 mM phenobarbital-supplemented MGM-450 insect medium) were different from
cells cultured in media containing 0.25 mM phenobarbital after previous
cultivation (cultivation for 80 hours in MGM-450 insect medium without
phenobarbital). This hysteretic phenomenon of transcriptome diversities
indicates multi-stability of the genome expression system.
| [
{
"created": "Wed, 7 Jan 2015 22:30:28 GMT",
"version": "v1"
}
] | 2016-02-17 | [
[
"Ogata",
"Norichika",
""
],
[
"Kozaki",
"Toshinori",
""
],
[
"Yokoyama",
"Takeshi",
""
],
[
"Hata",
"Tamako",
""
],
[
"Iwabuchi",
"Kikuo",
""
]
] | Cells must coordinate adjustments in genome expression to accommodate changes in their environment. We hypothesized that the amount of transcriptome change is proportional to the amount of environmental change. To capture the effects of environmental changes on the transcriptome, we compared transcriptome diversities (defined as the Shannon entropy of frequency distribution) of silkworm fat-body tissues cultured with several concentrations of phenobarbital. Although there was no proportional relationship, we did identify a drug concentration tipping point between 0.25 and 1.0 mM. Cells cultured in media containing lower drug concentrations than the tipping point showed uniformly high transcriptome diversities, while those cultured at higher drug concentrations than the tipping point showed uniformly low transcriptome diversities. The plasticity of transcriptome diversity was corroborated by cultivations of fat bodies in MGM-450 insect medium without phenobarbital and in 0.25 mM phenobarbital-supplemented MGM-450 insect medium after previous cultivation (cultivation for 80 hours in MGM-450 insect medium without phenobarbital, followed by cultivation for 10 hours in 1.0 mM phenobarbital-supplemented MGM-450 insect medium). Interestingly, the transcriptome diversities of cells cultured in media containing 0.25 mM phenobarbital after previous cultivation (cultivation for 80 hours in MGM-450 insect medium without phenobarbital, followed by cultivation for 10 hours in 1.0 mM phenobarbital-supplemented MGM-450 insect medium) were different from cells cultured in media containing 0.25 mM phenobarbital after previous cultivation (cultivation for 80 hours in MGM-450 insect medium without phenobarbital). This hysteretic phenomenon of transcriptome diversities indicates multi-stability of the genome expression system. |
2202.04991 | Delfim F. M. Torres | M\'arcia Lemos-Silva, Delfim F. M. Torres | A note on a prey-predator model with constant-effort harvesting | This is a preprint whose final form is published by Springer Nature
Switzerland AG in the book 'Dynamic Control and Optimization'. Submitted
30/Nov/2021; Accepted 10/Feb/2022 | null | 10.1007/978-3-031-17558-9_11 | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study a prey-predator model based on the classical Lotka-Volterra system
with Leslie-Gower and Holling IV schemes and a constant-effort harvesting. Our
goal is twofold: to present the model proposed by Cheng and Zhang in 2021,
pointing out some inconsistencies; to analyse the number and type of
equilibrium points of the model. We end by proving the stability of the
meaningful equilibrium point, according to the distribution of the eigenvalues.
| [
{
"created": "Thu, 10 Feb 2022 12:39:28 GMT",
"version": "v1"
}
] | 2022-10-03 | [
[
"Lemos-Silva",
"Márcia",
""
],
[
"Torres",
"Delfim F. M.",
""
]
] | We study a prey-predator model based on the classical Lotka-Volterra system with Leslie-Gower and Holling IV schemes and a constant-effort harvesting. Our goal is twofold: to present the model proposed by Cheng and Zhang in 2021, pointing out some inconsistencies; to analyse the number and type of equilibrium points of the model. We end by proving the stability of the meaningful equilibrium point, according to the distribution of the eigenvalues. |
1612.01605 | Ricard Sole | Bernat Corominas-Murtra, Lu\'is Seoane and Ricard Sol\'e | Zipf's law, unbounded complexity and open-ended evolution | 16 pages, 4 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major problem for evolutionary theory is understanding the so called {\em
open-ended} nature of evolutionary change, from its definition to its origins.
Open-ended evolution (OEE) refers to the unbounded increase in complexity that
seems to characterise evolution on multiple scales. This property seems to be a
characteristic feature of biological and technological evolution and is
strongly tied to the generative potential associated with combinatorics, which
allows the system to grow and expand their available state spaces.
Interestingly, many complex systems presumably displaying OEE, from language to
proteins, share a common statistical property: the presence of Zipf's law.
Given an inventory of basic items (such as words or protein domains) required
to build more complex structures (sentences or proteins) Zipf's law tells us
that most of these elements are rare whereas a few of them are extremely
common. Using Algorithmic Information Theory, in this paper we provide a
fundamental definition for open-endedness, which can be understood as {\em
postulates}. Its statistical counterpart, based on standard Shannon Information
theory, has the structure of a variational problem which is shown to lead to
Zipf's law as the expected consequence of an evolutionary process displaying
OEE. We further explore the problem of information conservation through an OEE
process and we conclude that statistical information (standard Shannon
information) is not conserved, resulting into the paradoxical situation in
which the increase of information content has the effect of erasing itself. We
prove that this paradox is solved if we consider non-statistical forms of
information. This last result implies that standard information theory may not
be a suitable theoretical framework to explore the persistence and increase of
the information content in OEE systems.
| [
{
"created": "Tue, 6 Dec 2016 00:43:51 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Jun 2018 11:04:16 GMT",
"version": "v2"
},
{
"created": "Tue, 7 Aug 2018 14:33:51 GMT",
"version": "v3"
}
] | 2018-08-08 | [
[
"Corominas-Murtra",
"Bernat",
""
],
[
"Seoane",
"Luís",
""
],
[
"Solé",
"Ricard",
""
]
] | A major problem for evolutionary theory is understanding the so called {\em open-ended} nature of evolutionary change, from its definition to its origins. Open-ended evolution (OEE) refers to the unbounded increase in complexity that seems to characterise evolution on multiple scales. This property seems to be a characteristic feature of biological and technological evolution and is strongly tied to the generative potential associated with combinatorics, which allows the system to grow and expand their available state spaces. Interestingly, many complex systems presumably displaying OEE, from language to proteins, share a common statistical property: the presence of Zipf's law. Given an inventory of basic items (such as words or protein domains) required to build more complex structures (sentences or proteins) Zipf's law tells us that most of these elements are rare whereas a few of them are extremely common. Using Algorithmic Information Theory, in this paper we provide a fundamental definition for open-endedness, which can be understood as {\em postulates}. Its statistical counterpart, based on standard Shannon Information theory, has the structure of a variational problem which is shown to lead to Zipf's law as the expected consequence of an evolutionary process displaying OEE. We further explore the problem of information conservation through an OEE process and we conclude that statistical information (standard Shannon information) is not conserved, resulting into the paradoxical situation in which the increase of information content has the effect of erasing itself. We prove that this paradox is solved if we consider non-statistical forms of information. This last result implies that standard information theory may not be a suitable theoretical framework to explore the persistence and increase of the information content in OEE systems. |
2308.04988 | Colin Bredenberg | Colin Bredenberg, Cristina Savin | Desiderata for normative models of synaptic plasticity | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Normative models of synaptic plasticity use a combination of mathematics and
computational simulations to arrive at predictions of behavioral and
network-level adaptive phenomena. In recent years, there has been an explosion
of theoretical work on these models, but experimental confirmation is
relatively limited. In this review, we organize work on normative plasticity
models in terms of a set of desiderata which, when satisfied, are designed to
guarantee that a model has a clear link between plasticity and adaptive
behavior, consistency with known biological evidence about neural plasticity,
and specific testable predictions. We then discuss how new models have begun to
improve on these criteria and suggest avenues for further development. As
prototypes, we provide detailed analyses of two specific models -- REINFORCE
and the Wake-Sleep algorithm. We provide a conceptual guide to help develop
neural learning theories that are precise, powerful, and experimentally
testable.
| [
{
"created": "Wed, 9 Aug 2023 14:42:10 GMT",
"version": "v1"
}
] | 2023-08-10 | [
[
"Bredenberg",
"Colin",
""
],
[
"Savin",
"Cristina",
""
]
] | Normative models of synaptic plasticity use a combination of mathematics and computational simulations to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work on these models, but experimental confirmation is relatively limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata which, when satisfied, are designed to guarantee that a model has a clear link between plasticity and adaptive behavior, consistency with known biological evidence about neural plasticity, and specific testable predictions. We then discuss how new models have begun to improve on these criteria and suggest avenues for further development. As prototypes, we provide detailed analyses of two specific models -- REINFORCE and the Wake-Sleep algorithm. We provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable. |
1612.06357 | Jonas Haslbeck | Jonas M B Haslbeck and Eiko I Fried | How Predictable are Symptoms in Psychopathological Networks? A
Reanalysis of 18 Published Datasets | 24 pages, 1 table, 4 figures | null | null | null | q-bio.NC physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background Network analyses on psychopathological data focus on the network
structure and its derivatives such as node centrality. One conclusion one can
draw from centrality measures is that the node with the highest centrality is
likely to be the node that is determined most by its neighboring nodes.
However, centrality is a relative measure: knowing that a node is highly
central gives no information about the extent to which it is determined by its
neighbors. Here we provide an absolute measure of determination (or
controllability) of a node - its predictability. We introduce predictability,
estimate the predictability of all nodes in 18 prior empirical network papers
on psychopathology, and statistically relate it to centrality.
Methods We carried out a literature review and collected 25 datasets from 18
published papers in the field (several mood and anxiety disorders, substance
abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art
net- work models to all datasets, and computed the predictability of all nodes.
Results Predictability was unrelated to sample size, moderately high in most
symptom networks, and differed considerable both within and between datasets.
Predictability was higher in community than clinical samples, highest for mood
and anxiety disorders, and lowest for psychosis.
Conclusions Predictability is an important additional characterization of
symptom networks because it gives an absolute measure of the controllability of
each node. It allows conclusions about how self-determined a symptom network
is, and may help to inform intervention strategies. Limitations of
predictability along with future directions are discussed.
| [
{
"created": "Mon, 28 Nov 2016 23:05:24 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Jun 2017 08:32:15 GMT",
"version": "v2"
}
] | 2017-06-21 | [
[
"Haslbeck",
"Jonas M B",
""
],
[
"Fried",
"Eiko I",
""
]
] | Background Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node - its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality. Methods We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art net- work models to all datasets, and computed the predictability of all nodes. Results Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis. Conclusions Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed. |
2204.04587 | Karina Laneri | Javier Armando Gutierrez and Karina Laneri and Juan Pablo Aparicio and
Gustavo Javier Sibona | Meteorological indicators of dengue epidemics in non-endemic Northwest
Argentina | 10 pages, 17 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last two decades dengue cases increased significantly throughout the
world. In several regions dengue re-emerged, particularly in Latin America,
where dengue cases not only increased but also occurred more frequently. It is
therefore necessary to understand the mechanisms that drive epidemic outbreaks
in non-endemic regions, to help in the design of control strategies. We develop
a stochastic model that includes climate variables, social structure, and
mobility between a non-endemic city and an endemic area. We choose as a case
study the non-endemic city of San Ram{\'o}n de la Nueva Or{\'a}n, located in
Northwest Argentina. Human mobility is intense through the border with Bolivia,
where dengue transmission is sustained during the whole year. City population
was modelled as a meta-population taking into account households and population
data for each patch. Climate variability was considered by including rainfall,
relative humidity and temperature time series into the models. Those climatic
variables were input of a mosquito population ecological model, which in turn
is coupled to an epidemiological model. Different hypotheses regarding people's
mobility between an endemic and non-endemic area are tested, taking into
account the local climatic variation, typical of the non-endemic city.
Simulations are qualitatively consistent with weekly clinical data reported
from 2009 to 2016. Our model results allow to explain the observed pattern of
outbreaks, that alternates large dengue epidemics and several years with
smaller outbreaks. We found that the number of vectors per host and an
effective reproductive number are proxies for large epidemics, both related
with climate variability such as rainfall and temperature, opening the
possibility to test these meteorological variables for forecast purposes.
| [
{
"created": "Sun, 10 Apr 2022 03:26:34 GMT",
"version": "v1"
}
] | 2022-04-12 | [
[
"Gutierrez",
"Javier Armando",
""
],
[
"Laneri",
"Karina",
""
],
[
"Aparicio",
"Juan Pablo",
""
],
[
"Sibona",
"Gustavo Javier",
""
]
] | In the last two decades dengue cases increased significantly throughout the world. In several regions dengue re-emerged, particularly in Latin America, where dengue cases not only increased but also occurred more frequently. It is therefore necessary to understand the mechanisms that drive epidemic outbreaks in non-endemic regions, to help in the design of control strategies. We develop a stochastic model that includes climate variables, social structure, and mobility between a non-endemic city and an endemic area. We choose as a case study the non-endemic city of San Ram{\'o}n de la Nueva Or{\'a}n, located in Northwest Argentina. Human mobility is intense through the border with Bolivia, where dengue transmission is sustained during the whole year. City population was modelled as a meta-population taking into account households and population data for each patch. Climate variability was considered by including rainfall, relative humidity and temperature time series into the models. Those climatic variables were input of a mosquito population ecological model, which in turn is coupled to an epidemiological model. Different hypotheses regarding people's mobility between an endemic and non-endemic area are tested, taking into account the local climatic variation, typical of the non-endemic city. Simulations are qualitatively consistent with weekly clinical data reported from 2009 to 2016. Our model results allow to explain the observed pattern of outbreaks, that alternates large dengue epidemics and several years with smaller outbreaks. We found that the number of vectors per host and an effective reproductive number are proxies for large epidemics, both related with climate variability such as rainfall and temperature, opening the possibility to test these meteorological variables for forecast purposes. |
0911.1720 | Carlos P. Roca | Carlos P. Roca, Jos\'e A. Cuesta and Angel S\'anchez | Evolutionary game theory: Temporal and spatial effects beyond replicator
dynamics | Review, 48 pages, 26 figures | Physics of Life Reviews 6, 208-249 (2009) | 10.1016/j.plrev.2009.08.001 | null | q-bio.PE cs.GT physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolutionary game dynamics is one of the most fruitful frameworks for
studying evolution in different disciplines, from Biology to Economics. Within
this context, the approach of choice for many researchers is the so-called
replicator equation, that describes mathematically the idea that those
individuals performing better have more offspring and thus their frequency in
the population grows. While very many interesting results have been obtained
with this equation in the three decades elapsed since it was first proposed, it
is important to realize the limits of its applicability. One particularly
relevant issue in this respect is that of non-mean-field effects, that may
arise from temporal fluctuations or from spatial correlations, both neglected
in the replicator equation. This review discusses these temporal and spatial
effects focusing on the non-trivial modifications they induce when compared to
the outcome of replicator dynamics. Alongside this question, the hypothesis of
linearity and its relation to the choice of the rule for strategy update is
also analyzed. The discussion is presented in terms of the emergence of
cooperation, as one of the current key problems in Biology and in other
disciplines.
| [
{
"created": "Mon, 9 Nov 2009 16:29:47 GMT",
"version": "v1"
}
] | 2009-11-14 | [
[
"Roca",
"Carlos P.",
""
],
[
"Cuesta",
"José A.",
""
],
[
"Sánchez",
"Angel",
""
]
] | Evolutionary game dynamics is one of the most fruitful frameworks for studying evolution in different disciplines, from Biology to Economics. Within this context, the approach of choice for many researchers is the so-called replicator equation, that describes mathematically the idea that those individuals performing better have more offspring and thus their frequency in the population grows. While very many interesting results have been obtained with this equation in the three decades elapsed since it was first proposed, it is important to realize the limits of its applicability. One particularly relevant issue in this respect is that of non-mean-field effects, that may arise from temporal fluctuations or from spatial correlations, both neglected in the replicator equation. This review discusses these temporal and spatial effects focusing on the non-trivial modifications they induce when compared to the outcome of replicator dynamics. Alongside this question, the hypothesis of linearity and its relation to the choice of the rule for strategy update is also analyzed. The discussion is presented in terms of the emergence of cooperation, as one of the current key problems in Biology and in other disciplines. |
2405.16861 | Wonho Zhung | Joongwon Lee, Wonho Zhung, Woo Youn Kim | NCIDiff: Non-covalent Interaction-generative Diffusion Model for
Improving Reliability of 3D Molecule Generation Inside Protein Pocket | null | null | null | null | q-bio.BM cs.LG physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Advancements in deep generative modeling have changed the paradigm of drug
discovery. Among such approaches, target-aware methods that exploit 3D
structures of protein pockets were spotlighted for generating ligand molecules
with their plausible binding modes. While docking scores superficially assess
the quality of generated ligands, closer inspection of the binding structures
reveals the inconsistency in local interactions between a pocket and generated
ligands. Here, we address the issue by explicitly generating non-covalent
interactions (NCIs), which are universal patterns throughout protein-ligand
complexes. Our proposed model, NCIDiff, simultaneously denoises NCI types of
protein-ligand edges along with a 3D graph of a ligand molecule during the
sampling. With the NCI-generating strategy, our model generates ligands with
more reliable NCIs, especially outperforming the baseline diffusion-based
models. We further adopted inpainting techniques on NCIs to further improve the
quality of the generated molecules. Finally, we showcase the applicability of
NCIDiff on drug design tasks for real-world settings with specialized
objectives by guiding the generation process with desired NCI patterns.
| [
{
"created": "Mon, 27 May 2024 06:26:55 GMT",
"version": "v1"
}
] | 2024-05-28 | [
[
"Lee",
"Joongwon",
""
],
[
"Zhung",
"Wonho",
""
],
[
"Kim",
"Woo Youn",
""
]
] | Advancements in deep generative modeling have changed the paradigm of drug discovery. Among such approaches, target-aware methods that exploit 3D structures of protein pockets were spotlighted for generating ligand molecules with their plausible binding modes. While docking scores superficially assess the quality of generated ligands, closer inspection of the binding structures reveals the inconsistency in local interactions between a pocket and generated ligands. Here, we address the issue by explicitly generating non-covalent interactions (NCIs), which are universal patterns throughout protein-ligand complexes. Our proposed model, NCIDiff, simultaneously denoises NCI types of protein-ligand edges along with a 3D graph of a ligand molecule during the sampling. With the NCI-generating strategy, our model generates ligands with more reliable NCIs, especially outperforming the baseline diffusion-based models. We further adopted inpainting techniques on NCIs to further improve the quality of the generated molecules. Finally, we showcase the applicability of NCIDiff on drug design tasks for real-world settings with specialized objectives by guiding the generation process with desired NCI patterns. |
1711.07387 | Sam Kriegman | Sam Kriegman, Nick Cheney, Josh Bongard | How morphological development can guide evolution | null | null | 10.1038/s41598-018-31868-7 | null | q-bio.PE cs.AI cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Organisms result from adaptive processes interacting across different time
scales. One such interaction is that between development and evolution. Models
have shown that development sweeps over several traits in a single agent,
sometimes exposing promising static traits. Subsequent evolution can then
canalize these rare traits. Thus, development can, under the right conditions,
increase evolvability. Here, we report on a previously unknown phenomenon when
embodied agents are allowed to develop and evolve: Evolution discovers body
plans robust to control changes, these body plans become genetically
assimilated, yet controllers for these agents are not assimilated. This allows
evolution to continue climbing fitness gradients by tinkering with the
developmental programs for controllers within these permissive body plans. This
exposes a previously unknown detail about the Baldwin effect: instead of all
useful traits becoming genetically assimilated, only traits that render the
agent robust to changes in other traits become assimilated. We refer to this as
differential canalization. This finding also has implications for the
evolutionary design of artificial and embodied agents such as robots: robots
robust to internal changes in their controllers may also be robust to external
changes in their environment, such as transferal from simulation to reality or
deployment in novel environments.
| [
{
"created": "Mon, 20 Nov 2017 15:51:34 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Dec 2017 00:46:09 GMT",
"version": "v2"
},
{
"created": "Wed, 2 May 2018 01:42:03 GMT",
"version": "v3"
},
{
"created": "Fri, 3 Aug 2018 23:55:21 GMT",
"version": "v4"
},
{
"created": "Fri, 7 Sep 2018 21:12:14 GMT",
"version": "v5"
}
] | 2018-09-19 | [
[
"Kriegman",
"Sam",
""
],
[
"Cheney",
"Nick",
""
],
[
"Bongard",
"Josh",
""
]
] | Organisms result from adaptive processes interacting across different time scales. One such interaction is that between development and evolution. Models have shown that development sweeps over several traits in a single agent, sometimes exposing promising static traits. Subsequent evolution can then canalize these rare traits. Thus, development can, under the right conditions, increase evolvability. Here, we report on a previously unknown phenomenon when embodied agents are allowed to develop and evolve: Evolution discovers body plans robust to control changes, these body plans become genetically assimilated, yet controllers for these agents are not assimilated. This allows evolution to continue climbing fitness gradients by tinkering with the developmental programs for controllers within these permissive body plans. This exposes a previously unknown detail about the Baldwin effect: instead of all useful traits becoming genetically assimilated, only traits that render the agent robust to changes in other traits become assimilated. We refer to this as differential canalization. This finding also has implications for the evolutionary design of artificial and embodied agents such as robots: robots robust to internal changes in their controllers may also be robust to external changes in their environment, such as transferal from simulation to reality or deployment in novel environments. |
2109.06405 | Fang Chen | Fang Chen, Te Wu and Long Wang | Evolutionary dynamics of zero-determinant strategies in repeated
multiplayer games | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since Press and Dyson's ingenious discovery of ZD (zero-determinant) strategy
in the repeated Prisoner's Dilemma game, several studies have confirmed the
existence of ZD strategy in repeated multiplayer social dilemmas. However, few
researches study the evolutionary performance of multiplayer ZD strategies,
especially from a theoretical perspective. Here, we use a newly proposed
state-clustering method to theoretically analyze the evolutionary dynamics of
two representative ZD strategies: generous ZD strategies and extortionate ZD
strategies. Apart from the competitions between the two strategies and some
classical strategies, we consider two new settings for multiplayer ZD
strategies: competitions in the whole ZD strategy space and competitions in the
space of all memory-1 strategies. Besides, we investigate the influence of
level of generosity and extortion on the evolutionary dynamics of generous and
extortionate ZD, which was commonly ignored in previous studies. Theoretical
results show players with limited generosity are at an advantageous place and
extortioners extorting more severely hold their ground more readily. Our
results may provide new insights into better understanding the evolutionary
dynamics of ZD strategies in repeated multiplayer games.
| [
{
"created": "Tue, 14 Sep 2021 02:41:02 GMT",
"version": "v1"
}
] | 2021-09-15 | [
[
"Chen",
"Fang",
""
],
[
"Wu",
"Te",
""
],
[
"Wang",
"Long",
""
]
] | Since Press and Dyson's ingenious discovery of ZD (zero-determinant) strategy in the repeated Prisoner's Dilemma game, several studies have confirmed the existence of ZD strategy in repeated multiplayer social dilemmas. However, few researches study the evolutionary performance of multiplayer ZD strategies, especially from a theoretical perspective. Here, we use a newly proposed state-clustering method to theoretically analyze the evolutionary dynamics of two representative ZD strategies: generous ZD strategies and extortionate ZD strategies. Apart from the competitions between the two strategies and some classical strategies, we consider two new settings for multiplayer ZD strategies: competitions in the whole ZD strategy space and competitions in the space of all memory-1 strategies. Besides, we investigate the influence of level of generosity and extortion on the evolutionary dynamics of generous and extortionate ZD, which was commonly ignored in previous studies. Theoretical results show players with limited generosity are at an advantageous place and extortioners extorting more severely hold their ground more readily. Our results may provide new insights into better understanding the evolutionary dynamics of ZD strategies in repeated multiplayer games. |
2405.20203 | Chiara Villa | Federica Padovano, Chiara Villa | The development of drug resistance in metastatic tumours under
chemotherapy: an evolutionary perspective | 35 pages, 10 Figures, 1 Supplementary material | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a mathematical model of the evolutionary dynamics of a metastatic
tumour under chemotherapy, comprising non-local partial differential equations
for the phenotype-structured cell populations in the primary tumour and its
metastasis. These equations are coupled with a physiologically-based
pharmacokinetic model of drug delivery, implementing a realistic delivery
schedule. The model is carefully calibrated from the literature, focusing on
BRAF-mutated melanoma treated with Dabrafenib as a case study. By means of
long-time asymptotic analysis, global sensitivity analysis and numerical
simulations, we explore the impact of cell migration from the primary to the
metastatic site, physiological aspects of the tumour sites and drug dose on the
development of drug resistance and treatment efficacy. Our findings provide a
possible explanation for empirical evidence indicating that chemotherapy may
foster metastatic spread and that metastatic sites may be less impacted by
chemotherapy.
| [
{
"created": "Thu, 30 May 2024 16:05:37 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Jun 2024 07:32:33 GMT",
"version": "v2"
}
] | 2024-06-21 | [
[
"Padovano",
"Federica",
""
],
[
"Villa",
"Chiara",
""
]
] | We present a mathematical model of the evolutionary dynamics of a metastatic tumour under chemotherapy, comprising non-local partial differential equations for the phenotype-structured cell populations in the primary tumour and its metastasis. These equations are coupled with a physiologically-based pharmacokinetic model of drug delivery, implementing a realistic delivery schedule. The model is carefully calibrated from the literature, focusing on BRAF-mutated melanoma treated with Dabrafenib as a case study. By means of long-time asymptotic analysis, global sensitivity analysis and numerical simulations, we explore the impact of cell migration from the primary to the metastatic site, physiological aspects of the tumour sites and drug dose on the development of drug resistance and treatment efficacy. Our findings provide a possible explanation for empirical evidence indicating that chemotherapy may foster metastatic spread and that metastatic sites may be less impacted by chemotherapy. |
1112.5604 | Pau Ru\'e | Pau Ru\'e, N\'uria Domedel-Puig, Jordi Garcia-Ojalvo, Antonio J. Pons | Integration of cellular signals in chattering environments | 9 pages, 6 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cells are constantly exposed to fluctuating environmental conditions.
External signals are sensed, processed and integrated by cellular signal
transduction networks, which translate input signals into specific cellular
responses by means of biochemical reactions. These networks have a complex
nature, and we are still far from having a complete characterization of the
process through which they integrate information, specially given the noisy
environment in which that information is embedded. Guided by the many instances
of constructive influences of noise that have been reported in the physical
sciences in the last decades, here we explore how multiple signals are
integrated in an eukaryotic cell in the presence of background noise, or
chatter. To that end, we use a Boolean model of a typical human signal
transduction network. Despite its complexity, we find that the network is able
to display simple patterns of signal integration. Furthermore, our
computational analysis shows that these integration patterns depend on the
levels of fluctuating background activity carried by other cell inputs. Taken
together, our results indicate that signal integration is sensitive to
environmental fluctuations, and that this background noise effectively
determines the information integration capabilities of the cell.
| [
{
"created": "Fri, 23 Dec 2011 15:37:01 GMT",
"version": "v1"
}
] | 2012-05-29 | [
[
"Rué",
"Pau",
""
],
[
"Domedel-Puig",
"Núria",
""
],
[
"Garcia-Ojalvo",
"Jordi",
""
],
[
"Pons",
"Antonio J.",
""
]
] | Cells are constantly exposed to fluctuating environmental conditions. External signals are sensed, processed and integrated by cellular signal transduction networks, which translate input signals into specific cellular responses by means of biochemical reactions. These networks have a complex nature, and we are still far from having a complete characterization of the process through which they integrate information, specially given the noisy environment in which that information is embedded. Guided by the many instances of constructive influences of noise that have been reported in the physical sciences in the last decades, here we explore how multiple signals are integrated in an eukaryotic cell in the presence of background noise, or chatter. To that end, we use a Boolean model of a typical human signal transduction network. Despite its complexity, we find that the network is able to display simple patterns of signal integration. Furthermore, our computational analysis shows that these integration patterns depend on the levels of fluctuating background activity carried by other cell inputs. Taken together, our results indicate that signal integration is sensitive to environmental fluctuations, and that this background noise effectively determines the information integration capabilities of the cell. |
1310.5091 | Leandro Nadaletti | Leandro P. Nadaletti, Beatriz S. L. P. de Lima, and Solange
Guimar\~aes | Synchronization as a unifying mechanism for protein folding | 6 pages, 5 figures; Reference added | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Different models such as diffusion-collision and nucleation-condensation have
been used to unravel how secondary and tertiary structures form during protein
folding. However, a simple mechanism based on physical principles that provide
an accurate description of kinetics and thermodynamics for such phenomena has
not yet been identified. This study introduces the hypothesis that the
synchronization of the peptide plane oscillatory movements throughout the
backbone must also play a key role in the folding mechanism. Based on that, we
draw a parallel between the folding process and the dynamics for a network of
coupled oscillators described by the Kuramoto model. The amino acid coupling
may explain the mean-field character of the force that propels an amino acid
sequence into a structure through self-organization. Thus, the pattern of
synchronized cluster formation and growing helps to solve the Levinthal's
paradox.Synchronization may also help us to understand the success of homology
structural modeling, allosteric effect, and the mechanism responsible for the
recognition of odorants by olfactory receptors.
| [
{
"created": "Fri, 18 Oct 2013 16:50:59 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Nov 2013 00:56:42 GMT",
"version": "v2"
},
{
"created": "Mon, 23 Jun 2014 18:26:19 GMT",
"version": "v3"
},
{
"created": "Tue, 14 Oct 2014 00:12:09 GMT",
"version": "v4"
}
] | 2014-10-15 | [
[
"Nadaletti",
"Leandro P.",
""
],
[
"de Lima",
"Beatriz S. L. P.",
""
],
[
"Guimarães",
"Solange",
""
]
] | Different models such as diffusion-collision and nucleation-condensation have been used to unravel how secondary and tertiary structures form during protein folding. However, a simple mechanism based on physical principles that provide an accurate description of kinetics and thermodynamics for such phenomena has not yet been identified. This study introduces the hypothesis that the synchronization of the peptide plane oscillatory movements throughout the backbone must also play a key role in the folding mechanism. Based on that, we draw a parallel between the folding process and the dynamics for a network of coupled oscillators described by the Kuramoto model. The amino acid coupling may explain the mean-field character of the force that propels an amino acid sequence into a structure through self-organization. Thus, the pattern of synchronized cluster formation and growing helps to solve the Levinthal's paradox.Synchronization may also help us to understand the success of homology structural modeling, allosteric effect, and the mechanism responsible for the recognition of odorants by olfactory receptors. |
2307.11325 | Shreya Ghosh | Matthew Hines, Gregory Glatzer, Shreya Ghosh, Prasenjit Mitra | Analysis of Elephant Movement in Sub-Saharan Africa: Ecological,
Climatic, and Conservation Perspectives | 11 pages, 17 figures, Accepted in ACM SIGCAS SIGCHI Conference on
Computing and Sustainable Societies (COMPASS 2023) | null | null | null | q-bio.PE cs.AI cs.IR cs.LG | http://creativecommons.org/licenses/by/4.0/ | The interaction between elephants and their environment has profound
implications for both ecology and conservation strategies. This study presents
an analytical approach to decipher the intricate patterns of elephant movement
in Sub-Saharan Africa, concentrating on key ecological drivers such as seasonal
variations and rainfall patterns. Despite the complexities surrounding these
influential factors, our analysis provides a holistic view of elephant
migratory behavior in the context of the dynamic African landscape. Our
comprehensive approach enables us to predict the potential impact of these
ecological determinants on elephant migration, a critical step in establishing
informed conservation strategies. This projection is particularly crucial given
the impacts of global climate change on seasonal and rainfall patterns, which
could substantially influence elephant movements in the future. The findings of
our work aim to not only advance the understanding of movement ecology but also
foster a sustainable coexistence of humans and elephants in Sub-Saharan Africa.
By predicting potential elephant routes, our work can inform strategies to
minimize human-elephant conflict, effectively manage land use, and enhance
anti-poaching efforts. This research underscores the importance of integrating
movement ecology and climatic variables for effective wildlife management and
conservation planning.
| [
{
"created": "Fri, 21 Jul 2023 03:23:17 GMT",
"version": "v1"
}
] | 2023-07-24 | [
[
"Hines",
"Matthew",
""
],
[
"Glatzer",
"Gregory",
""
],
[
"Ghosh",
"Shreya",
""
],
[
"Mitra",
"Prasenjit",
""
]
] | The interaction between elephants and their environment has profound implications for both ecology and conservation strategies. This study presents an analytical approach to decipher the intricate patterns of elephant movement in Sub-Saharan Africa, concentrating on key ecological drivers such as seasonal variations and rainfall patterns. Despite the complexities surrounding these influential factors, our analysis provides a holistic view of elephant migratory behavior in the context of the dynamic African landscape. Our comprehensive approach enables us to predict the potential impact of these ecological determinants on elephant migration, a critical step in establishing informed conservation strategies. This projection is particularly crucial given the impacts of global climate change on seasonal and rainfall patterns, which could substantially influence elephant movements in the future. The findings of our work aim to not only advance the understanding of movement ecology but also foster a sustainable coexistence of humans and elephants in Sub-Saharan Africa. By predicting potential elephant routes, our work can inform strategies to minimize human-elephant conflict, effectively manage land use, and enhance anti-poaching efforts. This research underscores the importance of integrating movement ecology and climatic variables for effective wildlife management and conservation planning. |
1210.2850 | Barbara Rakitsch | Barbara Rakitsch, Christoph Lippert, Hande Topa, Karsten Borgwardt,
Antti Honkela, Oliver Stegle | A mixed model approach for joint genetic analysis of alternatively
spliced transcript isoforms using RNA-Seq data | null | null | null | null | q-bio.GN q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | RNA-Seq technology allows for studying the transcriptional state of the cell
at an unprecedented level of detail. Beyond quantification of whole-gene
expression, it is now possible to disentangle the abundance of individual
alternatively spliced transcript isoforms of a gene. A central question is to
understand the regulatory processes that lead to differences in relative
abundance variation due to external and genetic factors. Here, we present a
mixed model approach that allows for (i) joint analysis and genetic mapping of
multiple transcript isoforms and (ii) mapping of isoform-specific effects.
Central to our approach is to comprehensively model the causes of variation and
correlation between transcript isoforms, including the genomic background and
technical quantification uncertainty. As a result, our method allows to
accurately test for shared as well as transcript-specific genetic regulation of
transcript isoforms and achieves substantially improved calibration of these
statistical tests. Experiments on genotype and RNA-Seq data from 126 human
HapMap individuals demonstrate that our model can help to obtain a more
fine-grained picture of the genetic basis of gene expression variation.
| [
{
"created": "Wed, 10 Oct 2012 09:56:06 GMT",
"version": "v1"
}
] | 2012-10-11 | [
[
"Rakitsch",
"Barbara",
""
],
[
"Lippert",
"Christoph",
""
],
[
"Topa",
"Hande",
""
],
[
"Borgwardt",
"Karsten",
""
],
[
"Honkela",
"Antti",
""
],
[
"Stegle",
"Oliver",
""
]
] | RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced transcript isoforms of a gene. A central question is to understand the regulatory processes that lead to differences in relative abundance variation due to external and genetic factors. Here, we present a mixed model approach that allows for (i) joint analysis and genetic mapping of multiple transcript isoforms and (ii) mapping of isoform-specific effects. Central to our approach is to comprehensively model the causes of variation and correlation between transcript isoforms, including the genomic background and technical quantification uncertainty. As a result, our method allows to accurately test for shared as well as transcript-specific genetic regulation of transcript isoforms and achieves substantially improved calibration of these statistical tests. Experiments on genotype and RNA-Seq data from 126 human HapMap individuals demonstrate that our model can help to obtain a more fine-grained picture of the genetic basis of gene expression variation. |
1610.06127 | Steven Frank | Steven A. Frank | Puzzles in modern biology. III. Two kinds of causality in age-related
disease | null | F1000Research 5:2533 (2016) | 10.12688/f1000research.9789.1 | null | q-bio.PE q-bio.MN q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | The two primary causal dimensions of age-related disease are rate and
function. Change in rate of disease development shifts the age of onset. Change
in physiological function provides necessary steps in disease progression. A
causal factor may alter the rate of physiological change, but that causal
factor itself may have no direct physiological role. Alternatively, a causal
factor may provide a necessary physiological function, but that causal factor
itself may not alter the rate of disease onset. The rate-function duality
provides the basis for solving puzzles of age-related disease. Causal factors
of cancer illustrate the duality between rate processes of discovery, such as
somatic mutation, and necessary physiological functions, such as invasive
penetration across tissue barriers. Examples from cancer suggest general
principles of age-related disease.
| [
{
"created": "Wed, 19 Oct 2016 17:49:45 GMT",
"version": "v1"
}
] | 2016-10-20 | [
[
"Frank",
"Steven A.",
""
]
] | The two primary causal dimensions of age-related disease are rate and function. Change in rate of disease development shifts the age of onset. Change in physiological function provides necessary steps in disease progression. A causal factor may alter the rate of physiological change, but that causal factor itself may have no direct physiological role. Alternatively, a causal factor may provide a necessary physiological function, but that causal factor itself may not alter the rate of disease onset. The rate-function duality provides the basis for solving puzzles of age-related disease. Causal factors of cancer illustrate the duality between rate processes of discovery, such as somatic mutation, and necessary physiological functions, such as invasive penetration across tissue barriers. Examples from cancer suggest general principles of age-related disease. |
q-bio/0512018 | Otger Camp\`as | O. Campas, Y. Kafri, K.B. Zeldovich, J. Casademunt, J.-F. Joanny | Collective dynamics of molecular motors pulling on fluid membranes | 5 pages, 5 figures | Phys. Rev. Lett. 97, 038101 (2006) | 10.1103/PhysRevLett.97.038101 | null | q-bio.SC physics.bio-ph | null | The collective dynamics of $N$ weakly coupled processive molecular motors are
considered theoretically. We show, using a discrete lattice model, that the
velocity-force curves strongly depend on the effective dynamic interactions
between motors and differ significantly from a simple mean field prediction.
They become essentially independent of $N$ if it is large enough. For strongly
biased motors such as kinesin this occurs if $N\gtrsim 5$. The study of a
two-state model shows that the existence of internal states can induce
effective interactions.
| [
{
"created": "Thu, 8 Dec 2005 20:43:54 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Campas",
"O.",
""
],
[
"Kafri",
"Y.",
""
],
[
"Zeldovich",
"K. B.",
""
],
[
"Casademunt",
"J.",
""
],
[
"Joanny",
"J. -F.",
""
]
] | The collective dynamics of $N$ weakly coupled processive molecular motors are considered theoretically. We show, using a discrete lattice model, that the velocity-force curves strongly depend on the effective dynamic interactions between motors and differ significantly from a simple mean field prediction. They become essentially independent of $N$ if it is large enough. For strongly biased motors such as kinesin this occurs if $N\gtrsim 5$. The study of a two-state model shows that the existence of internal states can induce effective interactions. |
1801.01385 | Sang-Yoon Kim | Sang-Yoon Kim, Woochang Lim | Effect of Inhibitory Spike-Timing-Dependent Plasticity on Fast Sparsely
Synchronized Rhythms in A Small-World Neuronal Network | arXiv admin note: text overlap with arXiv:1704.03150 | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the Watts-Strogatz small-world network (SWN) consisting of
inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal
population has adaptive dynamic synaptic strengths governed by the inhibitory
spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP,
fast sparsely synchronized rhythms, associated with diverse cognitive
functions, were found to appear in a range of large noise intensities for fixed
strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP
on fast sparse synchronization (FSS) by varying the noise intensity $D$. We
employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which
is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)].
Depending on values of $D$, population-averaged values of saturated synaptic
inhibition strengths are potentiated [long-term potentiation (LTP)] or
depressed [long-term depression (LTD)] in comparison with the initial mean
value, and dispersions from the mean values of LTP/LTD are much increased when
compared with the initial dispersion, independently of $D$. In most cases of
LTD where the effect of mean LTD is dominant in comparison with the effect of
dispersion, good FSS (with higher spiking measure) is found to get better via
LTD, while bad FSS (with lower spiking measure) is found to 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). 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 spike
times. Furthermore, we also investigate the effects of network architecture on
FSS by changing the rewiring probability $p$ of the SWN in the presence of
iSTDP.
| [
{
"created": "Wed, 3 Jan 2018 01:29:34 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jan 2018 09:46:39 GMT",
"version": "v2"
},
{
"created": "Fri, 11 May 2018 17:05:43 GMT",
"version": "v3"
},
{
"created": "Mon, 14 May 2018 04:59:47 GMT",
"version": "v4"
}
] | 2018-05-15 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | We consider the Watts-Strogatz small-world network (SWN) consisting of inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP, fast sparsely synchronized rhythms, associated with diverse cognitive functions, were found to appear in a range of large noise intensities for fixed strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP on fast sparse synchronization (FSS) by varying the noise intensity $D$. We employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)]. Depending on values of $D$, population-averaged values of saturated synaptic inhibition strengths are potentiated [long-term potentiation (LTP)] or depressed [long-term depression (LTD)] in comparison with the initial mean value, and dispersions from the mean values of LTP/LTD are much increased when compared with the initial dispersion, independently of $D$. In most cases of LTD where the effect of mean LTD is dominant in comparison with the effect of dispersion, good FSS (with higher spiking measure) is found to get better via LTD, while bad FSS (with lower spiking measure) is found to 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). 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 spike times. Furthermore, we also investigate the effects of network architecture on FSS by changing the rewiring probability $p$ of the SWN in the presence of iSTDP. |
q-bio/0607032 | Graziano Vernizzi | Michael Bon, Graziano Vernizzi, Henri Orland, A. Zee | Topological classification of RNA structures | 17 pages, 3 tables, 13 figures (high quality figures available on
request) | null | null | null | q-bio.BM cond-mat.soft q-bio.SC | null | We present a novel topological classification of RNA secondary structures
with pseudoknots. It is based on the topological genus of the circular diagram
associated to the RNA base-pair structure. The genus is a positive integer
number, whose value quantifies the topological complexity of the folded RNA
structure. In such a representation, planar diagrams correspond to pure RNA
secondary structures and have zero genus, whereas non planar diagrams
correspond to pseudoknotted structures and have higher genus. We analyze real
RNA structures from the databases wwPDB and Pseudobase, and classify them
according to their topological genus. We compare the results of our statistical
survey with existing theoretical and numerical models. We also discuss possible
applications of this classification and show how it can be used for identifying
new RNA structural motifs.
| [
{
"created": "Fri, 21 Jul 2006 14:26:22 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bon",
"Michael",
""
],
[
"Vernizzi",
"Graziano",
""
],
[
"Orland",
"Henri",
""
],
[
"Zee",
"A.",
""
]
] | We present a novel topological classification of RNA secondary structures with pseudoknots. It is based on the topological genus of the circular diagram associated to the RNA base-pair structure. The genus is a positive integer number, whose value quantifies the topological complexity of the folded RNA structure. In such a representation, planar diagrams correspond to pure RNA secondary structures and have zero genus, whereas non planar diagrams correspond to pseudoknotted structures and have higher genus. We analyze real RNA structures from the databases wwPDB and Pseudobase, and classify them according to their topological genus. We compare the results of our statistical survey with existing theoretical and numerical models. We also discuss possible applications of this classification and show how it can be used for identifying new RNA structural motifs. |
2012.11889 | Jiancheng Xu | Dongyang Xing, Suyan Tian, Yukun Chen, Jinmei Wang, Xuejuan Sun,
Shanji Li, Jiancheng Xu | Establishment of a diagnostic model to distinguish coronavirus disease
2019 from influenza A based on laboratory findings | 26 pages,3 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Coronavirus disease 2019 (COVID-19) and Influenza A are common
disease caused by viral infection. The clinical symptoms and transmission
routes of the two diseases are similar. However, there are no relevant studies
on laboratory diagnostic models to discriminate COVID-19 and influenza A. This
study aims at establishing a signature of laboratory findings to tell patients
with COVID-19 apart from those with influenza A perfectly. Materials: In this
study, 56 COVID-19 patients and 54 influenza A patients were included.
Laboratory findings, epidemiological characteristics and demographic data were
obtained from electronic medical record databases. Elastic network models,
followed by a stepwise logistic regression model were implemented to identify
indicators capable of discriminating COVID-19 and influenza A. A nomogram is
diagramed to show the resulting discriminative model. Results: The majority of
hematological and biochemical parameters in COVID-19 patients were
significantly different from those in influenza A patients. In the final model,
albumin/globulin (A/G), total bilirubin (TBIL) and erythrocyte specific volume
(HCT) were selected as predictors. Using an external dataset, the model was
validated to perform well. Conclusion: A diagnostic model of laboratory
findings was established, in which A/G, TBIL and HCT were included as highly
relevant indicators for the segmentation of COVID-19 and influenza A, providing
a complimentary means for the precise diagnosis of these two diseases.
| [
{
"created": "Tue, 22 Dec 2020 09:13:08 GMT",
"version": "v1"
}
] | 2020-12-23 | [
[
"Xing",
"Dongyang",
""
],
[
"Tian",
"Suyan",
""
],
[
"Chen",
"Yukun",
""
],
[
"Wang",
"Jinmei",
""
],
[
"Sun",
"Xuejuan",
""
],
[
"Li",
"Shanji",
""
],
[
"Xu",
"Jiancheng",
""
]
] | Background: Coronavirus disease 2019 (COVID-19) and Influenza A are common disease caused by viral infection. The clinical symptoms and transmission routes of the two diseases are similar. However, there are no relevant studies on laboratory diagnostic models to discriminate COVID-19 and influenza A. This study aims at establishing a signature of laboratory findings to tell patients with COVID-19 apart from those with influenza A perfectly. Materials: In this study, 56 COVID-19 patients and 54 influenza A patients were included. Laboratory findings, epidemiological characteristics and demographic data were obtained from electronic medical record databases. Elastic network models, followed by a stepwise logistic regression model were implemented to identify indicators capable of discriminating COVID-19 and influenza A. A nomogram is diagramed to show the resulting discriminative model. Results: The majority of hematological and biochemical parameters in COVID-19 patients were significantly different from those in influenza A patients. In the final model, albumin/globulin (A/G), total bilirubin (TBIL) and erythrocyte specific volume (HCT) were selected as predictors. Using an external dataset, the model was validated to perform well. Conclusion: A diagnostic model of laboratory findings was established, in which A/G, TBIL and HCT were included as highly relevant indicators for the segmentation of COVID-19 and influenza A, providing a complimentary means for the precise diagnosis of these two diseases. |
2210.06099 | Priya Chakraborty | Priya Chakraborty, Ushasi Roy, Mohit K. Jolly, Sayantari Ghosh | Spatio-temporal Pattern Formation due to Host-Circuit Interplay in Gene
Expression Dynamics | 33 pages, 11 figures | null | 10.1016/j.chaos.2022.112995 | null | q-bio.QM nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological systems are majorly dependent on their property of bistability in
order to exhibit nongenetic heterogeneity in terms of cellular morphology and
physiology. Spatial patterns of phenotypically heterogeneous cells, arising due
to underlying bistability, may play significant role in phenomena like biofilm
development, adaptation, cell motility etc. While nonlinear positive feedback
regulation, like cooperative heterodimer formation are the usual reason behind
bistability, similar dynamics can also occur as a consequence of host-circuit
interaction. In this paper, we have investigated the pattern formation by a
motif with non-cooperative positive feedback, that imposes a metabolic burden
on its host due to its expression. In a cellular array set inside diffusible
environment, we investigate spatio-temporal diffusion in one dimension as well
as in two dimension in the context of various initial conditions respectively.
Moreover, the number of cells exhibiting the same steady state, as well as
their spatial distribution has been quantified in terms of connected component
analysis. The effect of diffusion coefficient variation has been studied in
terms of stability of related states and time evolution of patterns.
| [
{
"created": "Wed, 12 Oct 2022 11:23:30 GMT",
"version": "v1"
}
] | 2023-01-25 | [
[
"Chakraborty",
"Priya",
""
],
[
"Roy",
"Ushasi",
""
],
[
"Jolly",
"Mohit K.",
""
],
[
"Ghosh",
"Sayantari",
""
]
] | Biological systems are majorly dependent on their property of bistability in order to exhibit nongenetic heterogeneity in terms of cellular morphology and physiology. Spatial patterns of phenotypically heterogeneous cells, arising due to underlying bistability, may play significant role in phenomena like biofilm development, adaptation, cell motility etc. While nonlinear positive feedback regulation, like cooperative heterodimer formation are the usual reason behind bistability, similar dynamics can also occur as a consequence of host-circuit interaction. In this paper, we have investigated the pattern formation by a motif with non-cooperative positive feedback, that imposes a metabolic burden on its host due to its expression. In a cellular array set inside diffusible environment, we investigate spatio-temporal diffusion in one dimension as well as in two dimension in the context of various initial conditions respectively. Moreover, the number of cells exhibiting the same steady state, as well as their spatial distribution has been quantified in terms of connected component analysis. The effect of diffusion coefficient variation has been studied in terms of stability of related states and time evolution of patterns. |
1910.10002 | Eric Sun | Eric D. Sun, Thomas C.T. Michaels, L. Mahadevan | Optimal control of aging in complex networks | null | null | 10.1073/pnas.2006375117 | null | q-bio.PE cond-mat.stat-mech physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many complex systems experience damage accumulation which leads to aging,
manifest as an increasing probability of system collapse with time. This
naturally raises the question of how to maximize health and longevity in an
aging system at minimal cost of maintenance and intervention. Here, we pose
this question in the context of a simple interdependent network model of aging
in complex systems, and use both optimal control theory and reinforcement
learning alongside a combination of analysis and simulation to determine
optimal maintenance protocols. These protocols may motivate the rational design
of strategies for promoting longevity in aging complex systems with potential
applications in therapeutic schedules and engineered system maintenance.
| [
{
"created": "Tue, 22 Oct 2019 14:22:06 GMT",
"version": "v1"
}
] | 2020-12-02 | [
[
"Sun",
"Eric D.",
""
],
[
"Michaels",
"Thomas C. T.",
""
],
[
"Mahadevan",
"L.",
""
]
] | Many complex systems experience damage accumulation which leads to aging, manifest as an increasing probability of system collapse with time. This naturally raises the question of how to maximize health and longevity in an aging system at minimal cost of maintenance and intervention. Here, we pose this question in the context of a simple interdependent network model of aging in complex systems, and use both optimal control theory and reinforcement learning alongside a combination of analysis and simulation to determine optimal maintenance protocols. These protocols may motivate the rational design of strategies for promoting longevity in aging complex systems with potential applications in therapeutic schedules and engineered system maintenance. |
1008.2591 | Thierry Rabilloud | Thierry Rabilloud (BBSI), Mireille Chevallet (BBSI), Sylvie Luche
(BBSI), C\'ecile Lelong (BBSI) | Two-dimensional gel electrophoresis in proteomics: past, present and
future | null | Journal of proteomics (2010) epub ahead of print | 10.1016/j.jprot.2010.05.016 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two-dimensional gel electrophoresis has been instrumental in the birth and
developments of proteomics, although it is no longer the exclusive separation
tool used in the field of proteomics. In this review, a historical perspective
is made, starting from the days where two-dimensional gels were used and the
word proteomics did not even exist. The events that have led to the birth of
proteomics are also recalled, ending with a description of the now well-known
limitations of two-dimensional gels in proteomics. However, the
often-underestimated advantages of two-dimensional gels are also underlined,
leading to a description of how and when to use two-dimensional gels for the
best in a proteomics approach. Taking support of these advantages (robustness,
resolution, and ability to separate entire, intact proteins), possible future
applications of this technique in proteomics are also mentioned.
| [
{
"created": "Mon, 16 Aug 2010 08:16:47 GMT",
"version": "v1"
}
] | 2010-08-17 | [
[
"Rabilloud",
"Thierry",
"",
"BBSI"
],
[
"Chevallet",
"Mireille",
"",
"BBSI"
],
[
"Luche",
"Sylvie",
"",
"BBSI"
],
[
"Lelong",
"Cécile",
"",
"BBSI"
]
] | Two-dimensional gel electrophoresis has been instrumental in the birth and developments of proteomics, although it is no longer the exclusive separation tool used in the field of proteomics. In this review, a historical perspective is made, starting from the days where two-dimensional gels were used and the word proteomics did not even exist. The events that have led to the birth of proteomics are also recalled, ending with a description of the now well-known limitations of two-dimensional gels in proteomics. However, the often-underestimated advantages of two-dimensional gels are also underlined, leading to a description of how and when to use two-dimensional gels for the best in a proteomics approach. Taking support of these advantages (robustness, resolution, and ability to separate entire, intact proteins), possible future applications of this technique in proteomics are also mentioned. |
q-bio/0703057 | Jacek Miekisz | Jacek Miekisz and Tadeusz Platkowski | Population dynamics with a stable efficient equilibrium | 12 pages | J. Theor. Biol. 237: 363 - 368 (2005) | null | null | q-bio.PE | null | We propose a game-theoretic dynamics of a population of replicating
individuals. It consists of two parts: the standard replicator one and a
migration between two different habitats. We consider symmetric two-player
games with two evolutionarily stable strategies: the efficient one in which the
population is in a state with a maximal payoff and the risk-dominant one where
players are averse to risk. We show that for a large range of parameters of our
dynamics, even if the initial conditions in both habitats are in the basin of
attraction of the risk-dominant equilibrium (with respect to the standard
replication dynamics without migration), in the long run most individuals play
the efficient strategy.
| [
{
"created": "Tue, 27 Mar 2007 10:12:51 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Miekisz",
"Jacek",
""
],
[
"Platkowski",
"Tadeusz",
""
]
] | We propose a game-theoretic dynamics of a population of replicating individuals. It consists of two parts: the standard replicator one and a migration between two different habitats. We consider symmetric two-player games with two evolutionarily stable strategies: the efficient one in which the population is in a state with a maximal payoff and the risk-dominant one where players are averse to risk. We show that for a large range of parameters of our dynamics, even if the initial conditions in both habitats are in the basin of attraction of the risk-dominant equilibrium (with respect to the standard replication dynamics without migration), in the long run most individuals play the efficient strategy. |
1801.08626 | Yunda Huang | Lily Zhang, Peter B. Gilbert, Edmund Capparelli, Yunda Huang | Pharmacokinetics Simulations for Studying Correlates of Prevention
Efficacy of Passive HIV-1 Antibody Prophylaxis in the Antibody Mediated
Prevention (AMP) Study | null | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A key objective in two phase 2b AMP clinical trials of VRC01 is to evaluate
whether drug concentration over time, as estimated by non-linear mixed effects
pharmacokinetics (PK) models, is associated with HIV infection rate. We
conducted a simulation study of marker sampling designs, and evaluated the
effect of study adherence and sub-cohort sample size on PK model estimates in
multiple-dose studies. With m=120, even under low adherence (about half of
study visits missing per participant), reasonably unbiased and consistent
estimates of most fixed and random effect terms were obtained. Coarsened marker
sampling schedules were also studied.
| [
{
"created": "Thu, 25 Jan 2018 22:43:57 GMT",
"version": "v1"
}
] | 2018-01-29 | [
[
"Zhang",
"Lily",
""
],
[
"Gilbert",
"Peter B.",
""
],
[
"Capparelli",
"Edmund",
""
],
[
"Huang",
"Yunda",
""
]
] | A key objective in two phase 2b AMP clinical trials of VRC01 is to evaluate whether drug concentration over time, as estimated by non-linear mixed effects pharmacokinetics (PK) models, is associated with HIV infection rate. We conducted a simulation study of marker sampling designs, and evaluated the effect of study adherence and sub-cohort sample size on PK model estimates in multiple-dose studies. With m=120, even under low adherence (about half of study visits missing per participant), reasonably unbiased and consistent estimates of most fixed and random effect terms were obtained. Coarsened marker sampling schedules were also studied. |
2103.04636 | Fares Al-Shargie | Fares Al-Shargie | Prefrontal cortex functional connectivity based on simultaneous record
of electrical and hemodynamic responses associated with mental stress | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | This paper investigates prefrontal cortex (PFC) functional connectivity based
on synchronized electrical and hemodynamic responses associated with mental
stress. The electrical response was based on alpha rhythmic of
Electroencephalography (EEG) signals and the hemodynamic responses were based
on the mean concentrations of oxygenated and deoxygenated hemoglobin measured
using functional Near-Infrared Spectroscopy (fNIRS). The aim is to explore the
effects of stress on the inter and intra hemispheric PFC functional
connectivity at narrow and wide frequency bands with 8- 13 Hz in EEG and
0.009-0.1Hz in fNIRS signals. The results demonstrated significantly reduce in
the functional connectivity on the dorsolateral PFC within the inter and intra
hemispheric PFC areas based in EEG alpha rhythmic and fNIRS oxygenated and
deoxygenated hemoglobin. The statistical analysis further demonstrated right
dorsolateral dominant to mental stress.
| [
{
"created": "Mon, 8 Mar 2021 09:48:03 GMT",
"version": "v1"
}
] | 2021-03-09 | [
[
"Al-Shargie",
"Fares",
""
]
] | This paper investigates prefrontal cortex (PFC) functional connectivity based on synchronized electrical and hemodynamic responses associated with mental stress. The electrical response was based on alpha rhythmic of Electroencephalography (EEG) signals and the hemodynamic responses were based on the mean concentrations of oxygenated and deoxygenated hemoglobin measured using functional Near-Infrared Spectroscopy (fNIRS). The aim is to explore the effects of stress on the inter and intra hemispheric PFC functional connectivity at narrow and wide frequency bands with 8- 13 Hz in EEG and 0.009-0.1Hz in fNIRS signals. The results demonstrated significantly reduce in the functional connectivity on the dorsolateral PFC within the inter and intra hemispheric PFC areas based in EEG alpha rhythmic and fNIRS oxygenated and deoxygenated hemoglobin. The statistical analysis further demonstrated right dorsolateral dominant to mental stress. |
0904.0376 | Philip von Doetinchem | S. Reibe, Ph. von Doetinchem, B. Madea | A new simulation-based model for calculating post-mortem intervals using
developmental data for Lucilia sericata (Dipt.: Calliphoridae) | 14 pages, 5 figures, 1 table | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Homicide investigations often depend on the determination of a minimum
post-mortem interval (PMI$_{min}$) by forensic entomologists. The age of the
most developed insect larvae (mostly blow fly larvae) gives reasonably reliable
information about the minimum time a person has been dead. Methods such as
isomegalen diagrams or ADH calculations can have problems in their reliability,
so we established in this study a new growth model to calculate the larval age
of \textit{Lucilia sericata} (Meigen 1826). This is based on the actual
non-linear development of the blow fly and is designed to include
uncertainties, e.g. for temperature values from the crime scene. We used
published data for the development of \textit{L. sericata} to estimate
non-linear functions describing the temperature dependent behavior of each
developmental state. For the new model it is most important to determine the
progress within one developmental state as correctly as possible since this
affects the accuracy of the PMI estimation by up to 75%. We found that PMI
calculations based on one mean temperature value differ by up to 65% from PMIs
based on an 12-hourly time temperature profile. Differences of 2\degree C in
the estimation of the crime scene temperature result in a deviation in PMI
calculation of 15 - 30%.
| [
{
"created": "Thu, 2 Apr 2009 12:49:19 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Sep 2009 08:58:01 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Mar 2010 18:46:26 GMT",
"version": "v3"
}
] | 2010-03-31 | [
[
"Reibe",
"S.",
""
],
[
"von Doetinchem",
"Ph.",
""
],
[
"Madea",
"B.",
""
]
] | Homicide investigations often depend on the determination of a minimum post-mortem interval (PMI$_{min}$) by forensic entomologists. The age of the most developed insect larvae (mostly blow fly larvae) gives reasonably reliable information about the minimum time a person has been dead. Methods such as isomegalen diagrams or ADH calculations can have problems in their reliability, so we established in this study a new growth model to calculate the larval age of \textit{Lucilia sericata} (Meigen 1826). This is based on the actual non-linear development of the blow fly and is designed to include uncertainties, e.g. for temperature values from the crime scene. We used published data for the development of \textit{L. sericata} to estimate non-linear functions describing the temperature dependent behavior of each developmental state. For the new model it is most important to determine the progress within one developmental state as correctly as possible since this affects the accuracy of the PMI estimation by up to 75%. We found that PMI calculations based on one mean temperature value differ by up to 65% from PMIs based on an 12-hourly time temperature profile. Differences of 2\degree C in the estimation of the crime scene temperature result in a deviation in PMI calculation of 15 - 30%. |
1603.08397 | Augusto Gonzalez | Augusto Gonzalez | Estimating the number of tissue resident macrophages | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I provide a simple estimation for the number of macrophages in a tissue,
arising from the hypothesis that they should keep infections below a certain
threshold, above which neutrophils are recruited from blood circulation. The
estimation reads Nm=a Ncel^{\alpha}/Nmax, where a is a numerical coefficient,
the exponent {\alpha} is near 2/3, and Nmax is the maximal number of pathogens
a macrophage may engulf in the time interval, tr, between pathogen
replications.
| [
{
"created": "Mon, 28 Mar 2016 15:04:22 GMT",
"version": "v1"
}
] | 2016-03-29 | [
[
"Gonzalez",
"Augusto",
""
]
] | I provide a simple estimation for the number of macrophages in a tissue, arising from the hypothesis that they should keep infections below a certain threshold, above which neutrophils are recruited from blood circulation. The estimation reads Nm=a Ncel^{\alpha}/Nmax, where a is a numerical coefficient, the exponent {\alpha} is near 2/3, and Nmax is the maximal number of pathogens a macrophage may engulf in the time interval, tr, between pathogen replications. |
q-bio/0606037 | Richard A. Blythe | R A Blythe | The propagation of a cultural or biological trait by neutral genetic
drift in a subdivided population | 17 pages, 8 figures, requires elsart5p.cls; substantially revised and
improved version; accepted for publication in Theoretical Population Biology | Theoretical Population Biology (2007) 71 454 | 10.1016/j.tpb.2007.01.006 | null | q-bio.PE | null | We study fixation probabilities and times as a consequence of neutral genetic
drift in subdivided populations, motivated by a model of the cultural
evolutionary process of language change that is described by the same
mathematics as the biological process. We focus on the growth of fixation times
with the number of subpopulations, and variation of fixation probabilities and
times with initial distributions of mutants. A general formula for the fixation
probability for arbitrary initial condition is derived by extending a duality
relation between forwards- and backwards-time properties of the model from a
panmictic to a subdivided population. From this we obtain new formulae,
formally exact in the limit of extremely weak migration, for the mean fixation
time from an arbitrary initial condition for Wright's island model, presenting
two cases as examples. For more general models of population subdivision,
formulae are introduced for an arbitrary number of mutants that are randomly
located, and a single mutant whose position is known. These formulae contain
parameters that typically have to be obtained numerically, a procedure we
follow for two contrasting clustered models. These data suggest that variation
of fixation time with the initial condition is slight, but depends strongly on
the nature of subdivision. In particular, we demonstrate conditions under which
the fixation time remains finite even in the limit of an infinite number of
demes. In many cases - except this last where fixation in a finite time is seen
- the time to fixation is shown to be in precise agreement with predictions
from formulae for the asymptotic effective population size.
| [
{
"created": "Mon, 26 Jun 2006 16:50:11 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Jan 2007 09:49:39 GMT",
"version": "v2"
}
] | 2015-05-26 | [
[
"Blythe",
"R A",
""
]
] | We study fixation probabilities and times as a consequence of neutral genetic drift in subdivided populations, motivated by a model of the cultural evolutionary process of language change that is described by the same mathematics as the biological process. We focus on the growth of fixation times with the number of subpopulations, and variation of fixation probabilities and times with initial distributions of mutants. A general formula for the fixation probability for arbitrary initial condition is derived by extending a duality relation between forwards- and backwards-time properties of the model from a panmictic to a subdivided population. From this we obtain new formulae, formally exact in the limit of extremely weak migration, for the mean fixation time from an arbitrary initial condition for Wright's island model, presenting two cases as examples. For more general models of population subdivision, formulae are introduced for an arbitrary number of mutants that are randomly located, and a single mutant whose position is known. These formulae contain parameters that typically have to be obtained numerically, a procedure we follow for two contrasting clustered models. These data suggest that variation of fixation time with the initial condition is slight, but depends strongly on the nature of subdivision. In particular, we demonstrate conditions under which the fixation time remains finite even in the limit of an infinite number of demes. In many cases - except this last where fixation in a finite time is seen - the time to fixation is shown to be in precise agreement with predictions from formulae for the asymptotic effective population size. |
1511.07768 | Yang-Yu Liu | Arunachalam Vinayagam, Travis E. Gibson, Ho-Joon Lee, Bahar Yilmazel,
Charles Roesel, Yanhui Hu, Young Kwon, Amitabh Sharma, Yang-Yu Liu, Norbert
Perrimon and Albert-L\'aszl\'o Barab\'asi | Controllability analysis of the directed human protein interaction
network identifies disease genes and drug targets | 31 pages, 4 figures | null | 10.1073/pnas.1603992113 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The protein-protein interaction (PPI) network is crucial for cellular
information processing and decision-making. With suitable inputs, PPI networks
drive the cells to diverse functional outcomes such as cell proliferation or
cell death. Here we characterize the structural controllability of a large
directed human PPI network comprised of 6,339 proteins and 34,813 interactions.
This allows us to classify proteins as "indispensable", "neutral" or
"dispensable", which correlates to increasing, no effect, or decreasing the
number of driver nodes in the network upon removal of that protein. We find
that 21% of the proteins in the PPI network are indispensable. Interestingly,
these indispensable proteins are the primary targets of disease-causing
mutations, human viruses, and drugs, suggesting that altering a network's
control property is critical for the transition between healthy and disease
states. Furthermore, analyzing copy number alterations data from 1,547 cancer
patients reveals that 56 genes that are frequently amplified or deleted in nine
different cancers are indispensable. Among the 56 genes, 46 of them have not
been previously associated with cancer. This suggests that controllability
analysis is very useful in identifying novel disease genes and potential drug
targets.
| [
{
"created": "Tue, 24 Nov 2015 15:55:33 GMT",
"version": "v1"
}
] | 2016-09-28 | [
[
"Vinayagam",
"Arunachalam",
""
],
[
"Gibson",
"Travis E.",
""
],
[
"Lee",
"Ho-Joon",
""
],
[
"Yilmazel",
"Bahar",
""
],
[
"Roesel",
"Charles",
""
],
[
"Hu",
"Yanhui",
""
],
[
"Kwon",
"Young",
""
],
[
"Sharma",
"Amitabh",
""
],
[
"Liu",
"Yang-Yu",
""
],
[
"Perrimon",
"Norbert",
""
],
[
"Barabási",
"Albert-László",
""
]
] | The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here we characterize the structural controllability of a large directed human PPI network comprised of 6,339 proteins and 34,813 interactions. This allows us to classify proteins as "indispensable", "neutral" or "dispensable", which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets. |
1206.0616 | Daniel Gamermann Dr. | R. Reyes, D. Gamermann, A. Montagud, D. Fuente, J. Triana, J. F.
Urchueg\'ia, P. Fern\'andez de C\'ordoba | Automation on the generation of genome scale metabolic models | 24 pages, 2 figures, 2 tables | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Nowadays, the reconstruction of genome scale metabolic models is
a non-automatized and interactive process based on decision taking. This
lengthy process usually requires a full year of one person's work in order to
satisfactory collect, analyze and validate the list of all metabolic reactions
present in a specific organism. In order to write this list, one manually has
to go through a huge amount of genomic, metabolomic and physiological
information. Currently, there is no optimal algorithm that allows one to
automatically go through all this information and generate the models taking
into account probabilistic criteria of unicity and completeness that a
biologist would consider. Results: This work presents the automation of a
methodology for the reconstruction of genome scale metabolic models for any
organism. The methodology that follows is the automatized version of the steps
implemented manually for the reconstruction of the genome scale metabolic model
of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for
the reconstruction are implemented in a computational platform (COPABI) that
generates the models from the probabilistic algorithms that have been
developed. Conclusions: For validation of the developed algorithm robustness,
the metabolic models of several organisms generated by the platform have been
studied together with published models that have been manually curated. Network
properties of the models like connectivity and average shortest mean path of
the different models have been compared and analyzed.
| [
{
"created": "Mon, 4 Jun 2012 13:41:38 GMT",
"version": "v1"
}
] | 2012-06-05 | [
[
"Reyes",
"R.",
""
],
[
"Gamermann",
"D.",
""
],
[
"Montagud",
"A.",
""
],
[
"Fuente",
"D.",
""
],
[
"Triana",
"J.",
""
],
[
"Urchuegía",
"J. F.",
""
],
[
"de Córdoba",
"P. Fernández",
""
]
] | Background: Nowadays, the reconstruction of genome scale metabolic models is a non-automatized and interactive process based on decision taking. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. Results: This work presents the automation of a methodology for the reconstruction of genome scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome scale metabolic model of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. Conclusions: For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models like connectivity and average shortest mean path of the different models have been compared and analyzed. |
1410.7350 | Logan Brooks | Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani,
Roni Rosenfeld | Flexible Modeling of Epidemics with an Empirical Bayes Framework | 52 pages | null | 10.1371/journal.pcbi.1004382 | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Seasonal influenza epidemics cause consistent, considerable, widespread loss
annually in terms of economic burden, morbidity, and mortality. With access to
accurate and reliable forecasts of a current or upcoming influenza epidemic's
behavior, policy makers can design and implement more effective
countermeasures. We developed a framework for in-season forecasts of epidemics
using a semiparametric Empirical Bayes framework, and applied it to predict the
weekly percentage of outpatient doctors visits for influenza-like illness, as
well as the season onset, duration, peak time, and peak height, with and
without additional data from Google Flu Trends, as part of the CDC's 2013--2014
"Predict the Influenza Season Challenge". Previous work on epidemic modeling
has focused on developing mechanistic models of disease behavior and applying
time series tools to explain historical data. However, these models may not
accurately capture the range of possible behaviors that we may see in the
future. Our approach instead produces possibilities for the epidemic curve of
the season of interest using modified versions of data from previous seasons,
allowing for reasonable variations in the timing, pace, and intensity of the
seasonal epidemics, as well as noise in observations. Since the framework does
not make strict domain-specific assumptions, it can easily be applied to other
diseases as well. Another important advantage of this method is that it
produces a complete posterior distribution for any desired forecasting target,
rather than mere point predictions. We report prospective
influenza-like-illness forecasts that were made for the 2013--2014 U.S.
influenza season, and compare the framework's cross-validated prediction error
on historical data to that of a variety of simpler baseline predictors.
| [
{
"created": "Mon, 27 Oct 2014 18:41:42 GMT",
"version": "v1"
}
] | 2016-02-17 | [
[
"Brooks",
"Logan C.",
""
],
[
"Farrow",
"David C.",
""
],
[
"Hyun",
"Sangwon",
""
],
[
"Tibshirani",
"Ryan J.",
""
],
[
"Rosenfeld",
"Roni",
""
]
] | Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic's behavior, policy makers can design and implement more effective countermeasures. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, as well as the season onset, duration, peak time, and peak height, with and without additional data from Google Flu Trends, as part of the CDC's 2013--2014 "Predict the Influenza Season Challenge". Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, these models may not accurately capture the range of possible behaviors that we may see in the future. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to other diseases as well. Another important advantage of this method is that it produces a complete posterior distribution for any desired forecasting target, rather than mere point predictions. We report prospective influenza-like-illness forecasts that were made for the 2013--2014 U.S. influenza season, and compare the framework's cross-validated prediction error on historical data to that of a variety of simpler baseline predictors. |
2008.09109 | Indrajit Ghosh | Indrajit Ghosh and Maia Martcheva | Modeling the effects of prosocial awareness on COVID-19 dynamics: A case
study on Colombia | null | null | 10.1007/s11071-021-06489-x | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | The ongoing COVID-19 pandemic has affected most of the countries on Earth. It
has become a pandemic outbreak with more than 24 million confirmed infections
and above 840 thousand deaths worldwide. In this study, we consider a
mathematical model on COVID-19 transmission with the prosocial awareness
effect. The proposed model can have four equilibrium states based on different
parametric conditions. The local and global stability conditions for awareness
free, disease-free equilibrium is studied. Using Lyapunov function theory and
LaSalle Invariance Principle, the disease-free equilibrium is shown globally
asymptotically stable under some parametric constraints. The existence of
unique awareness free, endemic equilibrium and unique endemic equilibrium is
presented. We calibrate our proposed model parameters to fit daily cases and
deaths from Colombia. Sensitivity analysis indicates that the transmission rate
and learning factor related to awareness of susceptibles are very crucial for
reduction in disease related deaths. Finally, we assess the impact of prosocial
awareness during the outbreak and compare this strategy with popular control
measures. Results indicate that prosocial awareness has competitive potential
to flatten the curve.
| [
{
"created": "Thu, 20 Aug 2020 17:53:00 GMT",
"version": "v1"
},
{
"created": "Sun, 30 Aug 2020 14:27:28 GMT",
"version": "v2"
}
] | 2021-05-21 | [
[
"Ghosh",
"Indrajit",
""
],
[
"Martcheva",
"Maia",
""
]
] | The ongoing COVID-19 pandemic has affected most of the countries on Earth. It has become a pandemic outbreak with more than 24 million confirmed infections and above 840 thousand deaths worldwide. In this study, we consider a mathematical model on COVID-19 transmission with the prosocial awareness effect. The proposed model can have four equilibrium states based on different parametric conditions. The local and global stability conditions for awareness free, disease-free equilibrium is studied. Using Lyapunov function theory and LaSalle Invariance Principle, the disease-free equilibrium is shown globally asymptotically stable under some parametric constraints. The existence of unique awareness free, endemic equilibrium and unique endemic equilibrium is presented. We calibrate our proposed model parameters to fit daily cases and deaths from Colombia. Sensitivity analysis indicates that the transmission rate and learning factor related to awareness of susceptibles are very crucial for reduction in disease related deaths. Finally, we assess the impact of prosocial awareness during the outbreak and compare this strategy with popular control measures. Results indicate that prosocial awareness has competitive potential to flatten the curve. |
1506.06581 | Ernest Montbrio | Ernest Montbri\'o, Diego Paz\'o, Alex Roxin | Macroscopic description for networks of spiking neurons | null | Phys. Rev. X 5, 021028 (2015) | 10.1103/PhysRevX.5.021028 | null | q-bio.NC cond-mat.dis-nn nlin.AO nlin.CD | http://creativecommons.org/licenses/by/3.0/ | A major goal of neuroscience, statistical physics and nonlinear dynamics is
to understand how brain function arises from the collective dynamics of
networks of spiking neurons. This challenge has been chiefly addressed through
large-scale numerical simulations. Alternatively, researchers have formulated
mean-field theories to gain insight into macroscopic states of large neuronal
networks in terms of the collective firing activity of the neurons, or the
firing rate. However, these theories have not succeeded in establishing an
exact correspondence between the firing rate of the network and the underlying
microscopic state of the spiking neurons. This has largely constrained the
range of applicability of such macroscopic descriptions, particularly when
trying to describe neuronal synchronization. Here we provide the derivation of
a set of exact macroscopic equations for a network of spiking neurons. Our
results reveal that the spike generation mechanism of individual neurons
introduces an effective coupling between two biophysically relevant macroscopic
quantities, the firing rate and the mean membrane potential, which together
govern the evolution of the neuronal network. The resulting equations exactly
describe all possible macroscopic dynamical states of the network, including
states of synchronous spiking activity. Finally we show that the firing rate
description is related, via a conformal map, with a low-dimensional description
in terms of the Kuramoto order parameter, called Ott-Antonsen theory. We
anticipate our results will be an important tool in investigating how large
networks of spiking neurons self-organize in time to process and encode
information in the brain.
| [
{
"created": "Mon, 22 Jun 2015 12:58:47 GMT",
"version": "v1"
}
] | 2015-06-23 | [
[
"Montbrió",
"Ernest",
""
],
[
"Pazó",
"Diego",
""
],
[
"Roxin",
"Alex",
""
]
] | A major goal of neuroscience, statistical physics and nonlinear dynamics is to understand how brain function arises from the collective dynamics of networks of spiking neurons. This challenge has been chiefly addressed through large-scale numerical simulations. Alternatively, researchers have formulated mean-field theories to gain insight into macroscopic states of large neuronal networks in terms of the collective firing activity of the neurons, or the firing rate. However, these theories have not succeeded in establishing an exact correspondence between the firing rate of the network and the underlying microscopic state of the spiking neurons. This has largely constrained the range of applicability of such macroscopic descriptions, particularly when trying to describe neuronal synchronization. Here we provide the derivation of a set of exact macroscopic equations for a network of spiking neurons. Our results reveal that the spike generation mechanism of individual neurons introduces an effective coupling between two biophysically relevant macroscopic quantities, the firing rate and the mean membrane potential, which together govern the evolution of the neuronal network. The resulting equations exactly describe all possible macroscopic dynamical states of the network, including states of synchronous spiking activity. Finally we show that the firing rate description is related, via a conformal map, with a low-dimensional description in terms of the Kuramoto order parameter, called Ott-Antonsen theory. We anticipate our results will be an important tool in investigating how large networks of spiking neurons self-organize in time to process and encode information in the brain. |
2401.04478 | Maximilian Schuh | Maximilian G. Schuh, Davide Boldini, Stephan A. Sieber | TwinBooster: Synergising Large Language Models with Barlow Twins and
Gradient Boosting for Enhanced Molecular Property Prediction | 13(+9) pages(+appendix), 5 figures, 11 tables | null | null | null | q-bio.BM cs.AI cs.CL cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | The success of drug discovery and development relies on the precise
prediction of molecular activities and properties. While in silico molecular
property prediction has shown remarkable potential, its use has been limited so
far to assays for which large amounts of data are available. In this study, we
use a fine-tuned large language model to integrate biological assays based on
their textual information, coupled with Barlow Twins, a Siamese neural network
using a novel self-supervised learning approach. This architecture uses both
assay information and molecular fingerprints to extract the true molecular
information. TwinBooster enables the prediction of properties of unseen
bioassays and molecules by providing state-of-the-art zero-shot learning tasks.
Remarkably, our artificial intelligence pipeline shows excellent performance on
the FS-Mol benchmark. This breakthrough demonstrates the application of deep
learning to critical property prediction tasks where data is typically scarce.
By accelerating the early identification of active molecules in drug discovery
and development, this method has the potential to help streamline the
identification of novel therapeutics.
| [
{
"created": "Tue, 9 Jan 2024 10:36:20 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Jan 2024 09:29:47 GMT",
"version": "v2"
}
] | 2024-01-31 | [
[
"Schuh",
"Maximilian G.",
""
],
[
"Boldini",
"Davide",
""
],
[
"Sieber",
"Stephan A.",
""
]
] | The success of drug discovery and development relies on the precise prediction of molecular activities and properties. While in silico molecular property prediction has shown remarkable potential, its use has been limited so far to assays for which large amounts of data are available. In this study, we use a fine-tuned large language model to integrate biological assays based on their textual information, coupled with Barlow Twins, a Siamese neural network using a novel self-supervised learning approach. This architecture uses both assay information and molecular fingerprints to extract the true molecular information. TwinBooster enables the prediction of properties of unseen bioassays and molecules by providing state-of-the-art zero-shot learning tasks. Remarkably, our artificial intelligence pipeline shows excellent performance on the FS-Mol benchmark. This breakthrough demonstrates the application of deep learning to critical property prediction tasks where data is typically scarce. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to help streamline the identification of novel therapeutics. |
1605.01039 | John Rhodes | Mark Layer and John A. Rhodes | Phylogenetic trees and Euclidean embeddings | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It was recently observed by de Vienne et al. that a simple square root
transformation of distances between taxa on a phylogenetic tree allowed for an
embedding of the taxa into Euclidean space. While the justification for this
was based on a diffusion model of continuous character evolution along the
tree, here we give a direct and elementary explanation for it that provides
substantial additional insight. We use this embedding to reinterpret the
differences between the NJ and BIONJ tree building algorithms, providing one
illustration of how this embedding reflects tree structures in data.
| [
{
"created": "Tue, 3 May 2016 19:33:45 GMT",
"version": "v1"
}
] | 2016-05-04 | [
[
"Layer",
"Mark",
""
],
[
"Rhodes",
"John A.",
""
]
] | It was recently observed by de Vienne et al. that a simple square root transformation of distances between taxa on a phylogenetic tree allowed for an embedding of the taxa into Euclidean space. While the justification for this was based on a diffusion model of continuous character evolution along the tree, here we give a direct and elementary explanation for it that provides substantial additional insight. We use this embedding to reinterpret the differences between the NJ and BIONJ tree building algorithms, providing one illustration of how this embedding reflects tree structures in data. |
1209.4469 | Martin Hediger | Martin R. Hediger, Luca De Vico, Julie B. Rannes, Christian J\"ackel,
Werner Besenmatter, Allan Svendsen, Jan H. Jensen | In silico screening of 393 mutants facilitates enzyme engineering of
amidase activity in CalB | null | null | 10.7717/peerj.145 | null | q-bio.BM physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our previously presented method for high throughput computational screening
of mutant activity (Hediger et al., arXiv:1203.2950) is benchmarked against
experimentally measured amidase activity for 22 mutants of Candida antarctica
lipase B (CalB). Using an appropriate cutoff criterion for the computed
barriers, the qualitative activity of 15 out of 22 mutants is correctly
predicted. The method identifies four of the six most active mutants with
>=3-fold wild type activity and seven out of the eight least active mutants
with <=0.5-fold wild type activity. The method is further used to screen all
sterically possible (386) double-, triple- and quadruple-mutants constructed
from the most active single mutants. Based on the benchmark test at least 20
new promising mutants are identified.
| [
{
"created": "Thu, 20 Sep 2012 09:21:16 GMT",
"version": "v1"
}
] | 2013-09-16 | [
[
"Hediger",
"Martin R.",
""
],
[
"De Vico",
"Luca",
""
],
[
"Rannes",
"Julie B.",
""
],
[
"Jäckel",
"Christian",
""
],
[
"Besenmatter",
"Werner",
""
],
[
"Svendsen",
"Allan",
""
],
[
"Jensen",
"Jan H.",
""
]
] | Our previously presented method for high throughput computational screening of mutant activity (Hediger et al., arXiv:1203.2950) is benchmarked against experimentally measured amidase activity for 22 mutants of Candida antarctica lipase B (CalB). Using an appropriate cutoff criterion for the computed barriers, the qualitative activity of 15 out of 22 mutants is correctly predicted. The method identifies four of the six most active mutants with >=3-fold wild type activity and seven out of the eight least active mutants with <=0.5-fold wild type activity. The method is further used to screen all sterically possible (386) double-, triple- and quadruple-mutants constructed from the most active single mutants. Based on the benchmark test at least 20 new promising mutants are identified. |
2403.16290 | Wayne Getz | Wayne M Getz | An Information Theory Treatment of Animal Movement Tracks | 21 pages, 2 tables, 1 figure | null | null | null | q-bio.PE cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | Position recordings of the two-dimensional tracks of animals moving over
landscapes has progressed over the past three decades from hourly to
second-by-second locations. Track segmentation methods for analyzing the
behavioral information in such relocation data has lagged somewhat behind, with
scales of analysis currently at the sub-hourly to minute level. A new approach
is needed to bring segmentation analysis down to a second-by-second level.
Here, a fine-scale approach is presented that rests heavily on concepts from
Shannon's Information Theory. In this paper, we first briefly review and update
concepts relating to movement path segmentation. We then discuss how cluster
analysis can be used to organize the smallest viable statistical movement
elements (StaMEs), which are $\mu$ steps long, and to code the next level of
movement elements called ``words'' that are $m \mu$ steps long. Centroids of
these word clusters are identified as canonical activity modes (CAMs). Unlike
current behavioral change point analysis and hidden Markov model segmentation
schemes, the approach presented here allows us to provide entropy measures for
movement paths, compute the coding efficiencies of derived StaMEs and CAMs, and
to assess error rates in the allocation of strings of $m$ StaMEs to CAM types.
In addition our approach allows us to employ the Jensen-Shannon divergence
measure to assess and compare the best choices for the various parameters
(number of steps in a StaME, number of StaME types, number of StaMEs in a word,
number of CAM types), as well as the best clustering methods for generating
segments that can then be used to interpret and predict sequences of higher
order segments. The theory presented here provides another tool in our toolbox
for dealing with the effects of global change on the movement and
redistribution of animals across altered landscapes.
| [
{
"created": "Sun, 24 Mar 2024 20:41:19 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Mar 2024 16:20:44 GMT",
"version": "v2"
},
{
"created": "Tue, 16 Apr 2024 17:52:34 GMT",
"version": "v3"
},
{
"created": "Fri, 31 May 2024 15:59:44 GMT",
"version": "v4"
}
] | 2024-06-03 | [
[
"Getz",
"Wayne M",
""
]
] | Position recordings of the two-dimensional tracks of animals moving over landscapes has progressed over the past three decades from hourly to second-by-second locations. Track segmentation methods for analyzing the behavioral information in such relocation data has lagged somewhat behind, with scales of analysis currently at the sub-hourly to minute level. A new approach is needed to bring segmentation analysis down to a second-by-second level. Here, a fine-scale approach is presented that rests heavily on concepts from Shannon's Information Theory. In this paper, we first briefly review and update concepts relating to movement path segmentation. We then discuss how cluster analysis can be used to organize the smallest viable statistical movement elements (StaMEs), which are $\mu$ steps long, and to code the next level of movement elements called ``words'' that are $m \mu$ steps long. Centroids of these word clusters are identified as canonical activity modes (CAMs). Unlike current behavioral change point analysis and hidden Markov model segmentation schemes, the approach presented here allows us to provide entropy measures for movement paths, compute the coding efficiencies of derived StaMEs and CAMs, and to assess error rates in the allocation of strings of $m$ StaMEs to CAM types. In addition our approach allows us to employ the Jensen-Shannon divergence measure to assess and compare the best choices for the various parameters (number of steps in a StaME, number of StaME types, number of StaMEs in a word, number of CAM types), as well as the best clustering methods for generating segments that can then be used to interpret and predict sequences of higher order segments. The theory presented here provides another tool in our toolbox for dealing with the effects of global change on the movement and redistribution of animals across altered landscapes. |
1209.6046 | Joshua Schraiber | Joshua G. Schraiber and Stephannie Shih and Montgomery Slatkin | Genomic tests of variation in inbreeding among individuals and among
chromosomes | 18 pages, 2 figures | Genetics December 1, 2012 vol. 192 no. 4 1477-1482 | 10.1534/genetics.112.145367 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the distribution of heterozygous sites in nine European and nine
Yoruban individuals whose genomic sequences were made publicly available by
Complete Genomics. We show that it is possible to obtain detailed information
about inbreeding when a relatively small set of whole-genome sequences is
available. Rather than focus on testing for deviations from Hardy-Weinberg
genotype frequencies at each site, we analyze the entire distribution of
heterozygotes conditioned on the number of copies of the derived
(non-chimpanzee) allele. Using Levene's exact test, we reject Hardy-Weinberg in
both populations. We generalized Levene's distribution to obtain the exact
distribution of the number of heterozygous individuals given that every
individual has the same inbreeding coefficient, F. We estimated F to be 0.0026
in Europeans and 0.0005 in Yorubans, but we could also reject the hypothesis
that F was the same in each individual. We used a composite likelihood method
to estimate F in each individual and within each chromosome. Variation in F
across chromosomes within individuals was too large to be consistent with
sampling effects alone. Furthermore, estimates of F for each chromosome in
different populations were not correlated. Our results show how detailed
comparisons of population genomic data can be made to theoretical predictions.
The application of methods to the Complete Genomics data set shows that the
extent of apparent inbreeding varies across chromosomes and across individuals,
and estimates of inbreeding coefficients are subject to unexpected levels of
variation which might be partly accounted for by selection.
| [
{
"created": "Wed, 26 Sep 2012 19:48:19 GMT",
"version": "v1"
}
] | 2012-12-14 | [
[
"Schraiber",
"Joshua G.",
""
],
[
"Shih",
"Stephannie",
""
],
[
"Slatkin",
"Montgomery",
""
]
] | We examine the distribution of heterozygous sites in nine European and nine Yoruban individuals whose genomic sequences were made publicly available by Complete Genomics. We show that it is possible to obtain detailed information about inbreeding when a relatively small set of whole-genome sequences is available. Rather than focus on testing for deviations from Hardy-Weinberg genotype frequencies at each site, we analyze the entire distribution of heterozygotes conditioned on the number of copies of the derived (non-chimpanzee) allele. Using Levene's exact test, we reject Hardy-Weinberg in both populations. We generalized Levene's distribution to obtain the exact distribution of the number of heterozygous individuals given that every individual has the same inbreeding coefficient, F. We estimated F to be 0.0026 in Europeans and 0.0005 in Yorubans, but we could also reject the hypothesis that F was the same in each individual. We used a composite likelihood method to estimate F in each individual and within each chromosome. Variation in F across chromosomes within individuals was too large to be consistent with sampling effects alone. Furthermore, estimates of F for each chromosome in different populations were not correlated. Our results show how detailed comparisons of population genomic data can be made to theoretical predictions. The application of methods to the Complete Genomics data set shows that the extent of apparent inbreeding varies across chromosomes and across individuals, and estimates of inbreeding coefficients are subject to unexpected levels of variation which might be partly accounted for by selection. |
1712.03346 | Sam Sinai | Sam Sinai, Eric Kelsic, George M. Church, Martin A. Nowak | Variational auto-encoding of protein sequences | Abstract for oral presentation at NIPS 2017 Workshop on Machine
Learning in Computational Biology | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proteins are responsible for the most diverse set of functions in biology.
The ability to extract information from protein sequences and to predict the
effects of mutations is extremely valuable in many domains of biology and
medicine. However the mapping between protein sequence and function is complex
and poorly understood. Here we present an embedding of natural protein
sequences using a Variational Auto-Encoder and use it to predict how mutations
affect protein function. We use this unsupervised approach to cluster natural
variants and learn interactions between sets of positions within a protein.
This approach generally performs better than baseline methods that consider no
interactions within sequences, and in some cases better than the
state-of-the-art approaches that use the inverse-Potts model. This generative
model can be used to computationally guide exploration of protein sequence
space and to better inform rational and automatic protein design.
| [
{
"created": "Sat, 9 Dec 2017 06:36:17 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Dec 2017 02:43:57 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Jan 2018 17:39:14 GMT",
"version": "v3"
}
] | 2018-01-04 | [
[
"Sinai",
"Sam",
""
],
[
"Kelsic",
"Eric",
""
],
[
"Church",
"George M.",
""
],
[
"Nowak",
"Martin A.",
""
]
] | Proteins are responsible for the most diverse set of functions in biology. The ability to extract information from protein sequences and to predict the effects of mutations is extremely valuable in many domains of biology and medicine. However the mapping between protein sequence and function is complex and poorly understood. Here we present an embedding of natural protein sequences using a Variational Auto-Encoder and use it to predict how mutations affect protein function. We use this unsupervised approach to cluster natural variants and learn interactions between sets of positions within a protein. This approach generally performs better than baseline methods that consider no interactions within sequences, and in some cases better than the state-of-the-art approaches that use the inverse-Potts model. This generative model can be used to computationally guide exploration of protein sequence space and to better inform rational and automatic protein design. |
2312.06159 | Miriam Shiffman | Miriam Shiffman, Ryan Giordano, Tamara Broderick | Could dropping a few cells change the takeaways from differential
expression? | null | null | null | null | q-bio.QM stat.CO stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Differential expression (DE) plays a fundamental role toward illuminating the
molecular mechanisms driving a difference between groups (e.g., due to
treatment or disease). While any analysis is run on particular cells/samples,
the intent is to generalize to future occurrences of the treatment or disease.
Implicitly, this step is justified by assuming that present and future samples
are independent and identically distributed from the same population. Though
this assumption is always false, we hope that any deviation from the assumption
is small enough that A) conclusions of the analysis still hold and B) standard
tools like standard error, significance, and power still reflect
generalizability. Conversely, we might worry about these deviations, and
reliance on standard tools, if conclusions could be substantively changed by
dropping a very small fraction of data. While checking every small fraction is
computationally intractable, recent work develops an approximation to identify
when such an influential subset exists. Building on this work, we develop a
metric for dropping-data robustness of DE; namely, we cast the analysis in a
form suitable to the approximation, extend the approximation to models with
data-dependent hyperparameters, and extend the notion of a data point from a
single cell to a pseudobulk observation. We then overcome the inherent
non-differentiability of gene set enrichment analysis to develop an additional
approximation for the robustness of top gene sets. We assess robustness of DE
for published single-cell RNA-seq data and discover that 1000s of genes can
have their results flipped by dropping <1% of the data, including 100s that are
sensitive to dropping a single cell (0.07%). Surprisingly, this non-robustness
extends to high-level takeaways; half of the top 10 gene sets can be changed by
dropping 1-2% of cells, and 2/10 can be changed by dropping a single cell.
| [
{
"created": "Mon, 11 Dec 2023 06:51:57 GMT",
"version": "v1"
}
] | 2023-12-12 | [
[
"Shiffman",
"Miriam",
""
],
[
"Giordano",
"Ryan",
""
],
[
"Broderick",
"Tamara",
""
]
] | Differential expression (DE) plays a fundamental role toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells/samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, this step is justified by assuming that present and future samples are independent and identically distributed from the same population. Though this assumption is always false, we hope that any deviation from the assumption is small enough that A) conclusions of the analysis still hold and B) standard tools like standard error, significance, and power still reflect generalizability. Conversely, we might worry about these deviations, and reliance on standard tools, if conclusions could be substantively changed by dropping a very small fraction of data. While checking every small fraction is computationally intractable, recent work develops an approximation to identify when such an influential subset exists. Building on this work, we develop a metric for dropping-data robustness of DE; namely, we cast the analysis in a form suitable to the approximation, extend the approximation to models with data-dependent hyperparameters, and extend the notion of a data point from a single cell to a pseudobulk observation. We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets. We assess robustness of DE for published single-cell RNA-seq data and discover that 1000s of genes can have their results flipped by dropping <1% of the data, including 100s that are sensitive to dropping a single cell (0.07%). Surprisingly, this non-robustness extends to high-level takeaways; half of the top 10 gene sets can be changed by dropping 1-2% of cells, and 2/10 can be changed by dropping a single cell. |
q-bio/0509044 | Ana Nunes | M. M. Telo da Gama and A. Nunes | Epidemics in small world networks | null | null | 10.1140/epjb/e2006-00099-7 | null | q-bio.PE | null | For many infectious diseases, a small-world network on an underlying regular
lattice is a suitable simplified model for the contact structure of the host
population. It is well known that the contact network, described in this
setting by a single parameter, the small-world parameter $p$, plays an
important role both in the short term and in the long term dynamics of epidemic
spread. We have studied the effect of the network structure on models of immune
for life diseases and found that in addition to the reduction of the effective
transmission rate, through the screening of infectives, spatial correlations
may strongly enhance the stochastic fluctuations. As a consequence, time series
of unforced Susceptible-Exposed-Infected-Recovered (SEIR) models provide
patterns of recurrent epidemics with realistic amplitudes, suggesting that
these models together with complex networks of contacts are the key ingredients
to describe the prevaccination dynamical patterns of diseases such as measles
and pertussis. We have also studied the role of the host contact strucuture in
pathogen antigenic variation, through its effect on the final outcome of an
invasion by a viral strain of a population where a very similar virus is
endemic. Similar viral strains are modelled by the same infection and
reinfection parameters, and by a given degree of cross immunity that represents
the antigenic distance between the competing strains. We have found, somewhat
surprisingly, that clustering on the network decreases the potential to sustain
pathogen diversity.
| [
{
"created": "Fri, 30 Sep 2005 17:35:17 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Oct 2005 22:41:36 GMT",
"version": "v2"
}
] | 2009-11-11 | [
[
"da Gama",
"M. M. Telo",
""
],
[
"Nunes",
"A.",
""
]
] | For many infectious diseases, a small-world network on an underlying regular lattice is a suitable simplified model for the contact structure of the host population. It is well known that the contact network, described in this setting by a single parameter, the small-world parameter $p$, plays an important role both in the short term and in the long term dynamics of epidemic spread. We have studied the effect of the network structure on models of immune for life diseases and found that in addition to the reduction of the effective transmission rate, through the screening of infectives, spatial correlations may strongly enhance the stochastic fluctuations. As a consequence, time series of unforced Susceptible-Exposed-Infected-Recovered (SEIR) models provide patterns of recurrent epidemics with realistic amplitudes, suggesting that these models together with complex networks of contacts are the key ingredients to describe the prevaccination dynamical patterns of diseases such as measles and pertussis. We have also studied the role of the host contact strucuture in pathogen antigenic variation, through its effect on the final outcome of an invasion by a viral strain of a population where a very similar virus is endemic. Similar viral strains are modelled by the same infection and reinfection parameters, and by a given degree of cross immunity that represents the antigenic distance between the competing strains. We have found, somewhat surprisingly, that clustering on the network decreases the potential to sustain pathogen diversity. |
q-bio/0503027 | Isaac Hubner | Isaac A. Hubner, Katherine A. Edmonds, and Eugene I. Shakhnovich | Nucleation and the transition state of the SH3 domain | In press at the Journal of Molecular Biology | null | null | null | q-bio.BM q-bio.OT | null | We present a verified computational model of the SH3 domain transition state
(TS) ensemble. This model was built for three separate SH3 domains using
experimental s in all-atom protein folding simulations. While averaging over
all conformations incorrectly considers non-TS conformations as transition
states, quantifying structures as pre-TS, TS, and post-TS by measurement of
their transmission coefficient (pfold, or probability to fold) allows for
rigorous conclusions regarding the structure of the folding nucleus and a full
mechanistic analysis of the folding process. Through analysis of the TS, we
observe a highly polarized nucleus in which many residues are solvent-exposed.
Mechanistic analysis suggests the hydrophobic core forms largely after an early
nucleation step. SH3 presents an ideal system for studying the
nucleation-condensation mechanism and highlights the synergistic relationship
between experiment and simulation in the study of protein folding.
| [
{
"created": "Thu, 17 Mar 2005 17:12:48 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Hubner",
"Isaac A.",
""
],
[
"Edmonds",
"Katherine A.",
""
],
[
"Shakhnovich",
"Eugene I.",
""
]
] | We present a verified computational model of the SH3 domain transition state (TS) ensemble. This model was built for three separate SH3 domains using experimental s in all-atom protein folding simulations. While averaging over all conformations incorrectly considers non-TS conformations as transition states, quantifying structures as pre-TS, TS, and post-TS by measurement of their transmission coefficient (pfold, or probability to fold) allows for rigorous conclusions regarding the structure of the folding nucleus and a full mechanistic analysis of the folding process. Through analysis of the TS, we observe a highly polarized nucleus in which many residues are solvent-exposed. Mechanistic analysis suggests the hydrophobic core forms largely after an early nucleation step. SH3 presents an ideal system for studying the nucleation-condensation mechanism and highlights the synergistic relationship between experiment and simulation in the study of protein folding. |
2304.02656 | Bingxin Zhou | Xinye Xiong, Bingxin Zhou, Yu Guang Wang | Graph Representation Learning for Interactive Biomolecule Systems | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Advances in deep learning models have revolutionized the study of biomolecule
systems and their mechanisms. Graph representation learning, in particular, is
important for accurately capturing the geometric information of biomolecules at
different levels. This paper presents a comprehensive review of the
methodologies used to represent biological molecules and systems as
computer-recognizable objects, such as sequences, graphs, and surfaces.
Moreover, it examines how geometric deep learning models, with an emphasis on
graph-based techniques, can analyze biomolecule data to enable drug discovery,
protein characterization, and biological system analysis. The study concludes
with an overview of the current state of the field, highlighting the challenges
that exist and the potential future research directions.
| [
{
"created": "Wed, 5 Apr 2023 08:00:50 GMT",
"version": "v1"
}
] | 2023-04-07 | [
[
"Xiong",
"Xinye",
""
],
[
"Zhou",
"Bingxin",
""
],
[
"Wang",
"Yu Guang",
""
]
] | Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms. Graph representation learning, in particular, is important for accurately capturing the geometric information of biomolecules at different levels. This paper presents a comprehensive review of the methodologies used to represent biological molecules and systems as computer-recognizable objects, such as sequences, graphs, and surfaces. Moreover, it examines how geometric deep learning models, with an emphasis on graph-based techniques, can analyze biomolecule data to enable drug discovery, protein characterization, and biological system analysis. The study concludes with an overview of the current state of the field, highlighting the challenges that exist and the potential future research directions. |
2005.00049 | Jean Dolbeault | Jean Dolbeault and Gabriel Turinici | Heterogeneous social interactions and the COVID-19 lockdown outcome in a
multi-group SEIR model | null | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study variants of the SEIR model for interpreting some qualitative
features of the statistics of the Covid-19 epidemic in France. Standard SEIR
models distinguish essentially two regimes: either the disease is controlled
and the number of infected people rapidly decreases, or the disease spreads and
contaminates a significant fraction of the population until herd immunity is
achieved. After lockdown, at first sight it seems that social distancing is not
enough to control the outbreak. We discuss here a possible explanation, namely
that the lockdown is creating social heterogeneity: even if a large majority of
the population complies with the lockdown rules, a small fraction of the
population still has to maintain a normal or high level of social interactions,
such as health workers, providers of essential services, etc. This results in
an apparent high level of epidemic propagation as measured through
re-estimations of the basic reproduction ratio. However, these measures are
limited to averages, while variance inside the population plays an essential
role on the peak and the size of the epidemic outbreak and tends to lower these
two indicators. We provide theoretical and numerical results to sustain such a
view.
| [
{
"created": "Thu, 30 Apr 2020 18:43:22 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Jun 2020 19:56:54 GMT",
"version": "v2"
}
] | 2020-06-25 | [
[
"Dolbeault",
"Jean",
""
],
[
"Turinici",
"Gabriel",
""
]
] | We study variants of the SEIR model for interpreting some qualitative features of the statistics of the Covid-19 epidemic in France. Standard SEIR models distinguish essentially two regimes: either the disease is controlled and the number of infected people rapidly decreases, or the disease spreads and contaminates a significant fraction of the population until herd immunity is achieved. After lockdown, at first sight it seems that social distancing is not enough to control the outbreak. We discuss here a possible explanation, namely that the lockdown is creating social heterogeneity: even if a large majority of the population complies with the lockdown rules, a small fraction of the population still has to maintain a normal or high level of social interactions, such as health workers, providers of essential services, etc. This results in an apparent high level of epidemic propagation as measured through re-estimations of the basic reproduction ratio. However, these measures are limited to averages, while variance inside the population plays an essential role on the peak and the size of the epidemic outbreak and tends to lower these two indicators. We provide theoretical and numerical results to sustain such a view. |
2404.05501 | Vivek Kumar Agarwal | Vivek Agarwal, Joshua Harvey, Dmitry Rinberg and Vasant Dhar | Data Science In Olfaction | 20 pages, 10 Figures, 2 Appendix, 1 Table | null | null | null | q-bio.NC cs.AI cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Advances in neural sensing technology are making it possible to observe the
olfactory process in great detail. In this paper, we conceptualize smell from a
Data Science and AI perspective, that relates the properties of odorants to how
they are sensed and analyzed in the olfactory system from the nose to the
brain. Drawing distinctions to color vision, we argue that smell presents
unique measurement challenges, including the complexity of stimuli, the high
dimensionality of the sensory apparatus, as well as what constitutes ground
truth. In the face of these challenges, we argue for the centrality of
odorant-receptor interactions in developing a theory of olfaction. Such a
theory is likely to find widespread industrial applications, and enhance our
understanding of smell, and in the longer-term, how it relates to other senses
and language. As an initial use case of the data, we present results using
machine learning-based classification of neural responses to odors as they are
recorded in the mouse olfactory bulb with calcium imaging.
| [
{
"created": "Mon, 8 Apr 2024 13:25:02 GMT",
"version": "v1"
}
] | 2024-04-09 | [
[
"Agarwal",
"Vivek",
""
],
[
"Harvey",
"Joshua",
""
],
[
"Rinberg",
"Dmitry",
""
],
[
"Dhar",
"Vasant",
""
]
] | Advances in neural sensing technology are making it possible to observe the olfactory process in great detail. In this paper, we conceptualize smell from a Data Science and AI perspective, that relates the properties of odorants to how they are sensed and analyzed in the olfactory system from the nose to the brain. Drawing distinctions to color vision, we argue that smell presents unique measurement challenges, including the complexity of stimuli, the high dimensionality of the sensory apparatus, as well as what constitutes ground truth. In the face of these challenges, we argue for the centrality of odorant-receptor interactions in developing a theory of olfaction. Such a theory is likely to find widespread industrial applications, and enhance our understanding of smell, and in the longer-term, how it relates to other senses and language. As an initial use case of the data, we present results using machine learning-based classification of neural responses to odors as they are recorded in the mouse olfactory bulb with calcium imaging. |
1306.2772 | Ellen Baake | Sandra Kluth, Ellen Baake | The Moran model with selection: Fixation probabilities, ancestral lines,
and an alternative particle representation | 21 pages, 8 figures | Theor. Pop. Biol. 90 (2013), 104-112 | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We reconsider the Moran model in continuous time with population size $N$,
two allelic types, and selection. We introduce a new particle representation,
which we call the labelled Moran model, and which has the same distribution of
type frequencies as the original Moran model, provided the initial values are
chosen appropriately. In the new model, individuals are labelled $1,2, \dots,
N$; neutral resampling events may take place between arbitrary labels, whereas
selective events only occur in the direction of increasing labels. With the
help of elementary methods only, we not only recover fixation probabilities,
but also obtain detailed insight into the number and nature of the selective
events that play a role in the fixation process forward in time.
| [
{
"created": "Wed, 12 Jun 2013 10:02:23 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Dec 2013 13:29:01 GMT",
"version": "v2"
}
] | 2013-12-09 | [
[
"Kluth",
"Sandra",
""
],
[
"Baake",
"Ellen",
""
]
] | We reconsider the Moran model in continuous time with population size $N$, two allelic types, and selection. We introduce a new particle representation, which we call the labelled Moran model, and which has the same distribution of type frequencies as the original Moran model, provided the initial values are chosen appropriately. In the new model, individuals are labelled $1,2, \dots, N$; neutral resampling events may take place between arbitrary labels, whereas selective events only occur in the direction of increasing labels. With the help of elementary methods only, we not only recover fixation probabilities, but also obtain detailed insight into the number and nature of the selective events that play a role in the fixation process forward in time. |
1904.10845 | Coralie Picoche | Coralie Picoche and Frederic Barraquand | How self-regulation, the storage effect and their interaction contribute
to coexistence in stochastic and seasonal environments | 27 pages, 9 figures, Theor Ecol (2019) | null | 10.1007/s12080-019-0420-9 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explaining coexistence in species-rich communities of primary producers
remains a challenge for ecologists because of their likely competition for
shared resources. Following Hutchinson's seminal suggestion, many theoreticians
have tried to create diversity through a fluctuating environment, which impairs
or slows down competitive exclusion. However, fluctuating-environment models
often only produce a dozen of coexisting species at best. Here, we investigate
how to create richer communities in fluctuating environments, using an
empirically parameterized model. Building on the forced Lotka-Volterra model of
Scranton and Vasseur (Theor Ecol 9(3):353-363, 2016), inspired by phytoplankton
communities, we have investigated the effect of two coexistence mechanisms,
namely the storage effect and higher intra- than interspecific competition
strengths (i.e., strong self-regulation). We tuned the intra/inter competition
ratio based on empirical analyses, in which self-regulation dominates
interspecific interactions. Although a strong self-regulation maintained more
species (50%) than the storage effect (25%), we show that none of the two
coexistence mechanisms considered could ensure the coexistence of all species
alone. Realistic seasonal environments only aggravated that picture, as they
decreased persistence relative to a random environment. However, strong
self-regulation and the storage effect combined superadditively so that all
species could persist with both mechanisms at work. Our results suggest that
combining different coexistence mechanisms into community models might be more
fruitful than trying to find which mechanism best explains diversity. We
additionally highlight that while biomass-trait distributions provide some
clues regarding coexistence mechanisms, they cannot indicate unequivocally
which mechanisms are at play.
| [
{
"created": "Wed, 24 Apr 2019 14:36:13 GMT",
"version": "v1"
}
] | 2019-04-25 | [
[
"Picoche",
"Coralie",
""
],
[
"Barraquand",
"Frederic",
""
]
] | Explaining coexistence in species-rich communities of primary producers remains a challenge for ecologists because of their likely competition for shared resources. Following Hutchinson's seminal suggestion, many theoreticians have tried to create diversity through a fluctuating environment, which impairs or slows down competitive exclusion. However, fluctuating-environment models often only produce a dozen of coexisting species at best. Here, we investigate how to create richer communities in fluctuating environments, using an empirically parameterized model. Building on the forced Lotka-Volterra model of Scranton and Vasseur (Theor Ecol 9(3):353-363, 2016), inspired by phytoplankton communities, we have investigated the effect of two coexistence mechanisms, namely the storage effect and higher intra- than interspecific competition strengths (i.e., strong self-regulation). We tuned the intra/inter competition ratio based on empirical analyses, in which self-regulation dominates interspecific interactions. Although a strong self-regulation maintained more species (50%) than the storage effect (25%), we show that none of the two coexistence mechanisms considered could ensure the coexistence of all species alone. Realistic seasonal environments only aggravated that picture, as they decreased persistence relative to a random environment. However, strong self-regulation and the storage effect combined superadditively so that all species could persist with both mechanisms at work. Our results suggest that combining different coexistence mechanisms into community models might be more fruitful than trying to find which mechanism best explains diversity. We additionally highlight that while biomass-trait distributions provide some clues regarding coexistence mechanisms, they cannot indicate unequivocally which mechanisms are at play. |
1610.04201 | Joseph Griffis | Joseph C. Griffis, Rodolphe Nenert, Jane B. Allendorfer, Jerzy P.
Szaflarski | Parallel ICA reveals linked patterns of structural damage and fMRI
language task activation in chronic post-stroke aphasia | 47 pages; 4 figures; 3 Tables; 3 Supplementary Figures; 2
Supplementary Tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Structural and functional MRI studies of patients with post-stroke language
deficits have contributed substantially to our understanding of how
cognitive-behavioral impairments relate to the location of structural damage
and to the activation of surviving brain regions during language processing,
respectively. However, very little is known about how inter-patient variability
in language task activation relates to variability in the structures affected
by stroke. Here, we used parallel independent component analysis (pICA) to
characterize links between patterns of structural damage and patterns of
functional MRI activation during semantic decisions. The pICA analysis revealed
a significant association between a lesion component featuring damage to left
posterior temporo-parietal cortex and the underlying deep white matter and an
fMRI component featuring (1) heightened activation in a primarily right
hemispheric network of frontal, temporal, and parietal regions, and (2) reduced
activation in areas associated with the semantic network activated by healthy
controls. Stronger loading parameters on both the lesion and fMRI activation
components were associated with poorer language test performance. Fiber
tracking suggests that lesions affecting the left posterior temporo-parietal
cortex and deep white matter may lead to the simultaneous disruption of
multiple long-range structural pathways connecting distal language areas.
Damage to the left posterior temporo-parietal cortex and underlying white
matter may (1) impede the language task-driven recruitment of canonical left
hemispheric language and other areas (e.g. the right anterior temporal lobe and
default mode regions) that likely support residual language function after
stroke, and (2) lead to the compensatory recruitment of right hemispheric
fronto-temporo-parietal networks for tasks requiring semantic processing.
| [
{
"created": "Thu, 13 Oct 2016 19:02:53 GMT",
"version": "v1"
}
] | 2016-10-14 | [
[
"Griffis",
"Joseph C.",
""
],
[
"Nenert",
"Rodolphe",
""
],
[
"Allendorfer",
"Jane B.",
""
],
[
"Szaflarski",
"Jerzy P.",
""
]
] | Structural and functional MRI studies of patients with post-stroke language deficits have contributed substantially to our understanding of how cognitive-behavioral impairments relate to the location of structural damage and to the activation of surviving brain regions during language processing, respectively. However, very little is known about how inter-patient variability in language task activation relates to variability in the structures affected by stroke. Here, we used parallel independent component analysis (pICA) to characterize links between patterns of structural damage and patterns of functional MRI activation during semantic decisions. The pICA analysis revealed a significant association between a lesion component featuring damage to left posterior temporo-parietal cortex and the underlying deep white matter and an fMRI component featuring (1) heightened activation in a primarily right hemispheric network of frontal, temporal, and parietal regions, and (2) reduced activation in areas associated with the semantic network activated by healthy controls. Stronger loading parameters on both the lesion and fMRI activation components were associated with poorer language test performance. Fiber tracking suggests that lesions affecting the left posterior temporo-parietal cortex and deep white matter may lead to the simultaneous disruption of multiple long-range structural pathways connecting distal language areas. Damage to the left posterior temporo-parietal cortex and underlying white matter may (1) impede the language task-driven recruitment of canonical left hemispheric language and other areas (e.g. the right anterior temporal lobe and default mode regions) that likely support residual language function after stroke, and (2) lead to the compensatory recruitment of right hemispheric fronto-temporo-parietal networks for tasks requiring semantic processing. |
2405.19587 | Simone Linz | Janosch D\"ocker, Simone Linz, Kristina Wicke | Bounding the softwired parsimony score of a phylogenetic network | null | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In comparison to phylogenetic trees, phylogenetic networks are more suitable
to represent complex evolutionary histories of species whose past includes
reticulation such as hybridisation or lateral gene transfer. However, the
reconstruction of phylogenetic networks remains challenging and computationally
expensive due to their intricate structural properties. For example, the small
parsimony problem that is solvable in polynomial time for phylogenetic trees,
becomes NP-hard on phylogenetic networks under softwired and parental
parsimony, even for a single binary character and structurally constrained
networks. To calculate the parsimony score of a phylogenetic network $N$, these
two parsimony notions consider different exponential-size sets of phylogenetic
trees that can be extracted from $N$ and infer the minimum parsimony score over
all trees in the set. In this paper, we ask: What is the maximum difference
between the parsimony score of any phylogenetic tree that is contained in the
set of considered trees and a phylogenetic tree whose parsimony score equates
to the parsimony score of $N$? Given a gap-free sequence alignment of
multi-state characters and a rooted binary level-$k$ phylogenetic network, we
use the novel concept of an informative blob to show that this difference is
bounded by $k+1$ times the softwired parsimony score of $N$. In particular, the
difference is independent of the alignment length and the number of character
states. We show that an analogous bound can be obtained for the softwired
parsimony score of semi-directed networks, while under parental parsimony on
the other hand, such a bound does not hold.
| [
{
"created": "Thu, 30 May 2024 00:39:40 GMT",
"version": "v1"
}
] | 2024-05-31 | [
[
"Döcker",
"Janosch",
""
],
[
"Linz",
"Simone",
""
],
[
"Wicke",
"Kristina",
""
]
] | In comparison to phylogenetic trees, phylogenetic networks are more suitable to represent complex evolutionary histories of species whose past includes reticulation such as hybridisation or lateral gene transfer. However, the reconstruction of phylogenetic networks remains challenging and computationally expensive due to their intricate structural properties. For example, the small parsimony problem that is solvable in polynomial time for phylogenetic trees, becomes NP-hard on phylogenetic networks under softwired and parental parsimony, even for a single binary character and structurally constrained networks. To calculate the parsimony score of a phylogenetic network $N$, these two parsimony notions consider different exponential-size sets of phylogenetic trees that can be extracted from $N$ and infer the minimum parsimony score over all trees in the set. In this paper, we ask: What is the maximum difference between the parsimony score of any phylogenetic tree that is contained in the set of considered trees and a phylogenetic tree whose parsimony score equates to the parsimony score of $N$? Given a gap-free sequence alignment of multi-state characters and a rooted binary level-$k$ phylogenetic network, we use the novel concept of an informative blob to show that this difference is bounded by $k+1$ times the softwired parsimony score of $N$. In particular, the difference is independent of the alignment length and the number of character states. We show that an analogous bound can be obtained for the softwired parsimony score of semi-directed networks, while under parental parsimony on the other hand, such a bound does not hold. |
1809.01878 | Juvid Aryaman | Juvid Aryaman, Iain G. Johnston, Nick S. Jones | Mitochondrial heterogeneity | null | null | 10.3389/fgene.2018.00718 | null | q-bio.SC | http://creativecommons.org/licenses/by-sa/4.0/ | Cell-to-cell heterogeneity drives a range of (patho)physiologically important
phenomena, such as cell fate and chemotherapeutic resistance. The role of
metabolism, and particularly mitochondria, is increasingly being recognised as
an important explanatory factor in cell-to-cell heterogeneity. Most eukaryotic
cells possess a population of mitochondria, in the sense that mitochondrial DNA
(mtDNA) is held in multiple copies per cell, where the sequence of each
molecule can vary. Hence intra-cellular mitochondrial heterogeneity is
possible, which can induce inter-cellular mitochondrial heterogeneity, and may
drive aspects of cellular noise. In this review, we discuss sources of
mitochondrial heterogeneity (variations between mitochondria in the same cell,
and mitochondrial variations between supposedly identical cells) from both
genetic and non-genetic perspectives, and mitochondrial genotype-phenotype
links. We discuss the apparent homeostasis of mtDNA copy number, the
observation of pervasive intra-cellular mtDNA mutation (we term
`microheteroplasmy') and developments in the understanding of inter-cellular
mtDNA mutation (`macroheteroplasmy'). We point to the relationship between
mitochondrial supercomplexes, cristal structure, pH and cardiolipin as a
potential amplifier of the mitochondrial genotype-phenotype link. We also
discuss mitochondrial membrane potential and networks as sources of
mitochondrial heterogeneity, and their influence upon the mitochondrial genome.
Finally, we revisit the idea of mitochondrial complementation as a means of
dampening mitochondrial genotype-phenotype links in light of recent
experimental developments. The diverse sources of mitochondrial heterogeneity,
as well as their increasingly recognised role in contributing to cellular
heterogeneity, highlights the need for future single-cell mitochondrial
measurements in the context of cellular noise studies.
| [
{
"created": "Thu, 6 Sep 2018 08:33:11 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Dec 2018 09:22:45 GMT",
"version": "v2"
}
] | 2019-02-11 | [
[
"Aryaman",
"Juvid",
""
],
[
"Johnston",
"Iain G.",
""
],
[
"Jones",
"Nick S.",
""
]
] | Cell-to-cell heterogeneity drives a range of (patho)physiologically important phenomena, such as cell fate and chemotherapeutic resistance. The role of metabolism, and particularly mitochondria, is increasingly being recognised as an important explanatory factor in cell-to-cell heterogeneity. Most eukaryotic cells possess a population of mitochondria, in the sense that mitochondrial DNA (mtDNA) is held in multiple copies per cell, where the sequence of each molecule can vary. Hence intra-cellular mitochondrial heterogeneity is possible, which can induce inter-cellular mitochondrial heterogeneity, and may drive aspects of cellular noise. In this review, we discuss sources of mitochondrial heterogeneity (variations between mitochondria in the same cell, and mitochondrial variations between supposedly identical cells) from both genetic and non-genetic perspectives, and mitochondrial genotype-phenotype links. We discuss the apparent homeostasis of mtDNA copy number, the observation of pervasive intra-cellular mtDNA mutation (we term `microheteroplasmy') and developments in the understanding of inter-cellular mtDNA mutation (`macroheteroplasmy'). We point to the relationship between mitochondrial supercomplexes, cristal structure, pH and cardiolipin as a potential amplifier of the mitochondrial genotype-phenotype link. We also discuss mitochondrial membrane potential and networks as sources of mitochondrial heterogeneity, and their influence upon the mitochondrial genome. Finally, we revisit the idea of mitochondrial complementation as a means of dampening mitochondrial genotype-phenotype links in light of recent experimental developments. The diverse sources of mitochondrial heterogeneity, as well as their increasingly recognised role in contributing to cellular heterogeneity, highlights the need for future single-cell mitochondrial measurements in the context of cellular noise studies. |
2105.00267 | Tiago Rodrigues | Kuan Lee, Ann Yang, Yen-Chu Lin, Daniel Reker, Goncalo J. L. Bernardes
and Tiago Rodrigues | Combating small molecule aggregation with machine learning | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Biological screens are plagued by false positive hits resulting from
aggregation. Thus, methods to triage small colloidally aggregating molecules
(SCAMs) are in high demand. Herein, we disclose a bespoke machine-learning tool
to confidently and intelligibly flag such entities. Our data demonstrate an
unprecedented utility of machine learning for predicting SCAMs, achieving 80%
of correct predictions in a challenging out-of-sample validation. The tool
outperformed a panel of expert chemists, who correctly predicted 61 +/- 7% of
the same test molecules in a Turing-like test. Further, the computational
routine provided insight into molecular features governing aggregation that had
remained hidden to expert intuition. Leveraging our tool, we quantify that up
to 15-20% of ligands in publicly available chemogenomic databases have the high
potential to aggregate at typical screening concentrations, imposing caution in
systems biology and drug design programs. Our approach provides a means to
augment human intuition, mitigate attrition and a pathway to accelerate future
molecular medicine.
| [
{
"created": "Sat, 1 May 2021 14:41:01 GMT",
"version": "v1"
}
] | 2021-05-04 | [
[
"Lee",
"Kuan",
""
],
[
"Yang",
"Ann",
""
],
[
"Lin",
"Yen-Chu",
""
],
[
"Reker",
"Daniel",
""
],
[
"Bernardes",
"Goncalo J. L.",
""
],
[
"Rodrigues",
"Tiago",
""
]
] | Biological screens are plagued by false positive hits resulting from aggregation. Thus, methods to triage small colloidally aggregating molecules (SCAMs) are in high demand. Herein, we disclose a bespoke machine-learning tool to confidently and intelligibly flag such entities. Our data demonstrate an unprecedented utility of machine learning for predicting SCAMs, achieving 80% of correct predictions in a challenging out-of-sample validation. The tool outperformed a panel of expert chemists, who correctly predicted 61 +/- 7% of the same test molecules in a Turing-like test. Further, the computational routine provided insight into molecular features governing aggregation that had remained hidden to expert intuition. Leveraging our tool, we quantify that up to 15-20% of ligands in publicly available chemogenomic databases have the high potential to aggregate at typical screening concentrations, imposing caution in systems biology and drug design programs. Our approach provides a means to augment human intuition, mitigate attrition and a pathway to accelerate future molecular medicine. |
1912.10262 | Lyudmila Kushnir | Lyudmila Kushnir, Sophie Den\`eve | Learning temporal structure of the input with a network of
integrate-and-fire neurons | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The task of the brain is to look for structure in the external input. We
study a network of integrate-and-fire neurons with several types of recurrent
connections that learns the structure of its time-varying feedforward input by
attempting to efficiently represent this input with spikes. The efficiency of
the representation arises from incorporating the structure of the input into
the decoder, which is implicit in the learned synaptic connectivity of the
network. While in the original work of [Boerlin, Machens, Den\`eve 2013] and
[Brendel et al., 2017] the structure learned by the network to make the
representation efficient was the low-dimensionality of the feedforward input,
in the present work it is its temporal dynamics. The network achieves the
efficiency by adjusting its synaptic weights in such a way, that for any neuron
in the network, the recurrent input cancels the feedforward for most of the
time. We show that if the only temporal structure that the input possesses is
that it changes slowly on the time scale of neuronal integration, the
dimensionality of the network dynamics is equal to the dimensionality of the
input. However, if the input follows a linear differential equation of the
first order, the efficiency of the representation can be increased by
increasing the dimensionality of the network dynamics in comparison to the
dimensionality of the input. If there is only one type of slow synaptic current
in the network, the increase is two-fold, while if there are two types of slow
synaptic currents that decay with different rates and whose amplitudes can be
adjusted separately, it is advantageous to make the increase three-fold. We
numerically simulate the network with synaptic weights that imply the most
efficient input representation in the above cases. We also propose a learning
rule by means of which the corresponding synaptic weights can be learned.
| [
{
"created": "Sat, 21 Dec 2019 13:04:30 GMT",
"version": "v1"
},
{
"created": "Sat, 28 Dec 2019 23:11:50 GMT",
"version": "v2"
},
{
"created": "Tue, 6 Oct 2020 19:50:24 GMT",
"version": "v3"
},
{
"created": "Sun, 11 Oct 2020 18:34:22 GMT",
"version": "v4"
}
] | 2020-10-13 | [
[
"Kushnir",
"Lyudmila",
""
],
[
"Denève",
"Sophie",
""
]
] | The task of the brain is to look for structure in the external input. We study a network of integrate-and-fire neurons with several types of recurrent connections that learns the structure of its time-varying feedforward input by attempting to efficiently represent this input with spikes. The efficiency of the representation arises from incorporating the structure of the input into the decoder, which is implicit in the learned synaptic connectivity of the network. While in the original work of [Boerlin, Machens, Den\`eve 2013] and [Brendel et al., 2017] the structure learned by the network to make the representation efficient was the low-dimensionality of the feedforward input, in the present work it is its temporal dynamics. The network achieves the efficiency by adjusting its synaptic weights in such a way, that for any neuron in the network, the recurrent input cancels the feedforward for most of the time. We show that if the only temporal structure that the input possesses is that it changes slowly on the time scale of neuronal integration, the dimensionality of the network dynamics is equal to the dimensionality of the input. However, if the input follows a linear differential equation of the first order, the efficiency of the representation can be increased by increasing the dimensionality of the network dynamics in comparison to the dimensionality of the input. If there is only one type of slow synaptic current in the network, the increase is two-fold, while if there are two types of slow synaptic currents that decay with different rates and whose amplitudes can be adjusted separately, it is advantageous to make the increase three-fold. We numerically simulate the network with synaptic weights that imply the most efficient input representation in the above cases. We also propose a learning rule by means of which the corresponding synaptic weights can be learned. |
0802.0522 | Jeremy England | Jeremy L. England and Vijay S. Pande | Potential for modulation of the hydrophobic effect inside chaperonins | null | null | 10.1529/biophysj.108.131037 | null | q-bio.BM | null | Despite the spontaneity of some in vitro protein folding reactions, native
folding in vivo often requires the participation of barrel-shaped multimeric
complexes known as chaperonins. Although it has long been known that chaperonin
substrates fold upon sequestration inside the chaperonin barrel, the precise
mechanism by which confinement within this space facilitates folding remains
unknown. In this study, we examine the possibility that the chaperonin mediates
a favorable reorganization of the solvent for the folding reaction. We begin by
discussing the effect of electrostatic charge on solvent-mediated hydrophobic
forces in an aqueous environment. Based on these initial physical arguments, we
construct a simple, phenomenological theory for the thermodynamics of density
and hydrogen bond order fluctuations in liquid water. Within the framework of
this model, we investigate the effect of confinement within a chaperonin-like
cavity on the configurational free energy of water by calculating solvent free
energies for cavities corresponding to the different conformational states in
the ATP- driven catalytic cycle of the prokaryotic chaperonin GroEL. Our
findings suggest that one function of chaperonins may be to trap unfolded
proteins and subsequently expose them to a micro-environment in which the
hydrophobic effect, a crucial thermodynamic driving force for folding, is
enhanced.
| [
{
"created": "Tue, 5 Feb 2008 00:10:39 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"England",
"Jeremy L.",
""
],
[
"Pande",
"Vijay S.",
""
]
] | Despite the spontaneity of some in vitro protein folding reactions, native folding in vivo often requires the participation of barrel-shaped multimeric complexes known as chaperonins. Although it has long been known that chaperonin substrates fold upon sequestration inside the chaperonin barrel, the precise mechanism by which confinement within this space facilitates folding remains unknown. In this study, we examine the possibility that the chaperonin mediates a favorable reorganization of the solvent for the folding reaction. We begin by discussing the effect of electrostatic charge on solvent-mediated hydrophobic forces in an aqueous environment. Based on these initial physical arguments, we construct a simple, phenomenological theory for the thermodynamics of density and hydrogen bond order fluctuations in liquid water. Within the framework of this model, we investigate the effect of confinement within a chaperonin-like cavity on the configurational free energy of water by calculating solvent free energies for cavities corresponding to the different conformational states in the ATP- driven catalytic cycle of the prokaryotic chaperonin GroEL. Our findings suggest that one function of chaperonins may be to trap unfolded proteins and subsequently expose them to a micro-environment in which the hydrophobic effect, a crucial thermodynamic driving force for folding, is enhanced. |
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