id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2002.03540 | Wenzhuo Zhang | Wen-Zhuo Zhang | A wave-pulse neural network for quasi-quantum coding | 5 pages, 2 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We design a physical wave-pulse neural network (WPNN) for both wave and pulse
propagation, which gives more degrees of freedom for neural coding than spike
neural networks (SNN). We define the rules and the information entropy of this
kind of neural network, where the signal speed, arrival time, and the length of
connections between neurons all become crucial parameters for signal coding. We
call it quasi-quantum coding (QQC) since the combination of wave and pulse
signals here behaves like a classical mimic of quantum wave-particle duality,
and can be studied by borrowing some concepts form quantum mechanics. We
present that the quasi-quantum coding can give efficient methods for both sound
and image recognitions. We also discuss the possibility of the wave-pulse
neural network and the quasi-quantum coding methods running on it in biological
brains where both neural oscillations and action potentials are important to
cognition.
| [
{
"created": "Thu, 6 Feb 2020 08:20:18 GMT",
"version": "v1"
}
] | 2020-02-11 | [
[
"Zhang",
"Wen-Zhuo",
""
]
] | We design a physical wave-pulse neural network (WPNN) for both wave and pulse propagation, which gives more degrees of freedom for neural coding than spike neural networks (SNN). We define the rules and the information entropy of this kind of neural network, where the signal speed, arrival time, and the length of connections between neurons all become crucial parameters for signal coding. We call it quasi-quantum coding (QQC) since the combination of wave and pulse signals here behaves like a classical mimic of quantum wave-particle duality, and can be studied by borrowing some concepts form quantum mechanics. We present that the quasi-quantum coding can give efficient methods for both sound and image recognitions. We also discuss the possibility of the wave-pulse neural network and the quasi-quantum coding methods running on it in biological brains where both neural oscillations and action potentials are important to cognition. |
q-bio/0403014 | Ignacio D. Peixoto | V. M. Kenkre, R. R. Parmenter, I. D. Peixoto, L. Sadasiv | A Theoretical Framework for the Analysis of the West Nile Virus Epidemic | 12 pages, 9 postscript figures | Mathematical and Computer Modelling vol. 42 (2005), issue 3/4,
pages 313-324. | 10.1016/j.mcm.2004.08.012 | null | q-bio.PE | null | We present a model for the growth of West Nile virus in mosquito and bird
populations based on observations of the initial epidemic in the U.S. Increase
of bird mortality as a result of infection, which is a feature of the epidemic,
is found to yield an effect which is observable in principle, viz., periodic
variations in the extent of infection. The vast difference between mosquito and
bird lifespans, another peculiarity of the system, is shown to lead to
interesting consequences regarding delay in the onset of the steady-state
infection. An outline of a framework is provided to treat mosquito diffusion
and bird migration.
| [
{
"created": "Sat, 13 Mar 2004 03:30:11 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Kenkre",
"V. M.",
""
],
[
"Parmenter",
"R. R.",
""
],
[
"Peixoto",
"I. D.",
""
],
[
"Sadasiv",
"L.",
""
]
] | We present a model for the growth of West Nile virus in mosquito and bird populations based on observations of the initial epidemic in the U.S. Increase of bird mortality as a result of infection, which is a feature of the epidemic, is found to yield an effect which is observable in principle, viz., periodic variations in the extent of infection. The vast difference between mosquito and bird lifespans, another peculiarity of the system, is shown to lead to interesting consequences regarding delay in the onset of the steady-state infection. An outline of a framework is provided to treat mosquito diffusion and bird migration. |
1406.3185 | Karthik Shankar | Karthik H. Shankar | Generic construction of scale-invariantly coarse grained memory | null | Lecture Notes in Artificial Intelligence, vol: 8955, pp: 175-184,
2015 | null | null | q-bio.NC cond-mat.dis-nn cs.AI | http://creativecommons.org/licenses/by/3.0/ | Encoding temporal information from the recent past as spatially distributed
activations is essential in order for the entire recent past to be
simultaneously accessible. Any biological or synthetic agent that relies on the
past to predict/plan the future, would be endowed with such a spatially
distributed temporal memory. Simplistically, we would expect that resource
limitations would demand the memory system to store only the most useful
information for future prediction. For natural signals in real world which show
scale free temporal fluctuations, the predictive information encoded in memory
is maximal if the past information is scale invariantly coarse grained. Here we
examine the general mechanism to construct a scale invariantly coarse grained
memory system. Remarkably, the generic construction is equivalent to encoding
the linear combinations of Laplace transform of the past information and their
approximated inverses. This reveals a fundamental construction constraint on
memory networks that attempt to maximize predictive information storage
relevant to the natural world.
| [
{
"created": "Thu, 12 Jun 2014 10:32:42 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Jan 2015 16:54:13 GMT",
"version": "v2"
}
] | 2015-01-05 | [
[
"Shankar",
"Karthik H.",
""
]
] | Encoding temporal information from the recent past as spatially distributed activations is essential in order for the entire recent past to be simultaneously accessible. Any biological or synthetic agent that relies on the past to predict/plan the future, would be endowed with such a spatially distributed temporal memory. Simplistically, we would expect that resource limitations would demand the memory system to store only the most useful information for future prediction. For natural signals in real world which show scale free temporal fluctuations, the predictive information encoded in memory is maximal if the past information is scale invariantly coarse grained. Here we examine the general mechanism to construct a scale invariantly coarse grained memory system. Remarkably, the generic construction is equivalent to encoding the linear combinations of Laplace transform of the past information and their approximated inverses. This reveals a fundamental construction constraint on memory networks that attempt to maximize predictive information storage relevant to the natural world. |
2308.01828 | Stav Marcus | Stav Marcus, Ari M Turner and Guy Bunin | Local and extensive fluctuations in sparsely-interacting ecological
communities | null | null | null | null | q-bio.PE cond-mat.dis-nn cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ecological communities with many species can be classified into dynamical
phases. In systems with all-to-all interactions, a phase where a fixed point is
always reached and a dynamically-fluctuating phase have been found. The
dynamics when interactions are sparse, with each species interacting with only
several others, has remained largely unexplored. Here we show that a new type
of phase appears in the phase diagram, where for the same control parameters
different communities may reach either a fixed point or a state where the
abundances of a finite subset of species fluctuate, and calculate the
probability for each outcome. These fluctuating species are organized around
short cycles in the interaction graph, and their abundances undergo large
non-linear fluctuations. We characterize the approach from this phase to a
phase with extensively many fluctuating species, and show that the probability
of fluctuations grows continuously to one as the transition is approached, and
that the number of fluctuating species diverges. This is qualitatively distinct
from the transition to extensive fluctuations coming from a fixed point phase,
which is marked by a loss of linear stability. The differences are traced back
to the emergent binary character of the dynamics when far away from short
cycles in the local fluctuations phase.
| [
{
"created": "Thu, 3 Aug 2023 15:39:26 GMT",
"version": "v1"
}
] | 2023-08-04 | [
[
"Marcus",
"Stav",
""
],
[
"Turner",
"Ari M",
""
],
[
"Bunin",
"Guy",
""
]
] | Ecological communities with many species can be classified into dynamical phases. In systems with all-to-all interactions, a phase where a fixed point is always reached and a dynamically-fluctuating phase have been found. The dynamics when interactions are sparse, with each species interacting with only several others, has remained largely unexplored. Here we show that a new type of phase appears in the phase diagram, where for the same control parameters different communities may reach either a fixed point or a state where the abundances of a finite subset of species fluctuate, and calculate the probability for each outcome. These fluctuating species are organized around short cycles in the interaction graph, and their abundances undergo large non-linear fluctuations. We characterize the approach from this phase to a phase with extensively many fluctuating species, and show that the probability of fluctuations grows continuously to one as the transition is approached, and that the number of fluctuating species diverges. This is qualitatively distinct from the transition to extensive fluctuations coming from a fixed point phase, which is marked by a loss of linear stability. The differences are traced back to the emergent binary character of the dynamics when far away from short cycles in the local fluctuations phase. |
1007.1340 | Philipp Altrock | Philipp M. Altrock, Chaytanya S. Gokhale, Arne Traulsen | Stochastic slowdown in evolutionary processes | 8 pages, 3 figures, accepted for publication | Phys. Rev. E 82, 011925 (2010) | 10.1103/PhysRevE.82.011925 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine birth--death processes with state dependent transition
probabilities and at least one absorbing boundary. In evolution, this describes
selection acting on two different types in a finite population where
reproductive events occur successively. If the two types have equal fitness the
system performs a random walk. If one type has a fitness advantage it is
favored by selection, which introduces a bias (asymmetry) in the transition
probabilities. How long does it take until advantageous mutants have invaded
and taken over? Surprisingly, we find that the average time of such a process
can increase, even if the mutant type always has a fitness advantage. We
discuss this finding for the Moran process and develop a simplified model which
allows a more intuitive understanding. We show that this effect can occur for
weak but non--vanishing bias (selection) in the state dependent transition
rates and infer the scaling with system size. We also address the Wright-Fisher
model commonly used in population genetics, which shows that this stochastic
slowdown is not restricted to birth-death processes.
| [
{
"created": "Thu, 8 Jul 2010 10:43:30 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Jul 2010 13:55:35 GMT",
"version": "v2"
}
] | 2010-10-12 | [
[
"Altrock",
"Philipp M.",
""
],
[
"Gokhale",
"Chaytanya S.",
""
],
[
"Traulsen",
"Arne",
""
]
] | We examine birth--death processes with state dependent transition probabilities and at least one absorbing boundary. In evolution, this describes selection acting on two different types in a finite population where reproductive events occur successively. If the two types have equal fitness the system performs a random walk. If one type has a fitness advantage it is favored by selection, which introduces a bias (asymmetry) in the transition probabilities. How long does it take until advantageous mutants have invaded and taken over? Surprisingly, we find that the average time of such a process can increase, even if the mutant type always has a fitness advantage. We discuss this finding for the Moran process and develop a simplified model which allows a more intuitive understanding. We show that this effect can occur for weak but non--vanishing bias (selection) in the state dependent transition rates and infer the scaling with system size. We also address the Wright-Fisher model commonly used in population genetics, which shows that this stochastic slowdown is not restricted to birth-death processes. |
2304.07295 | Yongquan Yang | Yongquan Yang, Jie Chen, Yani Wei, Mohammad Alobaidi and Hong Bu | Experts' cognition-driven safe noisy labels learning for precise
segmentation of residual tumor in breast cancer | null | null | null | null | q-bio.QM cs.AI eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Precise segmentation of residual tumor in breast cancer (PSRTBC) after
neoadjuvant chemotherapy is a fundamental key technique in the treatment
process of breast cancer. However, achieving PSRTBC is still a challenge, since
the breast cancer tissue and tumor cells commonly have complex and varied
morphological changes after neoadjuvant chemotherapy, which inevitably
increases the difficulty to produce a predictive model that has good
generalization with machine learning. To alleviate this situation, in this
paper, we propose an experts' cognition-driven safe noisy labels learning
(ECDSNLL) approach. In the concept of safe noisy labels learning, which is a
typical type of safe weakly supervised learning, ECDSNLL is constructed by
integrating the pathology experts' cognition about identifying residual tumor
in breast cancer and the artificial intelligence experts' cognition about data
modeling with provided data basis. We show the advantages of the proposed
ECDSNLL approach and its promising potentials in addressing PSRTBC. We also
release a better predictive model for achieving PSRTBC, which can be leveraged
to promote the development of related application software.
| [
{
"created": "Thu, 13 Apr 2023 03:46:40 GMT",
"version": "v1"
}
] | 2023-04-18 | [
[
"Yang",
"Yongquan",
""
],
[
"Chen",
"Jie",
""
],
[
"Wei",
"Yani",
""
],
[
"Alobaidi",
"Mohammad",
""
],
[
"Bu",
"Hong",
""
]
] | Precise segmentation of residual tumor in breast cancer (PSRTBC) after neoadjuvant chemotherapy is a fundamental key technique in the treatment process of breast cancer. However, achieving PSRTBC is still a challenge, since the breast cancer tissue and tumor cells commonly have complex and varied morphological changes after neoadjuvant chemotherapy, which inevitably increases the difficulty to produce a predictive model that has good generalization with machine learning. To alleviate this situation, in this paper, we propose an experts' cognition-driven safe noisy labels learning (ECDSNLL) approach. In the concept of safe noisy labels learning, which is a typical type of safe weakly supervised learning, ECDSNLL is constructed by integrating the pathology experts' cognition about identifying residual tumor in breast cancer and the artificial intelligence experts' cognition about data modeling with provided data basis. We show the advantages of the proposed ECDSNLL approach and its promising potentials in addressing PSRTBC. We also release a better predictive model for achieving PSRTBC, which can be leveraged to promote the development of related application software. |
1007.3311 | James Edwards | James R. Edwards, Mary R. Myerscough | Intelligent Decisions from the Hive Mind: Foragers and Nectar Receivers
of Apis mellifera Collaborate to Optimise Active Forager Numbers | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a differential equation-based mathematical model of nectar
foraging by the honey bee Apis mellifera. The model focuses on two behavioural
classes; nectar foragers and nectar receivers. Results generated from the model
are used to demonstrate how different classes within a collective can
collaborate to combine information and produce finely tuned decisions through
simple interactions. In particular we show the importance of the `search time'
- the time a returning forager takes to find an available nectar receiver - in
restricting the forager population to a level consistent with colony-wide
needs.
| [
{
"created": "Mon, 19 Jul 2010 23:54:04 GMT",
"version": "v1"
}
] | 2010-07-21 | [
[
"Edwards",
"James R.",
""
],
[
"Myerscough",
"Mary R.",
""
]
] | We present a differential equation-based mathematical model of nectar foraging by the honey bee Apis mellifera. The model focuses on two behavioural classes; nectar foragers and nectar receivers. Results generated from the model are used to demonstrate how different classes within a collective can collaborate to combine information and produce finely tuned decisions through simple interactions. In particular we show the importance of the `search time' - the time a returning forager takes to find an available nectar receiver - in restricting the forager population to a level consistent with colony-wide needs. |
2003.06933 | Amirhoshang Hoseinpour Dehkordi | Amirhoshang Hoseinpour Dehkordi, Majid Alizadeh, Pegah Derakhshan,
Peyman Babazadeh, Arash Jahandideh | Understanding Epidemic Data and Statistics: A case study of COVID-19 | 18 pages, 12 figures | null | 10.1002/jmv.25885 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The 2019-Novel-Coronavirus (COVID-19) has affected 116 countries (By March
12) and out of more than 118,000 confirmed cases. Understanding the
transmission dynamics of the infection in each country which affected on a
daily basis and evaluating the effectiveness of control policies is critical
for our further actions. To date, the statistics of COVID-19 reported cases
show more than 80 percent of infected had a mild case of disease, while around
14 percent of infected experienced a severe one and about 5 percent are
categorized as critical disease victims. Today's report (2020-03-12; daily
updates in the prepared website) shows the confirmed cases of COVID-19 in
China, South Korea, Italy, and Iran are 80932, 7869, 12462 and 10075;
respectively. Calculating the total Case Fatality Rate (CFR) of Italy
(2020-03-04), about 7.9% of confirmed cases passed away. Compared to South
Korea's rate of 0.76% (10 times lower than Italy) and China's 3.8% (50% lower
than Italy), the CFR of Italy is too high. There are some effective policies
that yield significant changes in the trend of cases. The lockdown policy in
China and Italy (the effect observed after 11 days), Shutdown of all
nonessential companies in Hubei (the effect observed after 5 days), combined
policy in South Korea and reducing working hours in Iran.
| [
{
"created": "Sun, 15 Mar 2020 21:56:15 GMT",
"version": "v1"
}
] | 2020-06-25 | [
[
"Dehkordi",
"Amirhoshang Hoseinpour",
""
],
[
"Alizadeh",
"Majid",
""
],
[
"Derakhshan",
"Pegah",
""
],
[
"Babazadeh",
"Peyman",
""
],
[
"Jahandideh",
"Arash",
""
]
] | The 2019-Novel-Coronavirus (COVID-19) has affected 116 countries (By March 12) and out of more than 118,000 confirmed cases. Understanding the transmission dynamics of the infection in each country which affected on a daily basis and evaluating the effectiveness of control policies is critical for our further actions. To date, the statistics of COVID-19 reported cases show more than 80 percent of infected had a mild case of disease, while around 14 percent of infected experienced a severe one and about 5 percent are categorized as critical disease victims. Today's report (2020-03-12; daily updates in the prepared website) shows the confirmed cases of COVID-19 in China, South Korea, Italy, and Iran are 80932, 7869, 12462 and 10075; respectively. Calculating the total Case Fatality Rate (CFR) of Italy (2020-03-04), about 7.9% of confirmed cases passed away. Compared to South Korea's rate of 0.76% (10 times lower than Italy) and China's 3.8% (50% lower than Italy), the CFR of Italy is too high. There are some effective policies that yield significant changes in the trend of cases. The lockdown policy in China and Italy (the effect observed after 11 days), Shutdown of all nonessential companies in Hubei (the effect observed after 5 days), combined policy in South Korea and reducing working hours in Iran. |
1901.02068 | James Hope Mr | J. Hope, K. Aristovich, C. A. R. Chapman, A. Volschenk, F.
Vanholsbeeck, A. McDaid | Extracting impedance changes from a frequency multiplexed signal during
neural activity in sciatic nerve of rat: preliminary study in-vitro | 15 pages, 8 figures | null | 10.1088/1361-6579/ab0c24 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: Establish suitable frequency spacing and demodulation steps to use
when extracting impedance changes from frequency division multiplexed (FDM)
carrier signals in peripheral nerve. Approach: Experiments were performed
in-vitro on cadavers immediately following euthanasia. Neural activity was
evoked via stimulation of nerves in the hind paw, while carrier signals were
injected, and recordings obtained, with a dual ring nerve cuff implanted on the
sciatic nerve. Frequency analysis of recorded compound action potentials (CAPs)
and extracted impedance changes, with the latter obtained using established
demodulation methods, were used to determine suitable frequency spacing of
carrier signals, and bandpass filter (BPF) bandwidth and order, for a frequency
multiplexed signal. Main results: CAPs and impedance changes were dominant in
the frequency band 200 to 500 Hz and 100 to 200 Hz, respectively. A Tukey
window was introduced to remove ringing from Gibbs phenomena. A +/- 750 Hz BPF
bandwidth was selected to encompass 99.99 % of the frequency power of the
impedance change. Modelling predicted a minimum BPF order of 16 for 2 kHz
spacing, and 10 for 4 kHz spacing, were required to avoid ringing from the
neighbouring carrier signal, while FDM experiments verified BPF orders of 12
and 8, respectively, were required. With a notch filter centred on the
neighbouring signal, a BPF order of at least 6 or 4 was required for 2 and 4
kHz, respectively. Significance: The results establish drive frequency spacing
and demodulation settings for use in FDM electrical impedance tomography (EIT)
experiments, as well as a framework for their selection, and, for the first
time, demonstrates the viability of FDM-EIT of neural activity on peripheral
nerve, which will be a central aspect of future real-time neural-EIT systems
and EIT-based neural prosthetics interfaces.
| [
{
"created": "Mon, 7 Jan 2019 21:18:50 GMT",
"version": "v1"
}
] | 2019-06-28 | [
[
"Hope",
"J.",
""
],
[
"Aristovich",
"K.",
""
],
[
"Chapman",
"C. A. R.",
""
],
[
"Volschenk",
"A.",
""
],
[
"Vanholsbeeck",
"F.",
""
],
[
"McDaid",
"A.",
""
]
] | Objective: Establish suitable frequency spacing and demodulation steps to use when extracting impedance changes from frequency division multiplexed (FDM) carrier signals in peripheral nerve. Approach: Experiments were performed in-vitro on cadavers immediately following euthanasia. Neural activity was evoked via stimulation of nerves in the hind paw, while carrier signals were injected, and recordings obtained, with a dual ring nerve cuff implanted on the sciatic nerve. Frequency analysis of recorded compound action potentials (CAPs) and extracted impedance changes, with the latter obtained using established demodulation methods, were used to determine suitable frequency spacing of carrier signals, and bandpass filter (BPF) bandwidth and order, for a frequency multiplexed signal. Main results: CAPs and impedance changes were dominant in the frequency band 200 to 500 Hz and 100 to 200 Hz, respectively. A Tukey window was introduced to remove ringing from Gibbs phenomena. A +/- 750 Hz BPF bandwidth was selected to encompass 99.99 % of the frequency power of the impedance change. Modelling predicted a minimum BPF order of 16 for 2 kHz spacing, and 10 for 4 kHz spacing, were required to avoid ringing from the neighbouring carrier signal, while FDM experiments verified BPF orders of 12 and 8, respectively, were required. With a notch filter centred on the neighbouring signal, a BPF order of at least 6 or 4 was required for 2 and 4 kHz, respectively. Significance: The results establish drive frequency spacing and demodulation settings for use in FDM electrical impedance tomography (EIT) experiments, as well as a framework for their selection, and, for the first time, demonstrates the viability of FDM-EIT of neural activity on peripheral nerve, which will be a central aspect of future real-time neural-EIT systems and EIT-based neural prosthetics interfaces. |
2404.04723 | Brian Camley | Kurmanbek Kaiyrbekov and Brian A. Camley | Does nematic order allow groups of elongated cells to sense electric
fields better? | null | null | null | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Collective response to external directional cues like electric fields plays a
pivotal role in processes such as tissue development, regeneration, and wound
healing. In this study we focus on the impact of anisotropy in cell shape and
local cell alignment on the collective response to electric fields. We model
elongated cells that have a different accuracy sensing the field depending on
their orientation with respect to the field. Elongated cells often line up with
their long axes in the same direction - "nematic order" - does this help the
group of cells sense the field more accurately? We use simulations of a simple
model to show that if cells orient themselves perpendicular to their average
velocity, alignment of a cell's long axis to its nearest neighbors' orientation
can enhance the directional response to electric fields. However, for cells to
benefit from aligning, their accuracy of sensing must be strongly dependent on
cell orientation. We also show that cell-cell adhesion modulates the accuracy
of cells in the group.
| [
{
"created": "Sat, 6 Apr 2024 20:20:24 GMT",
"version": "v1"
}
] | 2024-04-09 | [
[
"Kaiyrbekov",
"Kurmanbek",
""
],
[
"Camley",
"Brian A.",
""
]
] | Collective response to external directional cues like electric fields plays a pivotal role in processes such as tissue development, regeneration, and wound healing. In this study we focus on the impact of anisotropy in cell shape and local cell alignment on the collective response to electric fields. We model elongated cells that have a different accuracy sensing the field depending on their orientation with respect to the field. Elongated cells often line up with their long axes in the same direction - "nematic order" - does this help the group of cells sense the field more accurately? We use simulations of a simple model to show that if cells orient themselves perpendicular to their average velocity, alignment of a cell's long axis to its nearest neighbors' orientation can enhance the directional response to electric fields. However, for cells to benefit from aligning, their accuracy of sensing must be strongly dependent on cell orientation. We also show that cell-cell adhesion modulates the accuracy of cells in the group. |
2011.00297 | Jantine Broek PhD | Jantine A.C. Broek and Guillaume Drion | Generalisation of neuronal excitability allows for the identification of
an excitability change parameter that links to an experimentally measurable
value | 21 pages, 10 main figures, 3 supplementary figures | null | 10.5281/zenodo.4159691 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuronal excitability is the phenomena that describes action potential
generation due to a stimulus input. Commonly, neuronal excitability is divided
into two classes: Type I and Type II, both having different properties that
affect information processing, such as thresholding and gain scaling. These
properties can be mathematically studied using generalised phenomenological
models, such as the Fitzhugh-Nagumo model and the mirrored FHN. The FHN model
shows that each excitability type corresponds to one specific type of
bifurcation in the phase plane: Type I underlies a saddle-node on invariant
cycle bifurcation, and Type II a Hopf bifurcation. The difficulty of modelling
Type I excitability is that it is not only represented by its underlying
bifurcation, but also should be able to generate frequency while maintaining a
small depolarising current. Using the mFHN model, we show that this situation
is possible without modifying the phase portrait, due to the incorporation of a
slow regenerative variable. We show that in the singular limit of the mFHN
model, the time-scale separation can be chosen such that there is a
configuration of a classical phase portrait that allows for SNIC bifurcation,
zero-frequency onset and a depolarising current, such as observed in Type I
excitability. Using the definition of slow conductance, g_s, we show that these
mathematical findings for excitability change are translatable to reduced
conductance based models and also relates to an experimentally measurable
quantity. This not only allows for a measure of excitability change, but also
relates the mathematical parameters that indicate a physiological Type I
excitability to parameters that can be tuned during experiments.
| [
{
"created": "Sat, 31 Oct 2020 15:58:02 GMT",
"version": "v1"
}
] | 2020-11-03 | [
[
"Broek",
"Jantine A. C.",
""
],
[
"Drion",
"Guillaume",
""
]
] | Neuronal excitability is the phenomena that describes action potential generation due to a stimulus input. Commonly, neuronal excitability is divided into two classes: Type I and Type II, both having different properties that affect information processing, such as thresholding and gain scaling. These properties can be mathematically studied using generalised phenomenological models, such as the Fitzhugh-Nagumo model and the mirrored FHN. The FHN model shows that each excitability type corresponds to one specific type of bifurcation in the phase plane: Type I underlies a saddle-node on invariant cycle bifurcation, and Type II a Hopf bifurcation. The difficulty of modelling Type I excitability is that it is not only represented by its underlying bifurcation, but also should be able to generate frequency while maintaining a small depolarising current. Using the mFHN model, we show that this situation is possible without modifying the phase portrait, due to the incorporation of a slow regenerative variable. We show that in the singular limit of the mFHN model, the time-scale separation can be chosen such that there is a configuration of a classical phase portrait that allows for SNIC bifurcation, zero-frequency onset and a depolarising current, such as observed in Type I excitability. Using the definition of slow conductance, g_s, we show that these mathematical findings for excitability change are translatable to reduced conductance based models and also relates to an experimentally measurable quantity. This not only allows for a measure of excitability change, but also relates the mathematical parameters that indicate a physiological Type I excitability to parameters that can be tuned during experiments. |
2306.01629 | Forrest Sheldon | T. M. A. Fink and F. C. Sheldon | Number of attractors in the critical Kauffman model is exponential | 5 pages, 3 figures | null | null | null | q-bio.MN cond-mat.dis-nn | http://creativecommons.org/licenses/by/4.0/ | The Kauffman model is the archetypal model of genetic computation. It
highlights the importance of criticality, at which many biological systems seem
poised. In a series of advances, researchers have honed in on how the number of
attractors in the critical regime grows with network size. But a definitive
answer has proved elusive. We prove that, for the critical Kauffman model with
connectivity one, the number of attractors grows at least, and at most, as
$(2/\!\sqrt{e})^N$. This is the first proof that the number of attractors in a
critical Kauffman model grows exponentially.
| [
{
"created": "Fri, 2 Jun 2023 15:47:57 GMT",
"version": "v1"
}
] | 2023-06-05 | [
[
"Fink",
"T. M. A.",
""
],
[
"Sheldon",
"F. C.",
""
]
] | The Kauffman model is the archetypal model of genetic computation. It highlights the importance of criticality, at which many biological systems seem poised. In a series of advances, researchers have honed in on how the number of attractors in the critical regime grows with network size. But a definitive answer has proved elusive. We prove that, for the critical Kauffman model with connectivity one, the number of attractors grows at least, and at most, as $(2/\!\sqrt{e})^N$. This is the first proof that the number of attractors in a critical Kauffman model grows exponentially. |
1409.7915 | Delfim F. M. Torres | Helena Sofia Rodrigues, M. Teresa T. Monteiro, Delfim F. M. Torres,
Ana Clara Silva, Carla Sousa, Cl\'audia Concei\c{c}\~ao | Dengue in Madeira Island | This is a preprint of a paper whose final and definite form will be
published in the volume 'Mathematics of Planet Earth' that initiates the book
series 'CIM Series in Mathematical Sciences' (CIM-MS) published by Springer.
Submitted Oct/2013; Revised 16/July/2014 and 20/Sept/2014; Accepted
28/Sept/2014 | Dynamics, Games and Science, CIM Series in Mathematical Sciences 1
(2015), 593--605 | 10.1007/978-3-319-16118-1_32 | null | q-bio.PE math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dengue is a vector-borne disease and 40% of world population is at risk.
Dengue transcends international borders and can be found in tropical and
subtropical regions around the world, predominantly in urban and semi-urban
areas. A model for dengue disease transmission, composed by mutually-exclusive
compartments representing the human and vector dynamics, is presented in this
study. The data is from Madeira, a Portuguese island, where an unprecedented
outbreak was detected on October 2012. The aim of this work is to simulate the
repercussions of the control measures in the fight of the disease.
| [
{
"created": "Sun, 28 Sep 2014 14:36:28 GMT",
"version": "v1"
}
] | 2016-08-10 | [
[
"Rodrigues",
"Helena Sofia",
""
],
[
"Monteiro",
"M. Teresa T.",
""
],
[
"Torres",
"Delfim F. M.",
""
],
[
"Silva",
"Ana Clara",
""
],
[
"Sousa",
"Carla",
""
],
[
"Conceição",
"Cláudia",
""
]
] | Dengue is a vector-borne disease and 40% of world population is at risk. Dengue transcends international borders and can be found in tropical and subtropical regions around the world, predominantly in urban and semi-urban areas. A model for dengue disease transmission, composed by mutually-exclusive compartments representing the human and vector dynamics, is presented in this study. The data is from Madeira, a Portuguese island, where an unprecedented outbreak was detected on October 2012. The aim of this work is to simulate the repercussions of the control measures in the fight of the disease. |
1608.09009 | Pabitra Pal Choudhury | Suvankar Ghosh, Shankar Kumar Ghosh, Camellia Ray, Goutam Paul,
Pabitra Pal Choudhury, Raja Banerjee | Understanding the behavioural difference of PPCA among its homologs in
C7 family towards recognition of DXCA | Pages-13, Figures-6, Tables-4 | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Among all the proteins of Periplasmic C type cytochrome A (PPCA) family
obtained from cytochrome C7 found in Geobacter sulfurreducens, PPCA protein can
interact with Deoxycholate (DXCA), while its other homologs do not, as observed
from the crystal structures. Utilizing the concept of 'structure-function
relationship', an effort has been initiated towards understanding the driving
force for recognition of DXCA exclusively by PPCA among its homologs. Further,
a combinatorial analysis of the binding sequences (contiguous sequence of amino
acid residues of binding locations) is performed to build graph-theoretic
models, which show that PPCA differs from its homologues. Analysis of the
results suggests that the underlying impetus of recognition of DXCA by PPCA is
embedded in its primary sequence and 3D conformation.
| [
{
"created": "Thu, 17 Sep 2015 06:35:59 GMT",
"version": "v1"
}
] | 2016-09-01 | [
[
"Ghosh",
"Suvankar",
""
],
[
"Ghosh",
"Shankar Kumar",
""
],
[
"Ray",
"Camellia",
""
],
[
"Paul",
"Goutam",
""
],
[
"Choudhury",
"Pabitra Pal",
""
],
[
"Banerjee",
"Raja",
""
]
] | Among all the proteins of Periplasmic C type cytochrome A (PPCA) family obtained from cytochrome C7 found in Geobacter sulfurreducens, PPCA protein can interact with Deoxycholate (DXCA), while its other homologs do not, as observed from the crystal structures. Utilizing the concept of 'structure-function relationship', an effort has been initiated towards understanding the driving force for recognition of DXCA exclusively by PPCA among its homologs. Further, a combinatorial analysis of the binding sequences (contiguous sequence of amino acid residues of binding locations) is performed to build graph-theoretic models, which show that PPCA differs from its homologues. Analysis of the results suggests that the underlying impetus of recognition of DXCA by PPCA is embedded in its primary sequence and 3D conformation. |
1201.5531 | Jose Emilio Jimenez | J. E. Jimenez-Roldan and R. B. Freedman and R. A. R\"omer and S. A.
Wells | Rapid simulation of protein motion: merging flexibility, rigidity and
normal mode analyses | 34 pages, 22 Figures, Phys. Biol. 9 (2012) | Physical Biology 9, 016008-12 (2012) | 10.1088/1478-3975/9/1/016008 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein function frequently involves conformational changes with large
amplitude on timescales which are difficult and computationally expensive to
access using molecular dynamics. In this paper, we report on the combination of
three computationally inexpensive simulation methods-normal mode analysis using
the elastic network model, rigidity analysis using the pebble game algorithm,
and geometric simulation of protein motion-to explore conformational change
along normal mode eigenvectors. Using a combination of ELNEMO and FIRST/FRODA
software, large-amplitude motions in proteins with hundreds or thousands of
residues can be rapidly explored within minutes using desktop computing
resources. We apply the method to a representative set of six proteins covering
a range of sizes and structural characteristics and show that the method
identifies specific types of motion in each case and determines their amplitude
limits.
| [
{
"created": "Wed, 25 Jan 2012 15:31:35 GMT",
"version": "v1"
}
] | 2012-02-10 | [
[
"Jimenez-Roldan",
"J. E.",
""
],
[
"Freedman",
"R. B.",
""
],
[
"Römer",
"R. A.",
""
],
[
"Wells",
"S. A.",
""
]
] | Protein function frequently involves conformational changes with large amplitude on timescales which are difficult and computationally expensive to access using molecular dynamics. In this paper, we report on the combination of three computationally inexpensive simulation methods-normal mode analysis using the elastic network model, rigidity analysis using the pebble game algorithm, and geometric simulation of protein motion-to explore conformational change along normal mode eigenvectors. Using a combination of ELNEMO and FIRST/FRODA software, large-amplitude motions in proteins with hundreds or thousands of residues can be rapidly explored within minutes using desktop computing resources. We apply the method to a representative set of six proteins covering a range of sizes and structural characteristics and show that the method identifies specific types of motion in each case and determines their amplitude limits. |
2002.11013 | Nicholas Glykos | Dimitrios A. Mitsikas and Nicholas M. Glykos | On the propensity of Asn-Gly-containing heptapeptides to form
$\beta$-turn structures : comparison between ab initio quantum mechanical
calculations and Molecular Dynamics simulations | In the supplementary information file figures S5, S6 and S7 the
$\beta$-turn occupancies were wrong. They have been corrected | PLoS ONE (2020), 15(12): e0243429 | 10.1371/journal.pone.0243429 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Both molecular mechanical and quantum mechanical calculations play an
important role in describing the behavior and structure of molecules. In this
work, we compare for the same peptide systems the results obtained from folding
molecular dynamics simulations with previously reported results from quantum
mechanical calculations. More specifically, three molecular dynamics
simulations of 5 $\mu$s each in explicit water solvent were carried out for
three Asn-Gly-containing heptapeptides, in order to study their folding and
dynamics. Previous data, based on quantum mechanical calculations and the DFT
methods have shown that these peptides adopt $\beta$-turn structures in aqueous
solution, with type I' $\beta$-turn being the most preferred motif. The results
from our analyses indicate that for the given system the two methods diverge in
their predictions. The possibility of a force field-dependent deficiency is
examined as a possible source of the observed discrepancy.
| [
{
"created": "Tue, 25 Feb 2020 16:38:31 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Feb 2020 09:36:08 GMT",
"version": "v2"
}
] | 2021-01-07 | [
[
"Mitsikas",
"Dimitrios A.",
""
],
[
"Glykos",
"Nicholas M.",
""
]
] | Both molecular mechanical and quantum mechanical calculations play an important role in describing the behavior and structure of molecules. In this work, we compare for the same peptide systems the results obtained from folding molecular dynamics simulations with previously reported results from quantum mechanical calculations. More specifically, three molecular dynamics simulations of 5 $\mu$s each in explicit water solvent were carried out for three Asn-Gly-containing heptapeptides, in order to study their folding and dynamics. Previous data, based on quantum mechanical calculations and the DFT methods have shown that these peptides adopt $\beta$-turn structures in aqueous solution, with type I' $\beta$-turn being the most preferred motif. The results from our analyses indicate that for the given system the two methods diverge in their predictions. The possibility of a force field-dependent deficiency is examined as a possible source of the observed discrepancy. |
2007.01158 | Saeid Alirezazadeh | Saeid Alirezazadeh and Khadijeh Alibabaei and Stephen P. Hubbell | Species Area Relationship (SAR): Pattern Description with Geometrical
Approach | This work is done within 2017-2019. On the date of publishing on
ArXiv, Saeid Alirezazadeh is with C4 - Cloud Computing Competence Centre
(C4-UBI), Universidade da Beira Interior, Covilh\~{a}, Portugal, and Khadijeh
Alibabaei is with C-MAST Center for Mechanical and Aerospace Science and
Technologies, University of Beira Interior, Covilh\~{a}, Portugal | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several formulations are describing the pattern of species-area relationship,
log-log linear, semi-log linear, among others. These patterns mainly explain
the species-area relationship for large areas, and for the small area, they
provide significant differences from real data. We consider the geometric
position of individuals of species, and base on that, we find the probability
of observing at least one individual of the species. We apply a translation of
the well-studied problem of mixed salt-water in a tank to describe the formula
of SAR. For a rectangular sample area the species-area relationship follows the
pattern, with some simplification, $S=c|A^{\beta}+a|^z$, where $S$ is the
number of species in the area of size $A$ and $a,c,z$, and $\beta$ are
constants with $z<1$ and $\beta\leq1$. We also show how the constant $z$
relates to some macroecological patterns, namely spatial aggregation,
percentage of area coverage, and the core-satellite model. We exemplify our
method using data on tropical tree species from a 50ha plot in Barro Colorado
Island (BCI), Panama, using all individuals.
| [
{
"created": "Thu, 2 Jul 2020 14:46:52 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Jul 2020 14:24:39 GMT",
"version": "v2"
},
{
"created": "Wed, 8 Jul 2020 14:47:21 GMT",
"version": "v3"
},
{
"created": "Mon, 11 Jan 2021 16:52:53 GMT",
"version": "v4"
},
{
"crea... | 2021-07-06 | [
[
"Alirezazadeh",
"Saeid",
""
],
[
"Alibabaei",
"Khadijeh",
""
],
[
"Hubbell",
"Stephen P.",
""
]
] | Several formulations are describing the pattern of species-area relationship, log-log linear, semi-log linear, among others. These patterns mainly explain the species-area relationship for large areas, and for the small area, they provide significant differences from real data. We consider the geometric position of individuals of species, and base on that, we find the probability of observing at least one individual of the species. We apply a translation of the well-studied problem of mixed salt-water in a tank to describe the formula of SAR. For a rectangular sample area the species-area relationship follows the pattern, with some simplification, $S=c|A^{\beta}+a|^z$, where $S$ is the number of species in the area of size $A$ and $a,c,z$, and $\beta$ are constants with $z<1$ and $\beta\leq1$. We also show how the constant $z$ relates to some macroecological patterns, namely spatial aggregation, percentage of area coverage, and the core-satellite model. We exemplify our method using data on tropical tree species from a 50ha plot in Barro Colorado Island (BCI), Panama, using all individuals. |
2403.15842 | Kristina Wicke | Vincent Moulton, Andreas Spillner, Kristina Wicke | Phylogenetic diversity indices from an affine and projective viewpoint | 23 pages, 11 figures | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic diversity indices are commonly used to rank the elements in a
collection of species or populations for conservation purposes. The derivation
of these indices is typically based on some quantitative description of the
evolutionary history of the species in question, which is often given in terms
of a phylogenetic tree. Both rooted and unrooted phylogenetic trees can be
employed, and there are close connections between the indices that are derived
in these two different ways. In this paper, we introduce more general
phylogenetic diversity indices that can be derived from collections of subsets
(clusters) and collections of bipartitions (splits) of the given set of
species. Such indices could be useful, for example, in case there is some
uncertainty in the topology of the tree being used to derive a phylogenetic
diversity index. As well as characterizing some of the indices that we
introduce in terms of their special properties, we provide a link between
cluster-based and split-based phylogenetic diversity indices that uses a
discrete analogue of the classical link between affine and projective geometry.
This provides a unified framework for many of the various phylogenetic
diversity indices used in the literature based on rooted and unrooted
phylogenetic trees, generalizations and new proofs for previous results
concerning tree-based indices, and a way to define some new phylogenetic
diversity indices that naturally arise as affine or projective variants of each
other.
| [
{
"created": "Sat, 23 Mar 2024 13:47:43 GMT",
"version": "v1"
}
] | 2024-03-26 | [
[
"Moulton",
"Vincent",
""
],
[
"Spillner",
"Andreas",
""
],
[
"Wicke",
"Kristina",
""
]
] | Phylogenetic diversity indices are commonly used to rank the elements in a collection of species or populations for conservation purposes. The derivation of these indices is typically based on some quantitative description of the evolutionary history of the species in question, which is often given in terms of a phylogenetic tree. Both rooted and unrooted phylogenetic trees can be employed, and there are close connections between the indices that are derived in these two different ways. In this paper, we introduce more general phylogenetic diversity indices that can be derived from collections of subsets (clusters) and collections of bipartitions (splits) of the given set of species. Such indices could be useful, for example, in case there is some uncertainty in the topology of the tree being used to derive a phylogenetic diversity index. As well as characterizing some of the indices that we introduce in terms of their special properties, we provide a link between cluster-based and split-based phylogenetic diversity indices that uses a discrete analogue of the classical link between affine and projective geometry. This provides a unified framework for many of the various phylogenetic diversity indices used in the literature based on rooted and unrooted phylogenetic trees, generalizations and new proofs for previous results concerning tree-based indices, and a way to define some new phylogenetic diversity indices that naturally arise as affine or projective variants of each other. |
2103.11230 | Pedro Pessoa | Pedro Pessoa | Legendre transformation and information geometry for the maximum entropy
theory of ecology | Typos fixed | Phys. Sci. Forum 2021, 3(1), 1 | 10.3390/psf2021003001 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here I investigate some mathematical aspects of the maximum entropy theory of
ecology (METE). In particular I address the geometrical structure of METE
endowed by information geometry. As novel results, the macrostate entropy is
calculated analytically by the Legendre transformation of the log-normalizer in
METE. This result allows for the calculation of the metric terms in the
information geometry arising from METE and, by consequence, the covariance
matrix between METE variables.
| [
{
"created": "Sat, 20 Mar 2021 19:50:04 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Mar 2021 14:08:37 GMT",
"version": "v2"
},
{
"created": "Mon, 12 Apr 2021 18:43:54 GMT",
"version": "v3"
},
{
"created": "Sat, 21 Aug 2021 22:41:34 GMT",
"version": "v4"
}
] | 2021-11-09 | [
[
"Pessoa",
"Pedro",
""
]
] | Here I investigate some mathematical aspects of the maximum entropy theory of ecology (METE). In particular I address the geometrical structure of METE endowed by information geometry. As novel results, the macrostate entropy is calculated analytically by the Legendre transformation of the log-normalizer in METE. This result allows for the calculation of the metric terms in the information geometry arising from METE and, by consequence, the covariance matrix between METE variables. |
2407.15220 | Yuliya Burankova | Yuliya Burankova, Miriam Abele, Mohammad Bakhtiari, Christine von
T\"orne, Teresa Barth, Lisa Schweizer, Pieter Giesbertz, Johannes R. Schmidt,
Stefan Kalkhof, Janina M\"uller-Deile, Peter A van Veelen, Yassene Mohammed,
Elke Hammer, Lis Arend, Klaudia Adamowicz, Tanja Laske, Anne Hartebrodt,
Tobias Frisch, Chen Meng, Julian Matschinske, Julian Sp\"ath, Richard
R\"ottger, Veit Schw\"ammle, Stefanie M. Hauck, Stefan Lichtenthaler, Axel
Imhof, Matthias Mann, Christina Ludwig, Bernhard Kuster, Jan Baumbach, Olga
Zolotareva | Privacy-Preserving Multi-Center Differential Protein Abundance Analysis
with FedProt | 52 pages, 16 figures, 12 tables. Last two authors listed are joint
last authors | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Quantitative mass spectrometry has revolutionized proteomics by enabling
simultaneous quantification of thousands of proteins. Pooling patient-derived
data from multiple institutions enhances statistical power but raises
significant privacy concerns. Here we introduce FedProt, the first
privacy-preserving tool for collaborative differential protein abundance
analysis of distributed data, which utilizes federated learning and additive
secret sharing. In the absence of a multicenter patient-derived dataset for
evaluation, we created two, one at five centers from LFQ E.coli experiments and
one at three centers from TMT human serum. Evaluations using these datasets
confirm that FedProt achieves accuracy equivalent to DEqMS applied to pooled
data, with completely negligible absolute differences no greater than $\text{$4
\times 10^{-12}$}$. In contrast, -log10(p-values) computed by the most accurate
meta-analysis methods diverged from the centralized analysis results by up to
25-27. FedProt is available as a web tool with detailed documentation as a
FeatureCloud App.
| [
{
"created": "Sun, 21 Jul 2024 17:09:20 GMT",
"version": "v1"
}
] | 2024-07-23 | [
[
"Burankova",
"Yuliya",
""
],
[
"Abele",
"Miriam",
""
],
[
"Bakhtiari",
"Mohammad",
""
],
[
"von Törne",
"Christine",
""
],
[
"Barth",
"Teresa",
""
],
[
"Schweizer",
"Lisa",
""
],
[
"Giesbertz",
"Pieter",
""... | Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises significant privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived dataset for evaluation, we created two, one at five centers from LFQ E.coli experiments and one at three centers from TMT human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to DEqMS applied to pooled data, with completely negligible absolute differences no greater than $\text{$4 \times 10^{-12}$}$. In contrast, -log10(p-values) computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25-27. FedProt is available as a web tool with detailed documentation as a FeatureCloud App. |
2311.02594 | Chenyu Liu | Chenyu Liu, Yong Jin Kweon and Jun Ding | scBeacon: single-cell biomarker extraction via identifying paired cell
clusters across biological conditions with contrastive siamese networks | null | null | null | null | q-bio.GN cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the breakthroughs in biomarker discovery facilitated by differential
gene analysis, challenges remain, particularly at the single-cell level.
Traditional methodologies heavily rely on user-supplied cell annotations,
focusing on individually expressed data, often neglecting the critical
interactions between biological conditions, such as healthy versus diseased
states. In response, here we introduce scBeacon, an innovative framework built
upon a deep contrastive siamese network. scBeacon pioneers an unsupervised
approach, adeptly identifying matched cell populations across varied
conditions, enabling a refined differential gene analysis. By utilizing a
VQ-VAE framework, a contrastive siamese network, and a greedy iterative
strategy, scBeacon effectively pinpoints differential genes that hold potential
as key biomarkers. Comprehensive evaluations on a diverse array of datasets
validate scBeacon's superiority over existing single-cell differential gene
analysis tools. Its precision and adaptability underscore its significant role
in enhancing diagnostic accuracy in biomarker discovery. With the emphasis on
the importance of biomarkers in diagnosis, scBeacon is positioned to be a
pivotal asset in the evolution of personalized medicine and targeted
treatments.
| [
{
"created": "Sun, 5 Nov 2023 08:27:24 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Dec 2023 02:16:32 GMT",
"version": "v2"
}
] | 2023-12-29 | [
[
"Liu",
"Chenyu",
""
],
[
"Kweon",
"Yong Jin",
""
],
[
"Ding",
"Jun",
""
]
] | Despite the breakthroughs in biomarker discovery facilitated by differential gene analysis, challenges remain, particularly at the single-cell level. Traditional methodologies heavily rely on user-supplied cell annotations, focusing on individually expressed data, often neglecting the critical interactions between biological conditions, such as healthy versus diseased states. In response, here we introduce scBeacon, an innovative framework built upon a deep contrastive siamese network. scBeacon pioneers an unsupervised approach, adeptly identifying matched cell populations across varied conditions, enabling a refined differential gene analysis. By utilizing a VQ-VAE framework, a contrastive siamese network, and a greedy iterative strategy, scBeacon effectively pinpoints differential genes that hold potential as key biomarkers. Comprehensive evaluations on a diverse array of datasets validate scBeacon's superiority over existing single-cell differential gene analysis tools. Its precision and adaptability underscore its significant role in enhancing diagnostic accuracy in biomarker discovery. With the emphasis on the importance of biomarkers in diagnosis, scBeacon is positioned to be a pivotal asset in the evolution of personalized medicine and targeted treatments. |
2403.13569 | Guido Tiana | Francesco Borando and Guido Tiana | Effective model of protein--mediated interactions in chromatin | null | null | null | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein-mediated interactions are ubiquitous in the cellular environment, and
particularly in the nucleus, where they are responsible for the structuring of
chromatin. We show through molecular--dynamics simulations of a polymer
surrounded by binders that the strength of the binder-polymer interaction
separates an equilibrium from a non-equilibrium regime. In the equilibrium
regime, the system can be efficiently described by an effective model in which
the binders are traced out. Even in this case, the polymer display features
that are different from those of a standard homopolymer interacting with
two-body interactions. We then extend the effective model to deal with the case
where binders cannot be regarded as in equilibrium and a new phenomenology
appears, including local blobs in the polymer. Providing an effective
description of the system can be useful in clarifying the fundamental
mechanisms governing chromatin structuring.
| [
{
"created": "Wed, 20 Mar 2024 13:09:10 GMT",
"version": "v1"
}
] | 2024-03-21 | [
[
"Borando",
"Francesco",
""
],
[
"Tiana",
"Guido",
""
]
] | Protein-mediated interactions are ubiquitous in the cellular environment, and particularly in the nucleus, where they are responsible for the structuring of chromatin. We show through molecular--dynamics simulations of a polymer surrounded by binders that the strength of the binder-polymer interaction separates an equilibrium from a non-equilibrium regime. In the equilibrium regime, the system can be efficiently described by an effective model in which the binders are traced out. Even in this case, the polymer display features that are different from those of a standard homopolymer interacting with two-body interactions. We then extend the effective model to deal with the case where binders cannot be regarded as in equilibrium and a new phenomenology appears, including local blobs in the polymer. Providing an effective description of the system can be useful in clarifying the fundamental mechanisms governing chromatin structuring. |
q-bio/0409006 | Kerwyn Huang | Rahul V. Kulkarni, Kerwyn Casey Huang, Morten Kloster, and Ned S.
Wingreen | Pattern formation within Escherichia coli: diffusion, membrane
attachment, and self-interaction of MinD molecules | 4 pages, 3 figures, submitted to PRL | null | 10.1103/PhysRevLett.93.228103 | null | q-bio.SC | null | In E. coli, accurate cell division depends upon the oscillation of Min
proteins from pole to pole. We provide a model for the polar localization of
MinD based only on diffusion, a delay for nucleotide exchange, and different
rates of attachment to the bare membrane and the occupied membrane. We derive
analytically the probability density, and correspondingly the length scale, for
MinD attachment zones. Our simple analytical model illustrates the processes
giving rise to the observed localization of cellular MinD zones.
| [
{
"created": "Wed, 1 Sep 2004 21:01:03 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Kulkarni",
"Rahul V.",
""
],
[
"Huang",
"Kerwyn Casey",
""
],
[
"Kloster",
"Morten",
""
],
[
"Wingreen",
"Ned S.",
""
]
] | In E. coli, accurate cell division depends upon the oscillation of Min proteins from pole to pole. We provide a model for the polar localization of MinD based only on diffusion, a delay for nucleotide exchange, and different rates of attachment to the bare membrane and the occupied membrane. We derive analytically the probability density, and correspondingly the length scale, for MinD attachment zones. Our simple analytical model illustrates the processes giving rise to the observed localization of cellular MinD zones. |
2111.04338 | Adam Weisser | Adam Weisser | Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by
Optics and Communication | 626 pages, 124 figures, 13 tables, 1633 references | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A new theory of mammalian hearing is presented, which accounts for the
auditory image in the midbrain (inferior colliculus) of objects in the
acoustical environment of the listener. It is shown that the ear is a temporal
imaging system that comprises three transformations of the envelope functions:
cochlear group-delay dispersion, cochlear time lensing, and neural group-delay
dispersion. These elements are analogous to the optical transformations in
vision of diffraction between the object and the eye, spatial lensing by the
lens, and second diffraction between the lens and the retina. Unlike the eye,
it is established that the human auditory system is naturally defocused, so
that coherent stimuli do not react to the defocus, whereas completely
incoherent stimuli are impacted by it and may be blurred by design. It is
argued that the auditory system can use this differential focusing to enhance
or degrade the images of real-world acoustical objects that are partially
coherent. The theory is founded on coherence and temporal imaging theories that
were adopted from optics. In addition to the imaging transformations, the
corresponding inverse-domain modulation transfer functions are derived and
interpreted with consideration to the nonuniform neural sampling operation of
the auditory nerve. These ideas are used to rigorously initiate the concepts of
sharpness and blur in auditory imaging, auditory aberrations, and auditory
depth of field. In parallel, ideas from communication theory are used to show
that the organ of Corti functions as a multichannel phase-locked loop (PLL)
that constitutes the point of entry for auditory phase locking and hence
conserves the signal coherence. It provides an anchor for a dual coherent and
noncoherent auditory detection in the auditory brain that culminates in
auditory accommodation. Implications on hearing impairments are discussed as
well.
| [
{
"created": "Mon, 8 Nov 2021 08:54:39 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Nov 2021 08:18:51 GMT",
"version": "v2"
},
{
"created": "Fri, 12 Nov 2021 18:46:39 GMT",
"version": "v3"
},
{
"created": "Mon, 18 Apr 2022 17:00:58 GMT",
"version": "v4"
},
{
"cre... | 2024-06-04 | [
[
"Weisser",
"Adam",
""
]
] | A new theory of mammalian hearing is presented, which accounts for the auditory image in the midbrain (inferior colliculus) of objects in the acoustical environment of the listener. It is shown that the ear is a temporal imaging system that comprises three transformations of the envelope functions: cochlear group-delay dispersion, cochlear time lensing, and neural group-delay dispersion. These elements are analogous to the optical transformations in vision of diffraction between the object and the eye, spatial lensing by the lens, and second diffraction between the lens and the retina. Unlike the eye, it is established that the human auditory system is naturally defocused, so that coherent stimuli do not react to the defocus, whereas completely incoherent stimuli are impacted by it and may be blurred by design. It is argued that the auditory system can use this differential focusing to enhance or degrade the images of real-world acoustical objects that are partially coherent. The theory is founded on coherence and temporal imaging theories that were adopted from optics. In addition to the imaging transformations, the corresponding inverse-domain modulation transfer functions are derived and interpreted with consideration to the nonuniform neural sampling operation of the auditory nerve. These ideas are used to rigorously initiate the concepts of sharpness and blur in auditory imaging, auditory aberrations, and auditory depth of field. In parallel, ideas from communication theory are used to show that the organ of Corti functions as a multichannel phase-locked loop (PLL) that constitutes the point of entry for auditory phase locking and hence conserves the signal coherence. It provides an anchor for a dual coherent and noncoherent auditory detection in the auditory brain that culminates in auditory accommodation. Implications on hearing impairments are discussed as well. |
1912.10641 | Hiroki Ohta | Azusa Tanaka, Yasuhiro Ishitsuka, Hiroki Ohta, Akihiro Fujimoto,
Jun-ichirou Yasunaga, Masao Matsuoka | Systematic clustering algorithm for chromatin accessibility data and its
application to hematopoietic cells | 24 pages, 17 figures | PLOS Comput. Biol. 16(11), e1008422 (2020) | 10.1371/journal.pcbi.1008422 | null | q-bio.GN cond-mat.stat-mech q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The huge amount of data acquired by high-throughput sequencing requires data
reduction for effective analysis. Here we give a clustering algorithm for
genome-wide open chromatin data using a new data reduction method. This method
regards the genome as a string of $1$s and $0$s based on a set of peaks and
calculates the Hamming distances between the strings. This algorithm with the
systematically optimized set of peaks enables us to quantitatively evaluate
differences between samples of hematopoietic cells and classify cell types,
potentially leading to a better understanding of leukemia pathogenesis.
| [
{
"created": "Mon, 23 Dec 2019 06:34:36 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Nov 2020 19:00:27 GMT",
"version": "v2"
}
] | 2021-01-27 | [
[
"Tanaka",
"Azusa",
""
],
[
"Ishitsuka",
"Yasuhiro",
""
],
[
"Ohta",
"Hiroki",
""
],
[
"Fujimoto",
"Akihiro",
""
],
[
"Yasunaga",
"Jun-ichirou",
""
],
[
"Matsuoka",
"Masao",
""
]
] | The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of $1$s and $0$s based on a set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic cells and classify cell types, potentially leading to a better understanding of leukemia pathogenesis. |
1603.08386 | Chantriolnt-Andreas Kapourani | Chantriolnt-Andreas Kapourani and Guido Sanguinetti | Higher order methylation features for clustering and prediction in
epigenomic studies | 12 pages, 5 figures | Bioinformatics (2016) 32 (17): i405-i412 | 10.1093/bioinformatics/btw432 | null | q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: DNA methylation is an intensely studied epigenetic mark, yet its
functional role is incompletely understood. Attempts to quantitatively
associate average DNA methylation to gene expression yield poor correlations
outside of the well-understood methylation-switch at CpG islands.
Results: Here we use probabilistic machine learning to extract higher order
features associated with the methylation profile across a defined region. These
features quantitate precisely notions of shape of a methylation profile,
capturing spatial correlations in DNA methylation across genomic regions. Using
these higher order features across promoter-proximal regions, we are able to
construct a powerful machine learning predictor of gene expression,
significantly improving upon the predictive power of average DNA methylation
levels. Furthermore, we can use higher order features to cluster
promoter-proximal regions, showing that five major patterns of methylation
occur at promoters across different cell lines, and we provide evidence that
methylation beyond CpG islands may be related to regulation of gene expression.
Our results support previous reports of a functional role of spatial
correlations in methylation patterns, and provide a mean to quantitate such
features for downstream analyses.
Availability: https://github.com/andreaskapou/BPRMeth
| [
{
"created": "Mon, 28 Mar 2016 14:24:13 GMT",
"version": "v1"
}
] | 2016-11-17 | [
[
"Kapourani",
"Chantriolnt-Andreas",
""
],
[
"Sanguinetti",
"Guido",
""
]
] | Motivation: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-understood methylation-switch at CpG islands. Results: Here we use probabilistic machine learning to extract higher order features associated with the methylation profile across a defined region. These features quantitate precisely notions of shape of a methylation profile, capturing spatial correlations in DNA methylation across genomic regions. Using these higher order features across promoter-proximal regions, we are able to construct a powerful machine learning predictor of gene expression, significantly improving upon the predictive power of average DNA methylation levels. Furthermore, we can use higher order features to cluster promoter-proximal regions, showing that five major patterns of methylation occur at promoters across different cell lines, and we provide evidence that methylation beyond CpG islands may be related to regulation of gene expression. Our results support previous reports of a functional role of spatial correlations in methylation patterns, and provide a mean to quantitate such features for downstream analyses. Availability: https://github.com/andreaskapou/BPRMeth |
0911.4393 | Andrea Cavagna | Andrea Cavagna, Alessio Cimarelli, Irene Giardina, Giorgio Parisi,
Raffaele Santagati, Fabio Stefanini, Massimiliano Viale | Scale-free correlations in bird flocks | Submitted to PNAS | Proceedings of the National Academy of Sciences 107 (26),
11865-11870 (2010) | 10.1073/pnas.1005766107 | null | q-bio.PE cond-mat.stat-mech nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | From bird flocks to fish schools, animal groups often seem to react to
environmental perturbations as if of one mind. Most studies in collective
animal behaviour have aimed to understand how a globally ordered state may
emerge from simple behavioural rules. Less effort has been devoted to
understanding the origin of collective response, namely the way the group as a
whole reacts to its environment. Yet collective response is the adaptive key to
survivor, especially when strong predatory pressure is present. Here we argue
that collective response in animal groups is achieved through scale-free
behavioural correlations. By reconstructing the three-dimensional position and
velocity of individual birds in large flocks of starlings, we measured to what
extent the velocity fluctuations of different birds are correlated to each
other. We found that the range of such spatial correlation does not have a
constant value, but it scales with the linear size of the flock. This result
indicates that behavioural correlations are scale-free: the change in the
behavioural state of one animal affects and is affected by that of all other
animals in the group, no matter how large the group is. Scale-free correlations
extend maximally the effective perception range of the individuals, thus
compensating for the short-range nature of the direct inter-individual
interaction and enhancing global response to perturbations. Our results suggest
that flocks behave as critical systems, poised to respond maximally to
environmental perturbations.
| [
{
"created": "Mon, 23 Nov 2009 13:00:16 GMT",
"version": "v1"
}
] | 2014-10-10 | [
[
"Cavagna",
"Andrea",
""
],
[
"Cimarelli",
"Alessio",
""
],
[
"Giardina",
"Irene",
""
],
[
"Parisi",
"Giorgio",
""
],
[
"Santagati",
"Raffaele",
""
],
[
"Stefanini",
"Fabio",
""
],
[
"Viale",
"Massimiliano",
... | From bird flocks to fish schools, animal groups often seem to react to environmental perturbations as if of one mind. Most studies in collective animal behaviour have aimed to understand how a globally ordered state may emerge from simple behavioural rules. Less effort has been devoted to understanding the origin of collective response, namely the way the group as a whole reacts to its environment. Yet collective response is the adaptive key to survivor, especially when strong predatory pressure is present. Here we argue that collective response in animal groups is achieved through scale-free behavioural correlations. By reconstructing the three-dimensional position and velocity of individual birds in large flocks of starlings, we measured to what extent the velocity fluctuations of different birds are correlated to each other. We found that the range of such spatial correlation does not have a constant value, but it scales with the linear size of the flock. This result indicates that behavioural correlations are scale-free: the change in the behavioural state of one animal affects and is affected by that of all other animals in the group, no matter how large the group is. Scale-free correlations extend maximally the effective perception range of the individuals, thus compensating for the short-range nature of the direct inter-individual interaction and enhancing global response to perturbations. Our results suggest that flocks behave as critical systems, poised to respond maximally to environmental perturbations. |
0705.4062 | Eugene Shakhnovich | Konstantin Zeldovich, Peiqiu Chen, Eugene Shakhnovich | The Hypercube of Life: How Protein Stability Imposes Limits on Organism
Complexity and Speed of Molecular Evolution | null | null | null | null | q-bio.BM q-bio.PE | null | Classical population genetics a priori assigns fitness to alleles without
considering molecular or functional properties of proteins that these alleles
encode. Here we study population dynamics in a model where fitness can be
inferred from physical properties of proteins under a physiological assumption
that loss of stability of any protein encoded by an essential gene confers a
lethal phenotype. Accumulation of mutations in organisms containing Gamma genes
can then be represented as diffusion within the Gamma dimensional hypercube
with adsorbing boundaries which are determined, in each dimension, by loss of a
protein stability and, at higher stability, by lack of protein sequences.
Solving the diffusion equation whose parameters are derived from the data on
point mutations in proteins, we determine a universal distribution of protein
stabilities, in agreement with existing data. The theory provides a fundamental
relation between mutation rate, maximal genome size and thermodynamic response
of proteins to point mutations. It establishes a universal speed limit on rate
of molecular evolution by predicting that populations go extinct (via lethal
mutagenesis) when mutation rate exceeds approximately 6 mutations per essential
part of genome per replication for mesophilic organisms and 1 to 2 mutations
per genome per replication for thermophilic ones. Further, our results suggest
that in absence of error correction, modern RNA viruses and primordial genomes
must necessarily be very short. Several RNA viruses function close to the
evolutionary speed limit while error correction mechanisms used by DNA viruses
and non-mutant strains of bacteria featuring various genome lengths and
mutation rates have brought these organisms universally about 1000 fold below
the natural speed limit.
| [
{
"created": "Mon, 28 May 2007 16:00:37 GMT",
"version": "v1"
}
] | 2007-05-29 | [
[
"Zeldovich",
"Konstantin",
""
],
[
"Chen",
"Peiqiu",
""
],
[
"Shakhnovich",
"Eugene",
""
]
] | Classical population genetics a priori assigns fitness to alleles without considering molecular or functional properties of proteins that these alleles encode. Here we study population dynamics in a model where fitness can be inferred from physical properties of proteins under a physiological assumption that loss of stability of any protein encoded by an essential gene confers a lethal phenotype. Accumulation of mutations in organisms containing Gamma genes can then be represented as diffusion within the Gamma dimensional hypercube with adsorbing boundaries which are determined, in each dimension, by loss of a protein stability and, at higher stability, by lack of protein sequences. Solving the diffusion equation whose parameters are derived from the data on point mutations in proteins, we determine a universal distribution of protein stabilities, in agreement with existing data. The theory provides a fundamental relation between mutation rate, maximal genome size and thermodynamic response of proteins to point mutations. It establishes a universal speed limit on rate of molecular evolution by predicting that populations go extinct (via lethal mutagenesis) when mutation rate exceeds approximately 6 mutations per essential part of genome per replication for mesophilic organisms and 1 to 2 mutations per genome per replication for thermophilic ones. Further, our results suggest that in absence of error correction, modern RNA viruses and primordial genomes must necessarily be very short. Several RNA viruses function close to the evolutionary speed limit while error correction mechanisms used by DNA viruses and non-mutant strains of bacteria featuring various genome lengths and mutation rates have brought these organisms universally about 1000 fold below the natural speed limit. |
2112.05437 | Pratyush Kollepara | Pratyush K. Kollepara and Joel C. Miller | Questioning the use of global estimates of reproduction numbers, with
implications for policy | 5 pages, 3 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The basic reproduction number, $R_0$ is an important and widely used concept
in the study of infectious diseases. We briefly review the recent trend of
calculating the average of various $R_0$ estimates in systematic reviews aimed
at estimating the basic reproduction number of SARS-CoV-2, and discuss the
drawbacks and implications of using such averaging methods. Additionally, we
argue that even a theoretically grounded approach such as next generation
matrix could have practical impediments in its use. More generally, the
practice of associating an infectious disease with a single value of $R_0$ is
problematic, when the disease can, in fact have different reproduction numbers
in various populations.
| [
{
"created": "Fri, 10 Dec 2021 10:38:57 GMT",
"version": "v1"
}
] | 2021-12-13 | [
[
"Kollepara",
"Pratyush K.",
""
],
[
"Miller",
"Joel C.",
""
]
] | The basic reproduction number, $R_0$ is an important and widely used concept in the study of infectious diseases. We briefly review the recent trend of calculating the average of various $R_0$ estimates in systematic reviews aimed at estimating the basic reproduction number of SARS-CoV-2, and discuss the drawbacks and implications of using such averaging methods. Additionally, we argue that even a theoretically grounded approach such as next generation matrix could have practical impediments in its use. More generally, the practice of associating an infectious disease with a single value of $R_0$ is problematic, when the disease can, in fact have different reproduction numbers in various populations. |
2112.13021 | Reza Sameni | Reza Sameni | Noninvasive Fetal Electrocardiography: Models, Technologies and
Algorithms | null | In Innovative Technologies and Signal Processing in Perinatal
Medicine (pp. 99-146). Springer International Publishing (2020) | 10.1007/978-3-030-54403-4_5 | null | q-bio.QM cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fetal electrocardiogram (fECG) was first recorded from the maternal
abdominal surface in the early 1900s. During the past fifty years, the most
advanced electronics technologies and signal processing algorithms have been
used to convert noninvasive fetal electrocardiography into a reliable
technology for fetal cardiac monitoring. In this chapter, the major signal
processing techniques, which have been developed for the modeling, extraction
and analysis of the fECG from noninvasive maternal abdominal recordings are
reviewed and compared with one another in detail. The major topics of the
chapter include: 1) the electrophysiology of the fECG from the signal
processing viewpoint, 2) the mathematical model of the maternal volume
conduction media and the waveform models of the fECG acquired from body surface
leads, 3) the signal acquisition requirements, 4) model-based techniques for
fECG noise and interference cancellation, including adaptive filters and
semi-blind source separation techniques, and 5) recent algorithmic advances for
fetal motion tracking and online fECG extraction from few number of channels.
| [
{
"created": "Fri, 24 Dec 2021 10:16:23 GMT",
"version": "v1"
}
] | 2021-12-28 | [
[
"Sameni",
"Reza",
""
]
] | The fetal electrocardiogram (fECG) was first recorded from the maternal abdominal surface in the early 1900s. During the past fifty years, the most advanced electronics technologies and signal processing algorithms have been used to convert noninvasive fetal electrocardiography into a reliable technology for fetal cardiac monitoring. In this chapter, the major signal processing techniques, which have been developed for the modeling, extraction and analysis of the fECG from noninvasive maternal abdominal recordings are reviewed and compared with one another in detail. The major topics of the chapter include: 1) the electrophysiology of the fECG from the signal processing viewpoint, 2) the mathematical model of the maternal volume conduction media and the waveform models of the fECG acquired from body surface leads, 3) the signal acquisition requirements, 4) model-based techniques for fECG noise and interference cancellation, including adaptive filters and semi-blind source separation techniques, and 5) recent algorithmic advances for fetal motion tracking and online fECG extraction from few number of channels. |
q-bio/0604007 | Dmitry Kondrashov | Dmitry A. Kondrashov, Qiang Cui, and George N. Phillips Jr | Optimization and evaluation of a coarse-grained model of protein motion
using X-ray crystal data | 18 pages, 4 figures, 1 supplemental file (cnm_si.tex) | Biophysical Journal, vol 91 (8), 2006 | 10.1529/biophysj.106.085894 | null | q-bio.BM | null | Simple coarse-grained models, such as the Gaussian Network Model, have been
shown to capture some of the features of equilibrium protein dynamics. We
extend this model by using atomic contacts to define residue interactions and
introducing more than one interaction parameter between residues. We use
B-factors from 98 ultra-high resolution X-ray crystal structures to optimize
the interaction parameters. The average correlation between GNM fluctuation
predictions and the B-factors is 0.64 for the data set, consistent with a
previous large-scale study. By separating residue interactions into covalent
and noncovalent, we achieve an average correlation of 0.74, and addition of
ligands and cofactors further improves the correlation to 0.75. However,
further separating the noncovalent interactions into nonpolar, polar, and mixed
yields no significant improvement. The addition of simple chemical information
results in better prediction quality without increasing the size of the
coarse-grained model.
| [
{
"created": "Thu, 6 Apr 2006 18:56:36 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Kondrashov",
"Dmitry A.",
""
],
[
"Cui",
"Qiang",
""
],
[
"Phillips",
"George N.",
"Jr"
]
] | Simple coarse-grained models, such as the Gaussian Network Model, have been shown to capture some of the features of equilibrium protein dynamics. We extend this model by using atomic contacts to define residue interactions and introducing more than one interaction parameter between residues. We use B-factors from 98 ultra-high resolution X-ray crystal structures to optimize the interaction parameters. The average correlation between GNM fluctuation predictions and the B-factors is 0.64 for the data set, consistent with a previous large-scale study. By separating residue interactions into covalent and noncovalent, we achieve an average correlation of 0.74, and addition of ligands and cofactors further improves the correlation to 0.75. However, further separating the noncovalent interactions into nonpolar, polar, and mixed yields no significant improvement. The addition of simple chemical information results in better prediction quality without increasing the size of the coarse-grained model. |
2102.03682 | Mateusz Chwastyk | Mateusz Chwastyk, Marek Cieplak | Conformational Biases of {\alpha}-Synuclein and Formation of Transient
Knots | 28 pages, 9 figures, 1 table | J. Phys. Chem. B 2020, 124, 1, 11-19 | 10.1021/acs.jpcb.9b08481 | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | We study local conformational biases in the dynamics of {\alpha}-synuclein by
using all-atom simulations with explicit and implicit solvents. The biases are
related to the frequency of the specific contact formation. In both approaches,
the protein is intrinsically disordered, and its strongest bias is to make bend
and turn local structures. The explicit-solvent conformations can be
substantially more extended which allows for formation of transient trefoil
knots, both deep and shallow, that may last for up to 5 {\mu}s. The two-chain
self-association events, both short- and long-lived, are dominated by formation
of contacts in the central part of the sequence. This part tends to form
helices when bound to a micelle.
| [
{
"created": "Sat, 6 Feb 2021 23:21:28 GMT",
"version": "v1"
}
] | 2021-02-09 | [
[
"Chwastyk",
"Mateusz",
""
],
[
"Cieplak",
"Marek",
""
]
] | We study local conformational biases in the dynamics of {\alpha}-synuclein by using all-atom simulations with explicit and implicit solvents. The biases are related to the frequency of the specific contact formation. In both approaches, the protein is intrinsically disordered, and its strongest bias is to make bend and turn local structures. The explicit-solvent conformations can be substantially more extended which allows for formation of transient trefoil knots, both deep and shallow, that may last for up to 5 {\mu}s. The two-chain self-association events, both short- and long-lived, are dominated by formation of contacts in the central part of the sequence. This part tends to form helices when bound to a micelle. |
1504.00817 | Alexander Iomin | A. Iomin | Continuous Time Random Walk and Migration Proliferation Dichotomy | null | null | 10.1142/9789814730266_0004 | null | q-bio.CB cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A theory of fractional kinetics of glial cancer cells is presented. A role of
the migration-proliferation dichotomy in the fractional cancer cell dynamics in
the outer-invasive zone is discussed an explained in the framework of a
continuous time random walk. The main suggested model is based on a
construction of a 3D comb model, where the migration-proliferation dichotomy
becomes naturally apparent and the outer-invasive zone of glioma cancer is
considered as a fractal composite with a fractal dimension $\frD<3$.
| [
{
"created": "Fri, 3 Apr 2015 11:37:35 GMT",
"version": "v1"
}
] | 2016-03-23 | [
[
"Iomin",
"A.",
""
]
] | A theory of fractional kinetics of glial cancer cells is presented. A role of the migration-proliferation dichotomy in the fractional cancer cell dynamics in the outer-invasive zone is discussed an explained in the framework of a continuous time random walk. The main suggested model is based on a construction of a 3D comb model, where the migration-proliferation dichotomy becomes naturally apparent and the outer-invasive zone of glioma cancer is considered as a fractal composite with a fractal dimension $\frD<3$. |
2405.09595 | Noel Malod-Dognin | Natasa Przulj and Noel Malod-Dognin | Simplicity within biological complexity | 29 pages, 4 figures | null | null | null | q-bio.OT cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Heterogeneous, interconnected, systems-level, molecular data have become
increasingly available and key in precision medicine. We need to utilize them
to better stratify patients into risk groups, discover new biomarkers and
targets, repurpose known and discover new drugs to personalize medical
treatment. Existing methodologies are limited and a paradigm shift is needed to
achieve quantitative and qualitative breakthroughs. In this perspective paper,
we survey the literature and argue for the development of a comprehensive,
general framework for embedding of multi-scale molecular network data that
would enable their explainable exploitation in precision medicine in linear
time. Network embedding methods map nodes to points in low-dimensional space,
so that proximity in the learned space reflects the network's topology-function
relationships. They have recently achieved unprecedented performance on hard
problems of utilizing few omic data in various biomedical applications.
However, research thus far has been limited to special variants of the problems
and data, with the performance depending on the underlying topology-function
network biology hypotheses, the biomedical applications and evaluation metrics.
The availability of multi-omic data, modern graph embedding paradigms and
compute power call for a creation and training of efficient, explainable and
controllable models, having no potentially dangerous, unexpected behaviour,
that make a qualitative breakthrough. We propose to develop a general,
comprehensive embedding framework for multi-omic network data, from models to
efficient and scalable software implementation, and to apply it to biomedical
informatics. It will lead to a paradigm shift in computational and biomedical
understanding of data and diseases that will open up ways to solving some of
the major bottlenecks in precision medicine and other domains.
| [
{
"created": "Wed, 15 May 2024 13:32:45 GMT",
"version": "v1"
}
] | 2024-05-17 | [
[
"Przulj",
"Natasa",
""
],
[
"Malod-Dognin",
"Noel",
""
]
] | Heterogeneous, interconnected, systems-level, molecular data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known and discover new drugs to personalize medical treatment. Existing methodologies are limited and a paradigm shift is needed to achieve quantitative and qualitative breakthroughs. In this perspective paper, we survey the literature and argue for the development of a comprehensive, general framework for embedding of multi-scale molecular network data that would enable their explainable exploitation in precision medicine in linear time. Network embedding methods map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network's topology-function relationships. They have recently achieved unprecedented performance on hard problems of utilizing few omic data in various biomedical applications. However, research thus far has been limited to special variants of the problems and data, with the performance depending on the underlying topology-function network biology hypotheses, the biomedical applications and evaluation metrics. The availability of multi-omic data, modern graph embedding paradigms and compute power call for a creation and training of efficient, explainable and controllable models, having no potentially dangerous, unexpected behaviour, that make a qualitative breakthrough. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation, and to apply it to biomedical informatics. It will lead to a paradigm shift in computational and biomedical understanding of data and diseases that will open up ways to solving some of the major bottlenecks in precision medicine and other domains. |
1110.5225 | Tomas Tokar | Tom\'a\v{s} Tok\'ar and Jozef Uli\v{c}n\'y | Computational study of the mechanism of Bcl-2 apoptotic switch | null | Tokar T., Ulicny J., Computational study of the mechanism of Bcl-2
apoptotic switch, Physica A: Statistical Mechanics and its Applications,
Volume 391, Issue 23, 1 December 2012, Pages 6212-6225 | 10.1016/j.physa.2012.07.006 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Programmed cell death - apoptosis is one of the most studied biological
phenomenon of recent years. Apoptotic regulatory network contains several
significant control points, including probably the most important one - Bcl--2
apoptotic switch. There are two proposed hypotheses regarding its internal
working - the indirect activation and direct activation models. Since these
hypotheses form extreme poles of full continuum of intermediate models, we have
constructed more general model with these two models as extreme cases.
By studying relationship between model parameters and steady-state response
ultrasensitivity we have found optimal interaction pattern which reproduces
behavior of Bcl-2 apoptotic switch. Our results show, that stimulus-response
ultrasensitivity is negatively related to spontaneous activation of Bcl-2
effectors - subgroup of Bcl-2 proteins. We found that ultrasensitivity requires
effector's activation, mediated by another subgroup of Bcl-2 proteins -
activators. We have shown that the auto-activation of effectors forms
ultrasensitivity enhancing feedback loop, only if mediated by monomers, but not
by oligomers. Robustness analysis revealed that interaction pattern proposed by
direct activation hypothesis is able to conserve stimulus-response dependence
and preserve ultrasensitivity despite large changes of its internal parameters.
This ability is strongly reduced as for the intermediate to indirect side of
the models.
Computer simulation of the more general model presented here suggest, that
stimulus-response ultrasensitivity is an emergent property of the direct
activation model, that cannot originate within model of indirect activation.
Introduction of indirect-model-specific interactions does not provide better
explanation of Bcl-2 functioning compared to direct model.
| [
{
"created": "Mon, 24 Oct 2011 13:15:07 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Dec 2012 12:32:33 GMT",
"version": "v2"
}
] | 2015-05-30 | [
[
"Tokár",
"Tomáš",
""
],
[
"Uličný",
"Jozef",
""
]
] | Programmed cell death - apoptosis is one of the most studied biological phenomenon of recent years. Apoptotic regulatory network contains several significant control points, including probably the most important one - Bcl--2 apoptotic switch. There are two proposed hypotheses regarding its internal working - the indirect activation and direct activation models. Since these hypotheses form extreme poles of full continuum of intermediate models, we have constructed more general model with these two models as extreme cases. By studying relationship between model parameters and steady-state response ultrasensitivity we have found optimal interaction pattern which reproduces behavior of Bcl-2 apoptotic switch. Our results show, that stimulus-response ultrasensitivity is negatively related to spontaneous activation of Bcl-2 effectors - subgroup of Bcl-2 proteins. We found that ultrasensitivity requires effector's activation, mediated by another subgroup of Bcl-2 proteins - activators. We have shown that the auto-activation of effectors forms ultrasensitivity enhancing feedback loop, only if mediated by monomers, but not by oligomers. Robustness analysis revealed that interaction pattern proposed by direct activation hypothesis is able to conserve stimulus-response dependence and preserve ultrasensitivity despite large changes of its internal parameters. This ability is strongly reduced as for the intermediate to indirect side of the models. Computer simulation of the more general model presented here suggest, that stimulus-response ultrasensitivity is an emergent property of the direct activation model, that cannot originate within model of indirect activation. Introduction of indirect-model-specific interactions does not provide better explanation of Bcl-2 functioning compared to direct model. |
q-bio/0412011 | Hernan Garcia | Lacramioara Bintu, Nicolas E. Buchler, Hernan G. Garcia, Ulrich
Gerland, Terence Hwa, Jane' Kondev, Thomas Kuhlman and Rob Phillips | Transcriptional Regulation by the Numbers 2: Applications | 15 pages and 9 figures in PDF format | null | null | null | q-bio.MN q-bio.QM | null | With the increasing amount of experimental data on gene expression and
regulation, there is a growing need for quantitative models to describe the
data and relate them to the different contexts. The thermodynamic models
reviewed in the preceding paper provide a useful framework for the quantitative
analysis of bacterial transcription regulation. We review a number of
well-characterized bacterial promoters that are regulated by one or two species
of transcription factors, and apply the thermodynamic framework to these
promoters. We show that the framework allows one to quantify vastly different
forms of gene expression using a few parameters. As such, it provides a compact
description useful for higher-level studies, e.g., of genetic networks, without
the need to invoke the biochemical details of every component. Moreover, it can
be used to generate hypotheses on the likely mechanisms of transcriptional
control.
| [
{
"created": "Sun, 5 Dec 2004 22:47:57 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bintu",
"Lacramioara",
""
],
[
"Buchler",
"Nicolas E.",
""
],
[
"Garcia",
"Hernan G.",
""
],
[
"Gerland",
"Ulrich",
""
],
[
"Hwa",
"Terence",
""
],
[
"Kondev",
"Jane'",
""
],
[
"Kuhlman",
"Thomas",
""
],... | With the increasing amount of experimental data on gene expression and regulation, there is a growing need for quantitative models to describe the data and relate them to the different contexts. The thermodynamic models reviewed in the preceding paper provide a useful framework for the quantitative analysis of bacterial transcription regulation. We review a number of well-characterized bacterial promoters that are regulated by one or two species of transcription factors, and apply the thermodynamic framework to these promoters. We show that the framework allows one to quantify vastly different forms of gene expression using a few parameters. As such, it provides a compact description useful for higher-level studies, e.g., of genetic networks, without the need to invoke the biochemical details of every component. Moreover, it can be used to generate hypotheses on the likely mechanisms of transcriptional control. |
1911.05621 | Weikaixin Kong | Miaomiao Gao, Weikaixin Kong, Zhuo Huang and Zhengwei Xie | Identification of key genes related to the mechanism and prognosis of
lung squamous cell carcinoma using bioinformatics analysis | This work was supported by National key research and development
program of China(2018YFA0900200), National Natural Science Foundation of
China Grants (31771519,31871083) and Beijing Natural Science Foundation
(5182012, 7182087), the Ministry of Science and Technology of China Grant
2015CB559200. Funding for open access charge: National key research and
development program of China | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objectives Lung squamous cell carcinoma (LUSC) often diagnosed as advanced
with poor prognosis. The mechanisms of its pathogenesis and prognosis require
urgent elucidation. This study was performed to screen potential biomarkers
related to the occurrence, development and prognosis of LUSC to reveal unknown
physiological and pathological processes. Materials and Methods Using
bioinformatics analysis, the lung squamous cell carcinoma microarray datasets
from the GEO and TCGA databases were analyzed to identify differentially
expressed genes(DEGs). Furthermore, PPI and WGCNA network analysis were
integrated to identify the key genes closely related to the process of LUSC
development. In addition, survival analysis was performed to achieve a
prognostic model that accomplished a high level of prediction accuracy. Results
and Conclusion Eighty-five up-regulated and 39 down-regulated genes were
identified, on which functional and pathway enrichment analysis was conducted.
GO analysis demonstrated that up-regulated genes were principally enriched in
epidermal development and DNA unwinding in DNA replication. Down-regulated
genes were mainly involved in cell adhesion, signal transduction and positive
regulation of inflammatory response. After PPI and WGCNA network analysis,
eight genes, including AURKA, RAD51, TTK, AURKB, CCNA2, TPX2, KPNA2 and KIF23,
have been found to play a vital role in LUSC development. The prognostic model
contained 20 genes, 18 of which were detrimental to prognosis. The AUC of the
established prognostic model for predicting the survival of patients at 1, 3,
and 5 years was 0.828, 0.826 and 0.824, respectively. To conclude, this study
identified a number of biomarkers of significant interest for additional
investigation of the therapies and methods of prognosis of lung squamous cell
carcinoma.
| [
{
"created": "Wed, 13 Nov 2019 17:01:55 GMT",
"version": "v1"
}
] | 2019-11-14 | [
[
"Gao",
"Miaomiao",
""
],
[
"Kong",
"Weikaixin",
""
],
[
"Huang",
"Zhuo",
""
],
[
"Xie",
"Zhengwei",
""
]
] | Objectives Lung squamous cell carcinoma (LUSC) often diagnosed as advanced with poor prognosis. The mechanisms of its pathogenesis and prognosis require urgent elucidation. This study was performed to screen potential biomarkers related to the occurrence, development and prognosis of LUSC to reveal unknown physiological and pathological processes. Materials and Methods Using bioinformatics analysis, the lung squamous cell carcinoma microarray datasets from the GEO and TCGA databases were analyzed to identify differentially expressed genes(DEGs). Furthermore, PPI and WGCNA network analysis were integrated to identify the key genes closely related to the process of LUSC development. In addition, survival analysis was performed to achieve a prognostic model that accomplished a high level of prediction accuracy. Results and Conclusion Eighty-five up-regulated and 39 down-regulated genes were identified, on which functional and pathway enrichment analysis was conducted. GO analysis demonstrated that up-regulated genes were principally enriched in epidermal development and DNA unwinding in DNA replication. Down-regulated genes were mainly involved in cell adhesion, signal transduction and positive regulation of inflammatory response. After PPI and WGCNA network analysis, eight genes, including AURKA, RAD51, TTK, AURKB, CCNA2, TPX2, KPNA2 and KIF23, have been found to play a vital role in LUSC development. The prognostic model contained 20 genes, 18 of which were detrimental to prognosis. The AUC of the established prognostic model for predicting the survival of patients at 1, 3, and 5 years was 0.828, 0.826 and 0.824, respectively. To conclude, this study identified a number of biomarkers of significant interest for additional investigation of the therapies and methods of prognosis of lung squamous cell carcinoma. |
2307.12505 | Kanika Bansal | ItaloIvo Lima Dias Pinto, Javier Omar Garcia, Kanika Bansal | Optimizing parameter search for community detection in time evolving
networks of complex systems | 28 pages, 7 figures | null | null | null | q-bio.NC nlin.AO physics.data-an | http://creativecommons.org/licenses/by/4.0/ | Network representations have been effectively employed to analyze complex
systems across various areas and applications, leading to the development of
network science as a core tool to study systems with multiple components and
complex interactions. There is a growing interest in understanding the temporal
dynamics of complex networks to decode the underlying dynamic processes through
the temporal changes in network structure. Community detection algorithms,
which are specialized clustering algorithms, have been instrumental in studying
these temporal changes. They work by grouping nodes into communities based on
the structure and intensity of network connections over time aiming to maximize
modularity of the network partition. However, the performance of these
algorithms is highly influenced by the selection of resolution parameters of
the modularity function used, which dictate the scale of the represented
network, both in size of communities and the temporal resolution of dynamic
structure. The selection of these parameters has often been subjective and
heavily reliant on the characteristics of the data used to create the network
structure. Here, we introduce a method to objectively determine the values of
the resolution parameters based on the elements of self-organization. We
propose two key approaches: (1) minimization of the biases in spatial scale
network characterization and (2) maximization of temporal scale-freeness. We
demonstrate the effectiveness of these approaches using benchmark network
structures as well as real-world datasets. To implement our method, we also
provide an automated parameter selection software package that can be applied
to a wide range of complex systems.
| [
{
"created": "Mon, 24 Jul 2023 03:38:34 GMT",
"version": "v1"
}
] | 2023-07-25 | [
[
"Pinto",
"ItaloIvo Lima Dias",
""
],
[
"Garcia",
"Javier Omar",
""
],
[
"Bansal",
"Kanika",
""
]
] | Network representations have been effectively employed to analyze complex systems across various areas and applications, leading to the development of network science as a core tool to study systems with multiple components and complex interactions. There is a growing interest in understanding the temporal dynamics of complex networks to decode the underlying dynamic processes through the temporal changes in network structure. Community detection algorithms, which are specialized clustering algorithms, have been instrumental in studying these temporal changes. They work by grouping nodes into communities based on the structure and intensity of network connections over time aiming to maximize modularity of the network partition. However, the performance of these algorithms is highly influenced by the selection of resolution parameters of the modularity function used, which dictate the scale of the represented network, both in size of communities and the temporal resolution of dynamic structure. The selection of these parameters has often been subjective and heavily reliant on the characteristics of the data used to create the network structure. Here, we introduce a method to objectively determine the values of the resolution parameters based on the elements of self-organization. We propose two key approaches: (1) minimization of the biases in spatial scale network characterization and (2) maximization of temporal scale-freeness. We demonstrate the effectiveness of these approaches using benchmark network structures as well as real-world datasets. To implement our method, we also provide an automated parameter selection software package that can be applied to a wide range of complex systems. |
1909.02093 | John Halloran | John T. Halloran and David M. Rocke | Gradients of Generative Models for Improved Discriminative Analysis of
Tandem Mass Spectra | 13 pages. A partitioned version of this appeared in NIPS 2017 | null | null | null | q-bio.QM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tandem mass spectrometry (MS/MS) is a high-throughput technology used
toidentify the proteins in a complex biological sample, such as a drop of
blood. A collection of spectra is generated at the output of the process, each
spectrum of which is representative of a peptide (protein subsequence) present
in the original complex sample. In this work, we leverage the log-likelihood
gradients of generative models to improve the identification of such spectra.
In particular, we show that the gradient of a recently proposed dynamic
Bayesian network (DBN) may be naturally employed by a kernel-based
discriminative classifier. The resulting Fisher kernel substantially improves
upon recent attempts to combine generative and discriminative models for
post-processing analysis, outperforming all other methods on the evaluated
datasets. We extend the improved accuracy offered by the Fisher kernel
framework to other search algorithms by introducing Theseus, a DBN representing
a large number of widely used MS/MS scoring functions. Furthermore, with
gradient ascent and max-product inference at hand, we use Theseus to learn
model parameters without any supervision.
| [
{
"created": "Wed, 4 Sep 2019 20:29:04 GMT",
"version": "v1"
}
] | 2019-09-06 | [
[
"Halloran",
"John T.",
""
],
[
"Rocke",
"David M.",
""
]
] | Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A collection of spectra is generated at the output of the process, each spectrum of which is representative of a peptide (protein subsequence) present in the original complex sample. In this work, we leverage the log-likelihood gradients of generative models to improve the identification of such spectra. In particular, we show that the gradient of a recently proposed dynamic Bayesian network (DBN) may be naturally employed by a kernel-based discriminative classifier. The resulting Fisher kernel substantially improves upon recent attempts to combine generative and discriminative models for post-processing analysis, outperforming all other methods on the evaluated datasets. We extend the improved accuracy offered by the Fisher kernel framework to other search algorithms by introducing Theseus, a DBN representing a large number of widely used MS/MS scoring functions. Furthermore, with gradient ascent and max-product inference at hand, we use Theseus to learn model parameters without any supervision. |
1209.2975 | Emily Wall | Emily Wall, Frederic Guichard, and Antony R. Humphries | Synchronization in ecological systems by weak dispersal coupling with
time delay | Submitted to Theoretical Ecology on 06/09/2012, accepted for
publication on 21/01/2013 | Theor Ecol (2013) 6: 405 | 10.1007/s12080-013-0176-6 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most salient spatio-temporal patterns in population ecology is the
synchronization of fluctuating local populations across vast spatial extent.
Synchronization of abundance has been widely observed across a range of spatial
scales in relation to rate of dispersal among discrete populations. However,
the dependence of synchrony on patterns of among-patch movement across
heterogeneous landscapes has been largely ignored. Here we consider the
duration of movement between two predator-prey communities connected by weak
dispersal, and its effect on population synchrony. More specifically, we
introduce time delayed dispersal to incorporate the finite transmission time
between discrete populations across a continuous landscape. Reducing the system
to a phase model using weakly connected network theory, it is found that the
time delay is an important factor determining the nature and stability of
phase-locked states. Our analysis predicts enhanced convergence to stable
synchronous fluctuations in general, and a decreased ability of systems to
produce in-phase synchronization dynamics in the presence of delayed dispersal.
These results introduce delayed dispersal as a tool for understanding the
importance of dispersal time across a landscape matrix in affecting
metacommunity dynamics. They further highlight the importance of landscape and
dispersal patterns for predicting the onset of synchrony between weakly-coupled
populations.
| [
{
"created": "Thu, 13 Sep 2012 17:48:50 GMT",
"version": "v1"
},
{
"created": "Sun, 27 Jan 2013 14:56:08 GMT",
"version": "v2"
}
] | 2017-06-01 | [
[
"Wall",
"Emily",
""
],
[
"Guichard",
"Frederic",
""
],
[
"Humphries",
"Antony R.",
""
]
] | One of the most salient spatio-temporal patterns in population ecology is the synchronization of fluctuating local populations across vast spatial extent. Synchronization of abundance has been widely observed across a range of spatial scales in relation to rate of dispersal among discrete populations. However, the dependence of synchrony on patterns of among-patch movement across heterogeneous landscapes has been largely ignored. Here we consider the duration of movement between two predator-prey communities connected by weak dispersal, and its effect on population synchrony. More specifically, we introduce time delayed dispersal to incorporate the finite transmission time between discrete populations across a continuous landscape. Reducing the system to a phase model using weakly connected network theory, it is found that the time delay is an important factor determining the nature and stability of phase-locked states. Our analysis predicts enhanced convergence to stable synchronous fluctuations in general, and a decreased ability of systems to produce in-phase synchronization dynamics in the presence of delayed dispersal. These results introduce delayed dispersal as a tool for understanding the importance of dispersal time across a landscape matrix in affecting metacommunity dynamics. They further highlight the importance of landscape and dispersal patterns for predicting the onset of synchrony between weakly-coupled populations. |
2006.02949 | Dale Zhou | Dale Zhou, David M. Lydon-Staley, Perry Zurn, Danielle S. Bassett | The growth and form of knowledge networks by kinesthetic curiosity | null | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Throughout life, we might seek a calling, companions, skills, entertainment,
truth, self-knowledge, beauty, and edification. The practice of curiosity can
be viewed as an extended and open-ended search for valuable information with
hidden identity and location in a complex space of interconnected information.
Despite its importance, curiosity has been challenging to computationally model
because the practice of curiosity often flourishes without specific goals,
external reward, or immediate feedback. Here, we show how network science,
statistical physics, and philosophy can be integrated into an approach that
coheres with and expands the psychological taxonomies of specific-diversive and
perceptual-epistemic curiosity. Using this interdisciplinary approach, we
distill functional modes of curious information seeking as searching movements
in information space. The kinesthetic model of curiosity offers a vibrant
counterpart to the deliberative predictions of model-based reinforcement
learning. In doing so, this model unearths new computational opportunities for
identifying what makes curiosity curious.
| [
{
"created": "Thu, 4 Jun 2020 15:30:41 GMT",
"version": "v1"
}
] | 2020-06-05 | [
[
"Zhou",
"Dale",
""
],
[
"Lydon-Staley",
"David M.",
""
],
[
"Zurn",
"Perry",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Throughout life, we might seek a calling, companions, skills, entertainment, truth, self-knowledge, beauty, and edification. The practice of curiosity can be viewed as an extended and open-ended search for valuable information with hidden identity and location in a complex space of interconnected information. Despite its importance, curiosity has been challenging to computationally model because the practice of curiosity often flourishes without specific goals, external reward, or immediate feedback. Here, we show how network science, statistical physics, and philosophy can be integrated into an approach that coheres with and expands the psychological taxonomies of specific-diversive and perceptual-epistemic curiosity. Using this interdisciplinary approach, we distill functional modes of curious information seeking as searching movements in information space. The kinesthetic model of curiosity offers a vibrant counterpart to the deliberative predictions of model-based reinforcement learning. In doing so, this model unearths new computational opportunities for identifying what makes curiosity curious. |
2004.03495 | Marcelo Savi | Pedro V. Savi, Marcelo A. Savi, Beatriz Borges | A Mathematical Description of the Dynamics of Coronavirus Disease
(COVID-19): A Case Study of Brazil | 17 pages, 13 Figures | null | null | null | q-bio.PE nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper deals with the mathematical modeling and numerical simulations
related to the coronavirus dynamics. A description is developed based on the
framework of susceptible-exposed-infectious-recovered model. Initially, a model
verification is carried out calibrating system parameters with data from China,
Italy, Iran and Brazil. Afterward, numerical simulations are performed to
analyzed different scenarios of COVID-19 in Brazil. Results show the importance
of governmental and individual actions to control the number and the period of
the critical situations related to the pandemic.
| [
{
"created": "Tue, 7 Apr 2020 15:48:16 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Apr 2020 19:18:28 GMT",
"version": "v2"
}
] | 2020-04-27 | [
[
"Savi",
"Pedro V.",
""
],
[
"Savi",
"Marcelo A.",
""
],
[
"Borges",
"Beatriz",
""
]
] | This paper deals with the mathematical modeling and numerical simulations related to the coronavirus dynamics. A description is developed based on the framework of susceptible-exposed-infectious-recovered model. Initially, a model verification is carried out calibrating system parameters with data from China, Italy, Iran and Brazil. Afterward, numerical simulations are performed to analyzed different scenarios of COVID-19 in Brazil. Results show the importance of governmental and individual actions to control the number and the period of the critical situations related to the pandemic. |
2111.05315 | Cheng Shen | Cheng Shen, Adiyant Lamba, Meng Zhu, Ray Zhang, Changhuei Yang and
Magdalena Zernicka Goetz | Stain-free Detection of Embryo Polarization using Deep Learning | null | null | null | null | q-bio.QM cs.CV eess.IV physics.bio-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Polarization of the mammalian embryo at the right developmental time is
critical for its development to term and would be valuable in assessing the
potential of human embryos. However, tracking polarization requires invasive
fluorescence staining, impermissible in the in vitro fertilization clinic.
Here, we report the use of artificial intelligence to detect polarization from
unstained time-lapse movies of mouse embryos. We assembled a dataset of
bright-field movie frames from 8-cell-stage embryos, side-by-side with
corresponding images of fluorescent markers of cell polarization. We then used
an ensemble learning model to detect whether any bright-field frame showed an
embryo before or after onset of polarization. Our resulting model has an
accuracy of 85% for detecting polarization, significantly outperforming human
volunteers trained on the same data (61% accuracy). We discovered that our
self-learning model focuses upon the angle between cells as one known cue for
compaction, which precedes polarization, but it outperforms the use of this cue
alone. By compressing three-dimensional time-lapsed image data into
two-dimensions, we are able to reduce data to an easily manageable size for
deep learning processing. In conclusion, we describe a method for detecting a
key developmental feature of embryo development that avoids clinically
impermissible fluorescence staining.
| [
{
"created": "Mon, 8 Nov 2021 17:54:25 GMT",
"version": "v1"
}
] | 2021-11-10 | [
[
"Shen",
"Cheng",
""
],
[
"Lamba",
"Adiyant",
""
],
[
"Zhu",
"Meng",
""
],
[
"Zhang",
"Ray",
""
],
[
"Yang",
"Changhuei",
""
],
[
"Goetz",
"Magdalena Zernicka",
""
]
] | Polarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, impermissible in the in vitro fertilization clinic. Here, we report the use of artificial intelligence to detect polarization from unstained time-lapse movies of mouse embryos. We assembled a dataset of bright-field movie frames from 8-cell-stage embryos, side-by-side with corresponding images of fluorescent markers of cell polarization. We then used an ensemble learning model to detect whether any bright-field frame showed an embryo before or after onset of polarization. Our resulting model has an accuracy of 85% for detecting polarization, significantly outperforming human volunteers trained on the same data (61% accuracy). We discovered that our self-learning model focuses upon the angle between cells as one known cue for compaction, which precedes polarization, but it outperforms the use of this cue alone. By compressing three-dimensional time-lapsed image data into two-dimensions, we are able to reduce data to an easily manageable size for deep learning processing. In conclusion, we describe a method for detecting a key developmental feature of embryo development that avoids clinically impermissible fluorescence staining. |
0801.4395 | Baruch Vainas | Baruch Vainas | Transition from 12 to near-24 hours glucose circadian rhythm on
relaxation of a hyperglycemic condition | 10 pages, 3 figures | null | null | null | q-bio.QM | null | A composite, exponential relaxation function, modulated by a periodic
component, was used to fit to an experimental time series of blood glucose
levels. The 11 parameters function that allows for the detection of a possible
rhythm transition was fitted to the experimental time series using a genetic
algorithm. It has been found that the relaxation from a hyperglycemic condition
following a change in the anti-diabetic treatment, can be characterized by a
change from an initial 12 hours ultradian rhythm to a near-24 hours circadian
rhythm.
| [
{
"created": "Mon, 28 Jan 2008 22:27:12 GMT",
"version": "v1"
}
] | 2008-01-30 | [
[
"Vainas",
"Baruch",
""
]
] | A composite, exponential relaxation function, modulated by a periodic component, was used to fit to an experimental time series of blood glucose levels. The 11 parameters function that allows for the detection of a possible rhythm transition was fitted to the experimental time series using a genetic algorithm. It has been found that the relaxation from a hyperglycemic condition following a change in the anti-diabetic treatment, can be characterized by a change from an initial 12 hours ultradian rhythm to a near-24 hours circadian rhythm. |
2212.00430 | Robert Worden | Robert Worden | A Speed Limit for Evolution: Postscript | Unpublished paper | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | In 1995 I wrote a paper: "A Speed Limit for Evolution" whose main result was
that evolution must proceed rather slowly, in accordance with the earlier views
and intuitions of many authors. The abstract of the paper said: "The genetic
information expressed in some part of the phenotype of a species cannot
increase faster than a given rate, determined by the selection pressure on that
part. This rate is typically a small fraction of a bit per generation". This
result was derived in the presence of sexual reproduction and other effects
such as temporarily isolated sub-populations. In 1999 David Mackay published a
paper which apparently contradicted this result. In the abstract, he wrote "We
find striking differences between populations that have recombination and
populations that do not. If variation is produced by mutation alone, then the
entire population gains up to roughly 1 bit per generation. If variation is
created by recombination, the population can gain of the order of sqrt(G) bits
per generation." Mackay proposed that there were outstanding evolutionary
benefits to sexual reproduction, and that my result was too low by a very large
factor. He later repeated this result in a textbook he wrote in 2003. The
purpose of this note is to show that the key assumption of Mackays model, that
"fitness is a strictly additive trait" is so unrealistic as to render his
results irrelevant to any actual life form. In consequence, the speed limit I
derived is still valid, and has important consequences for human cognitive
evolution.
| [
{
"created": "Thu, 1 Dec 2022 11:00:00 GMT",
"version": "v1"
}
] | 2022-12-02 | [
[
"Worden",
"Robert",
""
]
] | In 1995 I wrote a paper: "A Speed Limit for Evolution" whose main result was that evolution must proceed rather slowly, in accordance with the earlier views and intuitions of many authors. The abstract of the paper said: "The genetic information expressed in some part of the phenotype of a species cannot increase faster than a given rate, determined by the selection pressure on that part. This rate is typically a small fraction of a bit per generation". This result was derived in the presence of sexual reproduction and other effects such as temporarily isolated sub-populations. In 1999 David Mackay published a paper which apparently contradicted this result. In the abstract, he wrote "We find striking differences between populations that have recombination and populations that do not. If variation is produced by mutation alone, then the entire population gains up to roughly 1 bit per generation. If variation is created by recombination, the population can gain of the order of sqrt(G) bits per generation." Mackay proposed that there were outstanding evolutionary benefits to sexual reproduction, and that my result was too low by a very large factor. He later repeated this result in a textbook he wrote in 2003. The purpose of this note is to show that the key assumption of Mackays model, that "fitness is a strictly additive trait" is so unrealistic as to render his results irrelevant to any actual life form. In consequence, the speed limit I derived is still valid, and has important consequences for human cognitive evolution. |
2008.01470 | Thomas R. Weikl | Batuhan Kav, Andrea Grafm\"uller, Emanuel Schneck, and Thomas R. Weikl | Weak carbohydrate-carbohydrate interactions in membrane adhesion are
fuzzy and generic | 12 pages, 9 figures | Nanoscale, 2020, DOI: 10.1039/D0NR03696J | 10.1039/D0NR03696J | null | q-bio.BM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Carbohydrates such as the trisaccharide motif LeX are key constituents of
cell surfaces. Despite intense research, the interactions between carbohydrates
of apposing cells or membranes are not well understood. In this article, we
investigate carbohydrate-carbohydrate interactions in membrane adhesion as well
as in solution with extensive atomistic molecular dynamics simulations that
exceed the simulation times of previous studies by orders of magnitude. For
LeX, we obtain association constants of soluble carbohydrates, adhesion
energies of lipid-anchored carbohydrates, and maximally sustained forces of
carbohydrate complexes in membrane adhesion that are in good agreement with
experimental results in the literature. Our simulations thus appear to provide
a realistic, detailed picture of LeX-LeX interactions in solution and during
membrane adhesion. In this picture, the LeX-LeX interactions are fuzzy, i.e.
LeX pairs interact in a large variety of short-lived, bound conformations. For
the synthetic tetrasaccharide Lac 2, which is composed of two lactose units, we
observe similarly fuzzy interactions and obtain association constants of both
soluble and lipid-anchored variants that are comparable to the corresponding
association constants of LeX. The fuzzy, weak carbohydrate-carbohydrate
interactions quantified in our simulations thus appear to be a generic feature
of small, neutral carbohydrates such as LeX and Lac 2.
| [
{
"created": "Tue, 4 Aug 2020 11:35:19 GMT",
"version": "v1"
}
] | 2020-08-05 | [
[
"Kav",
"Batuhan",
""
],
[
"Grafmüller",
"Andrea",
""
],
[
"Schneck",
"Emanuel",
""
],
[
"Weikl",
"Thomas R.",
""
]
] | Carbohydrates such as the trisaccharide motif LeX are key constituents of cell surfaces. Despite intense research, the interactions between carbohydrates of apposing cells or membranes are not well understood. In this article, we investigate carbohydrate-carbohydrate interactions in membrane adhesion as well as in solution with extensive atomistic molecular dynamics simulations that exceed the simulation times of previous studies by orders of magnitude. For LeX, we obtain association constants of soluble carbohydrates, adhesion energies of lipid-anchored carbohydrates, and maximally sustained forces of carbohydrate complexes in membrane adhesion that are in good agreement with experimental results in the literature. Our simulations thus appear to provide a realistic, detailed picture of LeX-LeX interactions in solution and during membrane adhesion. In this picture, the LeX-LeX interactions are fuzzy, i.e. LeX pairs interact in a large variety of short-lived, bound conformations. For the synthetic tetrasaccharide Lac 2, which is composed of two lactose units, we observe similarly fuzzy interactions and obtain association constants of both soluble and lipid-anchored variants that are comparable to the corresponding association constants of LeX. The fuzzy, weak carbohydrate-carbohydrate interactions quantified in our simulations thus appear to be a generic feature of small, neutral carbohydrates such as LeX and Lac 2. |
1302.6422 | Ruriko Yoshida | Grady Weyenberg and Peter Huggins and Christopher Schardl and Daniel K
Howe and Ruriko Yoshida | kdetrees: Nonparametric Estimation of Phylogenetic Tree Distributions | 3 figures | null | null | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: While the majority of gene histories found in a clade of
organisms are expected to be generated by a common process (e.g. the coalescent
process), it is well-known that numerous other coexisting processes (e.g.
horizontal gene transfers, gene duplication and subsequent
neofunctionalization) will cause some genes to exhibit a history quite distinct
from those of the majority of genes. Such "outlying" gene trees are considered
to be biologically interesting and identifying these genes has become an
important problem in phylogenetics.
Results: We propose and implement KDETREES, a nonparametric method of
estimating distributions of phylogenetic trees, with the goal of identifying
trees which are significantly different from the rest of the trees in the
sample. Our method compares favorably with a similar recently-published method,
featuring an improvement of one polynomial order of computational complexity
(to quadratic in the number of trees analyzed), with simulation studies
suggesting only a small penalty to classification accuracy. Application of
KDETREES to a set of Apicomplexa genes identified several unreliable sequence
alignments which had escaped previous detection, as well as a gene
independently reported as a possible case of horizontal gene transfer. We also
analyze a set of Epichloe genes, fungi symbiotic with grasses, successfully
identifying a contrived instance of paralogy.
Availability: Our method for estimating tree distributions and identifying
outlying trees is implemented as the R package KDETREES, and is available for
download from CRAN.
| [
{
"created": "Tue, 26 Feb 2013 13:03:06 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Aug 2013 01:22:34 GMT",
"version": "v2"
},
{
"created": "Tue, 22 Apr 2014 17:24:04 GMT",
"version": "v3"
}
] | 2014-04-23 | [
[
"Weyenberg",
"Grady",
""
],
[
"Huggins",
"Peter",
""
],
[
"Schardl",
"Christopher",
""
],
[
"Howe",
"Daniel K",
""
],
[
"Yoshida",
"Ruriko",
""
]
] | Motivation: While the majority of gene histories found in a clade of organisms are expected to be generated by a common process (e.g. the coalescent process), it is well-known that numerous other coexisting processes (e.g. horizontal gene transfers, gene duplication and subsequent neofunctionalization) will cause some genes to exhibit a history quite distinct from those of the majority of genes. Such "outlying" gene trees are considered to be biologically interesting and identifying these genes has become an important problem in phylogenetics. Results: We propose and implement KDETREES, a nonparametric method of estimating distributions of phylogenetic trees, with the goal of identifying trees which are significantly different from the rest of the trees in the sample. Our method compares favorably with a similar recently-published method, featuring an improvement of one polynomial order of computational complexity (to quadratic in the number of trees analyzed), with simulation studies suggesting only a small penalty to classification accuracy. Application of KDETREES to a set of Apicomplexa genes identified several unreliable sequence alignments which had escaped previous detection, as well as a gene independently reported as a possible case of horizontal gene transfer. We also analyze a set of Epichloe genes, fungi symbiotic with grasses, successfully identifying a contrived instance of paralogy. Availability: Our method for estimating tree distributions and identifying outlying trees is implemented as the R package KDETREES, and is available for download from CRAN. |
1412.1235 | Niviere Vincent | Vincent Nivi\`ere (LCBM - UMR 5249), Marc Fontecave (LCBM - UMR 5249) | Discovery of superoxide reductase: an historical perspective | null | Journal of Biological Inorganic Chemistry, 2004, 9, pp.119-23 | 10.1007/s00775-003-0519-7 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For more than 30 years, the only enzymatic system known to catalyze the
elimination of superoxide was superoxide dismutase, SOD. SOD has been found in
almost all organisms living in the presence of oxygen, including some anaerobic
bacteria, supporting the notion that superoxide is a key and general component
of oxidative stress. Recently, a new concept in the field of the mechanisms of
cellular defense against superoxide has emerged. It was discovered that
elimination of superoxide in some anaerobic and microaerophilic bacteria could
occur by reduction, a reaction catalyzed by a small metalloenzyme thus named
superoxide reductase, SOR. Having played a major role in this discovery, we
describe here how the concept of superoxide reduction emerged and how it was
experimentally substantiated independently in our laboratory.
| [
{
"created": "Wed, 3 Dec 2014 08:45:12 GMT",
"version": "v1"
}
] | 2014-12-04 | [
[
"Nivière",
"Vincent",
"",
"LCBM - UMR 5249"
],
[
"Fontecave",
"Marc",
"",
"LCBM - UMR 5249"
]
] | For more than 30 years, the only enzymatic system known to catalyze the elimination of superoxide was superoxide dismutase, SOD. SOD has been found in almost all organisms living in the presence of oxygen, including some anaerobic bacteria, supporting the notion that superoxide is a key and general component of oxidative stress. Recently, a new concept in the field of the mechanisms of cellular defense against superoxide has emerged. It was discovered that elimination of superoxide in some anaerobic and microaerophilic bacteria could occur by reduction, a reaction catalyzed by a small metalloenzyme thus named superoxide reductase, SOR. Having played a major role in this discovery, we describe here how the concept of superoxide reduction emerged and how it was experimentally substantiated independently in our laboratory. |
2005.12191 | Junhua Li | Junhua Li, Anastasios Bezerianos, Nitish Thakor | Cognitive State Analysis, Understanding, and Decoding from the
Perspective of Brain Connectivity | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cognitive states are involving in our daily life, which motivates us to
explore them and understand them by a vast variety of perspectives. Among these
perspectives, brain connectivity is increasingly receiving attention in recent
years. It is the right time to summarize the past achievements, serving as a
cornerstone for the upcoming progress in the field. In this chapter, the
definition of the cognitive state is first given and the cognitive states that
are frequently investigated are then outlined. The introduction of the methods
for estimating connectivity strength and graph theoretical metrics is followed.
Subsequently, each cognitive state is separately described and the progress in
cognitive state investigation is summarized, including analysis, understanding,
and decoding. We concentrate on the literature ascertaining macro-scale
representations of cognitive states from the perspective of brain connectivity
and give an overview of achievements related to cognitive states to date,
especially within the past ten years. The discussions and future prospects are
stated at the end of the chapter.
| [
{
"created": "Wed, 13 May 2020 11:08:42 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Sep 2020 23:05:23 GMT",
"version": "v2"
}
] | 2020-09-16 | [
[
"Li",
"Junhua",
""
],
[
"Bezerianos",
"Anastasios",
""
],
[
"Thakor",
"Nitish",
""
]
] | Cognitive states are involving in our daily life, which motivates us to explore them and understand them by a vast variety of perspectives. Among these perspectives, brain connectivity is increasingly receiving attention in recent years. It is the right time to summarize the past achievements, serving as a cornerstone for the upcoming progress in the field. In this chapter, the definition of the cognitive state is first given and the cognitive states that are frequently investigated are then outlined. The introduction of the methods for estimating connectivity strength and graph theoretical metrics is followed. Subsequently, each cognitive state is separately described and the progress in cognitive state investigation is summarized, including analysis, understanding, and decoding. We concentrate on the literature ascertaining macro-scale representations of cognitive states from the perspective of brain connectivity and give an overview of achievements related to cognitive states to date, especially within the past ten years. The discussions and future prospects are stated at the end of the chapter. |
1512.08074 | Nathan Baker | Suzette A. Pabit and Andrea M. Katz and Igor S. Tolokh and Aleksander
Drozdetski and Nathan Baker and Alexey V. Onufriev and Lois Pollack | Understanding Nucleic Acid Structural Changes by Comparing Wide-Angle
X-ray Scattering (WAXS) Experiments to Molecular Dynamics Simulations | null | null | 10.1063/1.4950814 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wide-angle x-ray scattering (WAXS) is emerging as a powerful tool for
increasing the resolution of solution structure measurements of biomolecules.
Compared to its better known complement, small angle x-ray scattering (SAXS),
WAXS targets higher scattering angles and can enhance structural studies of
molecules by accessing finer details of solution structures. Although the
extension from SAXS to WAXS is easy to implement experimentally, the
computational tools required to fully harness the power of WAXS are still under
development. Currently, WAXS is employed to study structural changes and ligand
binding in proteins; however the methods are not as fully developed for nucleic
acids. Here, we show how WAXS can qualitatively characterize nucleic acid
structures as well as the small but significant structural changes driven by
the addition of multivalent ions. We show the potential of WAXS to test
all-atom molecular dynamics (MD) simulations and to provide insight in
understanding how the trivalent ion cobalt(III) hexammine (CoHex) affects the
structure of RNA and DNA helices. We find that MD simulations capture the RNA
structural change that occurs due to addition of CoHex.
| [
{
"created": "Sat, 26 Dec 2015 05:20:46 GMT",
"version": "v1"
},
{
"created": "Sun, 15 May 2016 04:00:55 GMT",
"version": "v2"
}
] | 2016-06-22 | [
[
"Pabit",
"Suzette A.",
""
],
[
"Katz",
"Andrea M.",
""
],
[
"Tolokh",
"Igor S.",
""
],
[
"Drozdetski",
"Aleksander",
""
],
[
"Baker",
"Nathan",
""
],
[
"Onufriev",
"Alexey V.",
""
],
[
"Pollack",
"Lois",
""... | Wide-angle x-ray scattering (WAXS) is emerging as a powerful tool for increasing the resolution of solution structure measurements of biomolecules. Compared to its better known complement, small angle x-ray scattering (SAXS), WAXS targets higher scattering angles and can enhance structural studies of molecules by accessing finer details of solution structures. Although the extension from SAXS to WAXS is easy to implement experimentally, the computational tools required to fully harness the power of WAXS are still under development. Currently, WAXS is employed to study structural changes and ligand binding in proteins; however the methods are not as fully developed for nucleic acids. Here, we show how WAXS can qualitatively characterize nucleic acid structures as well as the small but significant structural changes driven by the addition of multivalent ions. We show the potential of WAXS to test all-atom molecular dynamics (MD) simulations and to provide insight in understanding how the trivalent ion cobalt(III) hexammine (CoHex) affects the structure of RNA and DNA helices. We find that MD simulations capture the RNA structural change that occurs due to addition of CoHex. |
1206.5811 | Andrey Dovzhenok | Andrey Dovzhenok, Leonid L. Rubchinsky | On the Origin of Tremor in Parkinson's Disease | 21 pages, 8 figures, submitted to PLoS One | (2012) PLoS ONE 7(7): e41598 | 10.1371/journal.pone.0041598 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The exact origin of tremor in Parkinson's disease remains unknown. We explain
why the existing data converge on the basal ganglia-thalamo-cortical loop as a
tremor generator and consider a conductance-based model of subthalamo-pallidal
circuits embedded into a simplified representation of the basal
ganglia-thalamo-cortical circuit to investigate the dynamics of this loop. We
show how variation of the strength of dopamine-modulated connections in the
basal ganglia-thalamo-cortical loop (representing the decreasing dopamine level
in Parkinson's disease) leads to the occurrence of tremor-like burst firing.
These tremor-like oscillations are suppressed when the connections are
modulated back to represent a higher dopamine level (as it would be the case in
dopaminergic therapy), as well as when the basal ganglia-thalamo-cortical loop
is broken (as would be the case for ablative anti-parkinsonian surgeries).
Thus, the proposed model provides an explanation for the basal
ganglia-thalamo-cortical loop mechanism of tremor generation. The strengthening
of the loop leads to tremor oscillations, while the weakening or disconnection
of the loop suppresses them. The loop origin of parkinsonian tremor also
suggests that new tremor-suppression therapies may have anatomical targets in
different cortical and subcortical areas as long as they are within the basal
ganglia-thalamo-cortical loop.
| [
{
"created": "Mon, 25 Jun 2012 20:00:33 GMT",
"version": "v1"
}
] | 2012-08-13 | [
[
"Dovzhenok",
"Andrey",
""
],
[
"Rubchinsky",
"Leonid L.",
""
]
] | The exact origin of tremor in Parkinson's disease remains unknown. We explain why the existing data converge on the basal ganglia-thalamo-cortical loop as a tremor generator and consider a conductance-based model of subthalamo-pallidal circuits embedded into a simplified representation of the basal ganglia-thalamo-cortical circuit to investigate the dynamics of this loop. We show how variation of the strength of dopamine-modulated connections in the basal ganglia-thalamo-cortical loop (representing the decreasing dopamine level in Parkinson's disease) leads to the occurrence of tremor-like burst firing. These tremor-like oscillations are suppressed when the connections are modulated back to represent a higher dopamine level (as it would be the case in dopaminergic therapy), as well as when the basal ganglia-thalamo-cortical loop is broken (as would be the case for ablative anti-parkinsonian surgeries). Thus, the proposed model provides an explanation for the basal ganglia-thalamo-cortical loop mechanism of tremor generation. The strengthening of the loop leads to tremor oscillations, while the weakening or disconnection of the loop suppresses them. The loop origin of parkinsonian tremor also suggests that new tremor-suppression therapies may have anatomical targets in different cortical and subcortical areas as long as they are within the basal ganglia-thalamo-cortical loop. |
2003.11614 | Maria Luisa Chiusano | Maria Luisa Chiusano | The modelling of COVID19 pathways sheds light on mechanisms,
opportunities and on controversial interpretations of medical treatments. v2 | null | null | null | null | q-bio.MN q-bio.PE | http://creativecommons.org/publicdomain/zero/1.0/ | The new coronavirus (2019-nCoV or SARS-CoV2), inducing the current pandemic
disease (COVID-19) and causing pneumoniae in humans, is dramatically increasing
in epidemic scale since its first appearance in Wuhan, China, in December 2019.
The first infection from epidemic coronaviruses in 2003 fostered the spread of
an overwhelming amount of related scientific efforts. The manifold aspects that
have been raised, as well as their redundancy offer precious information that
has been underexploited and needs to be critically re-evaluated, appropriately
used and offered to the whole community, from scientists, to medical doctors,
stakeholders and common people. These efforts will favour a holistic view on
the comprehension, prevention and development of strategies (pharmacological,
clinical etc) as well as common intervention against the new coronavirus
spreading. Here we describe a model that emerged from our analysis that was
focused on the Renin Angiotensin System (RAS) and the possible routes linking
it to the viral infection. because the infection is mediated by the viral
receptor on human cell membranes Angiotensin Converting Enzyme (ACE2), which is
a key component in RAS signalling. The model depicts the main pathways
determining the disease and the molecular framework for its establishment, and
can help to shed light on mechanisms involved in the infection. It promptly
gives an answer to some of the controversial, and still open, issues concerning
predisposing conditions and medical treatments that protect from or favour the
severity of the disease (such as the use of ACE inhibitors or ARBs/sartans), or
to the sex related biases in the affected population. The model highlights
novel opportunities for further investigations, diagnosis and appropriate
intervention to understand and fight COVID19.
| [
{
"created": "Wed, 25 Mar 2020 20:17:51 GMT",
"version": "v1"
}
] | 2020-03-28 | [
[
"Chiusano",
"Maria Luisa",
""
]
] | The new coronavirus (2019-nCoV or SARS-CoV2), inducing the current pandemic disease (COVID-19) and causing pneumoniae in humans, is dramatically increasing in epidemic scale since its first appearance in Wuhan, China, in December 2019. The first infection from epidemic coronaviruses in 2003 fostered the spread of an overwhelming amount of related scientific efforts. The manifold aspects that have been raised, as well as their redundancy offer precious information that has been underexploited and needs to be critically re-evaluated, appropriately used and offered to the whole community, from scientists, to medical doctors, stakeholders and common people. These efforts will favour a holistic view on the comprehension, prevention and development of strategies (pharmacological, clinical etc) as well as common intervention against the new coronavirus spreading. Here we describe a model that emerged from our analysis that was focused on the Renin Angiotensin System (RAS) and the possible routes linking it to the viral infection. because the infection is mediated by the viral receptor on human cell membranes Angiotensin Converting Enzyme (ACE2), which is a key component in RAS signalling. The model depicts the main pathways determining the disease and the molecular framework for its establishment, and can help to shed light on mechanisms involved in the infection. It promptly gives an answer to some of the controversial, and still open, issues concerning predisposing conditions and medical treatments that protect from or favour the severity of the disease (such as the use of ACE inhibitors or ARBs/sartans), or to the sex related biases in the affected population. The model highlights novel opportunities for further investigations, diagnosis and appropriate intervention to understand and fight COVID19. |
2201.07320 | Lucia Peixoto | Elizabeth Medina, Sarah Peterson, Kristan Singletary, and Lucia
Peixoto | Critical periods and Autism Spectrum Disorders, a role for sleep | 14 pages, 2 figures, 1 Table | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Brain development relies on both experience and genetically defined programs.
Time windows where certain brain circuits are particularly receptive to
external stimuli, resulting in heightened plasticity, are referred to as
critical periods. Sleep is thought to be essential for normal brain
development. Importantly, studies have shown that sleep enhances critical
period plasticity and promotes experience-dependent synaptic pruning in the
developing mammalian brain. Therefore, normal plasticity during critical
periods depends on proper sleep. Problems falling and staying asleep occur at a
higher rate in Autism Spectrum Disorder (ASD) relative to typical development.
In this review, we explore the potential link between sleep, critical period
plasticity, and ASD. First, we review the importance of critical period
plasticity in typical development and the role of sleep in this process. Next,
we summarize the evidence linking ASD with deficits in synaptic plasticity in
rodent models of high-confident ASD gene candidates. We then show that almost
all the high-confidence rodent models of ASD that show sleep deficits also
display plasticity deficits. Given how important sleep is for critical period
plasticity, it is essential to understand the connections between synaptic
plasticity, sleep, and brain development in ASD. However, studies investigating
sleep or plasticity during critical periods in ASD mouse models are lacking.
Therefore, we highlight an urgent need to consider developmental trajectory in
studies of sleep and plasticity in neurodevelopmental disorders.
| [
{
"created": "Tue, 18 Jan 2022 21:29:18 GMT",
"version": "v1"
}
] | 2022-01-20 | [
[
"Medina",
"Elizabeth",
""
],
[
"Peterson",
"Sarah",
""
],
[
"Singletary",
"Kristan",
""
],
[
"Peixoto",
"Lucia",
""
]
] | Brain development relies on both experience and genetically defined programs. Time windows where certain brain circuits are particularly receptive to external stimuli, resulting in heightened plasticity, are referred to as critical periods. Sleep is thought to be essential for normal brain development. Importantly, studies have shown that sleep enhances critical period plasticity and promotes experience-dependent synaptic pruning in the developing mammalian brain. Therefore, normal plasticity during critical periods depends on proper sleep. Problems falling and staying asleep occur at a higher rate in Autism Spectrum Disorder (ASD) relative to typical development. In this review, we explore the potential link between sleep, critical period plasticity, and ASD. First, we review the importance of critical period plasticity in typical development and the role of sleep in this process. Next, we summarize the evidence linking ASD with deficits in synaptic plasticity in rodent models of high-confident ASD gene candidates. We then show that almost all the high-confidence rodent models of ASD that show sleep deficits also display plasticity deficits. Given how important sleep is for critical period plasticity, it is essential to understand the connections between synaptic plasticity, sleep, and brain development in ASD. However, studies investigating sleep or plasticity during critical periods in ASD mouse models are lacking. Therefore, we highlight an urgent need to consider developmental trajectory in studies of sleep and plasticity in neurodevelopmental disorders. |
1207.2484 | Alan Lapedes | Alan Lapedes, Bertrand Giraud, Christopher Jarzynski | Using Sequence Alignments to Predict Protein Structure and Stability
With High Accuracy | This manuscript was originally written in 2002 and available from
http://library.lanl.gov/cgi-bin/getfile?01038177.pdf It's being deposited
here for greater ease of access | null | null | LA-UR-02-4481 | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a sequence-based probabilistic formalism that directly addresses
co-operative effects in networks of interacting positions in proteins,
providing significantly improved contact prediction, as well as accurate
quantitative prediction of free energy changes due to non-additive effects of
multiple mutations. In addition to these practical considerations, the
agreement of our sequence-based calculations with experimental data for both
structure and stability demonstrates a strong relation between the statistical
distribution of protein sequences produced by natural evolutionary processes,
and the thermodynamic stability of the structures to which these sequences
fold.
| [
{
"created": "Tue, 10 Jul 2012 20:24:42 GMT",
"version": "v1"
}
] | 2012-07-12 | [
[
"Lapedes",
"Alan",
""
],
[
"Giraud",
"Bertrand",
""
],
[
"Jarzynski",
"Christopher",
""
]
] | We present a sequence-based probabilistic formalism that directly addresses co-operative effects in networks of interacting positions in proteins, providing significantly improved contact prediction, as well as accurate quantitative prediction of free energy changes due to non-additive effects of multiple mutations. In addition to these practical considerations, the agreement of our sequence-based calculations with experimental data for both structure and stability demonstrates a strong relation between the statistical distribution of protein sequences produced by natural evolutionary processes, and the thermodynamic stability of the structures to which these sequences fold. |
1805.06002 | Xin Wang | Xin Wang and Yang-Yu Liu | Overcome Competitive Exclusion in Ecosystems | Manuscript 13 pages, 10 figures; SI 15 pages, 8 figures | iScience 2020;23(4):101009 | 10.1016/j.isci.2020.101009 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explaining biodiversity in nature is a fundamental problem in ecology. An
outstanding challenge is embodied in the so-called Competitive Exclusion
Principle: two species competing for one limiting resource cannot coexist at
constant population densities, or more generally, the number of consumer
species in steady coexistence cannot exceed that of resources. The fact that
competitive exclusion is rarely observed in natural ecosystems has not been
fully understood. Here we show that by forming chasing triplets among the
consumers and resources in the consumption process, the Competitive Exclusion
Principle can be naturally violated. The modeling framework developed here is
broadly applicable and can be used to explain the biodiversity of many
consumer-resource ecosystems and hence deepens our understanding of
biodiversity in nature.
| [
{
"created": "Tue, 15 May 2018 19:23:35 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Jun 2018 03:33:16 GMT",
"version": "v2"
},
{
"created": "Tue, 28 Aug 2018 22:06:25 GMT",
"version": "v3"
},
{
"created": "Wed, 12 Sep 2018 03:42:02 GMT",
"version": "v4"
},
{
"cr... | 2020-04-13 | [
[
"Wang",
"Xin",
""
],
[
"Liu",
"Yang-Yu",
""
]
] | Explaining biodiversity in nature is a fundamental problem in ecology. An outstanding challenge is embodied in the so-called Competitive Exclusion Principle: two species competing for one limiting resource cannot coexist at constant population densities, or more generally, the number of consumer species in steady coexistence cannot exceed that of resources. The fact that competitive exclusion is rarely observed in natural ecosystems has not been fully understood. Here we show that by forming chasing triplets among the consumers and resources in the consumption process, the Competitive Exclusion Principle can be naturally violated. The modeling framework developed here is broadly applicable and can be used to explain the biodiversity of many consumer-resource ecosystems and hence deepens our understanding of biodiversity in nature. |
0704.1908 | Radek Erban | Radek Erban, Jonathan Chapman and Philip Maini | A practical guide to stochastic simulations of reaction-diffusion
processes | 35 pages | null | null | null | q-bio.SC physics.ed-ph q-bio.QM | null | A practical introduction to stochastic modelling of reaction-diffusion
processes is presented. No prior knowledge of stochastic simulations is
assumed. The methods are explained using illustrative examples. The article
starts with the classical Gillespie algorithm for the stochastic modelling of
chemical reactions. Then stochastic algorithms for modelling molecular
diffusion are given. Finally, basic stochastic reaction-diffusion methods are
presented. The connections between stochastic simulations and deterministic
models are explained and basic mathematical tools (e.g. chemical master
equation) are presented. The article concludes with an overview of more
advanced methods and problems.
| [
{
"created": "Sun, 15 Apr 2007 17:50:38 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Nov 2007 03:47:24 GMT",
"version": "v2"
}
] | 2007-11-19 | [
[
"Erban",
"Radek",
""
],
[
"Chapman",
"Jonathan",
""
],
[
"Maini",
"Philip",
""
]
] | A practical introduction to stochastic modelling of reaction-diffusion processes is presented. No prior knowledge of stochastic simulations is assumed. The methods are explained using illustrative examples. The article starts with the classical Gillespie algorithm for the stochastic modelling of chemical reactions. Then stochastic algorithms for modelling molecular diffusion are given. Finally, basic stochastic reaction-diffusion methods are presented. The connections between stochastic simulations and deterministic models are explained and basic mathematical tools (e.g. chemical master equation) are presented. The article concludes with an overview of more advanced methods and problems. |
2310.08336 | Shesha Gopal Marehalli Srinivas | Shesha Gopal Marehalli Srinivas, Francesco Avanzini, Massimiliano
Esposito | Thermodynamics of Growth in Open Chemical Reaction Networks | null | null | null | null | q-bio.MN cond-mat.stat-mech | http://creativecommons.org/licenses/by/4.0/ | We identify the thermodynamic conditions necessary to observe indefinite
growth in homogeneous open chemical reaction networks (CRNs) satisfying mass
action kinetics. We also characterize the thermodynamic efficiency of growth by
considering the fraction of the chemical work supplied from the surroundings
that is converted into CRN free energy. We find that indefinite growth cannot
arise in CRNs chemostatted by fixing the concentration of some species at
constant values, or in continuous-flow stirred tank reactors. Indefinite growth
requires a constant net influx from the surroundings of at least one species.
In this case, unimolecular CRNs always generate equilibrium linear growth,
i.e., a continuous linear accumulation of species with equilibrium
concentrations and efficiency one. Multimolecular CRNs are necessary to
generate nonequilibrium growth, i.e., the continuous accumulation of species
with nonequilibrium concentrations. Pseudo-unimolecular CRNs - a subclass of
multimolecular CRNs - always generate asymptotic linear growth with zero
efficiency. Our findings demonstrate the importance of the CRN topology and the
chemostatting procedure in determining the dynamics and thermodynamics of
growth.
| [
{
"created": "Thu, 12 Oct 2023 13:48:55 GMT",
"version": "v1"
}
] | 2023-10-13 | [
[
"Srinivas",
"Shesha Gopal Marehalli",
""
],
[
"Avanzini",
"Francesco",
""
],
[
"Esposito",
"Massimiliano",
""
]
] | We identify the thermodynamic conditions necessary to observe indefinite growth in homogeneous open chemical reaction networks (CRNs) satisfying mass action kinetics. We also characterize the thermodynamic efficiency of growth by considering the fraction of the chemical work supplied from the surroundings that is converted into CRN free energy. We find that indefinite growth cannot arise in CRNs chemostatted by fixing the concentration of some species at constant values, or in continuous-flow stirred tank reactors. Indefinite growth requires a constant net influx from the surroundings of at least one species. In this case, unimolecular CRNs always generate equilibrium linear growth, i.e., a continuous linear accumulation of species with equilibrium concentrations and efficiency one. Multimolecular CRNs are necessary to generate nonequilibrium growth, i.e., the continuous accumulation of species with nonequilibrium concentrations. Pseudo-unimolecular CRNs - a subclass of multimolecular CRNs - always generate asymptotic linear growth with zero efficiency. Our findings demonstrate the importance of the CRN topology and the chemostatting procedure in determining the dynamics and thermodynamics of growth. |
1904.06667 | Johannes M\"uller | Thibaut Sellinger (1), Johannes M\"uller (2 and 3), Volker H\"osel
(2), Aur\'elien Tellier (1) ((1) Section of Population Genetics, Center of
Life and Food Sciences Weihenstephan, Technische Universit\"at M\"unchen,
Germany, (2) Center for Mathematics, Technische Universit\"at M\"unchen,
Germany, (3) Institute for Computational Biology, Helmholtz Center Munich,
Germany) | Are the better cooperators dormant or quiescent? | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the wealth of empirical and theoretical studies, the origin and
maintenance of cooperation is still an evolutionary riddle. In this context,
ecological life-history traits which affect the efficiency of selection may
play a role, though these are often ignored. We consider here species such as
bacteria, fungi, invertebrates and plants which exhibit resting stages in the
form of a quiescent state or a seedbank. When quiescent, individuals are
inactive and reproduce upon activation, while under seed bank parents produce
offspring remaining dormant for different amount of time. We assume weak
frequency-dependent selection modeled using game-theory and the prisoners
dilemma (cooperation/defect) as payoff matrix. The cooperators and defectors
are allowed to evolve different quiescence or dormancy times. By means of
singular perturbation theory we reduce the model to a one-dimensional equation
resembling the well known replicator equation, where the gain functions are
scaled with lumped parameters reflecting the time scale of the resting state of
the cooperators and defectors. If both time scales are identical cooperation
cannot persist in a homogeneous population. If, however, the time scale of the
cooperator is distinctively different from that of the defector, cooperation
may become a locally asymptotically stable strategy. Interestingly enough, in
the seedbank case the cooperator needs to be faster than the defector, while in
the quiescent case the cooperator has to be slower. We use adaptive dynamics to
identify situations where cooperation may evolve and form a convergent stable
ESS. We conclude by highlighting the relevance fo these results for many
non-model species and the maintenance of cooperation in microbial, invertebrate
or plant populations.
| [
{
"created": "Sun, 14 Apr 2019 09:58:29 GMT",
"version": "v1"
}
] | 2019-04-16 | [
[
"Sellinger",
"Thibaut",
"",
"2 and 3"
],
[
"Müller",
"Johannes",
"",
"2 and 3"
],
[
"Hösel",
"Volker",
""
],
[
"Tellier",
"Aurélien",
""
]
] | Despite the wealth of empirical and theoretical studies, the origin and maintenance of cooperation is still an evolutionary riddle. In this context, ecological life-history traits which affect the efficiency of selection may play a role, though these are often ignored. We consider here species such as bacteria, fungi, invertebrates and plants which exhibit resting stages in the form of a quiescent state or a seedbank. When quiescent, individuals are inactive and reproduce upon activation, while under seed bank parents produce offspring remaining dormant for different amount of time. We assume weak frequency-dependent selection modeled using game-theory and the prisoners dilemma (cooperation/defect) as payoff matrix. The cooperators and defectors are allowed to evolve different quiescence or dormancy times. By means of singular perturbation theory we reduce the model to a one-dimensional equation resembling the well known replicator equation, where the gain functions are scaled with lumped parameters reflecting the time scale of the resting state of the cooperators and defectors. If both time scales are identical cooperation cannot persist in a homogeneous population. If, however, the time scale of the cooperator is distinctively different from that of the defector, cooperation may become a locally asymptotically stable strategy. Interestingly enough, in the seedbank case the cooperator needs to be faster than the defector, while in the quiescent case the cooperator has to be slower. We use adaptive dynamics to identify situations where cooperation may evolve and form a convergent stable ESS. We conclude by highlighting the relevance fo these results for many non-model species and the maintenance of cooperation in microbial, invertebrate or plant populations. |
2305.03136 | David Brookes | David H. Brookes, Jakub Otwinowski, and Sam Sinai | Contrastive losses as generalized models of global epistasis | null | null | null | null | q-bio.PE cs.LG | http://creativecommons.org/licenses/by/4.0/ | Fitness functions map large combinatorial spaces of biological sequences to
properties of interest. Inferring these multimodal functions from experimental
data is a central task in modern protein engineering. Global epistasis models
are an effective and physically-grounded class of models for estimating fitness
functions from observed data. These models assume that a sparse latent function
is transformed by a monotonic nonlinearity to emit measurable fitness. Here we
demonstrate that minimizing contrastive loss functions, such as the
Bradley-Terry loss, is a simple and flexible technique for extracting the
sparse latent function implied by global epistasis. We argue by way of a
fitness-epistasis uncertainty principle that the nonlinearities in global
epistasis models can produce observed fitness functions that do not admit
sparse representations, and thus may be inefficient to learn from observations
when using a Mean Squared Error (MSE) loss (a common practice). We show that
contrastive losses are able to accurately estimate a ranking function from
limited data even in regimes where MSE is ineffective. We validate the
practical utility of this insight by showing contrastive loss functions result
in consistently improved performance on benchmark tasks.
| [
{
"created": "Thu, 4 May 2023 20:33:05 GMT",
"version": "v1"
},
{
"created": "Mon, 8 May 2023 00:59:43 GMT",
"version": "v2"
},
{
"created": "Fri, 1 Dec 2023 18:09:00 GMT",
"version": "v3"
}
] | 2023-12-04 | [
[
"Brookes",
"David H.",
""
],
[
"Otwinowski",
"Jakub",
""
],
[
"Sinai",
"Sam",
""
]
] | Fitness functions map large combinatorial spaces of biological sequences to properties of interest. Inferring these multimodal functions from experimental data is a central task in modern protein engineering. Global epistasis models are an effective and physically-grounded class of models for estimating fitness functions from observed data. These models assume that a sparse latent function is transformed by a monotonic nonlinearity to emit measurable fitness. Here we demonstrate that minimizing contrastive loss functions, such as the Bradley-Terry loss, is a simple and flexible technique for extracting the sparse latent function implied by global epistasis. We argue by way of a fitness-epistasis uncertainty principle that the nonlinearities in global epistasis models can produce observed fitness functions that do not admit sparse representations, and thus may be inefficient to learn from observations when using a Mean Squared Error (MSE) loss (a common practice). We show that contrastive losses are able to accurately estimate a ranking function from limited data even in regimes where MSE is ineffective. We validate the practical utility of this insight by showing contrastive loss functions result in consistently improved performance on benchmark tasks. |
1511.02976 | James Shine | James M. Shine, Patrick G. Bissett, Peter T. Bell, Oluwasanmi Koyejo,
Joshua H. Balsters, Krzysztof J. Gorgolewski, Craig A. Moodie, Russell A.
Poldrack | The Dynamics of Functional Brain Networks: Integrated Network States
during Cognitive Function | 38 pages, 4 figures | null | 10.1016/j.neuron.2016.09.018 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Higher brain function relies upon the ability to flexibly integrate
information across specialized communities of brain regions, however it is
unclear how this mechanism manifests over time. In this study, we use
time-resolved network analysis of functional magnetic resonance imaging data to
demonstrate that the human brain traverses between two functional states that
maximize either segregation into tight-knit communities or integration across
otherwise disparate neural regions. The integrated state enables faster and
more accurate performance on a cognitive task, and is associated with dilations
in pupil diameter, suggesting that ascending neuromodulatory systems may govern
the transition between these alternative modes of brain function. Our data
confirm a direct link between cognitive performance and the dynamic
reorganization of the network structure of the brain.
| [
{
"created": "Tue, 10 Nov 2015 02:57:08 GMT",
"version": "v1"
},
{
"created": "Wed, 3 Feb 2016 18:50:08 GMT",
"version": "v2"
},
{
"created": "Mon, 1 Aug 2016 03:46:56 GMT",
"version": "v3"
}
] | 2017-05-30 | [
[
"Shine",
"James M.",
""
],
[
"Bissett",
"Patrick G.",
""
],
[
"Bell",
"Peter T.",
""
],
[
"Koyejo",
"Oluwasanmi",
""
],
[
"Balsters",
"Joshua H.",
""
],
[
"Gorgolewski",
"Krzysztof J.",
""
],
[
"Moodie",
"Craig... | Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions, however it is unclear how this mechanism manifests over time. In this study, we use time-resolved network analysis of functional magnetic resonance imaging data to demonstrate that the human brain traverses between two functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. The integrated state enables faster and more accurate performance on a cognitive task, and is associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Our data confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain. |
1301.1077 | Christos Skiadas H | Christos H Skiadas and Charilaos Skiadas | A Quantitative Method for Estimating the Human Development Stages by
Based on the Health State Function Theory and the Resulting Deterioration
Process | 19 pages, 9 figures, 8 tables | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Health State Function theory is applied to find a quantitative estimate
of the Human Development Stages by defining and calculating the specific age
groups and subgroups. Early and late adolescence stages, first, second and
third stages of adult development are estimated along with the early, middle
and old age groups and subgroups. We briefly present the first exit time theory
used to find the health state function of a population and then we give the
details of the new theoretical approach with the appropriate applications to
support and validate the theoretical assumptions. Our approach is useful for
people working in several scientific fields and especially in medicine,
biology, anthropology, psychology, gerontology, probability and statistics. The
results are connected with the speed and acceleration of the deterioration of
the human organism during age as a consequence of the changes in the first,
second and third differences of the Health State Function and of the
Deterioration Function.
Keywords: Human development stages, Deterioration, Deterioration function,
Human Mortality Database, HMD, World Health Organization, WHO, Quantitative
methods, Health State Function, Erikson's stages of psychosocial development,
Piaget method, Sullivan method, Disability stages, Light disability, Moderate
disability, Severe disability stage, Old ages, Critical ages.
| [
{
"created": "Sun, 6 Jan 2013 23:42:59 GMT",
"version": "v1"
}
] | 2013-01-08 | [
[
"Skiadas",
"Christos H",
""
],
[
"Skiadas",
"Charilaos",
""
]
] | The Health State Function theory is applied to find a quantitative estimate of the Human Development Stages by defining and calculating the specific age groups and subgroups. Early and late adolescence stages, first, second and third stages of adult development are estimated along with the early, middle and old age groups and subgroups. We briefly present the first exit time theory used to find the health state function of a population and then we give the details of the new theoretical approach with the appropriate applications to support and validate the theoretical assumptions. Our approach is useful for people working in several scientific fields and especially in medicine, biology, anthropology, psychology, gerontology, probability and statistics. The results are connected with the speed and acceleration of the deterioration of the human organism during age as a consequence of the changes in the first, second and third differences of the Health State Function and of the Deterioration Function. Keywords: Human development stages, Deterioration, Deterioration function, Human Mortality Database, HMD, World Health Organization, WHO, Quantitative methods, Health State Function, Erikson's stages of psychosocial development, Piaget method, Sullivan method, Disability stages, Light disability, Moderate disability, Severe disability stage, Old ages, Critical ages. |
2301.12888 | Olivier Thouvenin | Tual Monfort, Salvatore Azzollini, Jeremy Brogard, Marilou
Cl\'emen\c{c}on, Am\'elie Slembrouck-Brec, Valerie Forster, Serge Picaud,
Olivier Goureau, Sacha Reichman, Olivier Thouvenin, and Kate Grieve | Dynamic Full-Field Optical Coherence Tomography module adapted to
commercial microscopes for longitudinal in vitro cell culture study | null | null | null | null | q-bio.QM physics.med-ph physics.optics | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Dynamic full-field optical coherence tomography (D-FFOCT) has recently
emerged as a label-free imaging tool, capable of resolving cell types and
organelles within 3D live samples, whilst monitoring their activity at tens of
milliseconds resolution. Here, a D-FFOCT module design is presented which can
be coupled to a commercial microscope with a stage top incubator, allowing
non-invasive label-free longitudinal imaging over periods of minutes to weeks
on the same sample. Long term volumetric imaging on human induced pluripotent
stem cell-derived retinal organoids is demonstrated, highlighting tissue and
cell organisation as well as cell shape, motility and division. Imaging on
retinal explants highlights single 3D cone and rod structures. An optimal
workflow for data acquisition, postprocessing and saving is demonstrated,
resulting in a time gain factor of 10 compared to prior state of the art.
Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated,
allowing rapid organoid screening.
| [
{
"created": "Mon, 30 Jan 2023 13:47:40 GMT",
"version": "v1"
}
] | 2023-01-31 | [
[
"Monfort",
"Tual",
""
],
[
"Azzollini",
"Salvatore",
""
],
[
"Brogard",
"Jeremy",
""
],
[
"Clémençon",
"Marilou",
""
],
[
"Slembrouck-Brec",
"Amélie",
""
],
[
"Forster",
"Valerie",
""
],
[
"Picaud",
"Serge",
... | Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organisation as well as cell shape, motility and division. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening. |
2201.04231 | Xin Tu | V. DeGruttola, M. Nakazawa, J. Liu, X. Tu, S. Little, S. Mehta | Modeling Homophily in Dynamic Networks with Application to HIV Molecular
Surveillance | null | null | null | null | q-bio.GN stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a novel approach to modeling homphily, i.e. the tendency
of nodes that share (or differ in) certain attributes to be linked; we consider
dynamic networks in which nodes can be added over time but not removed. Our
application is to HIV genetic linkage analysis that has been used to
investigate HIV transmission dynamics. In this setting, two HIV sequences from
different persons with HIV (PWH) are said to be linked if the genetic distance
between these sequences is less than a given threshold. Such linkage suggests
that that the nodes representing the two infected PWH, are close to each other
in a transmission network; such proximity would imply that either one of the
infected people directly transmitted the virus to the other or indirectly
transmitted it through a small number of intermediaries. These viral genetic
linkage networks are dynamic in the sense that, over time, a group or cluster
of genetically linked viral sequences may increase in size as new people are
infected by those in the cluster either directly or through intermediaries. Our
approach makes use of a logistic model to describe homophily with regard to
demographic and behavioral characteristics that is we investigate whether
similarities (or differences) between PWH in these characteristics impacts the
probability that their sequences are be linked. Such analyses provide
information about HIV transmission dynamics within a population.
| [
{
"created": "Wed, 29 Dec 2021 23:14:04 GMT",
"version": "v1"
}
] | 2022-01-13 | [
[
"DeGruttola",
"V.",
""
],
[
"Nakazawa",
"M.",
""
],
[
"Liu",
"J.",
""
],
[
"Tu",
"X.",
""
],
[
"Little",
"S.",
""
],
[
"Mehta",
"S.",
""
]
] | This paper describes a novel approach to modeling homphily, i.e. the tendency of nodes that share (or differ in) certain attributes to be linked; we consider dynamic networks in which nodes can be added over time but not removed. Our application is to HIV genetic linkage analysis that has been used to investigate HIV transmission dynamics. In this setting, two HIV sequences from different persons with HIV (PWH) are said to be linked if the genetic distance between these sequences is less than a given threshold. Such linkage suggests that that the nodes representing the two infected PWH, are close to each other in a transmission network; such proximity would imply that either one of the infected people directly transmitted the virus to the other or indirectly transmitted it through a small number of intermediaries. These viral genetic linkage networks are dynamic in the sense that, over time, a group or cluster of genetically linked viral sequences may increase in size as new people are infected by those in the cluster either directly or through intermediaries. Our approach makes use of a logistic model to describe homophily with regard to demographic and behavioral characteristics that is we investigate whether similarities (or differences) between PWH in these characteristics impacts the probability that their sequences are be linked. Such analyses provide information about HIV transmission dynamics within a population. |
1802.09272 | Kelin Xia | Kelin Xia | Persistent homology analysis of ion aggregation and hydrogen-bonding
network | 21 pages, 11 figures, 2 tables | Physical Chemistry Chemical Physics, 20, 13448-13460, 2018 | 10.1039/C8CP01552J | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the great advancement of experimental tools and theoretical models, a
quantitative characterization of the microscopic structures of ion aggregates
and its associated water hydrogen-bonding networks still remains a challenging
problem. In this paper, a newly-invented mathematical method called persistent
homology is introduced, for the first time, to quantitatively analyze the
intrinsic topological properties of ion aggregation systems and
hydrogen-bonding networks. Two most distinguishable properties of persistent
homology analysis of assembly systems are as follows. First, it does not
require a predefined bond length to construct the ion or hydrogen network.
Persistent homology results are determined by the morphological structure of
the data only. Second, it can directly measure the size of circles or holes in
ion aggregates and hydrogen-bonding networks. To validate our model, we
consider two well-studied systems, i.e., NaCl and KSCN solutions, generated
from molecular dynamics simulations. They are believed to represent two
morphological types of aggregation, i.e., local clusters and extended ion
network. It has been found that the two aggregation types have distinguishable
topological features and can be characterized by our topological model very
well. For hydrogen-bonding networks, KSCN systems demonstrate much more
dramatic variations in their local circle structures with the concentration
increase. A consistent increase of large-sized local circle structures is
observed and the sizes of these circles become more and more diverse. In
contrast, NaCl systems show no obvious increase of large-sized circles. Instead
a consistent decline of the average size of circle structures is observed and
the sizes of these circles become more and more uniformed with the
concentration increase.
| [
{
"created": "Mon, 26 Feb 2018 12:31:03 GMT",
"version": "v1"
}
] | 2019-03-08 | [
[
"Xia",
"Kelin",
""
]
] | Despite the great advancement of experimental tools and theoretical models, a quantitative characterization of the microscopic structures of ion aggregates and its associated water hydrogen-bonding networks still remains a challenging problem. In this paper, a newly-invented mathematical method called persistent homology is introduced, for the first time, to quantitatively analyze the intrinsic topological properties of ion aggregation systems and hydrogen-bonding networks. Two most distinguishable properties of persistent homology analysis of assembly systems are as follows. First, it does not require a predefined bond length to construct the ion or hydrogen network. Persistent homology results are determined by the morphological structure of the data only. Second, it can directly measure the size of circles or holes in ion aggregates and hydrogen-bonding networks. To validate our model, we consider two well-studied systems, i.e., NaCl and KSCN solutions, generated from molecular dynamics simulations. They are believed to represent two morphological types of aggregation, i.e., local clusters and extended ion network. It has been found that the two aggregation types have distinguishable topological features and can be characterized by our topological model very well. For hydrogen-bonding networks, KSCN systems demonstrate much more dramatic variations in their local circle structures with the concentration increase. A consistent increase of large-sized local circle structures is observed and the sizes of these circles become more and more diverse. In contrast, NaCl systems show no obvious increase of large-sized circles. Instead a consistent decline of the average size of circle structures is observed and the sizes of these circles become more and more uniformed with the concentration increase. |
2104.04604 | Jonathan Karr | Michael L. Blinov, John H. Gennari, Jonathan R. Karr, Ion I. Moraru,
David P. Nickerson and Herbert M. Sauro | Practical Resources for Enhancing the Reproducibility of Mechanistic
Modeling in Systems Biology | 11 pages, 1 figure | null | null | null | q-bio.QM q-bio.CB q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Although reproducibility is a core tenet of the scientific method, it remains
challenging to reproduce many results. Surprisingly, this also holds true for
computational results in domains such as systems biology where there have been
extensive standardization efforts. For example, Tiwari et al. recently found
that they could only repeat 50% of published simulation results in systems
biology. Toward improving the reproducibility of computational systems
research, we identified several resources that investigators can leverage to
make their research more accessible, executable, and comprehensible by others.
In particular, we identified several domain standards and curation services, as
well as powerful approaches pioneered by the software engineering industry that
we believe many investigators could adopt. Together, we believe these
approaches could substantially enhance the reproducibility of systems biology
research. In turn, we believe enhanced reproducibility would accelerate the
development of more sophisticated models that could inform precision medicine
and synthetic biology.
| [
{
"created": "Fri, 9 Apr 2021 21:09:27 GMT",
"version": "v1"
}
] | 2021-04-13 | [
[
"Blinov",
"Michael L.",
""
],
[
"Gennari",
"John H.",
""
],
[
"Karr",
"Jonathan R.",
""
],
[
"Moraru",
"Ion I.",
""
],
[
"Nickerson",
"David P.",
""
],
[
"Sauro",
"Herbert M.",
""
]
] | Although reproducibility is a core tenet of the scientific method, it remains challenging to reproduce many results. Surprisingly, this also holds true for computational results in domains such as systems biology where there have been extensive standardization efforts. For example, Tiwari et al. recently found that they could only repeat 50% of published simulation results in systems biology. Toward improving the reproducibility of computational systems research, we identified several resources that investigators can leverage to make their research more accessible, executable, and comprehensible by others. In particular, we identified several domain standards and curation services, as well as powerful approaches pioneered by the software engineering industry that we believe many investigators could adopt. Together, we believe these approaches could substantially enhance the reproducibility of systems biology research. In turn, we believe enhanced reproducibility would accelerate the development of more sophisticated models that could inform precision medicine and synthetic biology. |
2207.11174 | Jarek Duda Dr | Jarek Duda, Sabina Podlewska | Low cost prediction of probability distributions of molecular properties
for early virtual screening | 5 pages, 6 figures | null | null | null | q-bio.BM cs.LG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While there is a general focus on predictions of values, mathematically more
appropriate is prediction of probability distributions: with additional
possibilities like prediction of uncertainty, higher moments and quantiles. For
the purpose of the computer-aided drug design field, this article applies
Hierarchical Correlation Reconstruction approach, previously applied in the
analysis of demographic, financial and astronomical data. Instead of a single
linear regression to predict values, it uses multiple linear regressions to
independently predict multiple moments, finally combining them into predicted
probability distribution, here of several ADMET properties based on
substructural fingerprint developed by Klekota\&Roth. Discussed application
example is inexpensive selection of a percentage of molecules with properties
nearly certain to be in a predicted or chosen range during virtual screening.
Such an approach can facilitate the interpretation of the results as the
predictions characterized by high rate of uncertainty are automatically
detected. In addition, for each of the investigated predictive problems, we
detected crucial structural features, which should be carefully considered when
optimizing compounds towards particular property. The whole methodology
developed in the study constitutes therefore a great support for medicinal
chemists, as it enable fast rejection of compounds with the lowest potential of
desired physicochemical/ADMET characteristic and guides the compound
optimization process.
| [
{
"created": "Thu, 21 Jul 2022 13:29:26 GMT",
"version": "v1"
}
] | 2022-07-25 | [
[
"Duda",
"Jarek",
""
],
[
"Podlewska",
"Sabina",
""
]
] | While there is a general focus on predictions of values, mathematically more appropriate is prediction of probability distributions: with additional possibilities like prediction of uncertainty, higher moments and quantiles. For the purpose of the computer-aided drug design field, this article applies Hierarchical Correlation Reconstruction approach, previously applied in the analysis of demographic, financial and astronomical data. Instead of a single linear regression to predict values, it uses multiple linear regressions to independently predict multiple moments, finally combining them into predicted probability distribution, here of several ADMET properties based on substructural fingerprint developed by Klekota\&Roth. Discussed application example is inexpensive selection of a percentage of molecules with properties nearly certain to be in a predicted or chosen range during virtual screening. Such an approach can facilitate the interpretation of the results as the predictions characterized by high rate of uncertainty are automatically detected. In addition, for each of the investigated predictive problems, we detected crucial structural features, which should be carefully considered when optimizing compounds towards particular property. The whole methodology developed in the study constitutes therefore a great support for medicinal chemists, as it enable fast rejection of compounds with the lowest potential of desired physicochemical/ADMET characteristic and guides the compound optimization process. |
q-bio/0611005 | Hernan Garcia | Hernan G. Garcia (1), Paul Grayson (1), Lin Han (2), Mandar Inamdar
(2), Jane Kondev (3), Philip C. Nelson (4), Rob Phillips (2), Jonathan Widom
(5), Paul A. Wiggins (6) ((1) Department of Physics, California Institute of
Technology (2) Division of Engineering and Applied Science, California
Institute of Technology, (3) Department of Physics, Brandeis University, (4)
Department of Physics and Astronomy, University of Pennsylvania, (5)
Department of Biochemistry, Molecular Biology, and Cell Biology, Northwestern
University, (6) Whitehead Institute, Cambridge MA) | Biological Consequences of Tightly Bent DNA: The Other Life of a
Macromolecular Celebrity | 24 pages, 9 figures | null | null | null | q-bio.BM q-bio.QM | null | The mechanical properties of DNA play a critical role in many biological
functions. For example, DNA packing in viruses involves confining the viral
genome in a volume (the viral capsid) with dimensions that are comparable to
the DNA persistence length. Similarly, eukaryotic DNA is packed in DNA-protein
complexes (nucleosomes) in which DNA is tightly bent around protein spools. DNA
is also tightly bent by many proteins that regulate transcription, resulting in
a variation in gene expression that is amenable to quantitative analysis. In
these cases, DNA loops are formed with lengths that are comparable to or
smaller than the DNA persistence length. The aim of this review is to describe
the physical forces associated with tightly bent DNA in all of these settings
and to explore the biological consequences of such bending, as increasingly
accessible by single-molecule techniques.
| [
{
"created": "Wed, 1 Nov 2006 22:43:34 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Garcia",
"Hernan G.",
""
],
[
"Grayson",
"Paul",
""
],
[
"Han",
"Lin",
""
],
[
"Inamdar",
"Mandar",
""
],
[
"Kondev",
"Jane",
""
],
[
"Nelson",
"Philip C.",
""
],
[
"Phillips",
"Rob",
""
],
[
"Wido... | The mechanical properties of DNA play a critical role in many biological functions. For example, DNA packing in viruses involves confining the viral genome in a volume (the viral capsid) with dimensions that are comparable to the DNA persistence length. Similarly, eukaryotic DNA is packed in DNA-protein complexes (nucleosomes) in which DNA is tightly bent around protein spools. DNA is also tightly bent by many proteins that regulate transcription, resulting in a variation in gene expression that is amenable to quantitative analysis. In these cases, DNA loops are formed with lengths that are comparable to or smaller than the DNA persistence length. The aim of this review is to describe the physical forces associated with tightly bent DNA in all of these settings and to explore the biological consequences of such bending, as increasingly accessible by single-molecule techniques. |
1610.05512 | Gianni D'Angelo | Gianni D'Angelo, Salvatore Rampone | Towards a HPC-oriented parallel implementation of a learning algorithm
for bioinformatics applications | 14 pages, BMC Bioinformatics 2014, 15(Suppl 5):S2;
http://www.biomedcentral.com/1471-2105/15/S5/S2 | BMC Bioinformatics201415(Suppl 5):S2, 6 May 2014 | 10.1186/1471-2105-15-S5-S2 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: The huge quantity of data produced in Biomedical research needs
sophisticated algorithmic methodologies for its storage, analysis, and
processing. High Performance Computing (HPC) appears as a magic bullet in this
challenge. However, several hard to solve parallelization and load balancing
problems arise in this context. Here we discuss the HPC-oriented implementation
of a general purpose learning algorithm, originally conceived for DNA analysis
and recently extended to treat uncertainty on data (U BRAIN). The U-BRAIN
algorithm is a learning algorithm that finds a Boolean formula in disjunctive
normal form (DNF), of approximately minimum complexity, that is consistent with
a set of data (instances) which may have missing bits. The conjunctive terms of
the formula are computed in an iterative way by identifying, from the given
data, a family of sets of conditions that must be satisfied by all the positive
instances and violated by all the negative ones; such conditions allow the
computation of a set of coefficients (relevances) for each attribute (literal),
that form a probability distribution, allowing the selection of the term
literals. The great versatility that characterizes it, makes U-BRAIN applicable
in many of the fields in which there are data to be analyzed. However the
memory and the execution time required by the running are of O(n3) and of O(n5)
order, respectively, and so, the algorithm is unaffordable for huge data sets.
| [
{
"created": "Tue, 18 Oct 2016 09:54:14 GMT",
"version": "v1"
}
] | 2016-10-19 | [
[
"D'Angelo",
"Gianni",
""
],
[
"Rampone",
"Salvatore",
""
]
] | Background: The huge quantity of data produced in Biomedical research needs sophisticated algorithmic methodologies for its storage, analysis, and processing. High Performance Computing (HPC) appears as a magic bullet in this challenge. However, several hard to solve parallelization and load balancing problems arise in this context. Here we discuss the HPC-oriented implementation of a general purpose learning algorithm, originally conceived for DNA analysis and recently extended to treat uncertainty on data (U BRAIN). The U-BRAIN algorithm is a learning algorithm that finds a Boolean formula in disjunctive normal form (DNF), of approximately minimum complexity, that is consistent with a set of data (instances) which may have missing bits. The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones; such conditions allow the computation of a set of coefficients (relevances) for each attribute (literal), that form a probability distribution, allowing the selection of the term literals. The great versatility that characterizes it, makes U-BRAIN applicable in many of the fields in which there are data to be analyzed. However the memory and the execution time required by the running are of O(n3) and of O(n5) order, respectively, and so, the algorithm is unaffordable for huge data sets. |
q-bio/0511021 | Eytan Domany | Joseph Lotem, Dvir Netanely, Eytan Domany and Leo Sachs | Human cancers over express genes that are specific to a variety of
normal human tissues | To appear in PNAS | null | 10.1073/pnas.0509360102 | null | q-bio.TO q-bio.QM | null | We have analyzed gene expression data from 3 different kinds of samples:
normal human tissues, human cancer cell lines and leukemic cells from lymphoid
and myeloid leukemia pediatric patients. We have searched for genes that are
over expressed in human cancer and also show specific patterns of
tissue-dependent expression in normal tissues. Using the expression data of the
normal tissues we identified 4346 genes with a high variability of expression,
and clustered these genes according to their relative expression level. Of 91
stable clusters obtained, 24 clusters included genes preferentially expressed
either only in hematopoietic tissues or in hematopoietic and 1-2 other tissues;
28 clusters included genes preferentially expressed in various
non-hematopoietic tissues such as neuronal, testis, liver, kidney, muscle,
lung, pancreas and placenta. Analysis of the expression levels of these 2
groups of genes in the human cancer cell lines and leukemias, identified genes
that were highly expressed in cancer cells but not in their normal
counterparts, and were thus over expressed in the cancers. The different cancer
cell lines and leukemias varied in the number and identity of these over
expressed genes. The results indicate that many genes that are over expressed
in human cancer cells are specific to a variety of normal tissues, including
normal tissues other than those from which the cancer originated. It is
suggested that this general property of cancer cells plays a major role in
determining the behavior of the cancers, including their metastatic potential.
| [
{
"created": "Tue, 15 Nov 2005 08:19:58 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Lotem",
"Joseph",
""
],
[
"Netanely",
"Dvir",
""
],
[
"Domany",
"Eytan",
""
],
[
"Sachs",
"Leo",
""
]
] | We have analyzed gene expression data from 3 different kinds of samples: normal human tissues, human cancer cell lines and leukemic cells from lymphoid and myeloid leukemia pediatric patients. We have searched for genes that are over expressed in human cancer and also show specific patterns of tissue-dependent expression in normal tissues. Using the expression data of the normal tissues we identified 4346 genes with a high variability of expression, and clustered these genes according to their relative expression level. Of 91 stable clusters obtained, 24 clusters included genes preferentially expressed either only in hematopoietic tissues or in hematopoietic and 1-2 other tissues; 28 clusters included genes preferentially expressed in various non-hematopoietic tissues such as neuronal, testis, liver, kidney, muscle, lung, pancreas and placenta. Analysis of the expression levels of these 2 groups of genes in the human cancer cell lines and leukemias, identified genes that were highly expressed in cancer cells but not in their normal counterparts, and were thus over expressed in the cancers. The different cancer cell lines and leukemias varied in the number and identity of these over expressed genes. The results indicate that many genes that are over expressed in human cancer cells are specific to a variety of normal tissues, including normal tissues other than those from which the cancer originated. It is suggested that this general property of cancer cells plays a major role in determining the behavior of the cancers, including their metastatic potential. |
2004.10642 | Annika Hagemann | Annika Hagemann, Jens Wilting, Bita Samimizad, Florian Mormann, Viola
Priesemann | Assessing criticality in pre-seizure single-neuron activity of human
epileptic cortex | 19 pages, 8 Figures | PLOS Computational Biology, 17(3), e1008773 (2021) | 10.1371/journal.pcbi.1008773 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epileptic seizures are characterized by abnormal and excessive neural
activity, where cortical network dynamics seem to become unstable. However,
most of the time, during seizure-free periods, cortex of epilepsy patients
shows perfectly stable dynamics. This raises the question of how recurring
instability can arise in the light of this stable default state. In this work,
we examine two potential scenarios of seizure generation: (i) epileptic
cortical areas might generally operate closer to instability, which would make
epilepsy patients generally more susceptible to seizures, or (ii) epileptic
cortical areas might drift systematically towards instability before seizure
onset. We analyzed single-unit spike recordings from both the epileptogenic
(focal) and the nonfocal cortical hemispheres of 20 epilepsy patients. We
quantified the distance to instability in the framework of criticality, using a
novel estimator, which enables an unbiased inference from a small set of
recorded neurons. Surprisingly, we found no evidence for either scenario:
Neither did focal areas generally operate closer to instability, nor were
seizures preceded by a drift towards instability. In fact, our results from
both pre-seizure and seizure-free intervals suggest that despite epilepsy,
human cortex operates in the stable, slightly subcritical regime, just like
cortex of other healthy mammalians.
| [
{
"created": "Wed, 22 Apr 2020 15:38:08 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Apr 2021 14:53:58 GMT",
"version": "v2"
}
] | 2021-04-06 | [
[
"Hagemann",
"Annika",
""
],
[
"Wilting",
"Jens",
""
],
[
"Samimizad",
"Bita",
""
],
[
"Mormann",
"Florian",
""
],
[
"Priesemann",
"Viola",
""
]
] | Epileptic seizures are characterized by abnormal and excessive neural activity, where cortical network dynamics seem to become unstable. However, most of the time, during seizure-free periods, cortex of epilepsy patients shows perfectly stable dynamics. This raises the question of how recurring instability can arise in the light of this stable default state. In this work, we examine two potential scenarios of seizure generation: (i) epileptic cortical areas might generally operate closer to instability, which would make epilepsy patients generally more susceptible to seizures, or (ii) epileptic cortical areas might drift systematically towards instability before seizure onset. We analyzed single-unit spike recordings from both the epileptogenic (focal) and the nonfocal cortical hemispheres of 20 epilepsy patients. We quantified the distance to instability in the framework of criticality, using a novel estimator, which enables an unbiased inference from a small set of recorded neurons. Surprisingly, we found no evidence for either scenario: Neither did focal areas generally operate closer to instability, nor were seizures preceded by a drift towards instability. In fact, our results from both pre-seizure and seizure-free intervals suggest that despite epilepsy, human cortex operates in the stable, slightly subcritical regime, just like cortex of other healthy mammalians. |
2312.03016 | Shuo Zhang | Shuo Zhang, Lei Xie | Protein Language Model-Powered 3D Ligand Binding Site Prediction from
Protein Sequence | Accepted by the AI for Science (AI4Science) Workshop and the New
Frontiers of AI for Drug Discovery and Development (AI4D3) Workshop at
NeurIPS 2023 | null | null | null | q-bio.QM cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Prediction of ligand binding sites of proteins is a fundamental and important
task for understanding the function of proteins and screening potential drugs.
Most existing methods require experimentally determined protein holo-structures
as input. However, such structures can be unavailable on novel or less-studied
proteins. To tackle this limitation, we propose LaMPSite, which only takes
protein sequences and ligand molecular graphs as input for ligand binding site
predictions. The protein sequences are used to retrieve residue-level
embeddings and contact maps from the pre-trained ESM-2 protein language model.
The ligand molecular graphs are fed into a graph neural network to compute
atom-level embeddings. Then we compute and update the protein-ligand
interaction embedding based on the protein residue-level embeddings and ligand
atom-level embeddings, and the geometric constraints in the inferred protein
contact map and ligand distance map. A final pooling on protein-ligand
interaction embedding would indicate which residues belong to the binding
sites. Without any 3D coordinate information of proteins, our proposed model
achieves competitive performance compared to baseline methods that require 3D
protein structures when predicting binding sites. Given that less than 50% of
proteins have reliable structure information in the current stage, LaMPSite
will provide new opportunities for drug discovery.
| [
{
"created": "Tue, 5 Dec 2023 01:47:38 GMT",
"version": "v1"
}
] | 2023-12-07 | [
[
"Zhang",
"Shuo",
""
],
[
"Xie",
"Lei",
""
]
] | Prediction of ligand binding sites of proteins is a fundamental and important task for understanding the function of proteins and screening potential drugs. Most existing methods require experimentally determined protein holo-structures as input. However, such structures can be unavailable on novel or less-studied proteins. To tackle this limitation, we propose LaMPSite, which only takes protein sequences and ligand molecular graphs as input for ligand binding site predictions. The protein sequences are used to retrieve residue-level embeddings and contact maps from the pre-trained ESM-2 protein language model. The ligand molecular graphs are fed into a graph neural network to compute atom-level embeddings. Then we compute and update the protein-ligand interaction embedding based on the protein residue-level embeddings and ligand atom-level embeddings, and the geometric constraints in the inferred protein contact map and ligand distance map. A final pooling on protein-ligand interaction embedding would indicate which residues belong to the binding sites. Without any 3D coordinate information of proteins, our proposed model achieves competitive performance compared to baseline methods that require 3D protein structures when predicting binding sites. Given that less than 50% of proteins have reliable structure information in the current stage, LaMPSite will provide new opportunities for drug discovery. |
1607.01452 | Michael Hagan | Michael F. Hagan and Roya Zandi | Recent advances in coarse-grained modeling of virus assembly | 9 pages, 3 figures | Curr Opin Virol, 18, 36-43 (2016) | 10.1016/j.coviro.2016.02.012 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many virus families, tens to thousands of proteins assemble spontaneously
into a capsid (protein shell) while packaging the genomic nucleic acid. This
review summarizes recent advances in computational modeling of these dynamical
processes. We present an overview of recent technological and algorithmic
developments, which are enabling simulations to describe the large ranges of
length-and time-scales relevant to assembly, under conditions more closely
matched to experiments than in earlier work. We then describe two examples in
which computational modeling has recently provided an important complement to
experiments.
| [
{
"created": "Wed, 6 Jul 2016 01:13:37 GMT",
"version": "v1"
}
] | 2016-07-07 | [
[
"Hagan",
"Michael F.",
""
],
[
"Zandi",
"Roya",
""
]
] | In many virus families, tens to thousands of proteins assemble spontaneously into a capsid (protein shell) while packaging the genomic nucleic acid. This review summarizes recent advances in computational modeling of these dynamical processes. We present an overview of recent technological and algorithmic developments, which are enabling simulations to describe the large ranges of length-and time-scales relevant to assembly, under conditions more closely matched to experiments than in earlier work. We then describe two examples in which computational modeling has recently provided an important complement to experiments. |
1708.01242 | Marcus Aguiar de | Marcus A. M. de Aguiar, Erica A. Newman, Mathias M. Pires, Justin D.
Yeakel, David H. Hembry, Laura Burkle, Dominique Gravel, Paulo R. Guimaraes
Jr, Jimmy O'Donnell, Timothee Poisot, Marie-Josee Fortin | Revealing biases in the sampling of ecological interaction networks | 35 pages, 4 figures | PeerJ 7:e7566 2019 | 10.7717/peerj.7566 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The structure of ecological interactions is commonly understood through
analyses of interaction networks. However, these analyses may be sensitive to
sampling biases in both the interactors (the nodes of the network) and
interactions (the links between nodes). These issues may affect the accuracy of
empirically constructed ecological networks. We explore the properties of
sampled ecological networks by simulating large-scale ecological networks with
predetermined topologies, and sampling them with different mathematical
procedures. Several types of modular networks were generated, intended to
represent a wide variety of communities that vary in size and types of
ecological interactions. We sampled these networks with different sampling
designs that may be encountered in field experiments. The observed networks
generated by each sampling process were analyzed with respect to number and
size of components. We show that the sampling effort needed to estimate
underlying network properties depends both on the sampling design and on
network topology. Networks with random or scale-free modules require more
complete sampling compared to networks whose modules are nested or bipartite.
Overall, the structure of nested modules was the easiest to detect, regardless
of sampling design. Sampling according to species degree was consistently found
to be the most accurate strategy to estimate network structure. Conversely,
sampling according to module results in an accurate view of certain modules,
but fails to provide a global picture of the underlying network. We recommend
that these findings are incorporated into the design of projects aiming to
characterize large networks of species interactions in the field, to reduce
sampling biases. The software scripts developed to construct and sample
networks are provided for further explorations of network structure and
comparisons to real interaction data.
| [
{
"created": "Thu, 3 Aug 2017 17:36:09 GMT",
"version": "v1"
}
] | 2020-03-02 | [
[
"de Aguiar",
"Marcus A. M.",
""
],
[
"Newman",
"Erica A.",
""
],
[
"Pires",
"Mathias M.",
""
],
[
"Yeakel",
"Justin D.",
""
],
[
"Hembry",
"David H.",
""
],
[
"Burkle",
"Laura",
""
],
[
"Gravel",
"Dominique",
... | The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases in both the interactors (the nodes of the network) and interactions (the links between nodes). These issues may affect the accuracy of empirically constructed ecological networks. We explore the properties of sampled ecological networks by simulating large-scale ecological networks with predetermined topologies, and sampling them with different mathematical procedures. Several types of modular networks were generated, intended to represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different sampling designs that may be encountered in field experiments. The observed networks generated by each sampling process were analyzed with respect to number and size of components. We show that the sampling effort needed to estimate underlying network properties depends both on the sampling design and on network topology. Networks with random or scale-free modules require more complete sampling compared to networks whose modules are nested or bipartite. Overall, the structure of nested modules was the easiest to detect, regardless of sampling design. Sampling according to species degree was consistently found to be the most accurate strategy to estimate network structure. Conversely, sampling according to module results in an accurate view of certain modules, but fails to provide a global picture of the underlying network. We recommend that these findings are incorporated into the design of projects aiming to characterize large networks of species interactions in the field, to reduce sampling biases. The software scripts developed to construct and sample networks are provided for further explorations of network structure and comparisons to real interaction data. |
1206.1865 | Tobias Reichenbach | Tobias Reichenbach and A. J. Hudspeth | Frequency decoding of periodically timed action potentials through
distinct activity patterns in a random neural network | 16 pages, 5 figures, and supplementary information | null | 10.1088/1367-2630/14/11/113022 | null | q-bio.NC cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Frequency discrimination is a fundamental task of the auditory system. The
mammalian inner ear, or cochlea, provides a place code in which different
frequencies are detected at different spatial locations. However, a temporal
code based on spike timing is also available: action potentials evoked in an
auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the
stimulus-they exhibit phase locking-and thus provide temporal information about
the tone's frequency. In an accompanying psychoacoustic study, and in agreement
with previous experiments, we show that humans employ this temporal information
for discrimination of low frequencies. How might such temporal information be
read out in the brain? Here we demonstrate that recurrent random neural
networks in which connections between neurons introduce characteristic time
delays, and in which neurons require temporally coinciding inputs for spike
initiation, can perform sharp frequency discrimination when stimulated with
phase-locked inputs. Although the frequency resolution achieved by such
networks is limited by the noise in phase locking, the resolution for realistic
values reaches the tiny frequency difference of 0.2% that has been measured in
humans.
| [
{
"created": "Fri, 8 Jun 2012 20:09:44 GMT",
"version": "v1"
}
] | 2015-06-05 | [
[
"Reichenbach",
"Tobias",
""
],
[
"Hudspeth",
"A. J.",
""
]
] | Frequency discrimination is a fundamental task of the auditory system. The mammalian inner ear, or cochlea, provides a place code in which different frequencies are detected at different spatial locations. However, a temporal code based on spike timing is also available: action potentials evoked in an auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the stimulus-they exhibit phase locking-and thus provide temporal information about the tone's frequency. In an accompanying psychoacoustic study, and in agreement with previous experiments, we show that humans employ this temporal information for discrimination of low frequencies. How might such temporal information be read out in the brain? Here we demonstrate that recurrent random neural networks in which connections between neurons introduce characteristic time delays, and in which neurons require temporally coinciding inputs for spike initiation, can perform sharp frequency discrimination when stimulated with phase-locked inputs. Although the frequency resolution achieved by such networks is limited by the noise in phase locking, the resolution for realistic values reaches the tiny frequency difference of 0.2% that has been measured in humans. |
2006.03175 | Philip Gressman | Philip T. Gressman and Jennifer R. Peck | Simulating COVID-19 in a University Environment | 30 pages, 9 figures; fixed minor typos and rephrased some unclear
points | Mathematical Biosciences Volume 328, October 2020, 108436 | 10.1016/j.mbs.2020.108436 | null | q-bio.PE cs.MA cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Residential colleges and universities face unique challenges in providing
in-person instruction during the COVID-19 pandemic. Administrators are
currently faced with decisions about whether to open during the pandemic and
what modifications of their normal operations might be necessary to protect
students, faculty and staff. There is little information, however, on what
measures are likely to be most effective and whether existing interventions
could contain the spread of an outbreak on campus. We develop a full-scale
stochastic agent-based model to determine whether in-person instruction could
safely continue during the pandemic and evaluate the necessity of various
interventions. Simulation results indicate that large scale randomized testing,
contact-tracing, and quarantining are important components of a successful
strategy for containing campus outbreaks. High test specificity is critical for
keeping the size of the quarantine population manageable. Moving the largest
classes online is also crucial for controlling both the size of outbreaks and
the number of students in quarantine. Increased residential exposure can
significantly impact the size of an outbreak, but it is likely more important
to control non-residential social exposure among students. Finally, necessarily
high quarantine rates even in controlled outbreaks imply significant
absenteeism, indicating a need to plan for remote instruction of quarantined
students.
| [
{
"created": "Fri, 5 Jun 2020 00:04:03 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Jun 2020 16:46:02 GMT",
"version": "v2"
}
] | 2020-12-22 | [
[
"Gressman",
"Philip T.",
""
],
[
"Peck",
"Jennifer R.",
""
]
] | Residential colleges and universities face unique challenges in providing in-person instruction during the COVID-19 pandemic. Administrators are currently faced with decisions about whether to open during the pandemic and what modifications of their normal operations might be necessary to protect students, faculty and staff. There is little information, however, on what measures are likely to be most effective and whether existing interventions could contain the spread of an outbreak on campus. We develop a full-scale stochastic agent-based model to determine whether in-person instruction could safely continue during the pandemic and evaluate the necessity of various interventions. Simulation results indicate that large scale randomized testing, contact-tracing, and quarantining are important components of a successful strategy for containing campus outbreaks. High test specificity is critical for keeping the size of the quarantine population manageable. Moving the largest classes online is also crucial for controlling both the size of outbreaks and the number of students in quarantine. Increased residential exposure can significantly impact the size of an outbreak, but it is likely more important to control non-residential social exposure among students. Finally, necessarily high quarantine rates even in controlled outbreaks imply significant absenteeism, indicating a need to plan for remote instruction of quarantined students. |
1612.02243 | Ruggero G. Bettinardi | Ruggero G. Bettinardi, Gustavo Deco, Vasilis M. Karlaftis, Timothy J.
Van Hartevelt, Henrique M. Fernandes, Zoe Kourtzi, Morten L. Kringelbach and
Gorka Zamora-L\'opez | How structure sculpts function: unveiling the contribution of anatomical
connectivity to the brain's spontaneous correlation structure | null | Chaos 27, 047409 (2017) | 10.1063/1.4980099 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Intrinsic brain activity is characterized by highly structured co-activations
between different regions, whose origin is still under debate. In this paper,
we address the question whether it is possible to unveil how the underlying
anatomical connectivity shape the brain's spontaneous correlation structure. We
start from the assumption that in order for two nodes to exhibit large
covariation, they must be exposed to similar input patterns from the entire
network. We then acknowledge that information rarely spreads only along an
unique route, but rather travels along all possible paths. In real networks the
strength of local perturbations tends to decay as they propagate away from the
sources, leading to a progressive attenuation of the original information
content and, thus, of their influence. We use these notions to derive a novel
analytical measure, $\mathcal{T}$ , which quantifies the similarity of the
whole-network input patterns arriving at any two nodes only due to the
underlying topology, in what is a generalization of the matching index. We show
that this measure of topological similarity can indeed be used to predict the
contribution of network topology to the expected correlation structure, thus
unveiling the mechanism behind the tight but elusive relationship between
structure and function in complex networks. Finally, we use this measure to
investigate brain connectivity, showing that information about the topology
defined by the complex fabric of brain axonal pathways specifies to a large
extent the time-average functional connectivity observed at rest.
| [
{
"created": "Wed, 7 Dec 2016 13:47:58 GMT",
"version": "v1"
}
] | 2018-11-01 | [
[
"Bettinardi",
"Ruggero G.",
""
],
[
"Deco",
"Gustavo",
""
],
[
"Karlaftis",
"Vasilis M.",
""
],
[
"Van Hartevelt",
"Timothy J.",
""
],
[
"Fernandes",
"Henrique M.",
""
],
[
"Kourtzi",
"Zoe",
""
],
[
"Kringelbach",
... | Intrinsic brain activity is characterized by highly structured co-activations between different regions, whose origin is still under debate. In this paper, we address the question whether it is possible to unveil how the underlying anatomical connectivity shape the brain's spontaneous correlation structure. We start from the assumption that in order for two nodes to exhibit large covariation, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along an unique route, but rather travels along all possible paths. In real networks the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. We use these notions to derive a novel analytical measure, $\mathcal{T}$ , which quantifies the similarity of the whole-network input patterns arriving at any two nodes only due to the underlying topology, in what is a generalization of the matching index. We show that this measure of topological similarity can indeed be used to predict the contribution of network topology to the expected correlation structure, thus unveiling the mechanism behind the tight but elusive relationship between structure and function in complex networks. Finally, we use this measure to investigate brain connectivity, showing that information about the topology defined by the complex fabric of brain axonal pathways specifies to a large extent the time-average functional connectivity observed at rest. |
1603.04687 | Kanaka Rajan PhD | Kanaka Rajan, Christopher D Harvey, David W Tank | Recurrent Network Models Of Sequence Generation And Memory | 60 pages, 6 figures | Neuron 90, 1-15, April 6, 2016 Elsevier Inc, (2016) | 10.1016/j.neuron.2016.02.009 | null | q-bio.NC cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequential activation of neurons is a common feature of network activity
during a variety of behaviors, including working memory and decision making.
Previous network models for sequences and memory emphasized specialized
architectures in which a principled mechanism is pre-wired into their
connectivity. Here we demonstrate that, starting from random connectivity and
modifying a small fraction of connections, a largely disordered recur- rent
network can produce sequences and implement working memory efficiently. We use
this process, called Partial In-Network Training (PINning), to model and match
cellular resolution imaging data from the posterior parietal cortex during a
virtual memory- guided two-alternative forced-choice task. Analysis of the
connectivity reveals that sequences propagate by the cooperation between
recurrent synaptic interactions and external inputs, rather than through
feedforward or asymmetric connections. Together our results suggest that neural
sequences may emerge through learning from largely unstructured network
architectures.
| [
{
"created": "Mon, 14 Mar 2016 15:00:12 GMT",
"version": "v1"
}
] | 2016-03-16 | [
[
"Rajan",
"Kanaka",
""
],
[
"Harvey",
"Christopher D",
""
],
[
"Tank",
"David W",
""
]
] | Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here we demonstrate that, starting from random connectivity and modifying a small fraction of connections, a largely disordered recur- rent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network Training (PINning), to model and match cellular resolution imaging data from the posterior parietal cortex during a virtual memory- guided two-alternative forced-choice task. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures. |
q-bio/0408013 | Guido Tiana | R. A. Broglia, G. Tiana, D. Provasi, F. Simona, L. Sutto, F. Vasile
and M. Zanotti | Design of a folding inhibitor of the HIV-1 Protease | null | null | null | null | q-bio.BM | null | Being HIV-1-PR an essential enzyme in the viral life cycle, its inhibition
can control AIDS. Because the folding of single domain proteins, like HIV-1-PR
is controlled by local elementary structures (LES, folding units stabilized by
strongly interacting, highly conserved amino acids) which have evolved over
myriads of generations to recognize and strongly attract each other so as to
make the protein fold fast, we suggest a novel type of HIV-1-PR inhibitors
which interfere with the folding of the protein: short peptides displaying the
same amino acid sequence of that of LES. Theoretical and experimental evidence
for the specificity and efficiency of such inhibitors are presented.
| [
{
"created": "Mon, 16 Aug 2004 15:36:44 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Broglia",
"R. A.",
""
],
[
"Tiana",
"G.",
""
],
[
"Provasi",
"D.",
""
],
[
"Simona",
"F.",
""
],
[
"Sutto",
"L.",
""
],
[
"Vasile",
"F.",
""
],
[
"Zanotti",
"M.",
""
]
] | Being HIV-1-PR an essential enzyme in the viral life cycle, its inhibition can control AIDS. Because the folding of single domain proteins, like HIV-1-PR is controlled by local elementary structures (LES, folding units stabilized by strongly interacting, highly conserved amino acids) which have evolved over myriads of generations to recognize and strongly attract each other so as to make the protein fold fast, we suggest a novel type of HIV-1-PR inhibitors which interfere with the folding of the protein: short peptides displaying the same amino acid sequence of that of LES. Theoretical and experimental evidence for the specificity and efficiency of such inhibitors are presented. |
1502.02692 | Kristina Crona | Kristina Crona | Epistasis and Entropy | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epistasis is a key concept in the theory of adaptation. Indicators of
epistasis are of interest for large system where systematic fitness
measurements may not be possible. Some recent approaches depend on information
theory. We show that considering shared entropy for pairs of loci can be
misleading. The reason is that shared entropy does not imply epistasis for the
pair. This observation holds true also in the absence of higher order
epistasis. We discuss a refined approach for identifying pairwise interactions
using entropy.
| [
{
"created": "Sun, 1 Feb 2015 16:27:32 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Feb 2015 16:30:56 GMT",
"version": "v2"
},
{
"created": "Tue, 3 Mar 2015 20:24:25 GMT",
"version": "v3"
}
] | 2015-03-04 | [
[
"Crona",
"Kristina",
""
]
] | Epistasis is a key concept in the theory of adaptation. Indicators of epistasis are of interest for large system where systematic fitness measurements may not be possible. Some recent approaches depend on information theory. We show that considering shared entropy for pairs of loci can be misleading. The reason is that shared entropy does not imply epistasis for the pair. This observation holds true also in the absence of higher order epistasis. We discuss a refined approach for identifying pairwise interactions using entropy. |
2206.05307 | Sage Malingen | Sage Malingen and Padmini Rangamani | Modeling membrane curvature generation using mechanics and machine
learning | null | null | 10.1101/2022.06.06.495017 | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The deformation of cellular membranes regulates trafficking processes, such
as exocytosis and endocytosis. Classically, the Helfrich continuum model is
used to characterize the forces and mechanical parameters that cells tune to
accomplish membrane shape changes. While this classical model effectively
captures curvature generation, one of the core challenges in using it to
approximate a biological process is selecting a set of mechanical parameters
(including bending modulus and membrane tension) from a large set of reasonable
values. We used the Helfrich model to generate a large synthetic dataset from a
random sampling of realistic mechanical parameters and used this dataset to
train machine learning models. These models produced promising results,
accurately classifying model behavior and predicting membrane shape from
mechanical parameters. We also note emerging methods in machine learning that
can leverage the physical insight of the Helfrich model to improve performance
and draw greater insight into how cells control membrane shape change.
| [
{
"created": "Fri, 10 Jun 2022 18:08:12 GMT",
"version": "v1"
}
] | 2022-06-14 | [
[
"Malingen",
"Sage",
""
],
[
"Rangamani",
"Padmini",
""
]
] | The deformation of cellular membranes regulates trafficking processes, such as exocytosis and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces and mechanical parameters that cells tune to accomplish membrane shape changes. While this classical model effectively captures curvature generation, one of the core challenges in using it to approximate a biological process is selecting a set of mechanical parameters (including bending modulus and membrane tension) from a large set of reasonable values. We used the Helfrich model to generate a large synthetic dataset from a random sampling of realistic mechanical parameters and used this dataset to train machine learning models. These models produced promising results, accurately classifying model behavior and predicting membrane shape from mechanical parameters. We also note emerging methods in machine learning that can leverage the physical insight of the Helfrich model to improve performance and draw greater insight into how cells control membrane shape change. |
2207.02328 | Carlo Amodeo | Carlo Amodeo, Igor Fortel, Olusola Ajilore, Liang Zhan, Alex Leow,
Theja Tulabandhula | Unified Embeddings of Structural and Functional Connectome via a
Function-Constrained Structural Graph Variational Auto-Encoder | null | null | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Graph theoretical analyses have become standard tools in modeling functional
and anatomical connectivity in the brain. With the advent of connectomics, the
primary graphs or networks of interest are structural connectome (derived from
DTI tractography) and functional connectome (derived from resting-state fMRI).
However, most published connectome studies have focused on either structural or
functional connectome, yet complementary information between them, when
available in the same dataset, can be jointly leveraged to improve our
understanding of the brain. To this end, we propose a function-constrained
structural graph variational autoencoder (FCS-GVAE) capable of incorporating
information from both functional and structural connectome in an unsupervised
fashion. This leads to a joint low-dimensional embedding that establishes a
unified spatial coordinate system for comparing across different subjects. We
evaluate our approach using the publicly available OASIS-3 Alzheimer's disease
(AD) dataset and show that a variational formulation is necessary to optimally
encode functional brain dynamics. Further, the proposed joint embedding
approach can more accurately distinguish different patient sub-populations than
approaches that do not use complementary connectome information.
| [
{
"created": "Tue, 5 Jul 2022 21:39:13 GMT",
"version": "v1"
}
] | 2022-07-07 | [
[
"Amodeo",
"Carlo",
""
],
[
"Fortel",
"Igor",
""
],
[
"Ajilore",
"Olusola",
""
],
[
"Zhan",
"Liang",
""
],
[
"Leow",
"Alex",
""
],
[
"Tulabandhula",
"Theja",
""
]
] | Graph theoretical analyses have become standard tools in modeling functional and anatomical connectivity in the brain. With the advent of connectomics, the primary graphs or networks of interest are structural connectome (derived from DTI tractography) and functional connectome (derived from resting-state fMRI). However, most published connectome studies have focused on either structural or functional connectome, yet complementary information between them, when available in the same dataset, can be jointly leveraged to improve our understanding of the brain. To this end, we propose a function-constrained structural graph variational autoencoder (FCS-GVAE) capable of incorporating information from both functional and structural connectome in an unsupervised fashion. This leads to a joint low-dimensional embedding that establishes a unified spatial coordinate system for comparing across different subjects. We evaluate our approach using the publicly available OASIS-3 Alzheimer's disease (AD) dataset and show that a variational formulation is necessary to optimally encode functional brain dynamics. Further, the proposed joint embedding approach can more accurately distinguish different patient sub-populations than approaches that do not use complementary connectome information. |
1309.0408 | Troy Hernandez PhD | Troy Hernandez and Jie Yang | Descriptive Statistics of the Genome: Phylogenetic Classification of
Viruses | 14 pages, 4 tables, 1 figure | null | null | null | q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The typical process for classifying and submitting a newly sequenced virus to
the NCBI database involves two steps. First, a BLAST search is performed to
determine likely family candidates. That is followed by checking the candidate
families with the Pairwise Sequence Alignment tool for similar species. The
submitter's judgement is then used to determine the most likely species
classification. The aim of this paper is to show that this process can be
automated into a fast, accurate, one-step process using the proposed
alignment-free method and properly implemented machine learning techniques.
We present a new family of alignment-free vectorizations of the genome, the
generalized vector, that maintains the speed of existing alignment-free methods
while outperforming all available methods. This new alignment-free
vectorization uses the frequency of genomic words (k-mers), as is done in the
composition vector, and incorporates descriptive statistics of those k-mers'
positional information, as inspired by the natural vector.
We analyze 5 different characterizations of genome similarity using
$k$-nearest neighbor classification, and evaluate these on two collections of
viruses totaling over 10,000 viruses. We show that our proposed method performs
better than, or as well as, other methods at every level of the phylogenetic
hierarchy.
The data and R code is available upon request.
| [
{
"created": "Mon, 2 Sep 2013 13:56:40 GMT",
"version": "v1"
},
{
"created": "Sun, 20 Mar 2016 17:19:36 GMT",
"version": "v2"
}
] | 2016-03-22 | [
[
"Hernandez",
"Troy",
""
],
[
"Yang",
"Jie",
""
]
] | The typical process for classifying and submitting a newly sequenced virus to the NCBI database involves two steps. First, a BLAST search is performed to determine likely family candidates. That is followed by checking the candidate families with the Pairwise Sequence Alignment tool for similar species. The submitter's judgement is then used to determine the most likely species classification. The aim of this paper is to show that this process can be automated into a fast, accurate, one-step process using the proposed alignment-free method and properly implemented machine learning techniques. We present a new family of alignment-free vectorizations of the genome, the generalized vector, that maintains the speed of existing alignment-free methods while outperforming all available methods. This new alignment-free vectorization uses the frequency of genomic words (k-mers), as is done in the composition vector, and incorporates descriptive statistics of those k-mers' positional information, as inspired by the natural vector. We analyze 5 different characterizations of genome similarity using $k$-nearest neighbor classification, and evaluate these on two collections of viruses totaling over 10,000 viruses. We show that our proposed method performs better than, or as well as, other methods at every level of the phylogenetic hierarchy. The data and R code is available upon request. |
2103.16587 | Valeriia Demareva | Valeriia Demareva, Elena Mukhina, Tatiana Bobro | Does Double Biofeedback Affect Functional Hemispheric Asymmetry and
Activity? A Pilot Study | null | null | 10.3390/sym13060937 | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In the current pilot study, we attempt to find out how double neurofeedback
influences functional hemispheric asymmetry and activity. We examined 30
healthy participants (8 males; 22 females, mean age = 29; SD = 8). To measure
functional hemispheric asymmetry and activity, we used computer laterometry in
the "two-source" lead-lag dichotic paradigm. Double biofeedback included 8 min
of EEG oscillation recording with five minutes of basic mode. During the basic
mode, the current amplitude of the EEG oscillator gets transformed into
feedback sounds while the current amplitude of alpha EEG oscillator is used to
modulate the intensity of light signals. Double neurofeedback did not directly
influence the asymmetry itself but accelerated individual sound perception
characteristics during dichotic listening in the preceding effect paradigm.
Further research is needed to investigate the effect of double neurofeedback
training on functional brain activity and asymmetry, taking into account
participants' age, gender, and motivation.
| [
{
"created": "Tue, 30 Mar 2021 18:01:59 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Mar 2023 09:25:34 GMT",
"version": "v2"
}
] | 2023-03-20 | [
[
"Demareva",
"Valeriia",
""
],
[
"Mukhina",
"Elena",
""
],
[
"Bobro",
"Tatiana",
""
]
] | In the current pilot study, we attempt to find out how double neurofeedback influences functional hemispheric asymmetry and activity. We examined 30 healthy participants (8 males; 22 females, mean age = 29; SD = 8). To measure functional hemispheric asymmetry and activity, we used computer laterometry in the "two-source" lead-lag dichotic paradigm. Double biofeedback included 8 min of EEG oscillation recording with five minutes of basic mode. During the basic mode, the current amplitude of the EEG oscillator gets transformed into feedback sounds while the current amplitude of alpha EEG oscillator is used to modulate the intensity of light signals. Double neurofeedback did not directly influence the asymmetry itself but accelerated individual sound perception characteristics during dichotic listening in the preceding effect paradigm. Further research is needed to investigate the effect of double neurofeedback training on functional brain activity and asymmetry, taking into account participants' age, gender, and motivation. |
q-bio/0404035 | Jaewook Joo | Jaewook Joo, Joel L. Lebowitz | Pair approximation of the stochastic
susceptible-infected-recovered-susceptible epidemic model on the hypercubic
lattice | null | Phys. Rev. E, 70 (2004) 036114 | 10.1103/PhysRevE.70.036114 | null | q-bio.PE cond-mat.stat-mech | null | We investigate the time-evolution and steady states of the stochastic
susceptible-infected-recovered-susceptible(SIRS) epidemic model on one- and
two- dimensional lattices. We compare the behavior of this system, obtained
from computer simulations, with those obtained from the mean-field
approximation(MFA) and pair-approximation(PA). The former(latter) approximates
higher order moments in terms of first(second) order ones. We find that the PA
gives consistently better results than the MFA. In one dimension the
improvement is even qualitative.
| [
{
"created": "Mon, 26 Apr 2004 18:03:44 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Joo",
"Jaewook",
""
],
[
"Lebowitz",
"Joel L.",
""
]
] | We investigate the time-evolution and steady states of the stochastic susceptible-infected-recovered-susceptible(SIRS) epidemic model on one- and two- dimensional lattices. We compare the behavior of this system, obtained from computer simulations, with those obtained from the mean-field approximation(MFA) and pair-approximation(PA). The former(latter) approximates higher order moments in terms of first(second) order ones. We find that the PA gives consistently better results than the MFA. In one dimension the improvement is even qualitative. |
1112.2994 | Alexander Peyser | Alexander Peyser and Wolfgang Nonner | Electrostatic determinants of voltage sensitivity in ion channels:
Simulations of sliding-helix mechanisms | null | null | null | null | q-bio.BM physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electrical signaling via voltage-gated ion channels depends upon the function
of the voltage sensor (VS), identified with the S1-S4 domain of voltage-gated K
channels. Here we investigate some physical aspects of the sliding-helix model
of the VS using simulations based on VS charges, linear dielectrics and
whole-body motion. Model electrostatics in voltage-clamped boundary conditions
are solved using a boundary element method. The statistical mechanical
consequences of the electrostatic configurational energy are computed to gain
insight into the sliding-helix mechanism and to predict experimentally measured
ensemble properties such as gating charge displaced by an applied voltage.
Those consequences and ensemble properties are investigated for variations of:
S4 configuration ({\alpha}- and 3(10)-helical), intrinsic counter-charges,
protein polarizability, geometry of the gating canal, screening of S4 charges
by the baths, and protein charges located at the bath interfaces. We find that
the sliding helix VS has an inherent electrostatic stability and its function
as a VS is robust in the parameter space explored. Maximal charge displacement
is limited by geometry, specifically the range of movement where S4 charges and
counter-charges overlap in the region of weak dielectric. The steepness of
charge rearrangement in the physiological voltage range is sensitive to the
landscape of electrostatic energy: energy differences of <2 kT have substantial
consequences. Such variations of energy landscape are produced by all
variations of model features tested. The amount of free energy per unit voltage
that a sliding-helix VS can deliver to other parts of the channel (conductance
voltage sensitivity) is limited by both the maximal displaced charge and the
steepness of charge redistribution by voltage (sensor voltage sensitivity).
| [
{
"created": "Tue, 13 Dec 2011 18:27:15 GMT",
"version": "v1"
}
] | 2015-03-13 | [
[
"Peyser",
"Alexander",
""
],
[
"Nonner",
"Wolfgang",
""
]
] | Electrical signaling via voltage-gated ion channels depends upon the function of the voltage sensor (VS), identified with the S1-S4 domain of voltage-gated K channels. Here we investigate some physical aspects of the sliding-helix model of the VS using simulations based on VS charges, linear dielectrics and whole-body motion. Model electrostatics in voltage-clamped boundary conditions are solved using a boundary element method. The statistical mechanical consequences of the electrostatic configurational energy are computed to gain insight into the sliding-helix mechanism and to predict experimentally measured ensemble properties such as gating charge displaced by an applied voltage. Those consequences and ensemble properties are investigated for variations of: S4 configuration ({\alpha}- and 3(10)-helical), intrinsic counter-charges, protein polarizability, geometry of the gating canal, screening of S4 charges by the baths, and protein charges located at the bath interfaces. We find that the sliding helix VS has an inherent electrostatic stability and its function as a VS is robust in the parameter space explored. Maximal charge displacement is limited by geometry, specifically the range of movement where S4 charges and counter-charges overlap in the region of weak dielectric. The steepness of charge rearrangement in the physiological voltage range is sensitive to the landscape of electrostatic energy: energy differences of <2 kT have substantial consequences. Such variations of energy landscape are produced by all variations of model features tested. The amount of free energy per unit voltage that a sliding-helix VS can deliver to other parts of the channel (conductance voltage sensitivity) is limited by both the maximal displaced charge and the steepness of charge redistribution by voltage (sensor voltage sensitivity). |
1406.2500 | Istvan Kiss Z | P. Rattana, L. Berthouze, I.Z. Kiss | The impact of constrained rewiring on network structure and node
dynamics | null | null | 10.1103/PhysRevE.90.052806 | null | q-bio.PE nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study an adaptive spatial network. We consider an SIS
(susceptible-infectedsusceptible) epidemic on the network, with a link/contact
rewiring process constrained by spatial proximity. In particular, we assume
that susceptible nodes break links with infected nodes independently of
distance, and reconnect at random to susceptible nodes available within a given
radius. By systematically manipulating this radius we investigate the impact of
rewiring on the structure of the network and characteristics of the epidemic.
We adopt a step-by-step approach whereby we first study the impact of rewiring
on the network structure in the absence of an epidemic, then with nodes
assigned a disease status but without disease dynamics, and finally running
network and epidemic dynamics simultaneously. In the case of no labelling and
no epidemic dynamics, we provide both analytic and semi-analytic formulas for
the value of clustering achieved in the network. Our results also show that the
rewiring radius and the network's initial structure have a pronounced effect on
the endemic equilibrium, with increasingly large rewiring radiuses yielding
smaller disease prevalence.
| [
{
"created": "Tue, 10 Jun 2014 10:40:04 GMT",
"version": "v1"
}
] | 2016-11-25 | [
[
"Rattana",
"P.",
""
],
[
"Berthouze",
"L.",
""
],
[
"Kiss",
"I. Z.",
""
]
] | In this paper, we study an adaptive spatial network. We consider an SIS (susceptible-infectedsusceptible) epidemic on the network, with a link/contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance, and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labelling and no epidemic dynamics, we provide both analytic and semi-analytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network's initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence. |
1203.2737 | Jeremy Schofield | Jeremy Schofield, Paul Inder, and Raymond Kapral | Modeling of solvent flow effects in enzyme catalysis under physiological
conditions | 15 pages in double column format | null | 10.1063/1.4719539 | null | q-bio.BM cond-mat.soft physics.bio-ph physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A stochastic model for the dynamics of enzymatic catalysis in explicit,
effective solvents under physiological conditions is presented.
Analytically-computed first passage time densities of a diffusing particle in a
spherical shell with absorbing boundaries are combined with densities obtained
from explicit simulation to obtain the overall probability density for the
total reaction cycle time of the enzymatic system. The method is used to
investigate the catalytic transfer of a phosphoryl group in a phosphoglycerate
kinase-ADP-bis phosphoglycerate system, one of the steps of glycolysis. The
direct simulation of the enzyme-substrate binding and reaction is carried out
using an elastic network model for the protein, and the solvent motions are
described by multiparticle collision dynamics, which incorporates hydrodynamic
flow effects. Systems where solvent-enzyme coupling occurs through explicit
intermolecular interactions, as well as systems where this coupling is taken
into account by including the protein and substrate in the multiparticle
collision step, are investigated and compared with simulations where
hydrodynamic coupling is absent. It is demonstrated that the flow of solvent
particles around the enzyme facilitates the large-scale hinge motion of the
enzyme with bound substrates, and has a significant impact on the shape of the
probability densities and average time scales of substrate binding for
substrates near the enzyme, the closure of the enzyme after binding, and the
overall time of completion of the cycle.
| [
{
"created": "Tue, 13 Mar 2012 08:48:11 GMT",
"version": "v1"
}
] | 2015-06-04 | [
[
"Schofield",
"Jeremy",
""
],
[
"Inder",
"Paul",
""
],
[
"Kapral",
"Raymond",
""
]
] | A stochastic model for the dynamics of enzymatic catalysis in explicit, effective solvents under physiological conditions is presented. Analytically-computed first passage time densities of a diffusing particle in a spherical shell with absorbing boundaries are combined with densities obtained from explicit simulation to obtain the overall probability density for the total reaction cycle time of the enzymatic system. The method is used to investigate the catalytic transfer of a phosphoryl group in a phosphoglycerate kinase-ADP-bis phosphoglycerate system, one of the steps of glycolysis. The direct simulation of the enzyme-substrate binding and reaction is carried out using an elastic network model for the protein, and the solvent motions are described by multiparticle collision dynamics, which incorporates hydrodynamic flow effects. Systems where solvent-enzyme coupling occurs through explicit intermolecular interactions, as well as systems where this coupling is taken into account by including the protein and substrate in the multiparticle collision step, are investigated and compared with simulations where hydrodynamic coupling is absent. It is demonstrated that the flow of solvent particles around the enzyme facilitates the large-scale hinge motion of the enzyme with bound substrates, and has a significant impact on the shape of the probability densities and average time scales of substrate binding for substrates near the enzyme, the closure of the enzyme after binding, and the overall time of completion of the cycle. |
2110.10945 | Rasmus Gr{\o}nfeldt Winther | Rasmus Gr{\o}nfeldt Winther | Lewontin (1972) | To appear in a Routledge book anthology: /Remapping Race in a Global
Context/ edited by L. Lorusso and R.G. Winther (the author). 64 pages, 7
tables, 5 figures. For associated Dryad Data File see
https://datadryad.org/stash/dataset/doi:10.7291/D1F68R? | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Richard C. Lewontin is arguably the most influential evolutionary biologist
of the second half of the 20th century. In this chapter, I provide two windows
on his influential 1972 article "The Apportionment of Human Diversity": First,
I show how the fourteen publications that he cites influenced him and framed
his exploration; second, I present close readings of the five sections of the
article: "Introduction," "The Genes," "The Samples," "The Measure of
Diversity," and "The Results." I hope to illuminate the article's basic anatomy
and argumentative arc, and why it became such a historically important
document. In particular, I make explicit all of the mathematics (e.g., six
Shannon information measures) and the general population genetic theory
underlying this mathematics (e.g., the Wahlund effect). Lewontin did not make
this explicit in his article. Furthermore, in redoing all of his calculations,
I find that Lewontin made calculation errors (including rounding errors or
omitting diversity component values) for all the genes he analyzed except one
(P), and understated the among races diversity component, according to even
just his own calculations. In reproducing the original computation, I find that
the values of, respectively, within populations, among populations but within
races, and among races diversity apportionments shift slightly (86%, 7%, 7%);
here, in this "field guide" to Lewontin (1972), as well as in Winther (2022), I
discuss this change in light of the values produced in subsequent replications
of Lewontin's calculation with other statistics and data sets.
| [
{
"created": "Thu, 21 Oct 2021 07:30:00 GMT",
"version": "v1"
},
{
"created": "Sat, 6 Nov 2021 07:59:40 GMT",
"version": "v2"
}
] | 2021-11-09 | [
[
"Winther",
"Rasmus Grønfeldt",
""
]
] | Richard C. Lewontin is arguably the most influential evolutionary biologist of the second half of the 20th century. In this chapter, I provide two windows on his influential 1972 article "The Apportionment of Human Diversity": First, I show how the fourteen publications that he cites influenced him and framed his exploration; second, I present close readings of the five sections of the article: "Introduction," "The Genes," "The Samples," "The Measure of Diversity," and "The Results." I hope to illuminate the article's basic anatomy and argumentative arc, and why it became such a historically important document. In particular, I make explicit all of the mathematics (e.g., six Shannon information measures) and the general population genetic theory underlying this mathematics (e.g., the Wahlund effect). Lewontin did not make this explicit in his article. Furthermore, in redoing all of his calculations, I find that Lewontin made calculation errors (including rounding errors or omitting diversity component values) for all the genes he analyzed except one (P), and understated the among races diversity component, according to even just his own calculations. In reproducing the original computation, I find that the values of, respectively, within populations, among populations but within races, and among races diversity apportionments shift slightly (86%, 7%, 7%); here, in this "field guide" to Lewontin (1972), as well as in Winther (2022), I discuss this change in light of the values produced in subsequent replications of Lewontin's calculation with other statistics and data sets. |
1411.6330 | Brandon Barker | Lin Xu, Brandon Barker, Zhenglong Gu | Dynamic epistasis for different alleles of the same gene | 26 pages, 12 figures; Proc Natl Acad Sci U S A 109 | null | 10.1073/pnas.1121507109 | null | q-bio.MN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epistasis refers to the phenomenon in which phenotypic consequences caused by
mutation of one gene depend on one or more mutations at another gene. Epistasis
is critical for understanding many genetic and evolutionary processes,
including pathway organization, evolution of sexual reproduction, mutational
load, ploidy, genomic complexity, speciation, and the origin of life.
Nevertheless, current understandings for the genome-wide distribution of
epistasis are mostly inferred from interactions among one mutant type per gene,
whereas how epistatic interaction partners change dynamically for different
mutant alleles of the same gene is largely unknown. Here we address this issue
by combining predictions from flux balance analysis and data from a recently
published high-throughput experiment. Our results show that different alleles
can epistatically interact with very different gene sets. Furthermore, between
two random mutant alleles of the same gene, the chance for the allele with more
severe mutational consequence to develop a higher percentage of negative
epistasis than the other allele is 50-70% in eukaryotic organisms, but only
20-30% in bacteria and archaea. We developed a population genetics model that
predicts that the observed distribution for the sign of epistasis can speed up
the process of purging deleterious mutations in eukaryotic organisms. Our
results indicate that epistasis among genes can be dynamically rewired at the
genome level, and call on future efforts to revisit theories that can integrate
epistatic dynamics among genes in biological systems.
| [
{
"created": "Mon, 24 Nov 2014 02:34:24 GMT",
"version": "v1"
}
] | 2014-11-25 | [
[
"Xu",
"Lin",
""
],
[
"Barker",
"Brandon",
""
],
[
"Gu",
"Zhenglong",
""
]
] | Epistasis refers to the phenomenon in which phenotypic consequences caused by mutation of one gene depend on one or more mutations at another gene. Epistasis is critical for understanding many genetic and evolutionary processes, including pathway organization, evolution of sexual reproduction, mutational load, ploidy, genomic complexity, speciation, and the origin of life. Nevertheless, current understandings for the genome-wide distribution of epistasis are mostly inferred from interactions among one mutant type per gene, whereas how epistatic interaction partners change dynamically for different mutant alleles of the same gene is largely unknown. Here we address this issue by combining predictions from flux balance analysis and data from a recently published high-throughput experiment. Our results show that different alleles can epistatically interact with very different gene sets. Furthermore, between two random mutant alleles of the same gene, the chance for the allele with more severe mutational consequence to develop a higher percentage of negative epistasis than the other allele is 50-70% in eukaryotic organisms, but only 20-30% in bacteria and archaea. We developed a population genetics model that predicts that the observed distribution for the sign of epistasis can speed up the process of purging deleterious mutations in eukaryotic organisms. Our results indicate that epistasis among genes can be dynamically rewired at the genome level, and call on future efforts to revisit theories that can integrate epistatic dynamics among genes in biological systems. |
1507.01903 | Christoph Adami | Thomas LaBar, Christoph Adami and Arend Hintze | Does self-replication imply evolvability? | 8 pages, 5 figures. To appear in "Advances in Artificial Life":
Proceedings of the 13th European Conference on Artificial Life (ECAL 2015) | Proc. of the European Conference on Artificial Life, (P. Andrews,
L. Caves, R. Doursat, S. Hickinbotham, F. Polack, S. Stepney, T. Taylor & J.
Timmis, eds.) MIT Press (Cambridge, MA, 2015) pp. 596-602 | 10.7551/978-0-262-33027-5-ch103 | null | q-bio.PE nlin.AO q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The most prominent property of life on Earth is its ability to evolve. It is
often taken for granted that self-replication--the characteristic that makes
life possible--implies evolvability, but many examples such as the lack of
evolvability in computer viruses seem to challenge this view. Is evolvability
itself a property that needs to evolve, or is it automatically present within
any chemistry that supports sequences that can evolve in principle? Here, we
study evolvability in the digital life system Avida, where self-replicating
sequences written by hand are used to seed evolutionary experiments. We use 170
self-replicators that we found in a search through 3 billion randomly generated
sequences (at three different sequence lengths) to study the evolvability of
generic rather than hand-designed self-replicators. We find that most can
evolve but some are evolutionarily sterile. From this limited data set we are
led to conclude that evolvability is a likely--but not a guaranteed-- property
of random replicators in a digital chemistry.
| [
{
"created": "Tue, 7 Jul 2015 18:01:24 GMT",
"version": "v1"
}
] | 2015-11-18 | [
[
"LaBar",
"Thomas",
""
],
[
"Adami",
"Christoph",
""
],
[
"Hintze",
"Arend",
""
]
] | The most prominent property of life on Earth is its ability to evolve. It is often taken for granted that self-replication--the characteristic that makes life possible--implies evolvability, but many examples such as the lack of evolvability in computer viruses seem to challenge this view. Is evolvability itself a property that needs to evolve, or is it automatically present within any chemistry that supports sequences that can evolve in principle? Here, we study evolvability in the digital life system Avida, where self-replicating sequences written by hand are used to seed evolutionary experiments. We use 170 self-replicators that we found in a search through 3 billion randomly generated sequences (at three different sequence lengths) to study the evolvability of generic rather than hand-designed self-replicators. We find that most can evolve but some are evolutionarily sterile. From this limited data set we are led to conclude that evolvability is a likely--but not a guaranteed-- property of random replicators in a digital chemistry. |
1709.04588 | Patricio Maturana | Patricio Maturana Russel | Bayesian support for Evolution: detecting phylogenetic signal in a
subset of the primate family | null | null | 10.1007/978-3-319-91143-4_20 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theory of evolution states that the diversity of species can be explained
by descent with modification. Therefore, all living beings are related through
a common ancestor. This evolutionary process must have left traces in our
molecular composition. In this work, we present a randomization procedure in
order to determine if a group of 5 species of the primate family, namely,
macaque, guereza, orangutan, chimpanzee and human, has retained these traces in
its molecules. Firstly, we present the randomization methodology through two
toy examples, which allow to understand its logic. We then carry out a DNA data
analysis to assess if the group of primates contains phylogenetic information
which links them in a joint evolutionary history. This is carried out by
monitoring a Bayesian measure, called marginal likelihood, which we estimate by
using nested sampling. We found that it would be unusual to get the
relationship observed in the data among these primate species if they had not
shared a common ancestor. The results are in total agreement with the theory of
evolution.
| [
{
"created": "Thu, 14 Sep 2017 02:02:25 GMT",
"version": "v1"
}
] | 2018-08-09 | [
[
"Russel",
"Patricio Maturana",
""
]
] | The theory of evolution states that the diversity of species can be explained by descent with modification. Therefore, all living beings are related through a common ancestor. This evolutionary process must have left traces in our molecular composition. In this work, we present a randomization procedure in order to determine if a group of 5 species of the primate family, namely, macaque, guereza, orangutan, chimpanzee and human, has retained these traces in its molecules. Firstly, we present the randomization methodology through two toy examples, which allow to understand its logic. We then carry out a DNA data analysis to assess if the group of primates contains phylogenetic information which links them in a joint evolutionary history. This is carried out by monitoring a Bayesian measure, called marginal likelihood, which we estimate by using nested sampling. We found that it would be unusual to get the relationship observed in the data among these primate species if they had not shared a common ancestor. The results are in total agreement with the theory of evolution. |
1803.08575 | Alejandro Saettone | Alejandro Saettone, Jyoti Garg, Jean-Philippe Lambert, Syed
Nabeel-Shah, Marcelo Ponce, Alyson Burtch, Cristina Thuppu Mudalige,
Anne-Claude Gingras, Ronald E. Pearlman, Jeffrey Fillingham | The bromodomain-containing protein Ibd1 links multiple chromatin related
protein complexes to highly expressed genes in Tetrahymena thermophila | Published on BMC Epigenetics & Chromatin | Epigenetics & Chromatin (2018) 11:10 | 10.1186/s13072-018-0180-6 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: The chromatin remodelers of the SWI/SNF family are critical
transcriptional regulators. Recognition of lysine acetylation through a
bromodomain (BRD) component is key to SWI/SNF function; in most eukaryotes,
this function is attributed to SNF2/Brg1.
Results: Using affinity purification coupled to mass spectrometry (AP-MS) we
identified members of a SWI/SNF complex (SWI/SNFTt) in Tetrahymena thermophila.
SWI/SNFTt is composed of 11 proteins, Snf5Tt, Swi1Tt, Swi3Tt, Snf12Tt, Brg1Tt,
two proteins with potential chromatin interacting domains and four proteins
without orthologs to SWI/SNF proteins in yeast or mammals. SWI/SNFTt subunits
localize exclusively to the transcriptionally active macronucleus (MAC) during
growth and development, consistent with a role in transcription. While
Tetrahymena Brg1 does not contain a BRD, our AP-MS results identified a
BRD-containing SWI/SNFTt component, Ibd1 that associates with SWI/SNFTt during
growth but not development. AP-MS analysis of epitope-tagged Ibd1 revealed it
to be a subunit of several additional protein complexes, including putative
SWRTt, and SAGATt complexes as well as a putative H3K4-specific histone methyl
transferase complex. Recombinant Ibd1 recognizes acetyl-lysine marks on
histones correlated with active transcription. Consistent with our AP-MS and
histone array data suggesting a role in regulation of gene expression, ChIP-Seq
analysis of Ibd1 indicated that it primarily binds near promoters and within
gene bodies of highly expressed genes during growth.
Conclusions: Our results suggest that through recognizing specific histones
marks, Ibd1 targets active chromatin regions of highly expressed genes in
Tetrahymena where it subsequently might coordinate the recruitment of several
chromatin remodeling complexes to regulate the transcriptional landscape of
vegetatively growing Tetrahymena cells.
| [
{
"created": "Thu, 22 Mar 2018 20:22:04 GMT",
"version": "v1"
}
] | 2018-03-26 | [
[
"Saettone",
"Alejandro",
""
],
[
"Garg",
"Jyoti",
""
],
[
"Lambert",
"Jean-Philippe",
""
],
[
"Nabeel-Shah",
"Syed",
""
],
[
"Ponce",
"Marcelo",
""
],
[
"Burtch",
"Alyson",
""
],
[
"Mudalige",
"Cristina Thuppu"... | Background: The chromatin remodelers of the SWI/SNF family are critical transcriptional regulators. Recognition of lysine acetylation through a bromodomain (BRD) component is key to SWI/SNF function; in most eukaryotes, this function is attributed to SNF2/Brg1. Results: Using affinity purification coupled to mass spectrometry (AP-MS) we identified members of a SWI/SNF complex (SWI/SNFTt) in Tetrahymena thermophila. SWI/SNFTt is composed of 11 proteins, Snf5Tt, Swi1Tt, Swi3Tt, Snf12Tt, Brg1Tt, two proteins with potential chromatin interacting domains and four proteins without orthologs to SWI/SNF proteins in yeast or mammals. SWI/SNFTt subunits localize exclusively to the transcriptionally active macronucleus (MAC) during growth and development, consistent with a role in transcription. While Tetrahymena Brg1 does not contain a BRD, our AP-MS results identified a BRD-containing SWI/SNFTt component, Ibd1 that associates with SWI/SNFTt during growth but not development. AP-MS analysis of epitope-tagged Ibd1 revealed it to be a subunit of several additional protein complexes, including putative SWRTt, and SAGATt complexes as well as a putative H3K4-specific histone methyl transferase complex. Recombinant Ibd1 recognizes acetyl-lysine marks on histones correlated with active transcription. Consistent with our AP-MS and histone array data suggesting a role in regulation of gene expression, ChIP-Seq analysis of Ibd1 indicated that it primarily binds near promoters and within gene bodies of highly expressed genes during growth. Conclusions: Our results suggest that through recognizing specific histones marks, Ibd1 targets active chromatin regions of highly expressed genes in Tetrahymena where it subsequently might coordinate the recruitment of several chromatin remodeling complexes to regulate the transcriptional landscape of vegetatively growing Tetrahymena cells. |
1705.10170 | Rainer Kujala | Rainer Kujala, Enrico Glerean, Raj Kumar Pan, Iiro P.
J\"a\"askel\"ainen, Mikko Sams, Jari Saram\"aki | Graph coarse-graining reveals differences in the module-level structure
of functional brain networks | Manuscript + Supplementary materials | European Journal of Neuroscience, 2016, 44, 2673 | 10.1111/ejn.13392 | null | q-bio.NC physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network analysis is rapidly becoming a standard tool for studying functional
magnetic resonance imaging (fMRI) data. In this framework, different brain
areas are mapped to the nodes of a network, whose links depict functional
dependencies between the areas. The sizes of the areas that the nodes portray
vary between studies. Recently, it has been recommended that the original
volume elements, voxels, of the imaging experiment should be used as the
network nodes to avoid artefacts and biases. However, this results in a large
numbers of nodes and links, and the sheer amount of detail may obscure
important network features that are manifested on larger scales. One fruitful
approach to detecting such features is to partition networks into modules, i.e.
groups of nodes that are densely connected internally but have few connections
between them. However, attempting to understand how functional networks differ
by simply comparing their individual modular structures can be a daunting task,
and results may be hard to interpret. We show that instead of comparing
different partitions, it is beneficial to analyze differences in the
connectivity between and within the very same modules in networks obtained
under different conditions. We develop a network coarse-graining methodology
that provides easily interpretable results and allows assessing the statistical
significance of observed differences. The feasibility of the method is
demonstrated by analyzing fMRI data recorded from 13 healthy subjects during
rest and movie viewing. While independent partitioning of the networks
corresponding to the the two conditions yields few insights on their
differences, network coarse-graining allows us to pinpoint e.g. the increased
number of intra-module links within the visual cortex during movie viewing.
| [
{
"created": "Mon, 29 May 2017 13:18:37 GMT",
"version": "v1"
}
] | 2017-05-30 | [
[
"Kujala",
"Rainer",
""
],
[
"Glerean",
"Enrico",
""
],
[
"Pan",
"Raj Kumar",
""
],
[
"Jääskeläinen",
"Iiro P.",
""
],
[
"Sams",
"Mikko",
""
],
[
"Saramäki",
"Jari",
""
]
] | Network analysis is rapidly becoming a standard tool for studying functional magnetic resonance imaging (fMRI) data. In this framework, different brain areas are mapped to the nodes of a network, whose links depict functional dependencies between the areas. The sizes of the areas that the nodes portray vary between studies. Recently, it has been recommended that the original volume elements, voxels, of the imaging experiment should be used as the network nodes to avoid artefacts and biases. However, this results in a large numbers of nodes and links, and the sheer amount of detail may obscure important network features that are manifested on larger scales. One fruitful approach to detecting such features is to partition networks into modules, i.e. groups of nodes that are densely connected internally but have few connections between them. However, attempting to understand how functional networks differ by simply comparing their individual modular structures can be a daunting task, and results may be hard to interpret. We show that instead of comparing different partitions, it is beneficial to analyze differences in the connectivity between and within the very same modules in networks obtained under different conditions. We develop a network coarse-graining methodology that provides easily interpretable results and allows assessing the statistical significance of observed differences. The feasibility of the method is demonstrated by analyzing fMRI data recorded from 13 healthy subjects during rest and movie viewing. While independent partitioning of the networks corresponding to the the two conditions yields few insights on their differences, network coarse-graining allows us to pinpoint e.g. the increased number of intra-module links within the visual cortex during movie viewing. |
1902.05828 | Pavel Loskot | Pavel Loskot and Komlan Atitey and Lyudmila Mihaylova | Comprehensive review of models and methods for inferences in
bio-chemical reaction networks | 300 references, 10 tables, 3 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Key processes in biological and chemical systems are described by networks of
chemical reactions. From molecular biology to biotechnology applications,
computational models of reaction networks are used extensively to elucidate
their non-linear dynamics. Model dynamics are crucially dependent on parameter
values which are often estimated from observations. Over past decade, the
interest in parameter and state estimation in models of (bio-)chemical reaction
networks (BRNs) grew considerably. Statistical inference problems are also
encountered in many other tasks including model calibration, discrimination,
identifiability and checking as well as optimum experiment design, sensitivity
analysis, bifurcation analysis and other. The aim of this review paper is to
explore developments of past decade to understand what BRN models are commonly
used in literature, and for what inference tasks and inference methods. Initial
collection of about 700 publications excluding books in computational biology
and chemistry were screened to select over 260 research papers and 20 graduate
theses concerning estimation problems in BRNs. The paper selection was
performed as text mining using scripts to automate search for relevant keywords
and terms. The outcome are tables revealing the level of interest in different
inference tasks and methods for given models in literature as well as recent
trends. In addition, a brief survey of general estimation strategies is
provided to facilitate understanding of estimation methods which are used for
BRNs. Our findings indicate that many combinations of models, tasks and methods
are still relatively sparse representing new research opportunities to explore
those that have not been considered - perhaps for a good reason. The paper
concludes by discussing future research directions including research problems
which cannot be directly deduced from presented tables.
| [
{
"created": "Fri, 15 Feb 2019 14:52:35 GMT",
"version": "v1"
}
] | 2019-02-18 | [
[
"Loskot",
"Pavel",
""
],
[
"Atitey",
"Komlan",
""
],
[
"Mihaylova",
"Lyudmila",
""
]
] | Key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. Model dynamics are crucially dependent on parameter values which are often estimated from observations. Over past decade, the interest in parameter and state estimation in models of (bio-)chemical reaction networks (BRNs) grew considerably. Statistical inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability and checking as well as optimum experiment design, sensitivity analysis, bifurcation analysis and other. The aim of this review paper is to explore developments of past decade to understand what BRN models are commonly used in literature, and for what inference tasks and inference methods. Initial collection of about 700 publications excluding books in computational biology and chemistry were screened to select over 260 research papers and 20 graduate theses concerning estimation problems in BRNs. The paper selection was performed as text mining using scripts to automate search for relevant keywords and terms. The outcome are tables revealing the level of interest in different inference tasks and methods for given models in literature as well as recent trends. In addition, a brief survey of general estimation strategies is provided to facilitate understanding of estimation methods which are used for BRNs. Our findings indicate that many combinations of models, tasks and methods are still relatively sparse representing new research opportunities to explore those that have not been considered - perhaps for a good reason. The paper concludes by discussing future research directions including research problems which cannot be directly deduced from presented tables. |
q-bio/0406038 | Adriano Sousa A. O. Sousa | A.O. Sousa | Sympatric speciation in an age-structured population living on a lattice | 5 pages including 3 encapsulated postscript (*.eps) figures; To
appear in European Physical Journal B | null | 10.1140/epjb/e2004-00225-7 | null | q-bio.PE cond-mat.stat-mech | null | A square lattice is introduced into the Penna model for biological aging in
order to study the evolution of diploid sexual populations under certain
conditions when one single locus in the individual's genome is considered as
identifier of species. The simulation results show, after several generations,
the flourishing and coexistence of two separate species in the same
environment, i.e., one original species splits up into two on the same
territory (sympatric speciation). As well, the mortalities obtained are in a
good agreement with the Gompertz law of exponential increase of mortality with
age.
| [
{
"created": "Wed, 16 Jun 2004 20:14:26 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Sousa",
"A. O.",
""
]
] | A square lattice is introduced into the Penna model for biological aging in order to study the evolution of diploid sexual populations under certain conditions when one single locus in the individual's genome is considered as identifier of species. The simulation results show, after several generations, the flourishing and coexistence of two separate species in the same environment, i.e., one original species splits up into two on the same territory (sympatric speciation). As well, the mortalities obtained are in a good agreement with the Gompertz law of exponential increase of mortality with age. |
1308.4122 | Lianchun Yu | Lianchun Yu and Liwei Liu | The Optimal Size of Stochastic Hodgkin-Huxley Neuronal Systems for
Maximal Energy Efficiency in Coding of Pulse Signals | 22 pages, 10 figures | Phys. Rev. E 89, 032725 (2014) | 10.1103/PhysRevE.89.032725 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The generation and conduction of action potentials represents a fundamental
means of communication in the nervous system, and is a metabolically expensive
process. In this paper, we investigate the energy efficiency of neural systems
in a process of transfer pulse signals with action potentials. By computer
simulation of a stochastic version of Hodgkin-Huxley model with detailed
description of ion channel random gating, and analytically solve a bistable
neuron model that mimic the action potential generation with a particle
crossing the barrier of a double well, we find optimal number of ion channels
that maximize energy efficiency for a neuron. We also investigate the energy
efficiency of neuron population in which input pulse signals are represented
with synchronized spikes and read out with a downstream coincidence detector
neuron. We find an optimal combination of the number of neurons in neuron
population and the number of ion channels in each neuron that maximize the
energy efficiency. The energy efficiency depends on the characters of the input
signals, e.g., the pulse strength and the inter-pulse intervals. We argue that
trade-off between reliability of signal transmission and energy cost may
influence the size of the neural systems if energy use is constrained.
| [
{
"created": "Sat, 17 Aug 2013 05:13:14 GMT",
"version": "v1"
}
] | 2014-04-23 | [
[
"Yu",
"Lianchun",
""
],
[
"Liu",
"Liwei",
""
]
] | The generation and conduction of action potentials represents a fundamental means of communication in the nervous system, and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in a process of transfer pulse signals with action potentials. By computer simulation of a stochastic version of Hodgkin-Huxley model with detailed description of ion channel random gating, and analytically solve a bistable neuron model that mimic the action potential generation with a particle crossing the barrier of a double well, we find optimal number of ion channels that maximize energy efficiency for a neuron. We also investigate the energy efficiency of neuron population in which input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal combination of the number of neurons in neuron population and the number of ion channels in each neuron that maximize the energy efficiency. The energy efficiency depends on the characters of the input signals, e.g., the pulse strength and the inter-pulse intervals. We argue that trade-off between reliability of signal transmission and energy cost may influence the size of the neural systems if energy use is constrained. |
0912.2171 | Ulrich S. Schwarz | Christian B. Korn, Stefan Klumpp, Reinhard Lipowsky, Ulrich S. Schwarz | Stochastic simulations of cargo transport by processive molecular motors | 40 pages, Revtex with 13 figures, to appear in Journal of Chemical
Physics | J. Chem. Phys. 131:245107, 2009 | 10.1063/1.3279305 | null | q-bio.SC cond-mat.soft q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We use stochastic computer simulations to study the transport of a spherical
cargo particle along a microtubule-like track on a planar substrate by several
kinesin-like processive motors. Our newly developed adhesive motor dynamics
algorithm combines the numerical integration of a Langevin equation for the
motion of a sphere with kinetic rules for the molecular motors. The Langevin
part includes diffusive motion, the action of the pulling motors, and
hydrodynamic interactions between sphere and wall. The kinetic rules for the
motors include binding to and unbinding from the filament as well as active
motor steps. We find that the simulated mean transport length increases
exponentially with the number of bound motors, in good agreement with earlier
results. The number of motors in binding range to the motor track fluctuates in
time with a Poissonian distribution, both for springs and cables being used as
models for the linker mechanics. Cooperativity in the sense of equal load
sharing only occurs for high values for viscosity and attachment time.
| [
{
"created": "Fri, 11 Dec 2009 08:04:01 GMT",
"version": "v1"
}
] | 2010-02-24 | [
[
"Korn",
"Christian B.",
""
],
[
"Klumpp",
"Stefan",
""
],
[
"Lipowsky",
"Reinhard",
""
],
[
"Schwarz",
"Ulrich S.",
""
]
] | We use stochastic computer simulations to study the transport of a spherical cargo particle along a microtubule-like track on a planar substrate by several kinesin-like processive motors. Our newly developed adhesive motor dynamics algorithm combines the numerical integration of a Langevin equation for the motion of a sphere with kinetic rules for the molecular motors. The Langevin part includes diffusive motion, the action of the pulling motors, and hydrodynamic interactions between sphere and wall. The kinetic rules for the motors include binding to and unbinding from the filament as well as active motor steps. We find that the simulated mean transport length increases exponentially with the number of bound motors, in good agreement with earlier results. The number of motors in binding range to the motor track fluctuates in time with a Poissonian distribution, both for springs and cables being used as models for the linker mechanics. Cooperativity in the sense of equal load sharing only occurs for high values for viscosity and attachment time. |
2210.12064 | Shengjie Zheng | Shengjie Zheng, Ling Liu, Junjie Yang, Jianwei Zhang, Tao Su, Bin Yue,
Xiaojian Li | Embedded Silicon-Organic Integrated Neuromorphic System | This article need to update the corrected figure and data | null | null | null | q-bio.NC cs.NE | http://creativecommons.org/licenses/by/4.0/ | The development of artificial intelligence (AI) and robotics are both based
on the tenet of "science and technology are people-oriented", and both need to
achieve efficient communication with the human brain. Based on
multi-disciplinary research in systems neuroscience, computer architecture, and
functional organic materials, we proposed the concept of using AI to simulate
the operating principles and materials of the brain in hardware to develop
brain-inspired intelligence technology, and realized the preparation of
neuromorphic computing devices and basic materials. We simulated neurons and
neural networks in terms of material and morphology, using a variety of organic
polymers as the base materials for neuroelectronic devices, for building neural
interfaces as well as organic neural devices and silicon neural computational
modules. We assemble organic artificial synapses with simulated neurons from
silicon-based Field-Programmable Gate Array (FPGA) into organic artificial
neurons, the basic components of neural networks, and later construct
biological neural network models based on the interpreted neural circuits.
Finally, we also discuss how to further build neuromorphic devices based on
these organic artificial neurons, which have both a neural interface friendly
to nervous tissue and interact with information from real biological neural
networks.
| [
{
"created": "Tue, 18 Oct 2022 01:56:48 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Jun 2024 19:35:21 GMT",
"version": "v2"
}
] | 2024-06-27 | [
[
"Zheng",
"Shengjie",
""
],
[
"Liu",
"Ling",
""
],
[
"Yang",
"Junjie",
""
],
[
"Zhang",
"Jianwei",
""
],
[
"Su",
"Tao",
""
],
[
"Yue",
"Bin",
""
],
[
"Li",
"Xiaojian",
""
]
] | The development of artificial intelligence (AI) and robotics are both based on the tenet of "science and technology are people-oriented", and both need to achieve efficient communication with the human brain. Based on multi-disciplinary research in systems neuroscience, computer architecture, and functional organic materials, we proposed the concept of using AI to simulate the operating principles and materials of the brain in hardware to develop brain-inspired intelligence technology, and realized the preparation of neuromorphic computing devices and basic materials. We simulated neurons and neural networks in terms of material and morphology, using a variety of organic polymers as the base materials for neuroelectronic devices, for building neural interfaces as well as organic neural devices and silicon neural computational modules. We assemble organic artificial synapses with simulated neurons from silicon-based Field-Programmable Gate Array (FPGA) into organic artificial neurons, the basic components of neural networks, and later construct biological neural network models based on the interpreted neural circuits. Finally, we also discuss how to further build neuromorphic devices based on these organic artificial neurons, which have both a neural interface friendly to nervous tissue and interact with information from real biological neural networks. |
1303.5044 | Alan Bergland | Alan O. Bergland, Emily L. Behrman, Katherine R. O'Brien, Paul S.
Schmidt and Dmitri A. Petrov | Genomic evidence of rapid and stable adaptive oscillations over seasonal
time scales in Drosophila | 44 pages, 7 main figures, 7 supplemental figures, 3 tables | null | 10.1371/journal.pgen.1004775 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many species, genomic data have revealed pervasive adaptive evolution
indicated by the fixation of beneficial alleles. However, when selection
pressures are highly variable along a species range or through time adaptive
alleles may persist at intermediate frequencies for long periods. So called
balanced polymorphisms have long been understood to be an important component
of standing genetic variation yet direct evidence of the strength of balancing
selection and the stability and prevalence of balanced polymorphisms has
remained elusive. We hypothesized that environmental fluctuations between
seasons in a North American orchard would impose temporally variable selection
on Drosophila melanogaster and consequently maintain allelic variation at
polymorphisms adaptively evolving in response to climatic variation. We
identified hundreds of polymorphisms whose frequency oscillates among seasons
and argue that these loci are subject to strong, temporally variable selection.
We show that these polymorphisms respond to acute and persistent changes in
climate and are associated in predictable ways with seasonally variable
phenotypes. In addition, we show that adaptively oscillating polymorphisms are
likely millions of years old, with some likely predating the divergence between
D. melanogaster and D. simulans. Taken together, our results demonstrate that
rapid temporal fluctuations in climate over generational time promotes adaptive
genetic diversity at loci affecting polygenic phenotypes.
| [
{
"created": "Wed, 20 Mar 2013 19:42:07 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Jan 2014 18:02:02 GMT",
"version": "v2"
},
{
"created": "Mon, 24 Feb 2014 17:53:16 GMT",
"version": "v3"
}
] | 2014-11-10 | [
[
"Bergland",
"Alan O.",
""
],
[
"Behrman",
"Emily L.",
""
],
[
"O'Brien",
"Katherine R.",
""
],
[
"Schmidt",
"Paul S.",
""
],
[
"Petrov",
"Dmitri A.",
""
]
] | In many species, genomic data have revealed pervasive adaptive evolution indicated by the fixation of beneficial alleles. However, when selection pressures are highly variable along a species range or through time adaptive alleles may persist at intermediate frequencies for long periods. So called balanced polymorphisms have long been understood to be an important component of standing genetic variation yet direct evidence of the strength of balancing selection and the stability and prevalence of balanced polymorphisms has remained elusive. We hypothesized that environmental fluctuations between seasons in a North American orchard would impose temporally variable selection on Drosophila melanogaster and consequently maintain allelic variation at polymorphisms adaptively evolving in response to climatic variation. We identified hundreds of polymorphisms whose frequency oscillates among seasons and argue that these loci are subject to strong, temporally variable selection. We show that these polymorphisms respond to acute and persistent changes in climate and are associated in predictable ways with seasonally variable phenotypes. In addition, we show that adaptively oscillating polymorphisms are likely millions of years old, with some likely predating the divergence between D. melanogaster and D. simulans. Taken together, our results demonstrate that rapid temporal fluctuations in climate over generational time promotes adaptive genetic diversity at loci affecting polygenic phenotypes. |
q-bio/0505021 | Hugues Berry | Hugues Berry (INRIA Futurs), Olivier Temam (INRIA Futurs) | Characterizing Self-Developing Biological Neural Networks: A First Step
Towards their Application To Computing Systems | null | null | null | null | q-bio.NC cs.AR cs.NE nlin.AO | null | Carbon nanotubes are often seen as the only alternative technology to silicon
transistors. While they are the most likely short-term one, other longer-term
alternatives should be studied as well. While contemplating biological neurons
as an alternative component may seem preposterous at first sight, significant
recent progress in CMOS-neuron interface suggests this direction may not be
unrealistic; moreover, biological neurons are known to self-assemble into very
large networks capable of complex information processing tasks, something that
has yet to be achieved with other emerging technologies. The first step to
designing computing systems on top of biological neurons is to build an
abstract model of self-assembled biological neural networks, much like computer
architects manipulate abstract models of transistors and circuits. In this
article, we propose a first model of the structure of biological neural
networks. We provide empirical evidence that this model matches the biological
neural networks found in living organisms, and exhibits the small-world graph
structure properties commonly found in many large and self-organized systems,
including biological neural networks. More importantly, we extract the simple
local rules and characteristics governing the growth of such networks, enabling
the development of potentially large but realistic biological neural networks,
as would be needed for complex information processing/computing tasks. Based on
this model, future work will be targeted to understanding the evolution and
learning properties of such networks, and how they can be used to build
computing systems.
| [
{
"created": "Tue, 10 May 2005 19:51:16 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Berry",
"Hugues",
"",
"INRIA Futurs"
],
[
"Temam",
"Olivier",
"",
"INRIA Futurs"
]
] | Carbon nanotubes are often seen as the only alternative technology to silicon transistors. While they are the most likely short-term one, other longer-term alternatives should be studied as well. While contemplating biological neurons as an alternative component may seem preposterous at first sight, significant recent progress in CMOS-neuron interface suggests this direction may not be unrealistic; moreover, biological neurons are known to self-assemble into very large networks capable of complex information processing tasks, something that has yet to be achieved with other emerging technologies. The first step to designing computing systems on top of biological neurons is to build an abstract model of self-assembled biological neural networks, much like computer architects manipulate abstract models of transistors and circuits. In this article, we propose a first model of the structure of biological neural networks. We provide empirical evidence that this model matches the biological neural networks found in living organisms, and exhibits the small-world graph structure properties commonly found in many large and self-organized systems, including biological neural networks. More importantly, we extract the simple local rules and characteristics governing the growth of such networks, enabling the development of potentially large but realistic biological neural networks, as would be needed for complex information processing/computing tasks. Based on this model, future work will be targeted to understanding the evolution and learning properties of such networks, and how they can be used to build computing systems. |
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