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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2405.17745 | David Lipshutz | David Lipshutz and Eero P. Simoncelli | Shaping the distribution of neural responses with interneurons in a
recurrent circuit model | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Efficient coding theory posits that sensory circuits transform natural
signals into neural representations that maximize information transmission
subject to resource constraints. Local interneurons are thought to play an
important role in these transformations, shaping patterns of circuit activity
to facilitate and direct information flow. However, the relationship between
these coordinated, nonlinear, circuit-level transformations and the properties
of interneurons (e.g., connectivity, activation functions, response dynamics)
remains unknown. Here, we propose a normative computational model that
establishes such a relationship. Our model is derived from an optimal transport
objective that conceptualizes the circuit's input-response function as
transforming the inputs to achieve a target response distribution. The circuit,
which is comprised of primary neurons that are recurrently connected to a set
of local interneurons, continuously optimizes this objective by dynamically
adjusting both the synaptic connections between neurons as well as the
interneuron activation functions. In an application motivated by redundancy
reduction theory, we demonstrate that when the inputs are natural image
statistics and the target distribution is a spherical Gaussian, the circuit
learns a nonlinear transformation that significantly reduces statistical
dependencies in neural responses. Overall, our results provide a framework in
which the distribution of circuit responses is systematically and nonlinearly
controlled by adjustment of interneuron connectivity and activation functions.
| [
{
"created": "Tue, 28 May 2024 01:56:25 GMT",
"version": "v1"
}
] | 2024-05-29 | [
[
"Lipshutz",
"David",
""
],
[
"Simoncelli",
"Eero P.",
""
]
] | Efficient coding theory posits that sensory circuits transform natural signals into neural representations that maximize information transmission subject to resource constraints. Local interneurons are thought to play an important role in these transformations, shaping patterns of circuit activity to facilitate and direct information flow. However, the relationship between these coordinated, nonlinear, circuit-level transformations and the properties of interneurons (e.g., connectivity, activation functions, response dynamics) remains unknown. Here, we propose a normative computational model that establishes such a relationship. Our model is derived from an optimal transport objective that conceptualizes the circuit's input-response function as transforming the inputs to achieve a target response distribution. The circuit, which is comprised of primary neurons that are recurrently connected to a set of local interneurons, continuously optimizes this objective by dynamically adjusting both the synaptic connections between neurons as well as the interneuron activation functions. In an application motivated by redundancy reduction theory, we demonstrate that when the inputs are natural image statistics and the target distribution is a spherical Gaussian, the circuit learns a nonlinear transformation that significantly reduces statistical dependencies in neural responses. Overall, our results provide a framework in which the distribution of circuit responses is systematically and nonlinearly controlled by adjustment of interneuron connectivity and activation functions. |
q-bio/0511016 | Eytan Domany | T. Rozovskaia, O. Ravid-Amir, S. Tillib, G. Getz, E. Feinstein, H.
Agrawal, A. Nagler, E. Rappeport, I. Issaeva, Y. Matsuo, U. R. Kees, T.
Lapidot, F. Lo Coco, R. Foa, A. Mazo, T. Nakamura, C.M. Croce, G. Cimino, E.
Domany and E. Canaani | Expression profiles of acute lymphoblastic and myeloblastic leukemias
with ALL-1 rearrangements | null | PNAS vol 100, p 7853 (2003) | 10.1073/pnas.1132115100 | null | q-bio.QM q-bio.OT | null | The ALL-1 gene is directly involved in 5-10% of ALLs and AMLs by fusion to
other genes or through internal rearrangements. DNA microarrays were utilized
to determine expression profiles of ALLs and AMLs with ALL-1 rearrangements.
These profiles distinguish those tumors from other ALLs and AMLs. The
expression patterns of ALL-1-associated tumors, in particular ALLs, involve
oncogenes, tumor suppressors, anti apoptotic genes, drug resistance genes etc.,
and correlate with the aggressive nature of the tumors. The genes whose
expression differentiates between ALLs with and without ALL-1 rearrangement
were further divided into several groups enabling separation of ALL-1-
associated ALLs into two subclasses. Further, AMLs with partial duplication of
ALL-1 vary in their expression pattern from AMLs in which ALL-1 had undergone
fusion to other genes. The extensive analysis described here draws attention to
genes which might have a direct role in pathogenesis.
| [
{
"created": "Mon, 14 Nov 2005 19:10:51 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Rozovskaia",
"T.",
""
],
[
"Ravid-Amir",
"O.",
""
],
[
"Tillib",
"S.",
""
],
[
"Getz",
"G.",
""
],
[
"Feinstein",
"E.",
""
],
[
"Agrawal",
"H.",
""
],
[
"Nagler",
"A.",
""
],
[
"Rappeport",
"E.",
""
],
[
"Issaeva",
"I.",
""
],
[
"Matsuo",
"Y.",
""
],
[
"Kees",
"U. R.",
""
],
[
"Lapidot",
"T.",
""
],
[
"Coco",
"F. Lo",
""
],
[
"Foa",
"R.",
""
],
[
"Mazo",
"A.",
""
],
[
"Nakamura",
"T.",
""
],
[
"Croce",
"C. M.",
""
],
[
"Cimino",
"G.",
""
],
[
"Domany",
"E.",
""
],
[
"Canaani",
"E.",
""
]
] | The ALL-1 gene is directly involved in 5-10% of ALLs and AMLs by fusion to other genes or through internal rearrangements. DNA microarrays were utilized to determine expression profiles of ALLs and AMLs with ALL-1 rearrangements. These profiles distinguish those tumors from other ALLs and AMLs. The expression patterns of ALL-1-associated tumors, in particular ALLs, involve oncogenes, tumor suppressors, anti apoptotic genes, drug resistance genes etc., and correlate with the aggressive nature of the tumors. The genes whose expression differentiates between ALLs with and without ALL-1 rearrangement were further divided into several groups enabling separation of ALL-1- associated ALLs into two subclasses. Further, AMLs with partial duplication of ALL-1 vary in their expression pattern from AMLs in which ALL-1 had undergone fusion to other genes. The extensive analysis described here draws attention to genes which might have a direct role in pathogenesis. |
1903.06113 | Han Ching Ou | Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault and
Milind Tambe | Who and When to Screen: Multi-Round Active Screening for Recurrent
Infectious Diseases Under Uncertainty | 11 pages | null | null | null | q-bio.QM cs.SI q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Controlling recurrent infectious diseases is a vital yet complicated problem.
In this paper, we propose a novel active screening model (ACTS) and algorithms
to facilitate active screening for recurrent diseases (no permanent immunity)
under infection uncertainty. Our contributions are: (1) A new approach to
modeling multi-round network-based screening/contact tracing under uncertainty,
which is a common real-life practice in a variety of diseases; (2) Two novel
algorithms, Full- and Fast-REMEDY. Full-REMEDY considers the effect of future
actions and finds a policy that provides high solution quality, where
Fast-REMEDY scales linearly in the size of the network; (3) We evaluate Full-
and Fast-REMEDY on several real-world datasets which emulate human contact and
find that they control diseases better than the baselines. To the best of our
knowledge, this is the first work on multi-round active screening with
uncertainty for diseases with no permanent immunity.
| [
{
"created": "Wed, 13 Mar 2019 06:48:23 GMT",
"version": "v1"
}
] | 2019-03-15 | [
[
"Ou",
"Han-Ching",
""
],
[
"Sinha",
"Arunesh",
""
],
[
"Suen",
"Sze-Chuan",
""
],
[
"Perrault",
"Andrew",
""
],
[
"Tambe",
"Milind",
""
]
] | Controlling recurrent infectious diseases is a vital yet complicated problem. In this paper, we propose a novel active screening model (ACTS) and algorithms to facilitate active screening for recurrent diseases (no permanent immunity) under infection uncertainty. Our contributions are: (1) A new approach to modeling multi-round network-based screening/contact tracing under uncertainty, which is a common real-life practice in a variety of diseases; (2) Two novel algorithms, Full- and Fast-REMEDY. Full-REMEDY considers the effect of future actions and finds a policy that provides high solution quality, where Fast-REMEDY scales linearly in the size of the network; (3) We evaluate Full- and Fast-REMEDY on several real-world datasets which emulate human contact and find that they control diseases better than the baselines. To the best of our knowledge, this is the first work on multi-round active screening with uncertainty for diseases with no permanent immunity. |
1401.1422 | Xiao-Jun Tian | Hang Zhang, Xiao-Jun Tian, Abhishek Mukhopadhyay, K.S. Kim, Jianhua
Xing | Statistical Mechanics Model for the Dynamics of Collective Epigenetic
Histone Modification | Published in PRL | Physical Review Letters, 112, 068101 (2014) | 10.1103/PhysRevLett.112.068101 | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Epigenetic histone modifications play an important role in the maintenance of
different cell phenotypes. The exact molecular mechanism for inheritance of the
modification patterns over cell generations remains elusive. We construct a
Potts-type model based on experimentally observed nearest-neighbor enzyme
lateral interactions and nucleosome covalent modification state biased enzyme
recruitment. The model can lead to effective nonlocal interactions among
nucleosomes suggested in previous theoretical studies, and epigenetic memory is
robustly inheritable against stochastic cellular processes.
| [
{
"created": "Tue, 7 Jan 2014 15:55:35 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Jan 2014 20:03:56 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Feb 2014 20:52:52 GMT",
"version": "v3"
},
{
"created": "Thu, 27 Feb 2014 23:12:27 GMT",
"version": "v4"
}
] | 2014-03-03 | [
[
"Zhang",
"Hang",
""
],
[
"Tian",
"Xiao-Jun",
""
],
[
"Mukhopadhyay",
"Abhishek",
""
],
[
"Kim",
"K. S.",
""
],
[
"Xing",
"Jianhua",
""
]
] | Epigenetic histone modifications play an important role in the maintenance of different cell phenotypes. The exact molecular mechanism for inheritance of the modification patterns over cell generations remains elusive. We construct a Potts-type model based on experimentally observed nearest-neighbor enzyme lateral interactions and nucleosome covalent modification state biased enzyme recruitment. The model can lead to effective nonlocal interactions among nucleosomes suggested in previous theoretical studies, and epigenetic memory is robustly inheritable against stochastic cellular processes. |
2101.04908 | Vinny Pagano | Justin Weissberg and Vinny Pagano | Modeling Oyster Reef Reproductive Sustainability: Analyzing Gamete
Viability, Hydrodynamics, and Reef Structure to Facilitate Restoration of
$\textit{Crassostrea virginica}$ | 21 pages, 6 figures | null | null | null | q-bio.QM math.AP q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The eastern oyster is a keystone species and ecosystem engineer. However,
restoration efforts of wild oysters are often unsuccessful, in that they do not
produce a robust population of oysters that are able to successfully reproduce.
Furthermore, the dynamics of wild oyster fertilization is not yet well
understood. Through conducting an experiment predicated on quantifying the
influence of elementary aspects of fertilization kinetics--sperm concentration,
gamete age, and success rate--we found that, as stochastic as the mating
process may seem, there are correlations which fundamentally serve as the
framework for assessing long-term sustainability, reef structure, and
hydrodynamic parameters in relation to fertilization. We then focused on
mathematically defining a procedure which simulated a concentration
distribution of a single sperm and egg release where there existed conditions
necessary for breeding to take place. We found a very significant impact of
both gamete age and sperm concentration on fertilization rate ($p < 0.0001$).
Our hydrodynamic model demonstrates that distance can also drastically
influence broadcast spawning. This could be used as a foundation for developing
a flexible model for wild oyster fertilization based on placement, initial
seawater conditions, and size of the starting population. The results of this
research could be implemented into a more user-friendly program which would
accept multiple variables as inputs and output the probability of fertilization
given arbitrary values. By accounting for environmental deviations, this
generalization would increase its compatibility with the public and actualize
the project's intended purpose: enhance the planning of oyster reef restoration
projects.
| [
{
"created": "Wed, 13 Jan 2021 07:00:47 GMT",
"version": "v1"
}
] | 2021-01-14 | [
[
"Weissberg",
"Justin",
""
],
[
"Pagano",
"Vinny",
""
]
] | The eastern oyster is a keystone species and ecosystem engineer. However, restoration efforts of wild oysters are often unsuccessful, in that they do not produce a robust population of oysters that are able to successfully reproduce. Furthermore, the dynamics of wild oyster fertilization is not yet well understood. Through conducting an experiment predicated on quantifying the influence of elementary aspects of fertilization kinetics--sperm concentration, gamete age, and success rate--we found that, as stochastic as the mating process may seem, there are correlations which fundamentally serve as the framework for assessing long-term sustainability, reef structure, and hydrodynamic parameters in relation to fertilization. We then focused on mathematically defining a procedure which simulated a concentration distribution of a single sperm and egg release where there existed conditions necessary for breeding to take place. We found a very significant impact of both gamete age and sperm concentration on fertilization rate ($p < 0.0001$). Our hydrodynamic model demonstrates that distance can also drastically influence broadcast spawning. This could be used as a foundation for developing a flexible model for wild oyster fertilization based on placement, initial seawater conditions, and size of the starting population. The results of this research could be implemented into a more user-friendly program which would accept multiple variables as inputs and output the probability of fertilization given arbitrary values. By accounting for environmental deviations, this generalization would increase its compatibility with the public and actualize the project's intended purpose: enhance the planning of oyster reef restoration projects. |
1605.00562 | Mason A. Porter | Bernadette J. Stolz, Heather A. Harrington, and Mason A. Porter | Persistent homology of time-dependent functional networks constructed
from coupled time series | 17 pages (+3 pages in Supplementary Information), 11 figures in many
text (many with multiple parts) + others in SI, submitted | null | 10.1063/1.4978997 | null | q-bio.QM cond-mat.dis-nn math.AT nlin.AO q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We use topological data analysis to study "functional networks" that we
construct from time-series data from both experimental and synthetic sources.
We use persistent homology with a weight rank clique filtration to gain
insights into these functional networks, and we use persistence landscapes to
interpret our results. Our first example uses time-series output from networks
of coupled Kuramoto oscillators. Our second example consists of biological data
in the form of functional magnetic resonance imaging (fMRI) data that was
acquired from human subjects during a simple motor-learning task in which
subjects were monitored on three days in a five-day period. With these
examples, we demonstrate that (1) using persistent homology to study functional
networks provides fascinating insights into their properties and (2) the
position of the features in a filtration can sometimes play a more vital role
than persistence in the interpretation of topological features, even though
conventionally the latter is used to distinguish between signal and noise. We
find that persistent homology can detect differences in synchronization
patterns in our data sets over time, giving insight both on changes in
community structure in the networks and on increased synchronization between
brain regions that form loops in a functional network during motor learning.
For the motor-learning data, persistence landscapes also reveal that on average
the majority of changes in the network loops take place on the second of the
three days of the learning process.
| [
{
"created": "Mon, 2 May 2016 16:49:36 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Jul 2016 21:17:37 GMT",
"version": "v2"
},
{
"created": "Sat, 3 Dec 2016 21:21:41 GMT",
"version": "v3"
}
] | 2017-05-24 | [
[
"Stolz",
"Bernadette J.",
""
],
[
"Harrington",
"Heather A.",
""
],
[
"Porter",
"Mason A.",
""
]
] | We use topological data analysis to study "functional networks" that we construct from time-series data from both experimental and synthetic sources. We use persistent homology with a weight rank clique filtration to gain insights into these functional networks, and we use persistence landscapes to interpret our results. Our first example uses time-series output from networks of coupled Kuramoto oscillators. Our second example consists of biological data in the form of functional magnetic resonance imaging (fMRI) data that was acquired from human subjects during a simple motor-learning task in which subjects were monitored on three days in a five-day period. With these examples, we demonstrate that (1) using persistent homology to study functional networks provides fascinating insights into their properties and (2) the position of the features in a filtration can sometimes play a more vital role than persistence in the interpretation of topological features, even though conventionally the latter is used to distinguish between signal and noise. We find that persistent homology can detect differences in synchronization patterns in our data sets over time, giving insight both on changes in community structure in the networks and on increased synchronization between brain regions that form loops in a functional network during motor learning. For the motor-learning data, persistence landscapes also reveal that on average the majority of changes in the network loops take place on the second of the three days of the learning process. |
1605.05367 | Richard A Neher | Johanna Brodin, Fabio Zanini, Lina Thebo, Christa Lanz, G\"oran Bratt,
Richard A. Neher, Jan Albert | Establishment and stability of the latent HIV-1 DNA reservoir | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | HIV-1 infection currently cannot be cured because the virus persists as
integrated proviral DNA in long-lived cells despite years of suppressive
antiretroviral therapy (ART). To characterize establishment, turnover, and
evolution of viral DNA reservoirs we deep-sequenced the p17gag region of the
HIV-1 genome from samples obtained after 3-18 years of suppressive ART from 10
patients. For each of these patients, whole genome deep-sequencing data of
HIV-1 RNA populations before onset of ART were available from 6-12 longitudinal
plasma samples spanning 5-8 years of untreated infection. This enabled a
detailed analysis of the dynamics and origin of proviral DNA during ART. A
median of 14% (range 0-42%) of the p17gag DNA sequences were overtly defective
due to G-to-A hypermutation. The remaining sequences were remarkably similar to
previously observed RNA sequences and showed no evidence of evolution over many
years of suppressive ART. Most sequences from the DNA reservoirs were very
similar to viruses actively replicating in plasma (RNA sequences) shortly
before start of ART. The results do not support persistent HIV-1 replication as
a mechanism to maintain the HIV-1 reservoir during suppressive therapy. Rather,
the data indicate that viral DNA variants are turning over as long as patients
are untreated and that suppressive ART halts this turnover.
| [
{
"created": "Tue, 17 May 2016 21:02:01 GMT",
"version": "v1"
},
{
"created": "Tue, 24 May 2016 06:24:41 GMT",
"version": "v2"
}
] | 2016-05-25 | [
[
"Brodin",
"Johanna",
""
],
[
"Zanini",
"Fabio",
""
],
[
"Thebo",
"Lina",
""
],
[
"Lanz",
"Christa",
""
],
[
"Bratt",
"Göran",
""
],
[
"Neher",
"Richard A.",
""
],
[
"Albert",
"Jan",
""
]
] | HIV-1 infection currently cannot be cured because the virus persists as integrated proviral DNA in long-lived cells despite years of suppressive antiretroviral therapy (ART). To characterize establishment, turnover, and evolution of viral DNA reservoirs we deep-sequenced the p17gag region of the HIV-1 genome from samples obtained after 3-18 years of suppressive ART from 10 patients. For each of these patients, whole genome deep-sequencing data of HIV-1 RNA populations before onset of ART were available from 6-12 longitudinal plasma samples spanning 5-8 years of untreated infection. This enabled a detailed analysis of the dynamics and origin of proviral DNA during ART. A median of 14% (range 0-42%) of the p17gag DNA sequences were overtly defective due to G-to-A hypermutation. The remaining sequences were remarkably similar to previously observed RNA sequences and showed no evidence of evolution over many years of suppressive ART. Most sequences from the DNA reservoirs were very similar to viruses actively replicating in plasma (RNA sequences) shortly before start of ART. The results do not support persistent HIV-1 replication as a mechanism to maintain the HIV-1 reservoir during suppressive therapy. Rather, the data indicate that viral DNA variants are turning over as long as patients are untreated and that suppressive ART halts this turnover. |
q-bio/0402032 | Giovanni Imponente | Giovanni Imponente | Complex dynamics of the biological rhythms: gallbladder and heart cases | 3 pages, 8 figures, to appear on Physica A | null | 10.1016/j.physa.2004.02.052 | null | q-bio.TO nlin.CD | null | A theoretical analysis of the mechanisms underlying the dynamics of
gallbladder and heart pulsation could clarify the question regarding the
classification as chaotic of the associated behaviour, eventually related to a
normal and healthy beat; this analysis is particularly relevant in view of the
control of dynamics bifurcations arising in situations of disease. In this work
is presented a summary of the DFA method applied to gallbladder volume data for
a modest number of healthy and ill patients: the presence of signal correlation
is found in both cases, but the fit shapes differ from some critical values.
| [
{
"created": "Sun, 15 Feb 2004 21:00:32 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Imponente",
"Giovanni",
""
]
] | A theoretical analysis of the mechanisms underlying the dynamics of gallbladder and heart pulsation could clarify the question regarding the classification as chaotic of the associated behaviour, eventually related to a normal and healthy beat; this analysis is particularly relevant in view of the control of dynamics bifurcations arising in situations of disease. In this work is presented a summary of the DFA method applied to gallbladder volume data for a modest number of healthy and ill patients: the presence of signal correlation is found in both cases, but the fit shapes differ from some critical values. |
1810.01505 | Vivian Tyng | Vivian Tyng and Michael E. Kellman | Kinetic Model of Translational Autoregulation | 30 pages including 8 figures and TOC graphic. Submitted to J. Phys.
Chem. B | null | 10.1021/acs.jpcb.8b09503 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate dynamics of a kinetic model of inhibitory autoregulation as
exemplified when a protein inhibits its own production by interfering with its
messenger RNA, known in molecular biology as translational autoregulation. We
first show how linear models without feedback set the stage with a
nonequilibrium steady state that constitutes the target of the regulation.
However, regulation in the simple linear model is far from optimal. The
negative feedback mechanism whereby the protein "jams" the mRNA greatly
enhances the effectiveness of the control, with response to perturbation that
is targeted, rapid, and metabolically efficient. Understanding the full
dynamics of the system phase space is essential to understanding the
autoregulation process.
| [
{
"created": "Tue, 2 Oct 2018 20:49:12 GMT",
"version": "v1"
}
] | 2019-01-09 | [
[
"Tyng",
"Vivian",
""
],
[
"Kellman",
"Michael E.",
""
]
] | We investigate dynamics of a kinetic model of inhibitory autoregulation as exemplified when a protein inhibits its own production by interfering with its messenger RNA, known in molecular biology as translational autoregulation. We first show how linear models without feedback set the stage with a nonequilibrium steady state that constitutes the target of the regulation. However, regulation in the simple linear model is far from optimal. The negative feedback mechanism whereby the protein "jams" the mRNA greatly enhances the effectiveness of the control, with response to perturbation that is targeted, rapid, and metabolically efficient. Understanding the full dynamics of the system phase space is essential to understanding the autoregulation process. |
2209.10634 | David Lipshutz | David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii | Interneurons accelerate learning dynamics in recurrent neural networks
for statistical adaptation | 16 pages, 7 figures | null | null | null | q-bio.NC cs.LG cs.NE stat.ML | http://creativecommons.org/licenses/by/4.0/ | Early sensory systems in the brain rapidly adapt to fluctuating input
statistics, which requires recurrent communication between neurons.
Mechanistically, such recurrent communication is often indirect and mediated by
local interneurons. In this work, we explore the computational benefits of
mediating recurrent communication via interneurons compared with direct
recurrent connections. To this end, we consider two mathematically tractable
recurrent linear neural networks that statistically whiten their inputs -- one
with direct recurrent connections and the other with interneurons that mediate
recurrent communication. By analyzing the corresponding continuous synaptic
dynamics and numerically simulating the networks, we show that the network with
interneurons is more robust to initialization than the network with direct
recurrent connections in the sense that the convergence time for the synaptic
dynamics in the network with interneurons (resp. direct recurrent connections)
scales logarithmically (resp. linearly) with the spectrum of their
initialization. Our results suggest that interneurons are computationally
useful for rapid adaptation to changing input statistics. Interestingly, the
network with interneurons is an overparameterized solution of the whitening
objective for the network with direct recurrent connections, so our results can
be viewed as a recurrent linear neural network analogue of the implicit
acceleration phenomenon observed in overparameterized feedforward linear neural
networks.
| [
{
"created": "Wed, 21 Sep 2022 20:03:58 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Aug 2023 13:46:05 GMT",
"version": "v2"
}
] | 2023-08-25 | [
[
"Lipshutz",
"David",
""
],
[
"Pehlevan",
"Cengiz",
""
],
[
"Chklovskii",
"Dmitri B.",
""
]
] | Early sensory systems in the brain rapidly adapt to fluctuating input statistics, which requires recurrent communication between neurons. Mechanistically, such recurrent communication is often indirect and mediated by local interneurons. In this work, we explore the computational benefits of mediating recurrent communication via interneurons compared with direct recurrent connections. To this end, we consider two mathematically tractable recurrent linear neural networks that statistically whiten their inputs -- one with direct recurrent connections and the other with interneurons that mediate recurrent communication. By analyzing the corresponding continuous synaptic dynamics and numerically simulating the networks, we show that the network with interneurons is more robust to initialization than the network with direct recurrent connections in the sense that the convergence time for the synaptic dynamics in the network with interneurons (resp. direct recurrent connections) scales logarithmically (resp. linearly) with the spectrum of their initialization. Our results suggest that interneurons are computationally useful for rapid adaptation to changing input statistics. Interestingly, the network with interneurons is an overparameterized solution of the whitening objective for the network with direct recurrent connections, so our results can be viewed as a recurrent linear neural network analogue of the implicit acceleration phenomenon observed in overparameterized feedforward linear neural networks. |
1402.3676 | Fabiano Ribeiro | Fabiano Lemes Ribeiro | A Non-Phenomenological Model to Explain Population Growth Behaviors | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a non-phenomenological model of population growth that is
based on the interactions between the individuals that compose the system. It
is assumed that the individuals interact cooperatively and competitively. As a
consequence of this interaction, it is shown that some well-known
phenomenological population growth models (such as the Malthus, Verhulst,
Gompertz, Richards, Von Foerster, and power-law growth models) are special
cases of the model presented herein. Moreover, other ecological behaviors can
be seen as the emergent behavior of such interactions. For instance, the Allee
effect, which is the characteristic of some populations to increase the
population growth rate at a small population size, is observed. Whereas the
models presented in the literature explain the Allee effect with
phenomenological ideas, the model presented here explains this effect by the
interactions between the individuals. The model is tested with empirical data
to justify its formulation. Other interesting macroscopic emergent behavior
from the model proposed here is the observation of a regime of population
divergence at a finite time. It is interesting that this characteristic is
observed in humanity's global population growth. It is shown that in a regime
of cooperation, the model fits very well to the human population growth data
since 1000 A.D.
| [
{
"created": "Sat, 15 Feb 2014 11:49:15 GMT",
"version": "v1"
}
] | 2014-02-18 | [
[
"Ribeiro",
"Fabiano Lemes",
""
]
] | This paper proposes a non-phenomenological model of population growth that is based on the interactions between the individuals that compose the system. It is assumed that the individuals interact cooperatively and competitively. As a consequence of this interaction, it is shown that some well-known phenomenological population growth models (such as the Malthus, Verhulst, Gompertz, Richards, Von Foerster, and power-law growth models) are special cases of the model presented herein. Moreover, other ecological behaviors can be seen as the emergent behavior of such interactions. For instance, the Allee effect, which is the characteristic of some populations to increase the population growth rate at a small population size, is observed. Whereas the models presented in the literature explain the Allee effect with phenomenological ideas, the model presented here explains this effect by the interactions between the individuals. The model is tested with empirical data to justify its formulation. Other interesting macroscopic emergent behavior from the model proposed here is the observation of a regime of population divergence at a finite time. It is interesting that this characteristic is observed in humanity's global population growth. It is shown that in a regime of cooperation, the model fits very well to the human population growth data since 1000 A.D. |
1405.7892 | Grzegorz Nawrocki | Grzegorz Nawrocki and Marek Cieplak | Interactions of aqueous amino acids and proteins with the (110) surface
of ZnS in molecular dynamics simulations | null | J. Chem. Phys. 140, 095101 (2014) | 10.1063/1.4866763 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The growing usage of nanoparticles of zinc sulfide as quantum dots and
biosensors calls for a theoretical assessment of interactions of ZnS with
biomolecules. We employ the molecular-dynamics-based umbrella sampling method
to determine potentials of mean force for 20 single amino acids near the ZnS
(110) surface in aqueous solutions. We find that five amino acids do not bind
at all and the binding energy of the remaining amino acids does not exceed 4.3
kJ/mol. Such energies are comparable to those found for ZnO (and to hydrogen
bonds in proteins) but the nature of the specificity is different. Cysteine can
bind with ZnS in a covalent way, e.g. by forming the disulfide bond with S in
the solid. If this effect is included within a model incorporating the Morse
potential, then the potential well becomes much deeper - the binding energy is
close to 98 kJ/mol. We then consider tryptophan cage, a protein of 20 residues,
and characterize its events of adsorption to ZnS. We demonstrate the relevance
of interactions between the amino acids in the selection of optimal adsorbed
conformations and recognize the key role of cysteine in generation of lasting
adsorption. We show that ZnS is more hydrophobic than ZnO and that the density
profile of water is quite different than that forming near ZnO - it has only a
minor articulation into layers. Furthermore, the first layer of water is
disordered and mobile.
| [
{
"created": "Fri, 30 May 2014 15:42:30 GMT",
"version": "v1"
}
] | 2014-06-02 | [
[
"Nawrocki",
"Grzegorz",
""
],
[
"Cieplak",
"Marek",
""
]
] | The growing usage of nanoparticles of zinc sulfide as quantum dots and biosensors calls for a theoretical assessment of interactions of ZnS with biomolecules. We employ the molecular-dynamics-based umbrella sampling method to determine potentials of mean force for 20 single amino acids near the ZnS (110) surface in aqueous solutions. We find that five amino acids do not bind at all and the binding energy of the remaining amino acids does not exceed 4.3 kJ/mol. Such energies are comparable to those found for ZnO (and to hydrogen bonds in proteins) but the nature of the specificity is different. Cysteine can bind with ZnS in a covalent way, e.g. by forming the disulfide bond with S in the solid. If this effect is included within a model incorporating the Morse potential, then the potential well becomes much deeper - the binding energy is close to 98 kJ/mol. We then consider tryptophan cage, a protein of 20 residues, and characterize its events of adsorption to ZnS. We demonstrate the relevance of interactions between the amino acids in the selection of optimal adsorbed conformations and recognize the key role of cysteine in generation of lasting adsorption. We show that ZnS is more hydrophobic than ZnO and that the density profile of water is quite different than that forming near ZnO - it has only a minor articulation into layers. Furthermore, the first layer of water is disordered and mobile. |
2405.07245 | Matthew Andres Moreno | Matthew Andres Moreno and Santiago Rodriguez-Papa and Emily Dolson | Ecology, Spatial Structure, and Selection Pressure Induce Strong
Signatures in Phylogenetic Structure | null | null | null | null | q-bio.PE cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolutionary dynamics are shaped by a variety of fundamental, generic
drivers, including spatial structure, ecology, and selection pressure. These
drivers impact the trajectory of evolution, and have been hypothesized to
influence phylogenetic structure. Here, we set out to assess (1) if spatial
structure, ecology, and selection pressure leave detectable signatures in
phylogenetic structure, (2) the extent, in particular, to which ecology can be
detected and discerned in the presence of spatial structure, and (3) the extent
to which these phylogenetic signatures generalize across evolutionary systems.
To this end, we analyze phylogenies generated by manipulating spatial
structure, ecology, and selection pressure within three computational models of
varied scope and sophistication. We find that selection pressure, spatial
structure, and ecology have characteristic effects on phylogenetic metrics,
although these effects are complex and not always intuitive. Signatures have
some consistency across systems when using equivalent taxonomic unit
definitions (e.g., individual, genotype, species). Further, we find that
sufficiently strong ecology can be detected in the presence of spatial
structure. We also find that, while low-resolution phylogenetic reconstructions
can bias some phylogenetic metrics, high-resolution reconstructions
recapitulate them faithfully. Although our results suggest potential for
evolutionary inference of spatial structure, ecology, and selection pressure
through phylogenetic analysis, further methods development is needed to
distinguish these drivers' phylometric signatures from each other and to
appropriately normalize phylogenetic metrics. With such work, phylogenetic
analysis could provide a versatile toolkit to study large-scale evolving
populations.
| [
{
"created": "Sun, 12 May 2024 10:35:19 GMT",
"version": "v1"
}
] | 2024-05-14 | [
[
"Moreno",
"Matthew Andres",
""
],
[
"Rodriguez-Papa",
"Santiago",
""
],
[
"Dolson",
"Emily",
""
]
] | Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution, and have been hypothesized to influence phylogenetic structure. Here, we set out to assess (1) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure, (2) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure, and (3) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Further, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics. With such work, phylogenetic analysis could provide a versatile toolkit to study large-scale evolving populations. |
2011.04040 | Rafael Barrio | Hernan Barrio Zhang, Mariana Marquez-Machorro, Vito S. Hernandez,
Andres Molina, Limei Zhang, Tzipe Govezensky, Rafael A. Barrio | Analysis and modeling of low frequency local field oscillations in a
hippocampus circuit under osmotic challenge: the possible role of arginine
vasopressin circuit for hippocampal function | 11 pages, 12 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electrophysiological time series were taken simultaneously in two locations
in the hippocampus of a rat brain previously described as receiving innervation
from the osmosensitive vasopressinergic neurons of the hypothalamus. A
hyperosmotic saline solution injection was administered during the time of the
experiment. We analyze the recorded time series using different methods. We
detect a modification of the delta and theta oscillations just after the
perturbation caused by the injection. We compare the quality and information
that each one of the methods exhibit and we analyze the characteristics of the
perturbation based on a hypothesis that the strength of the functional
connections between the vasopressinergic hypothalamic magnocellular neurons and
their target in the hippocampus is modified by the perturbation. We built a
model of the hypothetic neural connections and numerically calculate the time
series produced by the system when simulating the perturbation caused by the
saline injection. The theoretical results resemble the experimental findings
concerning the frequency and amplitude alterations of the delta and theta
bands.
| [
{
"created": "Sun, 8 Nov 2020 18:06:05 GMT",
"version": "v1"
}
] | 2020-11-10 | [
[
"Zhang",
"Hernan Barrio",
""
],
[
"Marquez-Machorro",
"Mariana",
""
],
[
"Hernandez",
"Vito S.",
""
],
[
"Molina",
"Andres",
""
],
[
"Zhang",
"Limei",
""
],
[
"Govezensky",
"Tzipe",
""
],
[
"Barrio",
"Rafael A.",
""
]
] | Electrophysiological time series were taken simultaneously in two locations in the hippocampus of a rat brain previously described as receiving innervation from the osmosensitive vasopressinergic neurons of the hypothalamus. A hyperosmotic saline solution injection was administered during the time of the experiment. We analyze the recorded time series using different methods. We detect a modification of the delta and theta oscillations just after the perturbation caused by the injection. We compare the quality and information that each one of the methods exhibit and we analyze the characteristics of the perturbation based on a hypothesis that the strength of the functional connections between the vasopressinergic hypothalamic magnocellular neurons and their target in the hippocampus is modified by the perturbation. We built a model of the hypothetic neural connections and numerically calculate the time series produced by the system when simulating the perturbation caused by the saline injection. The theoretical results resemble the experimental findings concerning the frequency and amplitude alterations of the delta and theta bands. |
q-bio/0507019 | Georgy Karev | Georgy P. Karev, Faina S. Berezovskaya, and Eugene V. Koonin | Modeling genome evolution with a diffusion approximation of a
birth-and-death process | 22pages, 9 figures; submitted to Bioinformatics | null | null | null | q-bio.GN q-bio.PE | null | In our previous studies, we developed discrete-space Birth, Death and
Innovation Models (BDIM) of genome evolution. These models explain the origin
of the characteristic Pareto distribution of paralogous gene family sizes in
genomes, and model parameters that provide for the evolution of these
distributions within a realistic timeframe have been identified. Here we
develop the diffusion version of BDIM whose dynamics is described by the
Fokker-Plank equation and the stationary solution could be any specified Pareto
function. The diffusion models have time-dependent solutions of a special kind,
namely, the generalized self-similar solutions, which describe the transition
from one stationary distribution of the system to another; this provides for
the possibility of examining the temporal dynamics of genome evolution.
Analysis of the generalized self-similar solutions of the diffusion BDIM
reveals a biphasic curve of genome growth in which the initial, relatively
short, self-accelerating phase is followed by a prolonged phase of slow
deceleration. In biological terms, this regime of evolution can be tentatively
interpreted as a punctuated-equilibrium-like phenomenon such that whereby
evolutionary transitions are accompanied by rapid gene amplification and
innovation, followed by slow relaxation to a new stationary state.
| [
{
"created": "Wed, 13 Jul 2005 18:33:22 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Karev",
"Georgy P.",
""
],
[
"Berezovskaya",
"Faina S.",
""
],
[
"Koonin",
"Eugene V.",
""
]
] | In our previous studies, we developed discrete-space Birth, Death and Innovation Models (BDIM) of genome evolution. These models explain the origin of the characteristic Pareto distribution of paralogous gene family sizes in genomes, and model parameters that provide for the evolution of these distributions within a realistic timeframe have been identified. Here we develop the diffusion version of BDIM whose dynamics is described by the Fokker-Plank equation and the stationary solution could be any specified Pareto function. The diffusion models have time-dependent solutions of a special kind, namely, the generalized self-similar solutions, which describe the transition from one stationary distribution of the system to another; this provides for the possibility of examining the temporal dynamics of genome evolution. Analysis of the generalized self-similar solutions of the diffusion BDIM reveals a biphasic curve of genome growth in which the initial, relatively short, self-accelerating phase is followed by a prolonged phase of slow deceleration. In biological terms, this regime of evolution can be tentatively interpreted as a punctuated-equilibrium-like phenomenon such that whereby evolutionary transitions are accompanied by rapid gene amplification and innovation, followed by slow relaxation to a new stationary state. |
2106.07610 | Gordon Berman | Katherine E. Overman, Daniel M. Choi, Kawai Leung, Joshua W. Shaevitz,
Gordon J. Berman | Measuring the repertoire of age-related behavioral changes in Drosophila
melanogaster | null | null | 10.1371/journal.pcbi.1009867 | null | q-bio.QM physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Aging affects almost all aspects of an organism -- its morphology, its
physiology, its behavior. Isolating which biological mechanisms are regulating
these changes, however, has proven difficult, potentially due to our inability
to characterize the full repertoire of an animal's behavior across the
lifespan. Using data from fruit flies (D. melanogaster) we measure the full
repertoire of behaviors as a function of age. We observe a sexually dimorphic
pattern of changes in the behavioral repertoire during aging. Although the
stereotypy of the behaviors and the complexity of the repertoire overall
remains relatively unchanged, we find evidence that the observed alterations in
behavior can be explained by changing the fly's overall energy budget,
suggesting potential connections between metabolism, aging, and behavior.
| [
{
"created": "Mon, 14 Jun 2021 17:15:08 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Jun 2021 17:30:53 GMT",
"version": "v2"
}
] | 2022-05-04 | [
[
"Overman",
"Katherine E.",
""
],
[
"Choi",
"Daniel M.",
""
],
[
"Leung",
"Kawai",
""
],
[
"Shaevitz",
"Joshua W.",
""
],
[
"Berman",
"Gordon J.",
""
]
] | Aging affects almost all aspects of an organism -- its morphology, its physiology, its behavior. Isolating which biological mechanisms are regulating these changes, however, has proven difficult, potentially due to our inability to characterize the full repertoire of an animal's behavior across the lifespan. Using data from fruit flies (D. melanogaster) we measure the full repertoire of behaviors as a function of age. We observe a sexually dimorphic pattern of changes in the behavioral repertoire during aging. Although the stereotypy of the behaviors and the complexity of the repertoire overall remains relatively unchanged, we find evidence that the observed alterations in behavior can be explained by changing the fly's overall energy budget, suggesting potential connections between metabolism, aging, and behavior. |
2010.05116 | Hiroshi Isshiki Dr. | Hiroshi Isshiki and Masao Namiki | Application and Extension of Mean-Field Theory such as SIR to Discuss
the Non-Mean Field Problem of COVID-19 | 19 pages, 21 figures, 1 table | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The concept of the effective infection opportunity population (EIOP) was
incorporated into the SIQR model, and it was assumed that this EIOP would
change with the spread of infection, and this was named as the effective SIQR
model. When calculated with this model, the uninfected population S decreases
with the passage of time. However, when the EIOP N increases because of any
reason, the infection threshold becomes larger than 1. Even after the first
wave seems to have subsided, the infection begins to spread again. Firstly, we
find the curve of EIOP change so that the calculation result by this model
matches the data of the first and second waves. Then, we use this curve to fit
with only the data of the second wave alone, and the third wave is predicted.
In the case of new coronavirus infection, there are various restrictions on
data collection to identify individual coefficients of mathematical models, and
the true value is almost unknown. Therefore, the discussion in this paper is
only about data fitting for predictive calculation. Therefore, the simulation
on the true value is not aimed. However, since the data of infected persons
reflect the true values, the results of data fitting can be used for the
prediction of infected persons, isolated care recipients, inpatients, and
severely ill persons. They are useful for a qualitative understanding of
infection. The idea of EIOP is important in the sense that it connects the
mean-field and the non-mean field, but the existence of data is essential, and
the theory alone cannot simulate the non-mean field. We have developed two
methods for treating the non-mean field cases where we don't have enough data.
We have briefly introduced them.
| [
{
"created": "Sat, 10 Oct 2020 23:12:51 GMT",
"version": "v1"
}
] | 2020-10-13 | [
[
"Isshiki",
"Hiroshi",
""
],
[
"Namiki",
"Masao",
""
]
] | The concept of the effective infection opportunity population (EIOP) was incorporated into the SIQR model, and it was assumed that this EIOP would change with the spread of infection, and this was named as the effective SIQR model. When calculated with this model, the uninfected population S decreases with the passage of time. However, when the EIOP N increases because of any reason, the infection threshold becomes larger than 1. Even after the first wave seems to have subsided, the infection begins to spread again. Firstly, we find the curve of EIOP change so that the calculation result by this model matches the data of the first and second waves. Then, we use this curve to fit with only the data of the second wave alone, and the third wave is predicted. In the case of new coronavirus infection, there are various restrictions on data collection to identify individual coefficients of mathematical models, and the true value is almost unknown. Therefore, the discussion in this paper is only about data fitting for predictive calculation. Therefore, the simulation on the true value is not aimed. However, since the data of infected persons reflect the true values, the results of data fitting can be used for the prediction of infected persons, isolated care recipients, inpatients, and severely ill persons. They are useful for a qualitative understanding of infection. The idea of EIOP is important in the sense that it connects the mean-field and the non-mean field, but the existence of data is essential, and the theory alone cannot simulate the non-mean field. We have developed two methods for treating the non-mean field cases where we don't have enough data. We have briefly introduced them. |
2311.12032 | Aram Mohammed | Hemn Abdalla Mustafa, Tariq Abubakr Ahmad, Aram Akram Mohammed, Zainab
Sabah Lazim, Chopi Omer Ibrahim, Roshna Faeq Kak bra, Shvan Ramzi Salih | Effect of some plant extracts on hardwood cuttings of Bottlebrush
(Callistemon viminalis) | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | The study was conducted at the Collage of Agricultural Engineering Sciences,
University of Sulaimani, Kurdistan Region-Iraq so as to investigate response
hardwood cuttings of Callistemon viminalis to some plant extracts. The hardwood
cuttings were taken on 11 March 2021 and soaked separately in 3 and 6 g/L
aqueous extracts of moringa leaf, licorice root, willow shoot, fenugreek seed
and cinnamon bark for 1 hour. They were compared to the cuttings dipped in 3000
ppm IBA for 10s and control cuttings which were soaked in distilled water for 1
hour. The experiment laid out in CRD with three replications in a greenhouse,
and each replication included six cuttings which planted in a mixture of sand
and rice husk medium. The results showed that the highest (86.66%) rooting was
achieved in the cuttings treated with 6 g/L licorice extract and they were
significantly different with control cuttings (53.33%), but they were not
significantly different with 3000 ppm IBA (66.66%). Cinnamon 3g/L and fenugreek
3g/L extracts gave the lowest (6.66% and 33.33%, respectively) rooting and
other studied parameters. The cuttings dipped in 3000 ppm IBA gave the highest
(18.91) root number and the highest (66.66%) survival cuttings after
transplanting. The longest root (15.54 cm) was found in cuttings were treated
with 6 g/L moringa extract. The longest (5.83 cm) shoot was observed in treated
cuttings with 3 g/L willow extract. The highest chlorophyll a and b (10.08 and
4.62 mg/L, respectively) were observed in cuttings treated with 6 g/L willow
extract. Moreover, 3000 ppm IBA gave the highest (20.23%) total carbohydrate
and (1.77 mg/g) IAA content along with 6 g/L licorice, moringa and fenugreek
extracts, after 30 days from planting of the cuttings. Licorice root extract at
6 g/L fairly improved the measurements similar to 3000 ppm IBA throughout the
study.
| [
{
"created": "Sat, 9 Sep 2023 10:39:57 GMT",
"version": "v1"
}
] | 2023-11-22 | [
[
"Mustafa",
"Hemn Abdalla",
""
],
[
"Ahmad",
"Tariq Abubakr",
""
],
[
"Mohammed",
"Aram Akram",
""
],
[
"Lazim",
"Zainab Sabah",
""
],
[
"Ibrahim",
"Chopi Omer",
""
],
[
"bra",
"Roshna Faeq Kak",
""
],
[
"Salih",
"Shvan Ramzi",
""
]
] | The study was conducted at the Collage of Agricultural Engineering Sciences, University of Sulaimani, Kurdistan Region-Iraq so as to investigate response hardwood cuttings of Callistemon viminalis to some plant extracts. The hardwood cuttings were taken on 11 March 2021 and soaked separately in 3 and 6 g/L aqueous extracts of moringa leaf, licorice root, willow shoot, fenugreek seed and cinnamon bark for 1 hour. They were compared to the cuttings dipped in 3000 ppm IBA for 10s and control cuttings which were soaked in distilled water for 1 hour. The experiment laid out in CRD with three replications in a greenhouse, and each replication included six cuttings which planted in a mixture of sand and rice husk medium. The results showed that the highest (86.66%) rooting was achieved in the cuttings treated with 6 g/L licorice extract and they were significantly different with control cuttings (53.33%), but they were not significantly different with 3000 ppm IBA (66.66%). Cinnamon 3g/L and fenugreek 3g/L extracts gave the lowest (6.66% and 33.33%, respectively) rooting and other studied parameters. The cuttings dipped in 3000 ppm IBA gave the highest (18.91) root number and the highest (66.66%) survival cuttings after transplanting. The longest root (15.54 cm) was found in cuttings were treated with 6 g/L moringa extract. The longest (5.83 cm) shoot was observed in treated cuttings with 3 g/L willow extract. The highest chlorophyll a and b (10.08 and 4.62 mg/L, respectively) were observed in cuttings treated with 6 g/L willow extract. Moreover, 3000 ppm IBA gave the highest (20.23%) total carbohydrate and (1.77 mg/g) IAA content along with 6 g/L licorice, moringa and fenugreek extracts, after 30 days from planting of the cuttings. Licorice root extract at 6 g/L fairly improved the measurements similar to 3000 ppm IBA throughout the study. |
2302.07401 | Federico Bocci | Federico Bocci, Dongya Jia, Qing Nie, Mohit Kumar Jolly, Jose Onuchic | Theoretical and computational tools to model multistable gene regulatory
networks | 81 pages, 13 figures | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The last decade has witnessed a surge of theoretical and computational models
to describe the dynamics of complex gene regulatory networks, and how these
interactions can give rise to multistable and heterogeneous cell populations.
As the use of theoretical modeling to describe genetic and biochemical circuits
becomes more widespread, theoreticians with mathematical and physical
backgrounds routinely apply concepts from statistical physics, non-linear
dynamics, and network theory to biological systems. This review aims at
providing a clear overview of the most important methodologies applied in the
field while highlighting current and future challenges. It also includes
hands-on tutorials to solve and simulate some of the archetypical biological
system models used in the field. Furthermore, we provide concrete examples from
the existing literature for theoreticians that wish to explore this
fast-developing field. Whenever possible, we highlight the similarities and
differences between biochemical and regulatory networks and 'classical' systems
typically studied in non-equilibrium statistical and quantum mechanics.
| [
{
"created": "Tue, 14 Feb 2023 23:55:26 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Jun 2023 18:15:39 GMT",
"version": "v2"
}
] | 2023-06-28 | [
[
"Bocci",
"Federico",
""
],
[
"Jia",
"Dongya",
""
],
[
"Nie",
"Qing",
""
],
[
"Jolly",
"Mohit Kumar",
""
],
[
"Onuchic",
"Jose",
""
]
] | The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics. |
1503.03312 | Alexander K. Vidybida | Alexander K.Vidybida | Activity of any neuron with delayed feedback stimulated with Poisson
stream is non-Markov | 15 pages, 2 figures, 25 Refs | J Stat Phys (2015) 160:1507-1518 | 10.1007/s10955-015-1301-2 | null | q-bio.NC math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For a class of excitatory spiking neuron models with delayed feedback fed
with a Poisson stochastic process, it is proven that the stream of output
interspike intervals cannot be presented as a Markov process of any order.
Keywords: spiking neuron; Poisson stochastic process; probability density
function; delayed feedback; non-Markov stochastic process
| [
{
"created": "Wed, 11 Mar 2015 12:50:58 GMT",
"version": "v1"
}
] | 2015-08-19 | [
[
"Vidybida",
"Alexander K.",
""
]
] | For a class of excitatory spiking neuron models with delayed feedback fed with a Poisson stochastic process, it is proven that the stream of output interspike intervals cannot be presented as a Markov process of any order. Keywords: spiking neuron; Poisson stochastic process; probability density function; delayed feedback; non-Markov stochastic process |
1909.04586 | Prashant Raju | Prashant C. Raju | A Theory on Formatting Sensory Input for Cognition | I am moving in a completely different direction | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Over the last few decades, a lot of progress has been made in understanding
different aspects of the brain's ability to form abstract representations, but
a specific mechanism for how they are created and used remains to emerge. Here,
we review recent findings on the subject and we propose a mechanism for the
dynamics of forming abstract representations, where the formation of local
connectivity in neural networks determines the of search terms between the
prefrontal cortex and the hippocampus, as well as the amount of detail that is
transcribed into abstract representations.
| [
{
"created": "Sun, 8 Sep 2019 20:00:55 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Sep 2019 13:48:08 GMT",
"version": "v2"
},
{
"created": "Mon, 2 Dec 2019 15:22:02 GMT",
"version": "v3"
},
{
"created": "Sat, 4 Jan 2020 16:38:30 GMT",
"version": "v4"
},
{
"created": "Fri, 28 Feb 2020 00:22:09 GMT",
"version": "v5"
}
] | 2020-03-02 | [
[
"Raju",
"Prashant C.",
""
]
] | Over the last few decades, a lot of progress has been made in understanding different aspects of the brain's ability to form abstract representations, but a specific mechanism for how they are created and used remains to emerge. Here, we review recent findings on the subject and we propose a mechanism for the dynamics of forming abstract representations, where the formation of local connectivity in neural networks determines the of search terms between the prefrontal cortex and the hippocampus, as well as the amount of detail that is transcribed into abstract representations. |
2003.14283 | Martin Spousta | Martin Spousta | Parametric analysis of COVID-19 expansion in European countries in the
period of February to June 2020 | Rejected by a high-profile bio-math journal due to missing comparison
with established SIR models and due to insufficient medical impact of the
study. Unpublished | null | null | null | q-bio.PE physics.soc-ph q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | The data on number of registered cases of COVID-19 disease in twenty European
countries is analyzed by the least-squares fitting procedure with generic
analytic functions. Three regimes of the expansion of the disease are
identified and quantified -- early exponential expansion, damped exponential,
and linear expansion. Differences among countries in the early expansion period
are quantified. The velocity of the expansion in the exponential regime lies
within one standard deviation from the average value for 11 countries. The
number of infected individuals at the initial time is excessively high for
Italy, 7 standard deviations from the average value.
Method for predicting the expansion based on extrapolation in the parametric
space is presented. One-week predictions based on extrapolations have average
precision of 18% and 29% during the later period of the damped exponential
expansion for the case of Italy and Czechia, respectively. The method based on
extrapolations in the parametric space may provide an elementary method to
quantify the impact of restrictive measures on the spreading of the disease.
| [
{
"created": "Tue, 31 Mar 2020 15:14:05 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Apr 2021 12:16:53 GMT",
"version": "v2"
}
] | 2021-04-27 | [
[
"Spousta",
"Martin",
""
]
] | The data on number of registered cases of COVID-19 disease in twenty European countries is analyzed by the least-squares fitting procedure with generic analytic functions. Three regimes of the expansion of the disease are identified and quantified -- early exponential expansion, damped exponential, and linear expansion. Differences among countries in the early expansion period are quantified. The velocity of the expansion in the exponential regime lies within one standard deviation from the average value for 11 countries. The number of infected individuals at the initial time is excessively high for Italy, 7 standard deviations from the average value. Method for predicting the expansion based on extrapolation in the parametric space is presented. One-week predictions based on extrapolations have average precision of 18% and 29% during the later period of the damped exponential expansion for the case of Italy and Czechia, respectively. The method based on extrapolations in the parametric space may provide an elementary method to quantify the impact of restrictive measures on the spreading of the disease. |
q-bio/0401003 | Manoj Gopalakrishnan | Bindu. S. Govindan, W. B. Spillman, Jr., J. L. Robertson and W. R.
Huckle (Virginia Tech) | Acid-mediated tumor invasion: How does vasculature affect the growth
characteristics? | 26 pages, 8 fig (1 figure replaced, minor changes in text) | null | null | null | q-bio.CB q-bio.QM | null | We study the growth of an implanted a-vascular tumor seed in two-dimensions
based on a model where the mechanism of invasion is centered on tumor-induced
acidification of the micro-environment and consequent death of normal cells.
The spatial distribution of the acid density around the tumor is found using
mean-field analysis. By assuming that the viability of both normal and tumor
cells falls sharply below certain threshold values of the local pH, we
determine the conditions for the formation of a necrotic core at the center, as
well as its radius as a function of the tumor radius. We show that the mean
micro-vessel density (MVD) plays a pivotal role in determining the growth
characteristics of the tumor. When the MVD is sufficiently small, accumulation
of excess acid inside the tumor leads to the formation of a necrotic core,
which occupies a significant fraction of the total area in large tumors.
However, necrosis is reduced when the mean MVD inside the tumor is larger than
outside because of the more efficient removal of excess acid. At sufficiently
high MVD, necrosis is absent in the tumor, or confined to small regions mostly
devoid of micro-vessels. Quantitative estimates of MVD for these different
phases of growth are obtained, and verified using explicit cellular automaton
simulations. Recent experimental studies on the correlation between necrosis
and MVD support our main conclusions.
| [
{
"created": "Sun, 4 Jan 2004 21:11:42 GMT",
"version": "v1"
},
{
"created": "Sat, 10 Jan 2004 16:57:26 GMT",
"version": "v2"
}
] | 2012-08-27 | [
[
"Govindan",
"Bindu. S.",
"",
"Virginia Tech"
],
[
"Spillman,",
"W. B.",
"Jr.",
"Virginia Tech"
],
[
"Robertson",
"J. L.",
"",
"Virginia Tech"
],
[
"Huckle",
"W. R.",
"",
"Virginia Tech"
]
] | We study the growth of an implanted a-vascular tumor seed in two-dimensions based on a model where the mechanism of invasion is centered on tumor-induced acidification of the micro-environment and consequent death of normal cells. The spatial distribution of the acid density around the tumor is found using mean-field analysis. By assuming that the viability of both normal and tumor cells falls sharply below certain threshold values of the local pH, we determine the conditions for the formation of a necrotic core at the center, as well as its radius as a function of the tumor radius. We show that the mean micro-vessel density (MVD) plays a pivotal role in determining the growth characteristics of the tumor. When the MVD is sufficiently small, accumulation of excess acid inside the tumor leads to the formation of a necrotic core, which occupies a significant fraction of the total area in large tumors. However, necrosis is reduced when the mean MVD inside the tumor is larger than outside because of the more efficient removal of excess acid. At sufficiently high MVD, necrosis is absent in the tumor, or confined to small regions mostly devoid of micro-vessels. Quantitative estimates of MVD for these different phases of growth are obtained, and verified using explicit cellular automaton simulations. Recent experimental studies on the correlation between necrosis and MVD support our main conclusions. |
2111.08763 | Gerardo Aquino | Gerardo Aquino and Mauro Bologna | Effect of decreasing population growth-rate on deforestation and
population sustainability | 2 pages, 1 figure | Communicative & Integrative Biology, v. 14, 1, 261-63 (2021) | 10.1080/19420889.2021.2010394 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the effect of non-constant parameters on the human-forest
interaction logistic model coupled with human technological growth introduced
in "Deforestation and world population sustainability: a quantitative
analysis"[1]. In recent years in fact, a decrease in human population growth
rate has emerged which can be measured to about 1.7% drop per year since 1960
value which coincides with latest UN projections for next decades up to year
2100 [2]. We therefore consider here the effect of decreasing human population
growth-rate on the aforementioned model and we evaluate its effect on the
probability of survival of human civilisation without going through a
catastrophic collapse in population. We find that for realistic values of the
human population carrying capacity of the earth (measured by parameter beta)
this decrease would not affect previous results leading to a low probability of
avoiding a catastrophic collapse. For larger more optimistic values of beta
instead, a decrease in growth-rate would tilt the probability in favour of a
positive outcome, i.e. from 10-20% up to even 95% likelihood of avoiding
collapse.
| [
{
"created": "Tue, 16 Nov 2021 20:23:19 GMT",
"version": "v1"
}
] | 2021-12-21 | [
[
"Aquino",
"Gerardo",
""
],
[
"Bologna",
"Mauro",
""
]
] | We consider the effect of non-constant parameters on the human-forest interaction logistic model coupled with human technological growth introduced in "Deforestation and world population sustainability: a quantitative analysis"[1]. In recent years in fact, a decrease in human population growth rate has emerged which can be measured to about 1.7% drop per year since 1960 value which coincides with latest UN projections for next decades up to year 2100 [2]. We therefore consider here the effect of decreasing human population growth-rate on the aforementioned model and we evaluate its effect on the probability of survival of human civilisation without going through a catastrophic collapse in population. We find that for realistic values of the human population carrying capacity of the earth (measured by parameter beta) this decrease would not affect previous results leading to a low probability of avoiding a catastrophic collapse. For larger more optimistic values of beta instead, a decrease in growth-rate would tilt the probability in favour of a positive outcome, i.e. from 10-20% up to even 95% likelihood of avoiding collapse. |
2010.15214 | Julia Camps | Julia Camps, Brodie Lawson, Christopher Drovandi, Ana Minchole, Zhinuo
Jenny Wang, Vicente Grau, Kevin Burrage, Blanca Rodriguez | Inference of ventricular activation properties from non-invasive
electrocardiography | Submitted to Medical Image Analysis | null | 10.1016/j.media.2021.102143 | null | q-bio.TO eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The realisation of precision cardiology requires novel techniques for the
non-invasive characterisation of individual patients' cardiac function to
inform therapeutic and diagnostic decision-making. The electrocardiogram (ECG)
is the most widely used clinical tool for cardiac diagnosis. Its interpretation
is, however, confounded by functional and anatomical variability in heart and
torso. In this study, we develop new computational techniques to estimate key
ventricular activation properties for individual subjects by exploiting the
synergy between non-invasive electrocardiography and image-based
torso-biventricular modelling and simulation. More precisely, we present an
efficient sequential Monte Carlo approximate Bayesian computation-based
inference method, integrated with Eikonal simulations and torso-biventricular
models constructed based on clinical cardiac magnetic resonance (CMR) imaging.
The method also includes a novel strategy to treat combined continuous
(conduction speeds) and discrete (earliest activation sites) parameter spaces,
and an efficient dynamic time warping-based ECG comparison algorithm. We
demonstrate results from our inference method on a cohort of twenty virtual
subjects with cardiac volumes ranging from 74 cm3 to 171 cm3 and considering
low versus high resolution for the endocardial discretisation (which determines
possible locations of the earliest activation sites). Results show that our
method can successfully infer the ventricular activation properties from
non-invasive data, with higher accuracy for earliest activation sites,
endocardial speed, and sheet (transmural) speed in sinus rhythm, rather than
the fibre or sheet-normal speeds.
| [
{
"created": "Wed, 28 Oct 2020 20:25:44 GMT",
"version": "v1"
}
] | 2021-12-09 | [
[
"Camps",
"Julia",
""
],
[
"Lawson",
"Brodie",
""
],
[
"Drovandi",
"Christopher",
""
],
[
"Minchole",
"Ana",
""
],
[
"Wang",
"Zhinuo Jenny",
""
],
[
"Grau",
"Vicente",
""
],
[
"Burrage",
"Kevin",
""
],
[
"Rodriguez",
"Blanca",
""
]
] | The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients' cardiac function to inform therapeutic and diagnostic decision-making. The electrocardiogram (ECG) is the most widely used clinical tool for cardiac diagnosis. Its interpretation is, however, confounded by functional and anatomical variability in heart and torso. In this study, we develop new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography and image-based torso-biventricular modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical cardiac magnetic resonance (CMR) imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces, and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties from non-invasive data, with higher accuracy for earliest activation sites, endocardial speed, and sheet (transmural) speed in sinus rhythm, rather than the fibre or sheet-normal speeds. |
1501.04240 | Nathan Baker | Wenxiao Pan, Michael Daily, Nathan A. Baker | Numerical calculation of protein-ligand binding rates through solution
of the Smoluchowski equation using smooth particle hydrodynamics | null | BMC Biophys. 2015; 8: 7 | 10.1186/s13628-015-0021-y | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background. The calculation of diffusion-controlled ligand binding rates is
important for understanding enzyme mechanisms as well as designing enzyme
inhibitors. We demonstrate the accuracy and effectiveness of a Lagrangian
particle-based method, smoothed particle hydrodynamics (SPH), to study
diffusion in biomolecular systems by numerically solving the time-dependent
Smoluchowski equation for continuum diffusion.
Results. The numerical method is first verified in simple systems and then
applied to the calculation of ligand binding to an acetylcholinesterase
monomer. Unlike previous studies, a reactive Robin boundary condition (BC),
rather than the absolute absorbing (Dirichlet) boundary condition, is
considered on the reactive boundaries. This new boundary condition treatment
allows for the analysis of enzymes with "imperfect" reaction rates. Rates for
inhibitor binding to mAChE are calculated at various ionic strengths and
compared with experiment and other numerical methods. We find that imposition
of the Robin BC improves agreement between calculated and experimental reaction
rates.
Conclusions. Although this initial application focuses on a single monomer
system, our new method provides a framework to explore broader applications of
SPH in larger-scale biomolecular complexes by taking advantage of its
Lagrangian particle-based nature.
| [
{
"created": "Sat, 17 Jan 2015 22:46:02 GMT",
"version": "v1"
}
] | 2016-05-17 | [
[
"Pan",
"Wenxiao",
""
],
[
"Daily",
"Michael",
""
],
[
"Baker",
"Nathan A.",
""
]
] | Background. The calculation of diffusion-controlled ligand binding rates is important for understanding enzyme mechanisms as well as designing enzyme inhibitors. We demonstrate the accuracy and effectiveness of a Lagrangian particle-based method, smoothed particle hydrodynamics (SPH), to study diffusion in biomolecular systems by numerically solving the time-dependent Smoluchowski equation for continuum diffusion. Results. The numerical method is first verified in simple systems and then applied to the calculation of ligand binding to an acetylcholinesterase monomer. Unlike previous studies, a reactive Robin boundary condition (BC), rather than the absolute absorbing (Dirichlet) boundary condition, is considered on the reactive boundaries. This new boundary condition treatment allows for the analysis of enzymes with "imperfect" reaction rates. Rates for inhibitor binding to mAChE are calculated at various ionic strengths and compared with experiment and other numerical methods. We find that imposition of the Robin BC improves agreement between calculated and experimental reaction rates. Conclusions. Although this initial application focuses on a single monomer system, our new method provides a framework to explore broader applications of SPH in larger-scale biomolecular complexes by taking advantage of its Lagrangian particle-based nature. |
1404.4755 | Brandon Barker | Brandon Barker, Narayanan Sadagopan, Yiping Wang, Kieran Smallbone,
Christopher R. Myers, Hongwei Xi, Jason W. Locasale, Zhenglong Gu | A robust and efficient method for estimating enzyme complex abundance
and metabolic flux from expression data | 30 pages, 12 figures, 4 tables | null | 10.1016/j.compbiolchem.2015.08.002 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major theme in constraint-based modeling is unifying experimental data,
such as biochemical information about the reactions that can occur in a system
or the composition and localization of enzyme complexes, with highthroughput
data including expression data, metabolomics, or DNA sequencing. The desired
result is to increase predictive capability resulting in improved understanding
of metabolism. The approach typically employed when only gene (or protein)
intensities are available is the creation of tissue-specific models, which
reduces the available reactions in an organism model, and does not provide an
objective function for the estimation of fluxes, which is an important
limitation in many modeling applications. We develop a method, flux assignment
with LAD (least absolute deviation) convex objectives and normalization
(FALCON), that employs metabolic network reconstructions along with expression
data to estimate fluxes. In order to use such a method, accurate measures of
enzyme complex abundance are needed, so we first present a new algorithm that
addresses quantification of complex abundance. Our extensions to prior
techniques include the capability to work with large models and significantly
improved run-time performance even for smaller models, an improved analysis of
enzyme complex formation logic, the ability to handle very large enzyme complex
rules that may incorporate multiple isoforms, and depending on the model
constraints, either maintained or significantly improved correlation with
experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS,
and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not
required to compile the software, as intermediate C source code is available,
and binaries are provided for Linux x86-64 systems. FALCON requires use of the
COBRA Toolbox, also implemented in MATLAB.
| [
{
"created": "Fri, 18 Apr 2014 11:59:30 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Sep 2015 01:12:08 GMT",
"version": "v2"
}
] | 2015-09-10 | [
[
"Barker",
"Brandon",
""
],
[
"Sadagopan",
"Narayanan",
""
],
[
"Wang",
"Yiping",
""
],
[
"Smallbone",
"Kieran",
""
],
[
"Myers",
"Christopher R.",
""
],
[
"Xi",
"Hongwei",
""
],
[
"Locasale",
"Jason W.",
""
],
[
"Gu",
"Zhenglong",
""
]
] | A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with highthroughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability resulting in improved understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes, which is an important limitation in many modeling applications. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present a new algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation logic, the ability to handle very large enzyme complex rules that may incorporate multiple isoforms, and depending on the model constraints, either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available, and binaries are provided for Linux x86-64 systems. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB. |
1612.00036 | Eric Libby | Eric Libby and Joshua Grochow and Simon DeDeo and David Wolpert | A quantitative definition of organismality and its application to lichen | 21 pages, 6 figures | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The organism is a fundamental concept in biology. However there is no
universally accepted, formal, and yet broadly applicable definition of what an
organism is. Here we introduce a candidate definition. We adopt the view that
the "organism" is a functional concept, used by scientists to address
particular questions concerning the future state of a biological system, rather
than something wholly defined by that system. In this approach organisms are a
coarse-graining of a fine-grained dynamical model of a biological system.
Crucially, the coarse-graining of the system into organisms is chosen so that
their dynamics can be used by scientists to make accurate predictions of those
features of the biological system that interests them, and do so with minimal
computational burden. To illustrate our framework we apply it to a dynamic
model of lichen symbiosis---a system where either the lichen or its constituent
fungi and algae could reasonably be considered "organisms." We find that the
best choice for what organisms are in this scenario are complex mixtures of
many entities that do not resemble standard notions of organisms. When we
restrict our allowed coarse-grainings to more traditional types of organisms,
we find that ecological conditions, such as niche competition and predation
pressure, play a significant role in determining the best choice for organisms.
| [
{
"created": "Wed, 30 Nov 2016 21:21:47 GMT",
"version": "v1"
}
] | 2016-12-02 | [
[
"Libby",
"Eric",
""
],
[
"Grochow",
"Joshua",
""
],
[
"DeDeo",
"Simon",
""
],
[
"Wolpert",
"David",
""
]
] | The organism is a fundamental concept in biology. However there is no universally accepted, formal, and yet broadly applicable definition of what an organism is. Here we introduce a candidate definition. We adopt the view that the "organism" is a functional concept, used by scientists to address particular questions concerning the future state of a biological system, rather than something wholly defined by that system. In this approach organisms are a coarse-graining of a fine-grained dynamical model of a biological system. Crucially, the coarse-graining of the system into organisms is chosen so that their dynamics can be used by scientists to make accurate predictions of those features of the biological system that interests them, and do so with minimal computational burden. To illustrate our framework we apply it to a dynamic model of lichen symbiosis---a system where either the lichen or its constituent fungi and algae could reasonably be considered "organisms." We find that the best choice for what organisms are in this scenario are complex mixtures of many entities that do not resemble standard notions of organisms. When we restrict our allowed coarse-grainings to more traditional types of organisms, we find that ecological conditions, such as niche competition and predation pressure, play a significant role in determining the best choice for organisms. |
1308.5676 | Segei Taraskin | Sergei Taraskin and Francisco J. P\'erez-Reche | Effects of variable neighbourhood on spreading processes | null | Phys. Rev. E 88, 062815 (2013) | 10.1103/PhysRevE.88.062815 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A theoretical framework for the description of Susceptible-Infected-Removed
(SIR) spreading processes with synergistic transmission of infection on a
lattice is developed. The model incorporates explicitly the effects of a
time-dependent environment on the transmission of infection between hosts.
Exact solution of the model shows that time-dependence of the neighbourhood of
recipient hosts is a key factor for synergistic spreading processes. It is
demonstrated that the higher the connectivity of a lattice, the more prominent
is the effect of synergy on spread.
| [
{
"created": "Mon, 26 Aug 2013 14:55:07 GMT",
"version": "v1"
}
] | 2013-12-25 | [
[
"Taraskin",
"Sergei",
""
],
[
"Pérez-Reche",
"Francisco J.",
""
]
] | A theoretical framework for the description of Susceptible-Infected-Removed (SIR) spreading processes with synergistic transmission of infection on a lattice is developed. The model incorporates explicitly the effects of a time-dependent environment on the transmission of infection between hosts. Exact solution of the model shows that time-dependence of the neighbourhood of recipient hosts is a key factor for synergistic spreading processes. It is demonstrated that the higher the connectivity of a lattice, the more prominent is the effect of synergy on spread. |
1411.4106 | Mike Steel Prof. | Olivier Gascuel and Mike Steel | A 'stochastic safety radius' for distance-based tree reconstruction | 18 pages, 1 figure, 4 tables | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A variety of algorithms have been proposed for reconstructing trees that show
the evolutionary relationships between species by comparing differences in
genetic data across present-day taxa. If the leaf-to-leaf distances in a tree
can be accurately estimated, then it is possible to reconstruct this tree from
these estimated distances, using polynomial-time methods such as the popular
`Neighbor-Joining' algorithm. There is a precise combinatorial condition under
which distance-based methods are guaranteed to return a correct tree (in full
or in part) based on the requirement that the input distances all lie within
some `safety radius' of the true distances. Here, we explore a stochastic
analogue of this condition, and mathematically establish upper and lower bounds
on this `stochastic safety radius' for distance-based tree reconstruction
methods. Using simulations, we show how this notion provides a new way to
compare the performance of distance-based tree reconstruction methods. This may
help explain why Neighbor-Joining performs so well, as its stochastic safety
radius appears close to optimal (while its more classical safety radius is the
same as many other less accurate methods).
| [
{
"created": "Sat, 15 Nov 2014 02:39:13 GMT",
"version": "v1"
}
] | 2014-11-18 | [
[
"Gascuel",
"Olivier",
""
],
[
"Steel",
"Mike",
""
]
] | A variety of algorithms have been proposed for reconstructing trees that show the evolutionary relationships between species by comparing differences in genetic data across present-day taxa. If the leaf-to-leaf distances in a tree can be accurately estimated, then it is possible to reconstruct this tree from these estimated distances, using polynomial-time methods such as the popular `Neighbor-Joining' algorithm. There is a precise combinatorial condition under which distance-based methods are guaranteed to return a correct tree (in full or in part) based on the requirement that the input distances all lie within some `safety radius' of the true distances. Here, we explore a stochastic analogue of this condition, and mathematically establish upper and lower bounds on this `stochastic safety radius' for distance-based tree reconstruction methods. Using simulations, we show how this notion provides a new way to compare the performance of distance-based tree reconstruction methods. This may help explain why Neighbor-Joining performs so well, as its stochastic safety radius appears close to optimal (while its more classical safety radius is the same as many other less accurate methods). |
0706.0185 | Xianghong Qi | Xianghong Qi and John J. Portman | Excluded volume, local structural cooperativity,and the polymer physics
of protein folding rates | 12 pages,6 figures,1 page supporting information.To be published in
Proc.Natl.Acad.Sci.(USA)(2007) | null | 10.1073/pnas.0609321104 | null | q-bio.BM physics.bio-ph physics.chem-ph | null | A coarse-grained variational model is used to investigate the polymer
dynamics of barrier crossing for a diverse set of two-state folding proteins.
The model gives reliable folding rate predictions provided excluded volume
terms that induce minor structural cooperativity are included in the
interaction potential. In general, the cooperative folding routes have sharper
interfaces between folded and unfolded regions of the folding nucleus and
higher free energy barriers. The calculated free energy barriers are strongly
correlated with native topology as characterized by contact order. Increasing
the rigidity of the folding nucleus changes the local structure of the
transition state ensemble non-uniformly across the set of protein studied.
Neverthless, the calculated prefactors k0 are found to be relatively uniform
across the protein set, with variation in 1/k0 less than a factor of five. This
direct calculation justifies the common assumption that the prefactor is
roughly the same for all small two-state folding proteins. Using the barrier
heights obtained from the model and the best fit monomer relaxation time 30ns,
we find that 1/k0 (1-5)us (with average 1/k0 4us). This model can be extended
to study subtle aspects of folding such as the variation of the folding rate
with stability or solvent viscosity, and the onset of downhill folding.
| [
{
"created": "Fri, 1 Jun 2007 16:17:49 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Qi",
"Xianghong",
""
],
[
"Portman",
"John J.",
""
]
] | A coarse-grained variational model is used to investigate the polymer dynamics of barrier crossing for a diverse set of two-state folding proteins. The model gives reliable folding rate predictions provided excluded volume terms that induce minor structural cooperativity are included in the interaction potential. In general, the cooperative folding routes have sharper interfaces between folded and unfolded regions of the folding nucleus and higher free energy barriers. The calculated free energy barriers are strongly correlated with native topology as characterized by contact order. Increasing the rigidity of the folding nucleus changes the local structure of the transition state ensemble non-uniformly across the set of protein studied. Neverthless, the calculated prefactors k0 are found to be relatively uniform across the protein set, with variation in 1/k0 less than a factor of five. This direct calculation justifies the common assumption that the prefactor is roughly the same for all small two-state folding proteins. Using the barrier heights obtained from the model and the best fit monomer relaxation time 30ns, we find that 1/k0 (1-5)us (with average 1/k0 4us). This model can be extended to study subtle aspects of folding such as the variation of the folding rate with stability or solvent viscosity, and the onset of downhill folding. |
2402.05953 | Vikash Prasad | Ji Hwan Park, Vikash Prasad, Sydney Newsom, Fares Najar, Rakhi Rajan | idMotif: An Interactive Motif Identification in Protein Sequences | IEEE CGA | idMotif: An Interactive Motif Identification in Protein
Sequences," in IEEE Computer Graphics and Applications, 2023 | 10.1109/MCG.2023.3345742 | null | q-bio.QM cs.GR cs.HC cs.LG | http://creativecommons.org/licenses/by/4.0/ | This article introduces idMotif, a visual analytics framework designed to aid
domain experts in the identification of motifs within protein sequences.
Motifs, short sequences of amino acids, are critical for understanding the
distinct functions of proteins. Identifying these motifs is pivotal for
predicting diseases or infections. idMotif employs a deep learning-based method
for the categorization of protein sequences, enabling the discovery of
potential motif candidates within protein groups through local explanations of
deep learning model decisions. It offers multiple interactive views for the
analysis of protein clusters or groups and their sequences. A case study,
complemented by expert feedback, illustrates idMotif's utility in facilitating
the analysis and identification of protein sequences and motifs.
| [
{
"created": "Sun, 4 Feb 2024 06:51:03 GMT",
"version": "v1"
}
] | 2024-02-12 | [
[
"Park",
"Ji Hwan",
""
],
[
"Prasad",
"Vikash",
""
],
[
"Newsom",
"Sydney",
""
],
[
"Najar",
"Fares",
""
],
[
"Rajan",
"Rakhi",
""
]
] | This article introduces idMotif, a visual analytics framework designed to aid domain experts in the identification of motifs within protein sequences. Motifs, short sequences of amino acids, are critical for understanding the distinct functions of proteins. Identifying these motifs is pivotal for predicting diseases or infections. idMotif employs a deep learning-based method for the categorization of protein sequences, enabling the discovery of potential motif candidates within protein groups through local explanations of deep learning model decisions. It offers multiple interactive views for the analysis of protein clusters or groups and their sequences. A case study, complemented by expert feedback, illustrates idMotif's utility in facilitating the analysis and identification of protein sequences and motifs. |
1703.10680 | Christopher Marcotte | Christopher D Marcotte, Roman O Grigoriev | Dynamical mechanism of atrial fibrillation: a topological approach | 15 pages, 14 figures | null | 10.1063/1.5003259 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While spiral wave breakup has been implicated in the emergence of atrial
fibrillation, its role in maintaining this complex type of cardiac arrhythmia
is less clear. We used the Karma model of cardiac excitation to investigate the
dynamical mechanisms that sustain atrial fibrillation once it has been
established. The results of our numerical study show that spatiotemporally
chaotic dynamics in this regime can be described as a dynamical equilibrium
between topologically distinct types of transitions that increase or decrease
the number of wavelets, in general agreement with the multiple wavelets
hypothesis. Surprisingly, we found that the process of continuous excitation
waves breaking up into discontinuous pieces plays no role whatsoever in
maintaining spatiotemporal complexity. Instead this complexity is maintained as
a dynamical balance between wave coalescence -- a unique, previously
unidentified, topological process that increases the number of wavelets -- and
wave collapse -- a different topological process that decreases their number.
| [
{
"created": "Fri, 24 Mar 2017 22:35:02 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Sep 2017 19:12:28 GMT",
"version": "v2"
}
] | 2017-10-11 | [
[
"Marcotte",
"Christopher D",
""
],
[
"Grigoriev",
"Roman O",
""
]
] | While spiral wave breakup has been implicated in the emergence of atrial fibrillation, its role in maintaining this complex type of cardiac arrhythmia is less clear. We used the Karma model of cardiac excitation to investigate the dynamical mechanisms that sustain atrial fibrillation once it has been established. The results of our numerical study show that spatiotemporally chaotic dynamics in this regime can be described as a dynamical equilibrium between topologically distinct types of transitions that increase or decrease the number of wavelets, in general agreement with the multiple wavelets hypothesis. Surprisingly, we found that the process of continuous excitation waves breaking up into discontinuous pieces plays no role whatsoever in maintaining spatiotemporal complexity. Instead this complexity is maintained as a dynamical balance between wave coalescence -- a unique, previously unidentified, topological process that increases the number of wavelets -- and wave collapse -- a different topological process that decreases their number. |
1501.05353 | Stephanie Dodson | Stephanie Dodson, Darrell O. Ricke, Jeremy Kepner, Nelson Chiu, and
Anna Shcherbina | Rapid Sequence Identification of Potential Pathogens Using Techniques
from Sparse Linear Algebra | null | null | 10.1109/THS.2015.7225316 | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The decreasing costs and increasing speed and accuracy of DNA sample
collection, preparation, and sequencing has rapidly produced an enormous volume
of genetic data. However, fast and accurate analysis of the samples remains a
bottleneck. Here we present D$^{4}$RAGenS, a genetic sequence identification
algorithm that exhibits the Big Data handling and computational power of the
Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear
algebra and statistical properties to increase computational performance while
retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield
speed and precision tradeoffs, with applications in biodefense and medical
diagnostics. The D$^{4}$RAGenS analysis algorithm is tested over several
datasets, including three utilized for the Defense Threat Reduction Agency
(DTRA) metagenomic algorithm contest.
| [
{
"created": "Wed, 21 Jan 2015 23:58:38 GMT",
"version": "v1"
}
] | 2017-04-13 | [
[
"Dodson",
"Stephanie",
""
],
[
"Ricke",
"Darrell O.",
""
],
[
"Kepner",
"Jeremy",
""
],
[
"Chiu",
"Nelson",
""
],
[
"Shcherbina",
"Anna",
""
]
] | The decreasing costs and increasing speed and accuracy of DNA sample collection, preparation, and sequencing has rapidly produced an enormous volume of genetic data. However, fast and accurate analysis of the samples remains a bottleneck. Here we present D$^{4}$RAGenS, a genetic sequence identification algorithm that exhibits the Big Data handling and computational power of the Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear algebra and statistical properties to increase computational performance while retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield speed and precision tradeoffs, with applications in biodefense and medical diagnostics. The D$^{4}$RAGenS analysis algorithm is tested over several datasets, including three utilized for the Defense Threat Reduction Agency (DTRA) metagenomic algorithm contest. |
2310.06881 | Damiano Piovesan | Damiano Piovesan, Davide Zago, Parnal Joshi, M. Clara De Paolis
Kaluza, Mahta Mehdiabadi, Rashika Ramola, Alexander Miguel Monzon, Walter
Reade, Iddo Friedberg, Predrag Radivojac, Silvio C.E. Tosatto | CAFA-evaluator: A Python Tool for Benchmarking Ontological
Classification Methods | 5 pages | null | 10.1093/bioadv/vbae043 | null | q-bio.QM cs.PF | http://creativecommons.org/licenses/by/4.0/ | We present CAFA-evaluator, a powerful Python program designed to evaluate the
performance of prediction methods on targets with hierarchical concept
dependencies. It generalizes multi-label evaluation to modern ontologies where
the prediction targets are drawn from a directed acyclic graph and achieves
high efficiency by leveraging matrix computation and topological sorting. The
program requirements include a small number of standard Python libraries,
making CAFA-evaluator easy to maintain. The code replicates the Critical
Assessment of protein Function Annotation (CAFA) benchmarking, which evaluates
predictions of the consistent subgraphs in Gene Ontology. Owing to its
reliability and accuracy, the organizers have selected CAFA-evaluator as the
official CAFA evaluation software.
| [
{
"created": "Tue, 10 Oct 2023 10:51:47 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Mar 2024 15:07:01 GMT",
"version": "v2"
}
] | 2024-03-13 | [
[
"Piovesan",
"Damiano",
""
],
[
"Zago",
"Davide",
""
],
[
"Joshi",
"Parnal",
""
],
[
"Kaluza",
"M. Clara De Paolis",
""
],
[
"Mehdiabadi",
"Mahta",
""
],
[
"Ramola",
"Rashika",
""
],
[
"Monzon",
"Alexander Miguel",
""
],
[
"Reade",
"Walter",
""
],
[
"Friedberg",
"Iddo",
""
],
[
"Radivojac",
"Predrag",
""
],
[
"Tosatto",
"Silvio C. E.",
""
]
] | We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes multi-label evaluation to modern ontologies where the prediction targets are drawn from a directed acyclic graph and achieves high efficiency by leveraging matrix computation and topological sorting. The program requirements include a small number of standard Python libraries, making CAFA-evaluator easy to maintain. The code replicates the Critical Assessment of protein Function Annotation (CAFA) benchmarking, which evaluates predictions of the consistent subgraphs in Gene Ontology. Owing to its reliability and accuracy, the organizers have selected CAFA-evaluator as the official CAFA evaluation software. |
1203.4269 | Evgeny Mavrodiev Dr | Evgeny V. Mavrodiev, Alexander Madorsky | TAXON version 1.1: A simple way to generate uniform and fractionally
weighted three-item matrices from various kinds of biological data | 4 pages, 1 figure, 1 Supplement, 3 Supplemental examples | null | 10.1371/journal.pone.0048813 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An open-access program allowing three-item statement matrices to be generated
from data such as molecular sequences does not exist so far. The recently
developed LisBeth package (ver. 1.0) allows representing hypotheses of homology
among taxa or areas directly as rooted trees or as hierarchies; however, this
is not a standard matrix-based platform. Here we present "TAXON version 1.1"
(TAXON), a program designed for building three-item statement-matrices from
binary, additive (ordered) and non-additive (unordered) multistate characters,
with both fractional and uniform weighting of the resulted statements.
| [
{
"created": "Mon, 19 Mar 2012 21:29:33 GMT",
"version": "v1"
}
] | 2015-06-04 | [
[
"Mavrodiev",
"Evgeny V.",
""
],
[
"Madorsky",
"Alexander",
""
]
] | An open-access program allowing three-item statement matrices to be generated from data such as molecular sequences does not exist so far. The recently developed LisBeth package (ver. 1.0) allows representing hypotheses of homology among taxa or areas directly as rooted trees or as hierarchies; however, this is not a standard matrix-based platform. Here we present "TAXON version 1.1" (TAXON), a program designed for building three-item statement-matrices from binary, additive (ordered) and non-additive (unordered) multistate characters, with both fractional and uniform weighting of the resulted statements. |
1707.05998 | Stefan Engblom | Stefan Engblom, Per L\"otstedt, Lina Meinecke | Mesoscopic Modeling of Random Walk and Reactions in Crowded Media | null | Phys. Rev. E 98, 033304 (2018) | 10.1103/PhysRevE.98.033304 | null | q-bio.SC math.NA physics.comp-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a mesoscopic modeling framework for diffusion in a crowded
environment, particularly targeting applications in the modeling of living
cells. Through homogenization techniques we effectively coarse-grain a detailed
microscopic description into a previously developed internal state diffusive
framework. The observables in the mesoscopic model correspond to solutions of
macroscopic partial differential equations driven by stochastically varying
diffusion fields in space and time. Analytical solutions and numerical
experiments illustrate the framework.
| [
{
"created": "Wed, 19 Jul 2017 09:55:56 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Mar 2018 14:44:55 GMT",
"version": "v2"
}
] | 2018-09-19 | [
[
"Engblom",
"Stefan",
""
],
[
"Lötstedt",
"Per",
""
],
[
"Meinecke",
"Lina",
""
]
] | We develop a mesoscopic modeling framework for diffusion in a crowded environment, particularly targeting applications in the modeling of living cells. Through homogenization techniques we effectively coarse-grain a detailed microscopic description into a previously developed internal state diffusive framework. The observables in the mesoscopic model correspond to solutions of macroscopic partial differential equations driven by stochastically varying diffusion fields in space and time. Analytical solutions and numerical experiments illustrate the framework. |
2204.03950 | Sophie De Buyl | Dorota Youmbi Fouego, Sophie de Buyl | On the robustness of the in vivo cyanobacterial circadian clock | null | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We propose a revisited version of the in vivo model of the cyanobacterial
circadian clock. Our aim is to address the lack of robustness predicted for the
mutant cyanobacteria without transcriptional regulation of the original model.
For this, we rely on an in vitro model of the clock describing explicitly the
hexameric structure of the core protein of the clock. Our model is able to
reproduce oscillatory behavior for the mutant, as observed experimentally,
without finely tuned parameters.
| [
{
"created": "Fri, 8 Apr 2022 09:17:48 GMT",
"version": "v1"
}
] | 2022-04-11 | [
[
"Fouego",
"Dorota Youmbi",
""
],
[
"de Buyl",
"Sophie",
""
]
] | We propose a revisited version of the in vivo model of the cyanobacterial circadian clock. Our aim is to address the lack of robustness predicted for the mutant cyanobacteria without transcriptional regulation of the original model. For this, we rely on an in vitro model of the clock describing explicitly the hexameric structure of the core protein of the clock. Our model is able to reproduce oscillatory behavior for the mutant, as observed experimentally, without finely tuned parameters. |
1912.04861 | Anastasia Ignatieva | Anastasia Ignatieva, Jotun Hein, Paul A. Jenkins | A characterisation of the reconstructed birth-death process through time
rescaling | 32 pages, 5 figures | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The dynamics of a population exhibiting exponential growth can be modelled as
a birth-death process, which naturally captures the stochastic variation in
population size over time. In this article, we consider a supercritical
birth-death process, started at a random time in the past, and conditioned to
have n sampled individuals at the present. The genealogy of individuals sampled
at the present time is then described by the reversed reconstructed process
(RRP), which traces the ancestry of the sample backwards from the present. We
show that a simple, analytic, time rescaling of the RRP provides a
straightforward way to derive its inter-event times. The same rescaling
characterises other distributions underlying this process, obtained elsewhere
in the literature via more cumbersome calculations. We also consider the case
of incomplete sampling of the population, in which each leaf of the genealogy
is retained with an independent Bernoulli trial with probability $\psi$, and we
show that corresponding results for Bernoulli-sampled RRPs can be derived using
time rescaling, for any values of the underlying parameters. A central result
is the derivation of a scaling limit as $\psi$ approaches 0, corresponding to
the underlying population growing to infinity, using the time rescaling
formalism. We show that in this setting, after a linear time rescaling, the
event times are the order statistics of $n$ logistic random variables with mode
$\log(1/\psi)$; moreover, we show that the inter-event times are approximately
exponentially distributed.
| [
{
"created": "Tue, 10 Dec 2019 18:03:49 GMT",
"version": "v1"
},
{
"created": "Wed, 6 May 2020 14:32:06 GMT",
"version": "v2"
}
] | 2020-05-07 | [
[
"Ignatieva",
"Anastasia",
""
],
[
"Hein",
"Jotun",
""
],
[
"Jenkins",
"Paul A.",
""
]
] | The dynamics of a population exhibiting exponential growth can be modelled as a birth-death process, which naturally captures the stochastic variation in population size over time. In this article, we consider a supercritical birth-death process, started at a random time in the past, and conditioned to have n sampled individuals at the present. The genealogy of individuals sampled at the present time is then described by the reversed reconstructed process (RRP), which traces the ancestry of the sample backwards from the present. We show that a simple, analytic, time rescaling of the RRP provides a straightforward way to derive its inter-event times. The same rescaling characterises other distributions underlying this process, obtained elsewhere in the literature via more cumbersome calculations. We also consider the case of incomplete sampling of the population, in which each leaf of the genealogy is retained with an independent Bernoulli trial with probability $\psi$, and we show that corresponding results for Bernoulli-sampled RRPs can be derived using time rescaling, for any values of the underlying parameters. A central result is the derivation of a scaling limit as $\psi$ approaches 0, corresponding to the underlying population growing to infinity, using the time rescaling formalism. We show that in this setting, after a linear time rescaling, the event times are the order statistics of $n$ logistic random variables with mode $\log(1/\psi)$; moreover, we show that the inter-event times are approximately exponentially distributed. |
1606.08874 | William Jacobs | William M. Jacobs and Eugene I. Shakhnovich | Structure-based prediction of protein-folding transition paths | null | null | 10.1016/j.bpj.2016.06.031 | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a general theory to describe the distribution of protein-folding
transition paths. We show that transition paths follow a predictable sequence
of high-free-energy transient states that are separated by free-energy
barriers. Each transient state corresponds to the assembly of one or more
discrete, cooperative units, which are determined directly from the native
structure. We show that the transition state on a folding pathway is reached
when a small number of critical contacts are formed between a specific set of
substructures, after which folding proceeds downhill in free energy. This
approach suggests a natural resolution for distinguishing parallel folding
pathways and provides a simple means to predict the rate-limiting step in a
folding reaction. Our theory identifies a common folding mechanism for proteins
with diverse native structures and establishes general principles for the
self-assembly of polymers with specific interactions.
| [
{
"created": "Tue, 28 Jun 2016 20:25:32 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Jul 2016 19:52:24 GMT",
"version": "v2"
}
] | 2016-09-21 | [
[
"Jacobs",
"William M.",
""
],
[
"Shakhnovich",
"Eugene I.",
""
]
] | We propose a general theory to describe the distribution of protein-folding transition paths. We show that transition paths follow a predictable sequence of high-free-energy transient states that are separated by free-energy barriers. Each transient state corresponds to the assembly of one or more discrete, cooperative units, which are determined directly from the native structure. We show that the transition state on a folding pathway is reached when a small number of critical contacts are formed between a specific set of substructures, after which folding proceeds downhill in free energy. This approach suggests a natural resolution for distinguishing parallel folding pathways and provides a simple means to predict the rate-limiting step in a folding reaction. Our theory identifies a common folding mechanism for proteins with diverse native structures and establishes general principles for the self-assembly of polymers with specific interactions. |
2011.03518 | Prateeth Nayak | Prateeth Nayak, Andrew Silberfarb, Ran Chen, Tulay Muezzinoglu, John
Byrnes | Transformer Based Molecule Encoding for Property Prediction | Machine Learning for Molecules Workshop, NeurIPs2020 | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural methods of molecule property prediction require efficient encoding of
structure and property relationship to be accurate. Recent work using graph
algorithms shows limited generalization in the latent molecule encoding space.
We build a Transformer-based molecule encoder and property predictor network
with novel input featurization that performs significantly better than existing
methods. We adapt our model to semi-supervised learning to further perform well
on the limited experimental data usually available in practice.
| [
{
"created": "Thu, 5 Nov 2020 01:41:50 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Nov 2020 18:08:30 GMT",
"version": "v2"
}
] | 2020-11-26 | [
[
"Nayak",
"Prateeth",
""
],
[
"Silberfarb",
"Andrew",
""
],
[
"Chen",
"Ran",
""
],
[
"Muezzinoglu",
"Tulay",
""
],
[
"Byrnes",
"John",
""
]
] | Neural methods of molecule property prediction require efficient encoding of structure and property relationship to be accurate. Recent work using graph algorithms shows limited generalization in the latent molecule encoding space. We build a Transformer-based molecule encoder and property predictor network with novel input featurization that performs significantly better than existing methods. We adapt our model to semi-supervised learning to further perform well on the limited experimental data usually available in practice. |
2205.02291 | Kameron Harris | Seth Daetwiler and Angus Read and Jessica Stillwell and Kameron Decker
Harris | BrainViewer: interacting with spatial connectome data at the mesoscale | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Scientists construct connectomes, comprehensive descriptions of neuronal
connections across a brain, in order to better understand and model brain
function. Interactive visualizations of these pathways would enable exploratory
analysis of such information flows. Current tools can be used to see individual
tracing experiments which are used to build mesoscale connectomes of the mouse
brain, but not the brain network itself. We present a connectivity
visualization program called BrainViewer, which we use with a high-resolution
mouse cortical connectome. This has the ability to display connectomes from
other datasets when they become available and compare spatial connectivity
across multiple brain structures. Our tool, optimized for speed and
portability, presents a GUI visualization in 2-D top view and flatmap
projections, allowing users to select and explore the connections of every
source voxel to everywhere else in the cortex. Anatomists and other
neuroscientists will find BrainViewer useful for building understanding beyond
the known topography of cortical connectivity.
| [
{
"created": "Wed, 4 May 2022 19:05:24 GMT",
"version": "v1"
}
] | 2022-05-06 | [
[
"Daetwiler",
"Seth",
""
],
[
"Read",
"Angus",
""
],
[
"Stillwell",
"Jessica",
""
],
[
"Harris",
"Kameron Decker",
""
]
] | Scientists construct connectomes, comprehensive descriptions of neuronal connections across a brain, in order to better understand and model brain function. Interactive visualizations of these pathways would enable exploratory analysis of such information flows. Current tools can be used to see individual tracing experiments which are used to build mesoscale connectomes of the mouse brain, but not the brain network itself. We present a connectivity visualization program called BrainViewer, which we use with a high-resolution mouse cortical connectome. This has the ability to display connectomes from other datasets when they become available and compare spatial connectivity across multiple brain structures. Our tool, optimized for speed and portability, presents a GUI visualization in 2-D top view and flatmap projections, allowing users to select and explore the connections of every source voxel to everywhere else in the cortex. Anatomists and other neuroscientists will find BrainViewer useful for building understanding beyond the known topography of cortical connectivity. |
1701.05607 | Jorge Vel\'azquez-Castro PhD | Emilene Pliego Pliego, Jorge Vel\'azquez-Castro, Markus P. Eichhorn,
Andr\'es Fraguela Collar | Increased Efficiency in the Second-Hand Tire Trade Provides Opportunity
for Dengue Control | 24 pages, 7 figures | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dengue fever is increasing in geographical range, spread by invasion of its
vector mosquitoes. The trade in second-hand tires has been implicated as a
factor in this process as they act as mobile reservoirs of mosquito eggs and
larvae. Regional transportation of tires can create linkages between rural
areas with dengue to disease-free urban areas, potentially giving rise to
outbreaks even in areas with strong local control measures. In this work we
sought to model the dynamics of mosquito transportation via the tire trade, in
particular to predict its role in causing unexpected dengue outbreaks through
vertical transmission of the virus across generations of mosquitoes. We also
aimed to identify strategies for regulating the trade in second-hand tires,
improving disease control. We created a mathematical model which captures the
dynamics of dengue between rural and urban areas, taking into account the
movement, storage time of tires, and mosquito diapause. We simulate a series of
scenarios. First a mosquito population is introduced to a dengue-free area via
movement of tires, either as single or multiple events, increasing the
likelihood of a dengue outbreak. An endemic state can be induced regardless of
whether urban conditions for an outbreak are met, and an existing endemic state
can be enhanced by vector input. Finally we assess the potential for regulation
of tire processing as a means of reducing the transmission of dengue fever
using a specific case study from Puerto Rico. Our work demonstrates the
importance of the second-hand tire trade in modulating the spread of dengue
fever across regions, in particular its role in introducing dengue to
disease-free areas. We propose that regulation of tire storage and movement can
play a crucial role in containing outbreaks and dengue spread.
| [
{
"created": "Thu, 19 Jan 2017 21:23:19 GMT",
"version": "v1"
}
] | 2017-01-23 | [
[
"Pliego",
"Emilene Pliego",
""
],
[
"Velázquez-Castro",
"Jorge",
""
],
[
"Eichhorn",
"Markus P.",
""
],
[
"Collar",
"Andrés Fraguela",
""
]
] | Dengue fever is increasing in geographical range, spread by invasion of its vector mosquitoes. The trade in second-hand tires has been implicated as a factor in this process as they act as mobile reservoirs of mosquito eggs and larvae. Regional transportation of tires can create linkages between rural areas with dengue to disease-free urban areas, potentially giving rise to outbreaks even in areas with strong local control measures. In this work we sought to model the dynamics of mosquito transportation via the tire trade, in particular to predict its role in causing unexpected dengue outbreaks through vertical transmission of the virus across generations of mosquitoes. We also aimed to identify strategies for regulating the trade in second-hand tires, improving disease control. We created a mathematical model which captures the dynamics of dengue between rural and urban areas, taking into account the movement, storage time of tires, and mosquito diapause. We simulate a series of scenarios. First a mosquito population is introduced to a dengue-free area via movement of tires, either as single or multiple events, increasing the likelihood of a dengue outbreak. An endemic state can be induced regardless of whether urban conditions for an outbreak are met, and an existing endemic state can be enhanced by vector input. Finally we assess the potential for regulation of tire processing as a means of reducing the transmission of dengue fever using a specific case study from Puerto Rico. Our work demonstrates the importance of the second-hand tire trade in modulating the spread of dengue fever across regions, in particular its role in introducing dengue to disease-free areas. We propose that regulation of tire storage and movement can play a crucial role in containing outbreaks and dengue spread. |
1906.08962 | Roeland M.H. Merks | Elisabeth G. Rens and Roeland M.H. Merks | Cell Shape and Durotaxis Follow from Mechanical Cell-Substrate
Reciprocity and Focal Adhesion Dynamics: A Unifying Mathematical Model | 31 pages, 5 figures, 8 supplementary figures | null | null | null | q-bio.CB cond-mat.soft math.DS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many animal cells change their shape depending on the stiffness of the
substrate on which they are cultured: they assume small, rounded shapes in soft
ECMs, they elongate within stiffer ECMs, and flatten out on hard substrates.
Cells tend to prefer stiffer parts of the substrate, a phenomenon known as
durotaxis. Such mechanosensitive responses to ECM mechanics are key to
understanding the regulation of biological tissues by mechanical cues, as it
occurs, e.g., during angiogenesis and the alignment of cells in muscles and
tendons. Although it is well established that the mechanical cell-ECM
interactions are mediated by focal adhesions, the mechanosensitive molecular
complexes linking the cytoskeleton to the substrate, it is poorly understood
how the stiffness-dependent kinetics of the focal adhesions eventually produce
the observed interdependence of substrate stiffness and cell shape and cell
behavior. Here we show that the mechanosensitive behavior of single-focal
adhesions, cell contractility and substrate adhesivity together suffice to
explain the observed stiffness-dependent behavior of cells. We introduce a
multiscale computational model that is based upon the following assumptions:
(1) cells apply forces onto the substrate through FAs; (2) the FAs grow and
stabilize due to these forces; (3) within a given time-interval, the force that
the FAs experience is lower on soft substrates than on stiffer substrates due
to the time it takes to reach mechanical equilibrium; and (4) smaller FAs are
pulled from the substrate more easily than larger FAs. Our model combines the
cellular Potts model for the cells with a finite-element model for the
substrate, and describes each FA using differential equations. Together these
assumptions provide a unifying model for cell spreading, cell elongation and
durotaxis in response to substrate mechanics.
| [
{
"created": "Fri, 21 Jun 2019 06:09:00 GMT",
"version": "v1"
}
] | 2019-06-24 | [
[
"Rens",
"Elisabeth G.",
""
],
[
"Merks",
"Roeland M. H.",
""
]
] | Many animal cells change their shape depending on the stiffness of the substrate on which they are cultured: they assume small, rounded shapes in soft ECMs, they elongate within stiffer ECMs, and flatten out on hard substrates. Cells tend to prefer stiffer parts of the substrate, a phenomenon known as durotaxis. Such mechanosensitive responses to ECM mechanics are key to understanding the regulation of biological tissues by mechanical cues, as it occurs, e.g., during angiogenesis and the alignment of cells in muscles and tendons. Although it is well established that the mechanical cell-ECM interactions are mediated by focal adhesions, the mechanosensitive molecular complexes linking the cytoskeleton to the substrate, it is poorly understood how the stiffness-dependent kinetics of the focal adhesions eventually produce the observed interdependence of substrate stiffness and cell shape and cell behavior. Here we show that the mechanosensitive behavior of single-focal adhesions, cell contractility and substrate adhesivity together suffice to explain the observed stiffness-dependent behavior of cells. We introduce a multiscale computational model that is based upon the following assumptions: (1) cells apply forces onto the substrate through FAs; (2) the FAs grow and stabilize due to these forces; (3) within a given time-interval, the force that the FAs experience is lower on soft substrates than on stiffer substrates due to the time it takes to reach mechanical equilibrium; and (4) smaller FAs are pulled from the substrate more easily than larger FAs. Our model combines the cellular Potts model for the cells with a finite-element model for the substrate, and describes each FA using differential equations. Together these assumptions provide a unifying model for cell spreading, cell elongation and durotaxis in response to substrate mechanics. |
2007.03283 | Alan R. Champneys | Arkady Wey, Alan Champneys, Rosemary J Dyson, Nisreen A Alwan, Mary
Barker | The benefits of peer transparency in safe workplace operation post
pandemic lockdown | null | null | null | null | q-bio.PE math.DS physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The benefits, both in terms of productivity and public health, are
investigated for different levels of engagement with the test, trace and
isolate procedures in the context of a pandemic in which there is little or no
herd immunity. Simple mathematical modelling is used in the context of a
single, relatively closed workplace such as a factory or back-office where, in
normal operation, each worker has lengthy interactions with a fixed set of
colleagues. A discrete-time SEIR model on a fixed interaction graph is
simulated with parameters that are motivated by the recent COVID-19 pandemic in
the UK during a post-peak phase, including a small risk of viral infection from
outside the working environment. Two kinds of worker are assumed, transparents
who regularly test, share their results with colleagues and isolate as soon as
a contact tests positive for the disease, and opaques who do none of these.
Moreover, the simulations are constructed as a ``playable model" in which the
transparency level, disease parameters and mean interaction degree can be
varied by the user. The model is analysed in the continuum limit. All
simulations point to the double benefit of transparency in maximising
productivity and minimising overall infection rates. Based on these findings,
public policy implications are discussed on how to incentivise this mutually
beneficial behaviour in different kinds of workplace, and simple
recommendations are made.
| [
{
"created": "Tue, 7 Jul 2020 08:55:33 GMT",
"version": "v1"
}
] | 2020-07-08 | [
[
"Wey",
"Arkady",
""
],
[
"Champneys",
"Alan",
""
],
[
"Dyson",
"Rosemary J",
""
],
[
"Alwan",
"Nisreen A",
""
],
[
"Barker",
"Mary",
""
]
] | The benefits, both in terms of productivity and public health, are investigated for different levels of engagement with the test, trace and isolate procedures in the context of a pandemic in which there is little or no herd immunity. Simple mathematical modelling is used in the context of a single, relatively closed workplace such as a factory or back-office where, in normal operation, each worker has lengthy interactions with a fixed set of colleagues. A discrete-time SEIR model on a fixed interaction graph is simulated with parameters that are motivated by the recent COVID-19 pandemic in the UK during a post-peak phase, including a small risk of viral infection from outside the working environment. Two kinds of worker are assumed, transparents who regularly test, share their results with colleagues and isolate as soon as a contact tests positive for the disease, and opaques who do none of these. Moreover, the simulations are constructed as a ``playable model" in which the transparency level, disease parameters and mean interaction degree can be varied by the user. The model is analysed in the continuum limit. All simulations point to the double benefit of transparency in maximising productivity and minimising overall infection rates. Based on these findings, public policy implications are discussed on how to incentivise this mutually beneficial behaviour in different kinds of workplace, and simple recommendations are made. |
1902.06352 | Rosa Martinez-Corral | Rosa Martinez-Corral, Jintao Liu, Arthur Prindle, Gurol M. Suel and
Jordi Garcia-Ojalvo | Metabolic basis of brain-like electrical signalling in bacterial
communities | null | null | null | null | q-bio.NC q-bio.CB | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Information processing in the mammalian brain relies on a careful regulation
of the membrane potential dynamics of its constituent neurons, which propagates
across the neuronal tissue via electrical signalling. We recently reported the
existence of electrical signalling in a much simpler organism, the bacterium
Bacillus subtilis. In dense bacterial communities known as biofilms,
nutrient-deprived B. subtilis cells in the interior of the colony use
electrical communication to transmit stress signals to the periphery, which
interfere with the growth of peripheral cells and reduce nutrient consumption,
thereby relieving stress from the interior. Here we explicitly address the
interplay between metabolism and electrophysiology in bacterial biofilms, by
introducing a spatially-extended mathematical model that combines the metabolic
and electrical components of the phenomenon in a discretised reaction-diffusion
scheme. The model is experimentally validated by environmental and genetic
perturbations, and confirms that metabolic stress is transmitted through the
bacterial population via a potassium wave. Interestingly, this behaviour is
reminiscent of cortical spreading depression in the brain, characterised by a
wave of electrical activity mediated by potassium diffusion that has been
linked to various neurological disorders, calling for future studies on the
evolutionary link between the two phenomena.
| [
{
"created": "Sun, 17 Feb 2019 23:47:34 GMT",
"version": "v1"
}
] | 2019-02-19 | [
[
"Martinez-Corral",
"Rosa",
""
],
[
"Liu",
"Jintao",
""
],
[
"Prindle",
"Arthur",
""
],
[
"Suel",
"Gurol M.",
""
],
[
"Garcia-Ojalvo",
"Jordi",
""
]
] | Information processing in the mammalian brain relies on a careful regulation of the membrane potential dynamics of its constituent neurons, which propagates across the neuronal tissue via electrical signalling. We recently reported the existence of electrical signalling in a much simpler organism, the bacterium Bacillus subtilis. In dense bacterial communities known as biofilms, nutrient-deprived B. subtilis cells in the interior of the colony use electrical communication to transmit stress signals to the periphery, which interfere with the growth of peripheral cells and reduce nutrient consumption, thereby relieving stress from the interior. Here we explicitly address the interplay between metabolism and electrophysiology in bacterial biofilms, by introducing a spatially-extended mathematical model that combines the metabolic and electrical components of the phenomenon in a discretised reaction-diffusion scheme. The model is experimentally validated by environmental and genetic perturbations, and confirms that metabolic stress is transmitted through the bacterial population via a potassium wave. Interestingly, this behaviour is reminiscent of cortical spreading depression in the brain, characterised by a wave of electrical activity mediated by potassium diffusion that has been linked to various neurological disorders, calling for future studies on the evolutionary link between the two phenomena. |
1603.06401 | Souparno Roy | Souparno Roy, Ranjan Sengupta, Tarit Guhathakurata, Dipak Ghosh | Non-classicality in mental states : an experimental study with ambiguous
audio (music) stimuli | Conference submission;comments are welcome | null | null | null | q-bio.NC math.PR quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper attempts to address the question that whether the present physical
or mathematical theories are sufficient to understand the complexities of human
brain when it interacts with the external environment in the form of an
auditory stimulus.There have been efforts reporting that the introduction of
ambiguity in visual stimuli causes effects which can't be explained
classically.In this paper,it is investigated whether ambiguity in auditory
stimuli can introduce any non-classical effects in human brain.Simple
experiments were performed on normal subjects where they listened to an
ambiguous auditory signal and responded to a question with 'yes' or 'no'.The
outcome of the test showed that the classical formula of total probability does
not hold true in this case.Results were interesting and indicate that there is
a definite non-classicality in mental states in perception of ambiguous audio
stimuli.
| [
{
"created": "Fri, 18 Mar 2016 18:56:33 GMT",
"version": "v1"
}
] | 2016-03-22 | [
[
"Roy",
"Souparno",
""
],
[
"Sengupta",
"Ranjan",
""
],
[
"Guhathakurata",
"Tarit",
""
],
[
"Ghosh",
"Dipak",
""
]
] | This paper attempts to address the question that whether the present physical or mathematical theories are sufficient to understand the complexities of human brain when it interacts with the external environment in the form of an auditory stimulus.There have been efforts reporting that the introduction of ambiguity in visual stimuli causes effects which can't be explained classically.In this paper,it is investigated whether ambiguity in auditory stimuli can introduce any non-classical effects in human brain.Simple experiments were performed on normal subjects where they listened to an ambiguous auditory signal and responded to a question with 'yes' or 'no'.The outcome of the test showed that the classical formula of total probability does not hold true in this case.Results were interesting and indicate that there is a definite non-classicality in mental states in perception of ambiguous audio stimuli. |
2312.08120 | Peter S{\o}rensen | Peter Borgen S{\o}rensen, Anders Nielsen | Statistical model concept to quantify input and output of water,
nitrogen and phosphorus for lakes with partly gauged watersheds | 23 pages, 11 figures, 5 tables | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Valid mass load predictions of nutrients, in particular nitrogen (N) and
phosphorus (P), are needed for the limnological understanding of single lake
ecosystems as well as larger river/lake ecosystems. The mass of N and P that
enters a lake will determine the ecological state of the lake, and the mass
release from the lake will determine the ecological state of downstream
ecosystems. Hence, establishing sound quantifications of the external load is
crucial and e.g. contributes to the foundation of assessments of necessary
management interventions to improve or preserve the ecological integrity of
lakes. The external load of N and P is an integral of several pathways, each
having different contributions to the total mass load. Around the world,
balances of N and P have been derived for decades to support both lake water
quality monitoring and research, but it can be difficult and, thus, costly to
make detailed and sufficiently covering measurement campaigns in all
tributaries (surface as well as groundwater) in the watershed of the N and P
load including seasonality and temporal change from year to year. Thus, load
prediction is facing challenge of uncertainty due to unmeasured loads, which
can be a consequence of limited resources available for the water flow
recordings and water concentration measurements in inlets around the lake, or
simply due to invisible water flow taking place through the lake bottom. The
lake outlet will typically take place in one single river, so the outlet
recording seems easier to measure than inlets, however, the outlet may also
have unmeasured parts in cases where water is leaching out though the lake
bottom. In this paper, we propose a method that applies incomplete data sets
(incomplete in the sense of temporal frequency and percentage of gauged
watershed) to generate time series that predict the N and P loads entering and
leaving the lake.
| [
{
"created": "Wed, 13 Dec 2023 13:20:29 GMT",
"version": "v1"
}
] | 2023-12-14 | [
[
"Sørensen",
"Peter Borgen",
""
],
[
"Nielsen",
"Anders",
""
]
] | Valid mass load predictions of nutrients, in particular nitrogen (N) and phosphorus (P), are needed for the limnological understanding of single lake ecosystems as well as larger river/lake ecosystems. The mass of N and P that enters a lake will determine the ecological state of the lake, and the mass release from the lake will determine the ecological state of downstream ecosystems. Hence, establishing sound quantifications of the external load is crucial and e.g. contributes to the foundation of assessments of necessary management interventions to improve or preserve the ecological integrity of lakes. The external load of N and P is an integral of several pathways, each having different contributions to the total mass load. Around the world, balances of N and P have been derived for decades to support both lake water quality monitoring and research, but it can be difficult and, thus, costly to make detailed and sufficiently covering measurement campaigns in all tributaries (surface as well as groundwater) in the watershed of the N and P load including seasonality and temporal change from year to year. Thus, load prediction is facing challenge of uncertainty due to unmeasured loads, which can be a consequence of limited resources available for the water flow recordings and water concentration measurements in inlets around the lake, or simply due to invisible water flow taking place through the lake bottom. The lake outlet will typically take place in one single river, so the outlet recording seems easier to measure than inlets, however, the outlet may also have unmeasured parts in cases where water is leaching out though the lake bottom. In this paper, we propose a method that applies incomplete data sets (incomplete in the sense of temporal frequency and percentage of gauged watershed) to generate time series that predict the N and P loads entering and leaving the lake. |
1912.07354 | Yun Liu | Ellery Wulczyn, David F. Steiner, Zhaoyang Xu, Apaar Sadhwani, Hongwu
Wang, Isabelle Flament, Craig H. Mermel, Po-Hsuan Cameron Chen, Yun Liu,
Martin C. Stumpe | Deep learning-based survival prediction for multiple cancer types using
histopathology images | null | PLOS ONE (2020) | 10.1371/journal.pone.0233678 | null | q-bio.QM cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Prognostic information at diagnosis has important implications for cancer
treatment and monitoring. Although cancer staging, histopathological
assessment, molecular features, and clinical variables can provide useful
prognostic insights, improving risk stratification remains an active research
area. We developed a deep learning system (DLS) to predict disease specific
survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a
weakly-supervised approach without pixel-level annotations, and tested three
different survival loss functions. The DLS was developed using 9,086 slides
from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In
multivariable Cox regression analysis of the combined cohort including all 10
cancers, the DLS was significantly associated with disease specific survival
(hazard ratio of 1.58, 95% CI 1.28-1.70, p<0.0001) after adjusting for cancer
type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS
remained a significant predictor of survival in 5 of 10 cancer types. Compared
to a baseline model including stage, age, and sex, the c-index of the model
demonstrated an absolute 3.7% improvement (95% CI 1.0-6.5) in the combined
cohort. Additionally, our models stratified patients within individual cancer
stages, particularly stage II (p=0.025) and stage III (p<0.001). By developing
and evaluating prognostic models across multiple cancer types, this work
represents one of the most comprehensive studies exploring the direct
prediction of clinical outcomes using deep learning and histopathology images.
Our analysis demonstrates the potential for this approach to provide prognostic
information in multiple cancer types, and even within specific pathologic
stages. However, given the relatively small number of clinical events, we
observed wide confidence intervals, suggesting that future work will benefit
from larger datasets.
| [
{
"created": "Mon, 16 Dec 2019 13:47:36 GMT",
"version": "v1"
}
] | 2020-06-19 | [
[
"Wulczyn",
"Ellery",
""
],
[
"Steiner",
"David F.",
""
],
[
"Xu",
"Zhaoyang",
""
],
[
"Sadhwani",
"Apaar",
""
],
[
"Wang",
"Hongwu",
""
],
[
"Flament",
"Isabelle",
""
],
[
"Mermel",
"Craig H.",
""
],
[
"Chen",
"Po-Hsuan Cameron",
""
],
[
"Liu",
"Yun",
""
],
[
"Stumpe",
"Martin C.",
""
]
] | Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slides from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In multivariable Cox regression analysis of the combined cohort including all 10 cancers, the DLS was significantly associated with disease specific survival (hazard ratio of 1.58, 95% CI 1.28-1.70, p<0.0001) after adjusting for cancer type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS remained a significant predictor of survival in 5 of 10 cancer types. Compared to a baseline model including stage, age, and sex, the c-index of the model demonstrated an absolute 3.7% improvement (95% CI 1.0-6.5) in the combined cohort. Additionally, our models stratified patients within individual cancer stages, particularly stage II (p=0.025) and stage III (p<0.001). By developing and evaluating prognostic models across multiple cancer types, this work represents one of the most comprehensive studies exploring the direct prediction of clinical outcomes using deep learning and histopathology images. Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages. However, given the relatively small number of clinical events, we observed wide confidence intervals, suggesting that future work will benefit from larger datasets. |
1009.0456 | Luis G. Morelli | Leah Herrgen, Saul Ares, Luis G. Morelli, Christian Schroeter, Frank
Julicher and Andrew C. Oates | Intercellular Coupling Regulates the Period of the Segmentation Clock | 13 pages, 6 figures, 38 pages of supplemental information | Current Biology 20, 1244-1253 (2010) | 10.1016/j.cub.2010.06.034 | null | q-bio.TO nlin.AO nlin.PS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Coupled biological oscillators can tick with the same period. How this
collective period is established is a key question in understanding biological
clocks. We explore this question in the segmentation clock, a population of
coupled cellular oscillators in the vertebrate embryo that sets the rhythm of
somitogenesis, the morphological segmentation of the body axis. The oscillating
cells of the zebrafish segmentation clock are thought to possess noisy
autonomous periods, which are synchronized by intercellular coupling through
the Delta-Notch pathway. Here we ask whether Delta-Notch coupling additionally
influences the collective period of the segmentation clock. Using
multiple-embryo time-lapse microscopy, we show that disruption of Delta-Notch
intercellular coupling increases the period of zebrafish somitogenesis.
Embryonic segment length and the spatial wavelength of oscillating gene
expression also increase correspondingly, indicating an increase in the
segmentation clock's period. Using a theory based on phase oscillators in which
the collective period self-organizes because of time delays in coupling, we
estimate the cell-autonomous period, the coupling strength, and the coupling
delay from our data. Further supporting the role of coupling delays in the
clock, we predict and experimentally confirm an instability resulting from
decreased coupling delay time. Synchronization of cells by Delta-Notch coupling
regulates the collective period of the segmentation clock. Our identification
of the first segmentation clock period mutants is a critical step toward a
molecular understanding of temporal control in this system. We propose that
collective control of period via delayed coupling may be a general feature of
biological clocks.
| [
{
"created": "Thu, 2 Sep 2010 15:56:34 GMT",
"version": "v1"
}
] | 2010-09-03 | [
[
"Herrgen",
"Leah",
""
],
[
"Ares",
"Saul",
""
],
[
"Morelli",
"Luis G.",
""
],
[
"Schroeter",
"Christian",
""
],
[
"Julicher",
"Frank",
""
],
[
"Oates",
"Andrew C.",
""
]
] | Coupled biological oscillators can tick with the same period. How this collective period is established is a key question in understanding biological clocks. We explore this question in the segmentation clock, a population of coupled cellular oscillators in the vertebrate embryo that sets the rhythm of somitogenesis, the morphological segmentation of the body axis. The oscillating cells of the zebrafish segmentation clock are thought to possess noisy autonomous periods, which are synchronized by intercellular coupling through the Delta-Notch pathway. Here we ask whether Delta-Notch coupling additionally influences the collective period of the segmentation clock. Using multiple-embryo time-lapse microscopy, we show that disruption of Delta-Notch intercellular coupling increases the period of zebrafish somitogenesis. Embryonic segment length and the spatial wavelength of oscillating gene expression also increase correspondingly, indicating an increase in the segmentation clock's period. Using a theory based on phase oscillators in which the collective period self-organizes because of time delays in coupling, we estimate the cell-autonomous period, the coupling strength, and the coupling delay from our data. Further supporting the role of coupling delays in the clock, we predict and experimentally confirm an instability resulting from decreased coupling delay time. Synchronization of cells by Delta-Notch coupling regulates the collective period of the segmentation clock. Our identification of the first segmentation clock period mutants is a critical step toward a molecular understanding of temporal control in this system. We propose that collective control of period via delayed coupling may be a general feature of biological clocks. |
0708.0594 | Ila Fiete | Yoram Burak and Ila R. Fiete | Do we understand the emergent dynamics of grid cell activity? | null | J. Neurosci. 26(37) pp. 9352-4 (2006) | null | null | q-bio.NC q-bio.TO | null | We examine the qualitative and quantitative properties of continuous
attractor networks in explaining the dynamics of grid cells.
| [
{
"created": "Sat, 4 Aug 2007 00:34:47 GMT",
"version": "v1"
}
] | 2007-08-07 | [
[
"Burak",
"Yoram",
""
],
[
"Fiete",
"Ila R.",
""
]
] | We examine the qualitative and quantitative properties of continuous attractor networks in explaining the dynamics of grid cells. |
2403.03274 | James Lu | Samira Pakravan, Nikolaos Evangelou, Maxime Usdin, Logan Brooks and
James Lu | From Noise to Signal: Unveiling Treatment Effects from Digital Health
Data through Pharmacology-Informed Neural-SDE | 6 figures | null | null | null | q-bio.QM cs.AI cs.LG math.DS | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Digital health technologies (DHT), such as wearable devices, provide
personalized, continuous, and real-time monitoring of patient. These
technologies are contributing to the development of novel therapies and
personalized medicine. Gaining insight from these technologies requires
appropriate modeling techniques to capture clinically-relevant changes in
disease state. The data generated from these devices is characterized by being
stochastic in nature, may have missing elements, and exhibits considerable
inter-individual variability - thereby making it difficult to analyze using
traditional longitudinal modeling techniques. We present a novel
pharmacology-informed neural stochastic differential equation (SDE) model
capable of addressing these challenges. Using synthetic data, we demonstrate
that our approach is effective in identifying treatment effects and learning
causal relationships from stochastic data, thereby enabling counterfactual
simulation.
| [
{
"created": "Tue, 5 Mar 2024 19:13:57 GMT",
"version": "v1"
}
] | 2024-03-07 | [
[
"Pakravan",
"Samira",
""
],
[
"Evangelou",
"Nikolaos",
""
],
[
"Usdin",
"Maxime",
""
],
[
"Brooks",
"Logan",
""
],
[
"Lu",
"James",
""
]
] | Digital health technologies (DHT), such as wearable devices, provide personalized, continuous, and real-time monitoring of patient. These technologies are contributing to the development of novel therapies and personalized medicine. Gaining insight from these technologies requires appropriate modeling techniques to capture clinically-relevant changes in disease state. The data generated from these devices is characterized by being stochastic in nature, may have missing elements, and exhibits considerable inter-individual variability - thereby making it difficult to analyze using traditional longitudinal modeling techniques. We present a novel pharmacology-informed neural stochastic differential equation (SDE) model capable of addressing these challenges. Using synthetic data, we demonstrate that our approach is effective in identifying treatment effects and learning causal relationships from stochastic data, thereby enabling counterfactual simulation. |
2210.13561 | Thomas Fink | Thomas Fink, Yang-Hui He | Flowers of immortality | null | null | null | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There has been a recent surge of interest in what causes aging. This has been
matched by unprecedented research investment in the field from tech companies.
But, despite considerable effort from a broad range of researchers, we do not
have a rigorous mathematical theory of programmed aging. To address this, we
recently derived a mortality equation that governs the transition matrix of an
evolving population with a given maximum age. Here, we characterize the
spectrum of eigenvalues of the solution to this equation. The eigenvalues fall
into two classes. The complex and negative real eigenvalues, which we call the
flower, are always contained in the unit circle in the complex plane. They play
a negligible role in controlling the dynamics of an aging population. The
positive real eigenvalues, which we call the stem, are the only eigenvalues
which can exceed the unit circle. They control the most important properties of
the dynamics. In particular, the spectral radius increases with the maximum
allowed age. This suggests that programmed aging confers no advantage in a
constant environment. However, the spectral gap, which governs the rate of
convergence to equilibrium, decreases with the maximum allowed age. This opens
the door to an evolutionary advantage in a changing environment.
| [
{
"created": "Mon, 24 Oct 2022 19:32:19 GMT",
"version": "v1"
}
] | 2022-10-26 | [
[
"Fink",
"Thomas",
""
],
[
"He",
"Yang-Hui",
""
]
] | There has been a recent surge of interest in what causes aging. This has been matched by unprecedented research investment in the field from tech companies. But, despite considerable effort from a broad range of researchers, we do not have a rigorous mathematical theory of programmed aging. To address this, we recently derived a mortality equation that governs the transition matrix of an evolving population with a given maximum age. Here, we characterize the spectrum of eigenvalues of the solution to this equation. The eigenvalues fall into two classes. The complex and negative real eigenvalues, which we call the flower, are always contained in the unit circle in the complex plane. They play a negligible role in controlling the dynamics of an aging population. The positive real eigenvalues, which we call the stem, are the only eigenvalues which can exceed the unit circle. They control the most important properties of the dynamics. In particular, the spectral radius increases with the maximum allowed age. This suggests that programmed aging confers no advantage in a constant environment. However, the spectral gap, which governs the rate of convergence to equilibrium, decreases with the maximum allowed age. This opens the door to an evolutionary advantage in a changing environment. |
1509.07786 | Francis Beaudry | Raphael Santamaria, Marie-Chantal Giroux, Pascal Vachon and Francis
Beaudry | CYP3A Mediated Ketamine Metabolism is Severely Impaired in Liver S9
Fractions from Aging Sprague Dawley Rats | 16 pages, 1 table, 2 figures | null | null | null | q-bio.SC q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ketamine is widely used in veterinary medicine and in medicine. Ketamine is
metabolized to its active metabolite norketamine principally by liver CYP3A.
Drug metabolism alterations during aging have severe consequences particularly
in anesthesiology and very few studies on older animals were conducted for
ketamine. The objective of the present study is to assess the influence of
aging on CYP3A metabolism of ketamine. Liver S9 fractions from 3, 6, 12 and 18
month old male Sprague Dawley rats were prepared and Michaelis-Menten
parameters were determined for primary metabolic pathways. The derived maximum
enzyme velocity (i.e. Vmax) suggests a rapid saturation of the CYP3A enzyme
active sites in liver S9 fractions of 18-month old rats. Observed Vmax for
Liver S9 fractions from 3, 6 and 12 month old male Sprague Dawley rats were
2.39 (+-0.23), 2.61 (+-0.18), and 2.07 (+-0.07) respectively compared to 0.68
(+-0.02) for Liver S9 fractions from 18 month old male Sprague Dawley rats.
Interestingly, we observed a 6 to 7 fold change in the derived Km when
comparing Liver S9 fractions from 18 month old male Sprague Dawley rats with
Liver S9 fractions from younger rats. Our results suggest that rat CYP3A enzyme
undergoes conformational changes with age particularly in our geriatric group
(e.g. 18 month rats) leading significant decrease in the rate of formation of
norketamine. Moreover, our results strongly suggest a severe impairment of
CYP3A ketamine mediated metabolism.
| [
{
"created": "Fri, 25 Sep 2015 16:49:23 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Jun 2016 16:08:22 GMT",
"version": "v2"
}
] | 2016-06-14 | [
[
"Santamaria",
"Raphael",
""
],
[
"Giroux",
"Marie-Chantal",
""
],
[
"Vachon",
"Pascal",
""
],
[
"Beaudry",
"Francis",
""
]
] | Ketamine is widely used in veterinary medicine and in medicine. Ketamine is metabolized to its active metabolite norketamine principally by liver CYP3A. Drug metabolism alterations during aging have severe consequences particularly in anesthesiology and very few studies on older animals were conducted for ketamine. The objective of the present study is to assess the influence of aging on CYP3A metabolism of ketamine. Liver S9 fractions from 3, 6, 12 and 18 month old male Sprague Dawley rats were prepared and Michaelis-Menten parameters were determined for primary metabolic pathways. The derived maximum enzyme velocity (i.e. Vmax) suggests a rapid saturation of the CYP3A enzyme active sites in liver S9 fractions of 18-month old rats. Observed Vmax for Liver S9 fractions from 3, 6 and 12 month old male Sprague Dawley rats were 2.39 (+-0.23), 2.61 (+-0.18), and 2.07 (+-0.07) respectively compared to 0.68 (+-0.02) for Liver S9 fractions from 18 month old male Sprague Dawley rats. Interestingly, we observed a 6 to 7 fold change in the derived Km when comparing Liver S9 fractions from 18 month old male Sprague Dawley rats with Liver S9 fractions from younger rats. Our results suggest that rat CYP3A enzyme undergoes conformational changes with age particularly in our geriatric group (e.g. 18 month rats) leading significant decrease in the rate of formation of norketamine. Moreover, our results strongly suggest a severe impairment of CYP3A ketamine mediated metabolism. |
1511.01347 | Marie Chupeau | M. Chupeau, O. B\'enichou, S. Redner | Universality classes of foraging with resource renewal | null | Phys. Rev. E 93, 032403 (2016) | 10.1103/PhysRevE.93.032403 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We determine the impact of resource renewal on the lifetime of a forager that
depletes its environment and starves if it wanders too long without eating. In
the framework of the minimal starving random walk model with resource renewal,
there are three universal classes of behavior as a function of the renewal
time. For sufficiently rapid renewal, foragers are immortal, while foragers
have a finite lifetime otherwise. In one dimension, there is a third regime,
for sufficiently slow renewal, in which the lifetime of the forager is
independent of the renewal time. We outline an enumeration method to determine
the mean lifetime of the forager in the mortal regime.
| [
{
"created": "Wed, 4 Nov 2015 14:26:23 GMT",
"version": "v1"
},
{
"created": "Thu, 14 Jan 2016 15:07:09 GMT",
"version": "v2"
}
] | 2016-03-23 | [
[
"Chupeau",
"M.",
""
],
[
"Bénichou",
"O.",
""
],
[
"Redner",
"S.",
""
]
] | We determine the impact of resource renewal on the lifetime of a forager that depletes its environment and starves if it wanders too long without eating. In the framework of the minimal starving random walk model with resource renewal, there are three universal classes of behavior as a function of the renewal time. For sufficiently rapid renewal, foragers are immortal, while foragers have a finite lifetime otherwise. In one dimension, there is a third regime, for sufficiently slow renewal, in which the lifetime of the forager is independent of the renewal time. We outline an enumeration method to determine the mean lifetime of the forager in the mortal regime. |
0810.1404 | Phillip Staniczenko | Phillip P. A. Staniczenko, Chiu Fan Lee, and Nick S. Jones | Rapidly detecting disorder in rhythmic biological signals: A spectral
entropy measure to identify cardiac arrhythmias | 11 pages | Phys. Rev. E 79, 011915 (2009) | 10.1103/PhysRevE.79.011915 | null | q-bio.QM physics.bio-ph q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the use of a running measure of power spectrum disorder to
distinguish between the normal sinus rhythm of the heart and two forms of
cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral
entropy measure is motivated by characteristic differences in the spectra of
beat timings during the three rhythms. We plot patient data derived from
ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the
level and variance of spectral entropy values. Employing the spectral entropy
within an automatic arrhythmia detection algorithm enables the classification
of periods of atrial fibrillation from the time series of patients' beats. When
the algorithm is set to identify abnormal rhythms within 6 s it agrees with
85.7% of the annotations of professional rhythm assessors; for a response time
of 30 s this becomes 89.5%, and with 60 s it is 90.3%. The algorithm provides a
rapid way to detect atrial fibrillation, demonstrating usable response times as
low as 6 s. Measures of disorder in the frequency domain have practical
significance in a range of biological signals: the techniques described in this
paper have potential application for the rapid identification of disorder in
other rhythmic signals.
| [
{
"created": "Wed, 8 Oct 2008 11:53:53 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Feb 2009 15:45:38 GMT",
"version": "v2"
}
] | 2009-02-16 | [
[
"Staniczenko",
"Phillip P. A.",
""
],
[
"Lee",
"Chiu Fan",
""
],
[
"Jones",
"Nick S.",
""
]
] | We consider the use of a running measure of power spectrum disorder to distinguish between the normal sinus rhythm of the heart and two forms of cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral entropy measure is motivated by characteristic differences in the spectra of beat timings during the three rhythms. We plot patient data derived from ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the level and variance of spectral entropy values. Employing the spectral entropy within an automatic arrhythmia detection algorithm enables the classification of periods of atrial fibrillation from the time series of patients' beats. When the algorithm is set to identify abnormal rhythms within 6 s it agrees with 85.7% of the annotations of professional rhythm assessors; for a response time of 30 s this becomes 89.5%, and with 60 s it is 90.3%. The algorithm provides a rapid way to detect atrial fibrillation, demonstrating usable response times as low as 6 s. Measures of disorder in the frequency domain have practical significance in a range of biological signals: the techniques described in this paper have potential application for the rapid identification of disorder in other rhythmic signals. |
1604.04588 | Stephen Pankavich | Tyson Loudon and Stephen Pankavich | Mathematical Analysis and Dynamic Active Subspaces for a Long term model
of HIV | 26 pages, 17 figures | null | null | null | q-bio.PE math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, a long-term model of HIV infection dynamics was developed to
describe the entire time course of the disease. It consists of a large system
of ODEs with many parameters, and is expensive to simulate. In the current
paper, this model is analyzed by determining all infection-free steady states
and studying the local stability properties of the unique biologically-relevant
equilibrium. Active subspace methods are then used to perform a global
sensitivity analysis and study the dependence of an infected individual's
T-cell count on the parameter space. Building on these results, a
global-in-time approximation of the T-cell count is created by constructing
dynamic active subspaces and reduced order models are generated, thereby
allowing for inexpensive computation.
| [
{
"created": "Fri, 15 Apr 2016 18:13:48 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Apr 2016 20:35:33 GMT",
"version": "v2"
}
] | 2016-04-20 | [
[
"Loudon",
"Tyson",
""
],
[
"Pankavich",
"Stephen",
""
]
] | Recently, a long-term model of HIV infection dynamics was developed to describe the entire time course of the disease. It consists of a large system of ODEs with many parameters, and is expensive to simulate. In the current paper, this model is analyzed by determining all infection-free steady states and studying the local stability properties of the unique biologically-relevant equilibrium. Active subspace methods are then used to perform a global sensitivity analysis and study the dependence of an infected individual's T-cell count on the parameter space. Building on these results, a global-in-time approximation of the T-cell count is created by constructing dynamic active subspaces and reduced order models are generated, thereby allowing for inexpensive computation. |
2003.04537 | Quratul Ain Dr. | Qurat-ul-Ain and Muhammad Ismail | Metastatic melanoma-A review of current and future perspective | 12 pages, 1 figure | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Metastatic Melanoma, the fifth most common cancer in the western countries
and the most common malignancy diagnosed in United States present itself as the
most lethal treatment resistant cancer worldwide. In addition to the reactive
oxygen species(ROS), mutations in the genes encoding receptors and non-receptor
tyrosine/serene/threonine protein kinases are known to be involved in its
etiology. Kinases are molecular players of cell survival, growth, and
proliferation and migration that mediate their effects via various signal
transduction pathways. A number of such molecular players have been previously
found to be mutated and hyper phosphorylated in melanoma. Although, several
systemic therapies including cytotoxic chemotherapy, targeted drugs, hormonal
therapy, radiation therapy, bio-chemotherapy, and therapies that inhibit
negative regulation of immune system have been approved from U. S. Food and
Drug Administration (FDA) for metastatic melanoma treatment. However, no
systemic therapy has meaningfully changed its survival end points so far and
surgery still presents primary treatment option for advanced and metastatic
melanomaa due to its highly resistant nature towards systemic drugs, high rate
of severe, life-threatening, or fatal side effects, and un satisfactory overall
response rate. Therefore, there is still a need to develop therapies that
target the unique molecular profile of melanoma tumors.
| [
{
"created": "Fri, 6 Mar 2020 14:28:35 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Apr 2020 06:42:33 GMT",
"version": "v2"
},
{
"created": "Mon, 20 Apr 2020 14:51:47 GMT",
"version": "v3"
}
] | 2020-04-21 | [
[
"Qurat-ul-Ain",
"",
""
],
[
"Ismail",
"Muhammad",
""
]
] | Metastatic Melanoma, the fifth most common cancer in the western countries and the most common malignancy diagnosed in United States present itself as the most lethal treatment resistant cancer worldwide. In addition to the reactive oxygen species(ROS), mutations in the genes encoding receptors and non-receptor tyrosine/serene/threonine protein kinases are known to be involved in its etiology. Kinases are molecular players of cell survival, growth, and proliferation and migration that mediate their effects via various signal transduction pathways. A number of such molecular players have been previously found to be mutated and hyper phosphorylated in melanoma. Although, several systemic therapies including cytotoxic chemotherapy, targeted drugs, hormonal therapy, radiation therapy, bio-chemotherapy, and therapies that inhibit negative regulation of immune system have been approved from U. S. Food and Drug Administration (FDA) for metastatic melanoma treatment. However, no systemic therapy has meaningfully changed its survival end points so far and surgery still presents primary treatment option for advanced and metastatic melanomaa due to its highly resistant nature towards systemic drugs, high rate of severe, life-threatening, or fatal side effects, and un satisfactory overall response rate. Therefore, there is still a need to develop therapies that target the unique molecular profile of melanoma tumors. |
0803.1819 | Giovanni Paternostro | Diego Calzolari, Stefania Bruschi, Laurence Coquin, Jennifer
Schofield, Jacob Feala, John C. Reed, Andrew D. McCulloch, Giovanni
Paternostro | Search algorithms as a framework for the optimization of drug
combinations | 36 pages, 10 figures, revised version | PLoS Comp Biol 4(12):1-14, e1000249, 2008 | 10.1371/journal.pcbi.1000249 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Combination therapies are often needed for effective clinical outcomes in the
management of complex diseases, but presently they are generally based on
empirical clinical experience. Here we suggest a novel application of search
algorithms, originally developed for digital communication, modified to
optimize combinations of therapeutic interventions. In biological experiments
measuring the restoration of the decline with age in heart function and
exercise capacity in Drosophila melanogaster, we found that search algorithms
correctly identified optimal combinations of four drugs with only one third of
the tests performed in a fully factorial search. In experiments identifying
combinations of three doses of up to six drugs for selective killing of human
cancer cells, search algorithms resulted in a highly significant enrichment of
selective combinations compared with random searches. In simulations using a
network model of cell death, we found that the search algorithms identified the
optimal combinations of 6-9 interventions in 80-90% of tests, compared with
15-30% for an equivalent random search. These findings suggest that modified
search algorithms from information theory have the potential to enhance the
discovery of novel therapeutic drug combinations. This report also helps to
frame a biomedical problem that will benefit from an interdisciplinary effort
and suggests a general strategy for its solution.
| [
{
"created": "Wed, 12 Mar 2008 18:13:06 GMT",
"version": "v1"
},
{
"created": "Sat, 24 May 2008 02:43:02 GMT",
"version": "v2"
},
{
"created": "Mon, 13 Oct 2008 22:45:59 GMT",
"version": "v3"
}
] | 2009-01-13 | [
[
"Calzolari",
"Diego",
""
],
[
"Bruschi",
"Stefania",
""
],
[
"Coquin",
"Laurence",
""
],
[
"Schofield",
"Jennifer",
""
],
[
"Feala",
"Jacob",
""
],
[
"Reed",
"John C.",
""
],
[
"McCulloch",
"Andrew D.",
""
],
[
"Paternostro",
"Giovanni",
""
]
] | Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution. |
1704.08669 | Wilhelm Braun | Wilhelm Braun, R\"udiger Thul, Andr\'e Longtin | Evolution of moments and correlations in non-renewal escape-time
processes | 14 pages, 12 figures, 1 appendix. Accepted for publication in
Physical Review E | null | 10.1103/PhysRevE.95.052127 | null | q-bio.NC math.PR physics.comp-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theoretical description of non-renewal stochastic systems is a challenge.
Analytical results are often not available or can only be obtained under strong
conditions, limiting their applicability. Also, numerical results have mostly
been obtained by ad-hoc Monte--Carlo simulations, which are usually
computationally expensive when a high degree of accuracy is needed. To gain
quantitative insight into these systems under general conditions, we here
introduce a numerical iterated first-passage time approach based on solving the
time-dependent Fokker-Planck equation (FPE) to describe the statistics of
non-renewal stochastic systems. We illustrate the approach using
spike-triggered neuronal adaptation in the leaky and perfect integrate-and-fire
model, respectively. The transition to stationarity of first-passage time
moments and their sequential correlations occur on a non-trivial timescale that
depends on all system parameters. Surprisingly this is so for both single
exponential and scale-free power-law adaptation. The method works beyond the
small noise and timescale separation approximations. It shows excellent
agreement with direct Monte Carlo simulations, which allows for the computation
of transient and stationary distributions. We compare different methods to
compute the evolution of the moments and serial correlation coefficients (SCC),
and discuss the challenge of reliably computing the SCC which we find to be
very sensitive to numerical inaccuracies for both the leaky and perfect
integrate-and-fire models. In conclusion, our methods provide a general picture
of non-renewal dynamics in a wide range of stochastic systems exhibiting short
and long-range correlations.
| [
{
"created": "Thu, 27 Apr 2017 17:33:32 GMT",
"version": "v1"
}
] | 2017-06-07 | [
[
"Braun",
"Wilhelm",
""
],
[
"Thul",
"Rüdiger",
""
],
[
"Longtin",
"André",
""
]
] | The theoretical description of non-renewal stochastic systems is a challenge. Analytical results are often not available or can only be obtained under strong conditions, limiting their applicability. Also, numerical results have mostly been obtained by ad-hoc Monte--Carlo simulations, which are usually computationally expensive when a high degree of accuracy is needed. To gain quantitative insight into these systems under general conditions, we here introduce a numerical iterated first-passage time approach based on solving the time-dependent Fokker-Planck equation (FPE) to describe the statistics of non-renewal stochastic systems. We illustrate the approach using spike-triggered neuronal adaptation in the leaky and perfect integrate-and-fire model, respectively. The transition to stationarity of first-passage time moments and their sequential correlations occur on a non-trivial timescale that depends on all system parameters. Surprisingly this is so for both single exponential and scale-free power-law adaptation. The method works beyond the small noise and timescale separation approximations. It shows excellent agreement with direct Monte Carlo simulations, which allows for the computation of transient and stationary distributions. We compare different methods to compute the evolution of the moments and serial correlation coefficients (SCC), and discuss the challenge of reliably computing the SCC which we find to be very sensitive to numerical inaccuracies for both the leaky and perfect integrate-and-fire models. In conclusion, our methods provide a general picture of non-renewal dynamics in a wide range of stochastic systems exhibiting short and long-range correlations. |
2311.15572 | Jason Kim | Jason Z. Kim, Bart Larsen, Linden Parkes | Shaping dynamical neural computations using spatiotemporal constraints | 7 figures, 18 pages | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | Dynamics play a critical role in computation. The principled evolution of
states over time enables both biological and artificial networks to represent
and integrate information to make decisions. In the past few decades,
significant multidisciplinary progress has been made in bridging the gap
between how we understand biological versus artificial computation, including
how insights gained from one can translate to the other. Research has revealed
that neurobiology is a key determinant of brain network architecture, which
gives rise to spatiotemporally constrained patterns of activity that underlie
computation. Here, we discuss how neural systems use dynamics for computation,
and claim that the biological constraints that shape brain networks may be
leveraged to improve the implementation of artificial neural networks. To
formalize this discussion, we consider a natural artificial analog of the brain
that has been used extensively to model neural computation: the recurrent
neural network (RNN). In both the brain and the RNN, we emphasize the common
computational substrate atop which dynamics occur -- the connectivity between
neurons -- and we explore the unique computational advantages offered by
biophysical constraints such as resource efficiency, spatial embedding, and
neurodevelopment.
| [
{
"created": "Mon, 27 Nov 2023 06:45:31 GMT",
"version": "v1"
}
] | 2023-11-28 | [
[
"Kim",
"Jason Z.",
""
],
[
"Larsen",
"Bart",
""
],
[
"Parkes",
"Linden",
""
]
] | Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur -- the connectivity between neurons -- and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment. |
2308.12586 | Dylan Antonio Talabis | Dylan Antonio Talabis, Eduardo Mendoza | Network transformation-based analysis of biochemical systems | 40 pages. arXiv admin note: text overlap with arXiv:1109.2923 by
other authors | null | null | null | q-bio.MN math.DS | http://creativecommons.org/licenses/by/4.0/ | A dynamical system obtains a wide variety of kinetic realizations, which is
advantageous for the analysis of biochemical systems. A reaction network,
derived from a dynamical system, may or may not possess some properties needed
for a thorough analysis. We improve and extend the work of M. Johnston
\cite{JOHN2014} and Hong et al. \cite{HONG2023} on network translations to
network transformations, where the network is modified while preserving the
dynamical system. These transformations can shrink, extend, or retain the
stoichiometric subspace. Here, we show that positive dependent network can be
translated to a weakly reversible network. Using the kinetic realizations of
(1) calcium signaling in the olfactory system and (2) metabolic insulin
signaling, we demonstrate the benefits of transformed systems with positive
deficiency for analyzing biochemical systems. Furthermore, we present an
algorithm for a network transformation of a weakly reversible non-complex
factorizable kinetic (NFK) system to a weakly reversible complex factorizable
kinetic (CFK) system, thereby enhancing the Subspace Coincidence Theorem for
NFK systems of Nazareno et al. \cite{NAZA2019}. Finally, using the transformed
kinetic realization of monolignol biosynthesis in \textit{Populus xylem}, we
study the structural and kinetic properties of transformed systems,including
the invariance of concordance and variation of injectivity and
mono-/multi-stationarity under network transformation.
| [
{
"created": "Thu, 24 Aug 2023 06:24:43 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Apr 2024 14:35:58 GMT",
"version": "v2"
}
] | 2024-05-01 | [
[
"Talabis",
"Dylan Antonio",
""
],
[
"Mendoza",
"Eduardo",
""
]
] | A dynamical system obtains a wide variety of kinetic realizations, which is advantageous for the analysis of biochemical systems. A reaction network, derived from a dynamical system, may or may not possess some properties needed for a thorough analysis. We improve and extend the work of M. Johnston \cite{JOHN2014} and Hong et al. \cite{HONG2023} on network translations to network transformations, where the network is modified while preserving the dynamical system. These transformations can shrink, extend, or retain the stoichiometric subspace. Here, we show that positive dependent network can be translated to a weakly reversible network. Using the kinetic realizations of (1) calcium signaling in the olfactory system and (2) metabolic insulin signaling, we demonstrate the benefits of transformed systems with positive deficiency for analyzing biochemical systems. Furthermore, we present an algorithm for a network transformation of a weakly reversible non-complex factorizable kinetic (NFK) system to a weakly reversible complex factorizable kinetic (CFK) system, thereby enhancing the Subspace Coincidence Theorem for NFK systems of Nazareno et al. \cite{NAZA2019}. Finally, using the transformed kinetic realization of monolignol biosynthesis in \textit{Populus xylem}, we study the structural and kinetic properties of transformed systems,including the invariance of concordance and variation of injectivity and mono-/multi-stationarity under network transformation. |
1711.00989 | Michael Pan | Michael Pan, Peter J. Gawthrop, Joseph Cursons, Kenneth Tran, and
Edmund J. Crampin | The cardiac Na$^+$/K$^+$ ATPase: An updated, thermodynamically
consistent model | null | Physiome (2020) | 10.36903/physiome.12871070.v1 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Na$^+$/K$^+$ ATPase is an essential component of cardiac
electrophysiology, maintaining physiological Na$^+$ and K$^+$ concentrations
over successive heart beats. Terkildsen et al. (2007) developed a model of the
ventricular myocyte Na$^+$/K$^+$ ATPase to study extracellular potassium
accumulation during ischaemia, demonstrating the ability to recapitulate a wide
range of experimental data, but unfortunately there was no archived code
associated with the original manuscript. Here we detail an updated version of
the model and provide CellML and MATLAB code to ensure reproducibility and
reusability. We note some errors within the original formulation which have
been corrected to ensure that the model is thermodynamically consistent, and
although this required some reparameterisation, the resulting model still
provides a good fit to experimental measurements that demonstrate the
dependence of Na$^+$/K$^+$ ATPase pumping rate upon membrane voltage and
metabolite concentrations. To demonstrate thermodynamic consistency we also
developed a bond graph version of the model. We hope that these models will be
useful for community efforts to assemble a whole-cell cardiomyocyte model which
facilitates the investigation of cellular energetics.
| [
{
"created": "Fri, 3 Nov 2017 01:09:05 GMT",
"version": "v1"
}
] | 2020-09-08 | [
[
"Pan",
"Michael",
""
],
[
"Gawthrop",
"Peter J.",
""
],
[
"Cursons",
"Joseph",
""
],
[
"Tran",
"Kenneth",
""
],
[
"Crampin",
"Edmund J.",
""
]
] | The Na$^+$/K$^+$ ATPase is an essential component of cardiac electrophysiology, maintaining physiological Na$^+$ and K$^+$ concentrations over successive heart beats. Terkildsen et al. (2007) developed a model of the ventricular myocyte Na$^+$/K$^+$ ATPase to study extracellular potassium accumulation during ischaemia, demonstrating the ability to recapitulate a wide range of experimental data, but unfortunately there was no archived code associated with the original manuscript. Here we detail an updated version of the model and provide CellML and MATLAB code to ensure reproducibility and reusability. We note some errors within the original formulation which have been corrected to ensure that the model is thermodynamically consistent, and although this required some reparameterisation, the resulting model still provides a good fit to experimental measurements that demonstrate the dependence of Na$^+$/K$^+$ ATPase pumping rate upon membrane voltage and metabolite concentrations. To demonstrate thermodynamic consistency we also developed a bond graph version of the model. We hope that these models will be useful for community efforts to assemble a whole-cell cardiomyocyte model which facilitates the investigation of cellular energetics. |
2205.15421 | Adri\'an Hern\'andez | Adri\'an Hern\'andez and Jos\'e M. Amig\'o | Multilayer adaptive networks in neuronal processing | 11 pages, 2 fugures | The European Physical Journal Special Topics 227, 1039-1049 (2018) | 10.1140/epjst/e2018-800037-y | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | The connectome is a wiring diagram mapping all the neural connections in the
brain. At the cellular level, it provides a map of the neurons and synapses
within a part or all of the brain of an organism. In recent years, significant
advances have been made in the study of the connectome via network science and
graph theory. This analysis is fundamental to understand neurotransmission
(fast synaptic transmission) networks. However, neurons use other forms of
communication as neuromodulation that, instead of conveying excitation or
inhibition, change neuronal and synaptic properties. This additional
neuromodulatory layers condition and reconfigure the connectome. In this paper,
we propose that multilayer adaptive networks, in which different synaptic and
neurochemical layers interact, are the appropriate framework to explain
neuronal processing. Then, we describe a simplified multilayer adaptive network
model that accounts for these extra-layers of interaction and analyse the
emergence of interesting computational capabilities.
| [
{
"created": "Mon, 30 May 2022 20:35:24 GMT",
"version": "v1"
}
] | 2022-06-01 | [
[
"Hernández",
"Adrián",
""
],
[
"Amigó",
"José M.",
""
]
] | The connectome is a wiring diagram mapping all the neural connections in the brain. At the cellular level, it provides a map of the neurons and synapses within a part or all of the brain of an organism. In recent years, significant advances have been made in the study of the connectome via network science and graph theory. This analysis is fundamental to understand neurotransmission (fast synaptic transmission) networks. However, neurons use other forms of communication as neuromodulation that, instead of conveying excitation or inhibition, change neuronal and synaptic properties. This additional neuromodulatory layers condition and reconfigure the connectome. In this paper, we propose that multilayer adaptive networks, in which different synaptic and neurochemical layers interact, are the appropriate framework to explain neuronal processing. Then, we describe a simplified multilayer adaptive network model that accounts for these extra-layers of interaction and analyse the emergence of interesting computational capabilities. |
1711.07397 | Eleonora Alfinito Dr. | R. Cataldo, F. Ciriaco, E. Alfinito | A validation strategy for in silico generated aptamers | 17 pages, 9 figures | null | null | null | q-bio.QM cond-mat.other physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The selection of high-affinity aptamers is of paramount interest for clinical
and technological applications. A novel strategy is proposed to validate the
reliability of the 3D structures of aptamers, produced in silico by using free
software. The procedure consists of three steps: a. the production of a large
set of conformations for each candidate aptamer, b. the rigid docking upon the
receptor, c. the topological and electrical characterization of the products.
Steps a. and b. allow a global binding score of the ligand-receptor complexes
based on the distribution of the "effective affinity", i.e. the sum of the
conformational and the docking energy. Step c. employs a complex network
approach (Proteotronics) to characterize the electrical properties of the
aptamers and the ligand-receptor complexes. The test-bed is represented by a
group of anti- Angiopoietin-2 aptamers. In a previous literature these aptamers
were processed both in vitro and in silico, by using an approach different from
that here presented, and finally tested with a SPS experiment. Computational
expectations and experimental outcomes did not agree, while our results show a
good agreement with the known measurements. The devised procedure is not
aptamer-specific and, integrating structure production with structure
selection, candidates itself as a quite complete theoretical approach for
aptamer selection.
| [
{
"created": "Mon, 20 Nov 2017 16:34:00 GMT",
"version": "v1"
},
{
"created": "Mon, 21 May 2018 08:10:56 GMT",
"version": "v2"
}
] | 2018-05-22 | [
[
"Cataldo",
"R.",
""
],
[
"Ciriaco",
"F.",
""
],
[
"Alfinito",
"E.",
""
]
] | The selection of high-affinity aptamers is of paramount interest for clinical and technological applications. A novel strategy is proposed to validate the reliability of the 3D structures of aptamers, produced in silico by using free software. The procedure consists of three steps: a. the production of a large set of conformations for each candidate aptamer, b. the rigid docking upon the receptor, c. the topological and electrical characterization of the products. Steps a. and b. allow a global binding score of the ligand-receptor complexes based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energy. Step c. employs a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the ligand-receptor complexes. The test-bed is represented by a group of anti- Angiopoietin-2 aptamers. In a previous literature these aptamers were processed both in vitro and in silico, by using an approach different from that here presented, and finally tested with a SPS experiment. Computational expectations and experimental outcomes did not agree, while our results show a good agreement with the known measurements. The devised procedure is not aptamer-specific and, integrating structure production with structure selection, candidates itself as a quite complete theoretical approach for aptamer selection. |
1004.5465 | Sanzo Miyazawa | Sanzo Miyazawa | Selective Constraints on Amino Acids Estimated by a Mechanistic Codon
Substitution Model with Multiple Nucleotide Changes | Table 9 in this article includes corrections for errata in the Table
9 published in 10.1371/journal.pone.0017244. Supporting information is
attached at the end of the article, and a computer-readable dataset of the ML
estimates of selective constraints is available from
10.1371/journal.pone.0017244 | PLoS One, 6, e17244/pp. 1-22, 2011 | 10.1371/journal.pone.0017244 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Empirical substitution matrices represent the average tendencies of
substitutions over various protein families by sacrificing gene-level
resolution. We develop a codon-based model, in which mutational tendencies of
codon, a genetic code, and the strength of selective constraints against amino
acid replacements can be tailored to a given gene. First, selective constraints
averaged over proteins are estimated by maximizing the likelihood of each 1-PAM
matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution
matrices. Then, selective constraints specific to given proteins are
approximated as a linear function of those estimated from the empirical
substitution matrices.
Akaike information criterion (AIC) values indicate that a model allowing
multiple nucleotide changes fits the empirical substitution matrices
significantly better. Also, the ML estimates of transition-transversion bias
obtained from these empirical matrices are not so large as previously
estimated. The selective constraints are characteristic of proteins rather than
species. However, their relative strengths among amino acid pairs can be
approximated not to depend very much on protein families but amino acid pairs,
because the present model, in which selective constraints are approximated to
be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can
provide a good fit to other empirical substitution matrices including cpREV for
chloroplast proteins and mtREV for vertebrate mitochondrial proteins.
The present codon-based model with the ML estimates of selective constraints
and with adjustable mutation rates of nucleotide would be useful as a simple
substitution model in ML and Bayesian inferences of molecular phylogenetic
trees, and enables us to obtain biologically meaningful information at both
nucleotide and amino acid levels from codon and protein sequences.
| [
{
"created": "Fri, 30 Apr 2010 08:04:51 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Aug 2011 08:31:48 GMT",
"version": "v2"
}
] | 2011-08-31 | [
[
"Miyazawa",
"Sanzo",
""
]
] | Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution. We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene. First, selective constraints averaged over proteins are estimated by maximizing the likelihood of each 1-PAM matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution matrices. Then, selective constraints specific to given proteins are approximated as a linear function of those estimated from the empirical substitution matrices. Akaike information criterion (AIC) values indicate that a model allowing multiple nucleotide changes fits the empirical substitution matrices significantly better. Also, the ML estimates of transition-transversion bias obtained from these empirical matrices are not so large as previously estimated. The selective constraints are characteristic of proteins rather than species. However, their relative strengths among amino acid pairs can be approximated not to depend very much on protein families but amino acid pairs, because the present model, in which selective constraints are approximated to be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can provide a good fit to other empirical substitution matrices including cpREV for chloroplast proteins and mtREV for vertebrate mitochondrial proteins. The present codon-based model with the ML estimates of selective constraints and with adjustable mutation rates of nucleotide would be useful as a simple substitution model in ML and Bayesian inferences of molecular phylogenetic trees, and enables us to obtain biologically meaningful information at both nucleotide and amino acid levels from codon and protein sequences. |
1310.8091 | Janusz Szwabi\'nski | Andrzej P\k{e}kalski and Janusz Szwabi\'nski | Role of detritus in a spatial food web model with diffusion | 11 pages, 12 figures | Phys. Rev. E 89, 052714 (2014) | 10.1103/PhysRevE.89.052714 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the central themes in modern ecology is the enduring debate on whether
there is a relationship between the complexity of a biological community and
its stability. In this paper, we focus on the role of detritus and spatial
dispersion on the stability of ecosystems. Using Monte Carlo simulations we
analyze two three level models of food webs: a grazing one with the basal
species (i.e. primary producers) having unlimited food resources and a detrital
one in which the basal species uses detritus as a food resource. While the vast
majority of theoretical studies neglects detritus, from our results it follows
that the detrital food web is more stable than its grazing counterpart, because
the interactions mediated by detritus damp out fluctuations in species'
densities. Since the detritus model is the more complex one in terms of
interaction patterns, our results provide new evidence for the advocates of the
complexity as one of the factors enhancing stability of ecosystems.
| [
{
"created": "Wed, 30 Oct 2013 10:21:10 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Jan 2014 11:41:35 GMT",
"version": "v2"
},
{
"created": "Mon, 14 Apr 2014 08:11:43 GMT",
"version": "v3"
}
] | 2014-06-11 | [
[
"Pękalski",
"Andrzej",
""
],
[
"Szwabiński",
"Janusz",
""
]
] | One of the central themes in modern ecology is the enduring debate on whether there is a relationship between the complexity of a biological community and its stability. In this paper, we focus on the role of detritus and spatial dispersion on the stability of ecosystems. Using Monte Carlo simulations we analyze two three level models of food webs: a grazing one with the basal species (i.e. primary producers) having unlimited food resources and a detrital one in which the basal species uses detritus as a food resource. While the vast majority of theoretical studies neglects detritus, from our results it follows that the detrital food web is more stable than its grazing counterpart, because the interactions mediated by detritus damp out fluctuations in species' densities. Since the detritus model is the more complex one in terms of interaction patterns, our results provide new evidence for the advocates of the complexity as one of the factors enhancing stability of ecosystems. |
q-bio/0609020 | Arne Traulsen | Arne Traulsen, Martin A. Nowak, and Jorge M. Pacheco | Stochastic Dynamics of Invasion and Fixation | null | Physical Review E 74, 011909, 2006 | 10.1103/PhysRevE.74.011909 | null | q-bio.PE | null | We study evolutionary game dynamics in finite populations. We analyze an
evolutionary process, which we call pairwise comparison, for which we adopt the
ubiquitous Fermi distribution function from statistical mechanics. The inverse
temperature in this process controls the intensity of selection, leading to a
unified framework for evolutionary dynamics at all intensities of selection,
from random drift to imitation dynamics. We derive, for the first time, a
simple closed formula which determines the feasibility of cooperation in finite
populations, whenever cooperation is modeled in terms of any symmetric
two-person game. In contrast with previous results, the present formula is
valid at all intensities of selection and for any initial condition. We
investigate the evolutionary dynamics of cooperators in finite populations, and
study the interplay between intensity of selection and the remnants of interior
fixed points in infinite populations, as a function of a given initial number
of cooperators, showing how this interplay strongly affects the approach to
fixation of a given trait in finite populations, leading to counter-intuitive
results at different intensities of selection.
| [
{
"created": "Wed, 13 Sep 2006 22:17:37 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Traulsen",
"Arne",
""
],
[
"Nowak",
"Martin A.",
""
],
[
"Pacheco",
"Jorge M.",
""
]
] | We study evolutionary game dynamics in finite populations. We analyze an evolutionary process, which we call pairwise comparison, for which we adopt the ubiquitous Fermi distribution function from statistical mechanics. The inverse temperature in this process controls the intensity of selection, leading to a unified framework for evolutionary dynamics at all intensities of selection, from random drift to imitation dynamics. We derive, for the first time, a simple closed formula which determines the feasibility of cooperation in finite populations, whenever cooperation is modeled in terms of any symmetric two-person game. In contrast with previous results, the present formula is valid at all intensities of selection and for any initial condition. We investigate the evolutionary dynamics of cooperators in finite populations, and study the interplay between intensity of selection and the remnants of interior fixed points in infinite populations, as a function of a given initial number of cooperators, showing how this interplay strongly affects the approach to fixation of a given trait in finite populations, leading to counter-intuitive results at different intensities of selection. |
1410.4465 | Andrea Giansanti | Antonio Deiana and Andrea Giansanti | On the abundance of intrinsically disordered proteins in the human
proteome and its relation to diseases: there is no enrichment | Paper presented at the meeting:" The CISB scientific activity: recent
and seminal achievements" (Rome, May 29-30 2014 - Palazzo Baleani-Aula Magna.
To be published in a special issue of the electronic journal BIOPHYSICS AND
BIOENGINEERING LETTERS (http://ojs.uniroma1.it/index.php/CISB-BBL) | null | null | null | q-bio.BM q-bio.GN | http://creativecommons.org/licenses/by/3.0/ | Intrinsically disordered proteins are fascinating the community of protein
science since the last decade, at least. There is a well-established line of
research that intends to reveal the crucial role played by intrinsically
disordered proteins (IDPs) in the development of human diseases. The main
argument is that IDPs are differentially more present in groups of
disease-related proteins. In this note we compare the frequency of disorder in
human proteins, both disease-related and not. The frequency of disorder is
comparable in the two sub-groups of proteins. Disorder is widespread in human
proteins, but it is not a specific pre-requisite of proteins involved in the
development of cancer, cardiovascular diseases, diabetes and neurodegenerative
diseases. A tendency of cancer-related proteins to be statistically more
disordered than the rest of human proteins is confirmed.
| [
{
"created": "Thu, 16 Oct 2014 15:25:18 GMT",
"version": "v1"
}
] | 2014-10-17 | [
[
"Deiana",
"Antonio",
""
],
[
"Giansanti",
"Andrea",
""
]
] | Intrinsically disordered proteins are fascinating the community of protein science since the last decade, at least. There is a well-established line of research that intends to reveal the crucial role played by intrinsically disordered proteins (IDPs) in the development of human diseases. The main argument is that IDPs are differentially more present in groups of disease-related proteins. In this note we compare the frequency of disorder in human proteins, both disease-related and not. The frequency of disorder is comparable in the two sub-groups of proteins. Disorder is widespread in human proteins, but it is not a specific pre-requisite of proteins involved in the development of cancer, cardiovascular diseases, diabetes and neurodegenerative diseases. A tendency of cancer-related proteins to be statistically more disordered than the rest of human proteins is confirmed. |
1912.12551 | Ryan Langendorf | Ryan E. Langendorf and Debra S. Goldberg | Aligning Statistical Dynamics Captures Biological Network Functioning | Supplementary Information included, 35 pages total, 8 main text & 4
SI figures, and 3 SI tables. Accompanying software at
https://cran.r-project.org/package=netcom | null | null | null | q-bio.QM q-bio.MN stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Empirical studies of graphs have contributed enormously to our understanding
of complex systems. Known today as network science, what was originally a
theoretical study of graphs has grown into a more scientific exploration of
communities spanning the physical, biological, and social. However, as the
quantity and types of networks have grown so has their heterogeneity in quality
and specificity. This has hampered efforts to develop general network theory
capable of inferring functioning and predicting dynamics across study systems.
We have successfully approached this challenge by aligning networks to each
other rather than comparing parameter estimates from individually fitted models
or properties of edge topologies. By comparing the predictability of
statistical dynamics originating from each network's constituent nodes we were
able to build a functional classifier that distinguished underlying processes
in both synthetic and real-world network data spanning the entire biological
scale from cellular machinery to ecosystems.
| [
{
"created": "Sun, 29 Dec 2019 00:06:57 GMT",
"version": "v1"
}
] | 2020-01-01 | [
[
"Langendorf",
"Ryan E.",
""
],
[
"Goldberg",
"Debra S.",
""
]
] | Empirical studies of graphs have contributed enormously to our understanding of complex systems. Known today as network science, what was originally a theoretical study of graphs has grown into a more scientific exploration of communities spanning the physical, biological, and social. However, as the quantity and types of networks have grown so has their heterogeneity in quality and specificity. This has hampered efforts to develop general network theory capable of inferring functioning and predicting dynamics across study systems. We have successfully approached this challenge by aligning networks to each other rather than comparing parameter estimates from individually fitted models or properties of edge topologies. By comparing the predictability of statistical dynamics originating from each network's constituent nodes we were able to build a functional classifier that distinguished underlying processes in both synthetic and real-world network data spanning the entire biological scale from cellular machinery to ecosystems. |
2205.06196 | Asuka Takai | Asuka Takai, Qiushi Fu, Yuzuru Doibata, Giuseppe Lisi, Toshiki
Tsuchiya, Keivan Mojtahedi, Toshinori Yoshioka, Mitsuo Kawato, Jun Morimoto,
Marco Santello | Two are not always better than one: Role specialization is an important
determinant of collaborative task performance | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Collaboration frequently yields better results in decision making, learning,
and haptic interactions than when these actions are performed individually.
However, is collaboration always superior to solo actions, or do its benefits
depend on whether collaborating individuals have different or the same roles?
To answer this question, we asked human subjects to perform virtual-reality
collaborative and individual beam transportation tasks. These tasks were
simulated in real-time by coupling the motion of a pair of hand-held robotic
manipulanda to the virtual beam using virtual spring-dampers. For the task to
be considered successful, participants had to complete it within temporal and
spatial constraints. While the visual feedback remained the same, the
underlying dynamics of the beam were altered to create two distinctive task
contexts which were determined by a moving pivot constraint. When the pivot was
placed at the center of the beam, two hands contribute to the task with
symmetric mechanical leverage (symmetric context). When the pivot was placed at
the left side of the beam, two hands contribute to the task with asymmetric
mechanical leverage (asymmetric context). Participants performed these task
contexts either individually with both hands (solo), or collaboratively by
pairing one hand with another one (dyads). We found that dyads in the
asymmetric context performed better than solos. In contrast, solos performed
the symmetric context better than dyads. Importantly, we found that two hands
took different roles in the asymmetric context for both solos and dyads. In
contrast, the contribution from each hand was statistically indistinguishable
in the symmetric context. Our findings suggest that better performance in dyads
than solos is not a general phenomenon, but rather that collaboration yields
better performance only when role specialization emerges in dyadic
interactions.
| [
{
"created": "Thu, 12 May 2022 16:35:06 GMT",
"version": "v1"
}
] | 2022-05-13 | [
[
"Takai",
"Asuka",
""
],
[
"Fu",
"Qiushi",
""
],
[
"Doibata",
"Yuzuru",
""
],
[
"Lisi",
"Giuseppe",
""
],
[
"Tsuchiya",
"Toshiki",
""
],
[
"Mojtahedi",
"Keivan",
""
],
[
"Yoshioka",
"Toshinori",
""
],
[
"Kawato",
"Mitsuo",
""
],
[
"Morimoto",
"Jun",
""
],
[
"Santello",
"Marco",
""
]
] | Collaboration frequently yields better results in decision making, learning, and haptic interactions than when these actions are performed individually. However, is collaboration always superior to solo actions, or do its benefits depend on whether collaborating individuals have different or the same roles? To answer this question, we asked human subjects to perform virtual-reality collaborative and individual beam transportation tasks. These tasks were simulated in real-time by coupling the motion of a pair of hand-held robotic manipulanda to the virtual beam using virtual spring-dampers. For the task to be considered successful, participants had to complete it within temporal and spatial constraints. While the visual feedback remained the same, the underlying dynamics of the beam were altered to create two distinctive task contexts which were determined by a moving pivot constraint. When the pivot was placed at the center of the beam, two hands contribute to the task with symmetric mechanical leverage (symmetric context). When the pivot was placed at the left side of the beam, two hands contribute to the task with asymmetric mechanical leverage (asymmetric context). Participants performed these task contexts either individually with both hands (solo), or collaboratively by pairing one hand with another one (dyads). We found that dyads in the asymmetric context performed better than solos. In contrast, solos performed the symmetric context better than dyads. Importantly, we found that two hands took different roles in the asymmetric context for both solos and dyads. In contrast, the contribution from each hand was statistically indistinguishable in the symmetric context. Our findings suggest that better performance in dyads than solos is not a general phenomenon, but rather that collaboration yields better performance only when role specialization emerges in dyadic interactions. |
q-bio/0309025 | Carson C. Chow | Carson C. Chow, John A. White, Jason Ritt, and Nancy Kopell | Frequency control in synchronized networks of inhibitory neurons | 18 pages, 3 figures, Kluwer.sty. J. Comp. Neurosci. (in press).
Originally submitted to the neuro-sys archive which was never publicly
announced (was 9803001) | null | null | null | q-bio.NC | null | We analyze the control of frequency for a synchronized inhibitory neuronal
network. The analysis is done for a reduced membrane model with a
biophysically-based synaptic influence. We argue that such a reduced model can
quantitatively capture the frequency behavior of a larger class of neuronal
models. We show that in different parameter regimes, the network frequency
depends in different ways on the intrinsic and synaptic time constants. Only in
one portion of the parameter space, called `phasic', is the network period
proportional to the synaptic decay time. These results are discussed in
connection with previous work of the authors, which showed that for mildly
heterogeneous networks, the synchrony breaks down, but coherence is preserved
much more for systems in the phasic regime than in the other regimes. These
results imply that for mildly heterogeneous networks, the existence of a
coherent rhythm implies a linear dependence of the network period on synaptic
decay time, and a much weaker dependence on the drive to the cells. We give
experimental evidence for this conclusion.
| [
{
"created": "Fri, 20 Mar 1998 21:12:18 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Chow",
"Carson C.",
""
],
[
"White",
"John A.",
""
],
[
"Ritt",
"Jason",
""
],
[
"Kopell",
"Nancy",
""
]
] | We analyze the control of frequency for a synchronized inhibitory neuronal network. The analysis is done for a reduced membrane model with a biophysically-based synaptic influence. We argue that such a reduced model can quantitatively capture the frequency behavior of a larger class of neuronal models. We show that in different parameter regimes, the network frequency depends in different ways on the intrinsic and synaptic time constants. Only in one portion of the parameter space, called `phasic', is the network period proportional to the synaptic decay time. These results are discussed in connection with previous work of the authors, which showed that for mildly heterogeneous networks, the synchrony breaks down, but coherence is preserved much more for systems in the phasic regime than in the other regimes. These results imply that for mildly heterogeneous networks, the existence of a coherent rhythm implies a linear dependence of the network period on synaptic decay time, and a much weaker dependence on the drive to the cells. We give experimental evidence for this conclusion. |
2105.14224 | Fan Hu | Fan Hu, Lei Wang, Yishen Hu, Dongqi Wang, Weijie Wang, Jianbing Jiang,
Nan Li and Peng Yin | A Novel Framework Integrating AI Model and Enzymological Experiments
Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and
Activity-based Probe | null | Briefings in Bioinformatics, 2021 | 10.1093/bib/bbab301 | null | q-bio.MN cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The identification of protein-ligand interaction plays a key role in
biochemical research and drug discovery. Although deep learning has recently
shown great promise in discovering new drugs, there remains a gap between deep
learning-based and experimental approaches. Here we propose a novel framework,
named AIMEE, integrating AI Model and Enzymology Experiments, to identify
inhibitors against 3CL protease of SARS-CoV-2, which has taken a significant
toll on people across the globe. From a bioactive chemical library, we have
conducted two rounds of experiments and identified six novel inhibitors with a
hit rate of 29.41%, and four of them showed an IC50 value less than 3 {\mu}M.
Moreover, we explored the interpretability of the central model in AIMEE,
mapping the deep learning extracted features to domain knowledge of chemical
properties. Based on this knowledge, a commercially available compound was
selected and proven to be an activity-based probe of 3CLpro. This work
highlights the great potential of combining deep learning models and
biochemical experiments for intelligent iteration and expanding the boundaries
of drug discovery.
| [
{
"created": "Sat, 29 May 2021 06:23:05 GMT",
"version": "v1"
}
] | 2021-08-10 | [
[
"Hu",
"Fan",
""
],
[
"Wang",
"Lei",
""
],
[
"Hu",
"Yishen",
""
],
[
"Wang",
"Dongqi",
""
],
[
"Wang",
"Weijie",
""
],
[
"Jiang",
"Jianbing",
""
],
[
"Li",
"Nan",
""
],
[
"Yin",
"Peng",
""
]
] | The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery. Although deep learning has recently shown great promise in discovering new drugs, there remains a gap between deep learning-based and experimental approaches. Here we propose a novel framework, named AIMEE, integrating AI Model and Enzymology Experiments, to identify inhibitors against 3CL protease of SARS-CoV-2, which has taken a significant toll on people across the globe. From a bioactive chemical library, we have conducted two rounds of experiments and identified six novel inhibitors with a hit rate of 29.41%, and four of them showed an IC50 value less than 3 {\mu}M. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and proven to be an activity-based probe of 3CLpro. This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and expanding the boundaries of drug discovery. |
2010.02522 | Rafael Bermeo | Kanhaya Lal (CERMAV), Rafael Bermeo (CERMAV), Serge P\'erez (CERMAV) | Computational tools for drawing, building and displaying carbohydrates:
a visual guide | null | Beilstein Journal of Organic Chemistry, Beilstein-Institut, 2020,
16, pp.2448-2468 | 10.3762/bjoc.16.199 | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Drawing and visualisation of molecular structures are some of the most common
tasks carried out in structural glycobiology, typically using various software.
In this perspective article, we outline developments in the computational tools
for the sketching, visualisation and modelling of glycans. The article also
provides details on the standard representation of glycans, and
glycoconjugates, which helps the communication of structure details within the
scientific community. We highlight the comparative analysis of the available
tools which could help researchers to perform various tasks related to
structure representation and model building of glycans. These tools can be
useful for glycobiologists or any researcher looking for a ready to use, simple
program for the sketching or building of glycans.
| [
{
"created": "Tue, 6 Oct 2020 06:53:16 GMT",
"version": "v1"
}
] | 2020-10-07 | [
[
"Lal",
"Kanhaya",
"",
"CERMAV"
],
[
"Bermeo",
"Rafael",
"",
"CERMAV"
],
[
"Pérez",
"Serge",
"",
"CERMAV"
]
] | Drawing and visualisation of molecular structures are some of the most common tasks carried out in structural glycobiology, typically using various software. In this perspective article, we outline developments in the computational tools for the sketching, visualisation and modelling of glycans. The article also provides details on the standard representation of glycans, and glycoconjugates, which helps the communication of structure details within the scientific community. We highlight the comparative analysis of the available tools which could help researchers to perform various tasks related to structure representation and model building of glycans. These tools can be useful for glycobiologists or any researcher looking for a ready to use, simple program for the sketching or building of glycans. |
2405.17833 | Mike Steel Prof. | Mike Steel | Neutral phylogenetic models and their role in tree-based biodiversity
measures | 19 pages, 5 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A wide variety of stochastic models of cladogenesis (based on speciation and
extinction) lead to an identical distribution on phylogenetic tree shapes once
the edge lengths are ignored. By contrast, the distribution of the tree's edge
lengths is generally quite sensitive to the underlying model. In this paper, we
review the impact of different model choices on tree shape and edge length
distribution, and its impact for studying the properties of phylogenetic
diversity (PD) as a measure of biodiversity, and the loss of PD as species
become extinct at the present. We also compare PD with a stochastic model of
feature diversity, and investigate some mathematical links and inequalities
between these two measures plus their predictions concerning the loss of
biodiversity under extinction at the present.
| [
{
"created": "Tue, 28 May 2024 05:10:11 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Aug 2024 04:33:14 GMT",
"version": "v2"
}
] | 2024-08-07 | [
[
"Steel",
"Mike",
""
]
] | A wide variety of stochastic models of cladogenesis (based on speciation and extinction) lead to an identical distribution on phylogenetic tree shapes once the edge lengths are ignored. By contrast, the distribution of the tree's edge lengths is generally quite sensitive to the underlying model. In this paper, we review the impact of different model choices on tree shape and edge length distribution, and its impact for studying the properties of phylogenetic diversity (PD) as a measure of biodiversity, and the loss of PD as species become extinct at the present. We also compare PD with a stochastic model of feature diversity, and investigate some mathematical links and inequalities between these two measures plus their predictions concerning the loss of biodiversity under extinction at the present. |
1603.04173 | Rafal Paprocki Mr | Temesgen Gebrehiwot, Rafal Paprocki, Artem Lenskiy | Analysis of Blink Rate Variability during reading and memory testing | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we investigated how statistical properties of the blink rate
variability changes during two mental tasks: reading a passage and memory
testing. To construct time series of inter-blink intervals (blink rate
variability) we detected exact blink time in EEG recordings using our blink
detection algorithm. We found that among 13 subjects, all subjects blinked less
during reading session. Moreover, standard deviation of the blink rate
variability is higher during reading. Thus, we conclude that the variability of
inter-blink intervals decreases during tasks that require concentration and
intense mental activity.
| [
{
"created": "Mon, 14 Mar 2016 09:20:04 GMT",
"version": "v1"
},
{
"created": "Sun, 27 Mar 2016 03:08:32 GMT",
"version": "v2"
}
] | 2016-03-29 | [
[
"Gebrehiwot",
"Temesgen",
""
],
[
"Paprocki",
"Rafal",
""
],
[
"Lenskiy",
"Artem",
""
]
] | In this paper we investigated how statistical properties of the blink rate variability changes during two mental tasks: reading a passage and memory testing. To construct time series of inter-blink intervals (blink rate variability) we detected exact blink time in EEG recordings using our blink detection algorithm. We found that among 13 subjects, all subjects blinked less during reading session. Moreover, standard deviation of the blink rate variability is higher during reading. Thus, we conclude that the variability of inter-blink intervals decreases during tasks that require concentration and intense mental activity. |
1706.04541 | Luigi Frunzo | Luigi Frunzo | Modeling sorption of emerging contaminants in biofilms | 24 pages, 11 figure, original paper | null | null | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A mathematical model for emerging contaminants sorption in multispecies
biofilms, based on a continuum approach and mass conservation principles is
presented. Diffusion of contaminants within the biofilm is described using a
diffusion-reaction equation. Binding sites formation and occupation are modeled
by two systems of hyperbolic partial differential equations are mutually
connected through the two growth rate terms. The model is completed with a
system of hyperbolic equations governing the microbial species growth within
the biofilm; a system of parabolic equations for substrates diffusion and
reaction and a nonlinear ordinary differential equation describing the free
boundary evolution. Two real special cases are modelled. The first one
describes the dynamics of a free sorbent component diffusing and reacting in a
multispecies biofilm. In the second illustrative case, the fate of two
different contaminants has been modelled.
| [
{
"created": "Mon, 5 Jun 2017 18:16:49 GMT",
"version": "v1"
}
] | 2017-06-15 | [
[
"Frunzo",
"Luigi",
""
]
] | A mathematical model for emerging contaminants sorption in multispecies biofilms, based on a continuum approach and mass conservation principles is presented. Diffusion of contaminants within the biofilm is described using a diffusion-reaction equation. Binding sites formation and occupation are modeled by two systems of hyperbolic partial differential equations are mutually connected through the two growth rate terms. The model is completed with a system of hyperbolic equations governing the microbial species growth within the biofilm; a system of parabolic equations for substrates diffusion and reaction and a nonlinear ordinary differential equation describing the free boundary evolution. Two real special cases are modelled. The first one describes the dynamics of a free sorbent component diffusing and reacting in a multispecies biofilm. In the second illustrative case, the fate of two different contaminants has been modelled. |
2311.05223 | Alenka Copic | Alenka {\v{C}}opi{\v{c}} (CRBM), Thibaud Dieudonn\'e (I2BC), Guillaume
Lenoir (I2BC) | Phosphatidylserine transport in cell life and death | null | Current Opinion in Cell Biology, 2023, Special Issue on Membrane
Trafficking, 83, pp.102192 | 10.1016/j.ceb.2023.102192 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phosphatidylserine (PS) is a negatively-charged glycerophospholipid found
mainly in the plasma membrane (PM) and in the late secretory/endocytic
compartments, where it regulates cellular activity and can mediate apoptosis.
Export of PS from the endoplasmic reticulum, its site of synthesis, to other
compartments, and its transbilayer asymmetry must therefore be precisely
regulated. We review recent findings on non-vesicular transport of PS by lipid
transfer proteins (LTPs) at membrane contact sites, on PS flip-flop between
membrane leaflets by flippases and scramblases, and on PS nano-clustering at
the PM. We also discuss emerging data on cooperation between scramblases and
LTPs, how perturbation of PS distribution can lead to disease, and the specific
role of PS in viral infection.
| [
{
"created": "Thu, 9 Nov 2023 09:12:31 GMT",
"version": "v1"
}
] | 2023-11-10 | [
[
"{Č}opi{č}",
"Alenka",
"",
"CRBM"
],
[
"Dieudonné",
"Thibaud",
"",
"I2BC"
],
[
"Lenoir",
"Guillaume",
"",
"I2BC"
]
] | Phosphatidylserine (PS) is a negatively-charged glycerophospholipid found mainly in the plasma membrane (PM) and in the late secretory/endocytic compartments, where it regulates cellular activity and can mediate apoptosis. Export of PS from the endoplasmic reticulum, its site of synthesis, to other compartments, and its transbilayer asymmetry must therefore be precisely regulated. We review recent findings on non-vesicular transport of PS by lipid transfer proteins (LTPs) at membrane contact sites, on PS flip-flop between membrane leaflets by flippases and scramblases, and on PS nano-clustering at the PM. We also discuss emerging data on cooperation between scramblases and LTPs, how perturbation of PS distribution can lead to disease, and the specific role of PS in viral infection. |
1808.06578 | Paul Smolen | Paul Smolen, Douglas A. Baxter, John H. Byrne | Paradoxical LTP maintenance with inhibition of protein synthesis and the
proteasome suggests a novel protein synthesis requirement for early LTP
reversal | 23 pages, 5 figures. Accepted to Journal of Theoretical Biology | null | null | null | q-bio.NC q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The transition from early long-term potentiation (E-LTP) to late LTP (L-LTP)
involves protein synthesis and degradation. L-LTP is blocked by inhibiting
either protein synthesis or proteasome-dependent degradation prior to and
during a tetanic stimulus, but paradoxically, L-LTP is not blocked when
synthesis and degradation are inhibited simultaneously, suggesting
counter-acting positive and negative proteins regulate L-LTP. To investigate
this paradox, we modeled LTP at the Schaffer collateral synapse. Nine
differential equations describe the levels of positive and negative regulator
proteins (PP and NP) and transitions among five discrete synaptic states, a
basal state (BAS), three E-LTP states (EP1, EP2, ED), and a L-LTP state (LP). A
stimulus initiates the transition from BAS to EP1 and from EP1 to EP2,
initiates the synthesis of PP and NP, and activates the ubiquitin-proteasome
system (UPS). UPS mediates transitions of EP1 and EP2 to ED and the degradation
of NP. The conversion of E-LTP to L-LTP is mediated by a PP-dependent
transition from ED to LP. NP mediates reversal of EP2 to BAS. This model
simulates empirical observations: 1) normal L-LTP, 2) block by either
proteasome inhibitor or protein synthesis inhibitor alone, and 3) preservation
of L-LTP when both inhibitors are applied together. Elements of this abstract
model can be correlated with specific molecules and processes. Moreover, the
model makes testable predictions, such as a unique synaptic state ED that
precedes the transition to L-LTP, and a time window for the action of the UPS
(during the transitions from EP1 and EP2 to ED). Tests of these predictions
will provide insights into the processes of long-term synaptic plasticity.
| [
{
"created": "Mon, 20 Aug 2018 17:39:15 GMT",
"version": "v1"
}
] | 2018-08-21 | [
[
"Smolen",
"Paul",
""
],
[
"Baxter",
"Douglas A.",
""
],
[
"Byrne",
"John H.",
""
]
] | The transition from early long-term potentiation (E-LTP) to late LTP (L-LTP) involves protein synthesis and degradation. L-LTP is blocked by inhibiting either protein synthesis or proteasome-dependent degradation prior to and during a tetanic stimulus, but paradoxically, L-LTP is not blocked when synthesis and degradation are inhibited simultaneously, suggesting counter-acting positive and negative proteins regulate L-LTP. To investigate this paradox, we modeled LTP at the Schaffer collateral synapse. Nine differential equations describe the levels of positive and negative regulator proteins (PP and NP) and transitions among five discrete synaptic states, a basal state (BAS), three E-LTP states (EP1, EP2, ED), and a L-LTP state (LP). A stimulus initiates the transition from BAS to EP1 and from EP1 to EP2, initiates the synthesis of PP and NP, and activates the ubiquitin-proteasome system (UPS). UPS mediates transitions of EP1 and EP2 to ED and the degradation of NP. The conversion of E-LTP to L-LTP is mediated by a PP-dependent transition from ED to LP. NP mediates reversal of EP2 to BAS. This model simulates empirical observations: 1) normal L-LTP, 2) block by either proteasome inhibitor or protein synthesis inhibitor alone, and 3) preservation of L-LTP when both inhibitors are applied together. Elements of this abstract model can be correlated with specific molecules and processes. Moreover, the model makes testable predictions, such as a unique synaptic state ED that precedes the transition to L-LTP, and a time window for the action of the UPS (during the transitions from EP1 and EP2 to ED). Tests of these predictions will provide insights into the processes of long-term synaptic plasticity. |
1101.5008 | Anne-Claire Haury | Anne-Claire Haury (CBIO), Pierre Gestraud, Jean-Philippe Vert (CBIO) | The influence of feature selection methods on accuracy, stability and
interpretability of molecular signatures | null | PLoS ONE (2011) 6(12): e28210 | 10.1371/journal.pone.0028210 | null | q-bio.QM stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Biomarker discovery from high-dimensional data is a crucial
problem with enormous applications in biology and medicine. It is also
extremely challenging from a statistical viewpoint, but surprisingly few
studies have investigated the relative strengths and weaknesses of the plethora
of existing feature selection methods. Methods: We compare 32 feature selection
methods on 4 public gene expression datasets for breast cancer prognosis, in
terms of predictive performance, stability and functional interpretability of
the signatures they produce. Results: We observe that the feature selection
method has a significant influence on the accuracy, stability and
interpretability of signatures. Simple filter methods generally outperform more
complex embedded or wrapper methods, and ensemble feature selection has
generally no positive effect. Overall a simple Student's t-test seems to
provide the best results. Availability: Code and data are publicly available at
http://cbio.ensmp.fr/~ahaury/.
| [
{
"created": "Wed, 26 Jan 2011 09:04:05 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Jun 2011 07:17:10 GMT",
"version": "v2"
}
] | 2012-09-17 | [
[
"Haury",
"Anne-Claire",
"",
"CBIO"
],
[
"Gestraud",
"Pierre",
"",
"CBIO"
],
[
"Vert",
"Jean-Philippe",
"",
"CBIO"
]
] | Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/. |
0907.0514 | Koichi Takahashi | Koichi Takahashi, Sorin Tanase-Nicola, Pieter Rein ten Wolde | Spatio-temporal correlations can drastically change the response of a
MAPK pathway | null | null | 10.1073/pnas.0906885107 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multisite covalent modification of proteins is omnipresent in eukaryotic
cells. A well-known example is the mitogen-activated protein kinase (MAPK)
cascade, where in each layer of the cascade a protein is phosphorylated at two
sites. It has long been known that the response of a MAPK pathway strongly
depends on whether the enzymes that modify the protein act processively or
distributively: distributive mechanism, in which the enzyme molecules have to
release the substrate molecules in between the modification of the two sites,
can generate an ultrasensitive response and lead to hysteresis and bistability.
We study by Green's Function Reaction Dynamics, a stochastic scheme that makes
it possible to simulate biochemical networks at the particle level and in time
and space, a dual phosphorylation cycle in which the enzymes act according to a
distributive mechanism. We find that the response of this network can differ
dramatically from that predicted by a mean-field analysis based on the chemical
rate equations. In particular, rapid rebindings of the enzyme molecules to the
substrate molecules after modification of the first site can markedly speed up
the response, and lead to loss of ultrasensitivity and bistability. In essence,
rapid enzyme-substrate rebindings can turn a distributive mechanism into a
processive mechanism. We argue that slow ADP release by the enzymes can protect
the system against these rapid rebindings, thus enabling ultrasensitivity and
bistability.
| [
{
"created": "Fri, 3 Jul 2009 15:36:25 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Takahashi",
"Koichi",
""
],
[
"Tanase-Nicola",
"Sorin",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | Multisite covalent modification of proteins is omnipresent in eukaryotic cells. A well-known example is the mitogen-activated protein kinase (MAPK) cascade, where in each layer of the cascade a protein is phosphorylated at two sites. It has long been known that the response of a MAPK pathway strongly depends on whether the enzymes that modify the protein act processively or distributively: distributive mechanism, in which the enzyme molecules have to release the substrate molecules in between the modification of the two sites, can generate an ultrasensitive response and lead to hysteresis and bistability. We study by Green's Function Reaction Dynamics, a stochastic scheme that makes it possible to simulate biochemical networks at the particle level and in time and space, a dual phosphorylation cycle in which the enzymes act according to a distributive mechanism. We find that the response of this network can differ dramatically from that predicted by a mean-field analysis based on the chemical rate equations. In particular, rapid rebindings of the enzyme molecules to the substrate molecules after modification of the first site can markedly speed up the response, and lead to loss of ultrasensitivity and bistability. In essence, rapid enzyme-substrate rebindings can turn a distributive mechanism into a processive mechanism. We argue that slow ADP release by the enzymes can protect the system against these rapid rebindings, thus enabling ultrasensitivity and bistability. |
2005.00921 | Ashutosh Mahajan Dr | Ashutosh Mahajan, Ravi Solanki and A.S. Namitha | An Epidemic Model SIPHERD and its application for prediction of the
spread of COVID-19 infection for India and USA | 6 pages, 11 figures | null | 10.1016/j.chaos.2020.110156 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an epidemic model SIPHERD in which three categories of infection
carriers Symptomatic, Purely Asymptomatic, and Exposed are considered with
different rates of transmission of infection that are taken dependent on the
lockdown and social distancing. The rate of detection of the infected carriers
is taken dependent on the tests done per day. The model is applied for the
COVID outbreak in Germany and South Korea to validate its predictive
capabilities and then applied to India and the United States for the prediction
of its spread with different lockdown situations and testing in the coming
months.
| [
{
"created": "Sat, 2 May 2020 21:11:36 GMT",
"version": "v1"
},
{
"created": "Mon, 11 May 2020 18:39:01 GMT",
"version": "v2"
}
] | 2020-08-26 | [
[
"Mahajan",
"Ashutosh",
""
],
[
"Solanki",
"Ravi",
""
],
[
"Namitha",
"A. S.",
""
]
] | We propose an epidemic model SIPHERD in which three categories of infection carriers Symptomatic, Purely Asymptomatic, and Exposed are considered with different rates of transmission of infection that are taken dependent on the lockdown and social distancing. The rate of detection of the infected carriers is taken dependent on the tests done per day. The model is applied for the COVID outbreak in Germany and South Korea to validate its predictive capabilities and then applied to India and the United States for the prediction of its spread with different lockdown situations and testing in the coming months. |
0805.2936 | Mike Steel Prof. | Mike Steel and Beata Faller | Markovian log-supermodularity, and its applications in phylogenetics | 8 pages, 2 figures | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We establish a log-supermodularity property for probability distributions on
binary patterns observed at the tips of a tree that are generated under any
2--state Markov process. We illustrate the applicability of this result in
phylogenetics by deriving an inequality relevant to estimating expected future
phylogenetic diversity under a model of species extinction. In a further
application of the log-supermodularity property, we derive a purely
combinatorial inequality for the parsimony score of a binary character. The
proofs of our results exploit two classical theorems in the combinatorics of
finite sets.
| [
{
"created": "Mon, 19 May 2008 20:53:56 GMT",
"version": "v1"
}
] | 2008-05-21 | [
[
"Steel",
"Mike",
""
],
[
"Faller",
"Beata",
""
]
] | We establish a log-supermodularity property for probability distributions on binary patterns observed at the tips of a tree that are generated under any 2--state Markov process. We illustrate the applicability of this result in phylogenetics by deriving an inequality relevant to estimating expected future phylogenetic diversity under a model of species extinction. In a further application of the log-supermodularity property, we derive a purely combinatorial inequality for the parsimony score of a binary character. The proofs of our results exploit two classical theorems in the combinatorics of finite sets. |
q-bio/0610039 | Ram\'on D\'iaz-Uriarte | Andreu Alibes, Edward R. Morrissey, Andres Canada, Oscar M. Rueda,
David Casado, Patricio Yankilevich, Ramon Diaz-Uriarte | Asterias: a parallelized web-based suite for the analysis of expression
and aCGH data | web based application; 3 figures | null | null | null | q-bio.GN q-bio.OT | null | Asterias (\url{http://www.asterias.info}) is an integrated collection of
freely-accessible web tools for the analysis of gene expression and aCGH data.
Most of the tools use parallel computing (via MPI). Most of our applications
allow the user to obtain additional information for user-selected genes by
using clickable links in tables and/or figures. Our tools include:
normalization of expression and aCGH data; converting between different types
of gene/clone and protein identifiers; filtering and imputation; finding
differentially expressed genes related to patient class and survival data;
searching for models of class prediction; using random forests to search for
minimal models for class prediction or for large subsets of genes with
predictive capacity; searching for molecular signatures and predictive genes
with survival data; detecting regions of genomic DNA gain or loss. The
capability to send results between different applications, access to additional
functional information, and parallelized computation make our suite unique and
exploit features only available to web-based applications.
| [
{
"created": "Sun, 22 Oct 2006 13:03:26 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Alibes",
"Andreu",
""
],
[
"Morrissey",
"Edward R.",
""
],
[
"Canada",
"Andres",
""
],
[
"Rueda",
"Oscar M.",
""
],
[
"Casado",
"David",
""
],
[
"Yankilevich",
"Patricio",
""
],
[
"Diaz-Uriarte",
"Ramon",
""
]
] | Asterias (\url{http://www.asterias.info}) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI). Most of our applications allow the user to obtain additional information for user-selected genes by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data; converting between different types of gene/clone and protein identifiers; filtering and imputation; finding differentially expressed genes related to patient class and survival data; searching for models of class prediction; using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity; searching for molecular signatures and predictive genes with survival data; detecting regions of genomic DNA gain or loss. The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications. |
1807.01457 | Vaibhav Madhok | Vaibhav Madhok | Evolutionary dynamics from deterministic microscopic ecological
processes: A toy model for evolutionary processes | A rigorous development on arXiv:1601.07830 | Phys. Rev. E 101, 032411 (2020) | 10.1103/PhysRevE.101.032411 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The central goal of a dynamical theory of evolution is to abstract the mean
evolutionary trajectory in the trait space by considering ecological processes
at the level of the individual. In this work, we develop such a theory for a
new class of deterministic individual based models describing individual births
and deaths, which captures the essential features of standard stochastic
individual-based models and become identical with the latter under maximal
competition. The key motivation is to derive the canonical equation of adaptive
dynamics from this microscopic ecological model, which can be regarded as a
"toy model" for evolution, in a simple way and give it an intuitive geometric
interpretation. Another goal is to study evolution and sympatric speciation
under "maximal" competition. We show that these models, in the deterministic
limit of adaptive dynamics, lead to the same equations that describe the
unraveling of the mean evolutionary trajectory as those obtained from the
standard stochastic models. We further study conditions under which these
models lead to evolutionary branching and find them to be similar with those
obtained from the standard stochastic models. We find that though deterministic
models result in a strong competition that leads to a speed up in the temporal
dynamics of a population cloud in the phenotypic space as well as an increase
in the rate of generation of biodiversity, it does not seem to result in an
absolute increase in biodiversity as far as total number of species are
concerned. Hence, the "toy model" essentially captures all the features of the
standard stochastic model. Interestingly, the notion of a fitness function does
not explicitly enter in our derivation of the canonical equation, thereby
advocating a mechanistic view of evolution.
| [
{
"created": "Wed, 4 Jul 2018 06:22:14 GMT",
"version": "v1"
}
] | 2020-03-25 | [
[
"Madhok",
"Vaibhav",
""
]
] | The central goal of a dynamical theory of evolution is to abstract the mean evolutionary trajectory in the trait space by considering ecological processes at the level of the individual. In this work, we develop such a theory for a new class of deterministic individual based models describing individual births and deaths, which captures the essential features of standard stochastic individual-based models and become identical with the latter under maximal competition. The key motivation is to derive the canonical equation of adaptive dynamics from this microscopic ecological model, which can be regarded as a "toy model" for evolution, in a simple way and give it an intuitive geometric interpretation. Another goal is to study evolution and sympatric speciation under "maximal" competition. We show that these models, in the deterministic limit of adaptive dynamics, lead to the same equations that describe the unraveling of the mean evolutionary trajectory as those obtained from the standard stochastic models. We further study conditions under which these models lead to evolutionary branching and find them to be similar with those obtained from the standard stochastic models. We find that though deterministic models result in a strong competition that leads to a speed up in the temporal dynamics of a population cloud in the phenotypic space as well as an increase in the rate of generation of biodiversity, it does not seem to result in an absolute increase in biodiversity as far as total number of species are concerned. Hence, the "toy model" essentially captures all the features of the standard stochastic model. Interestingly, the notion of a fitness function does not explicitly enter in our derivation of the canonical equation, thereby advocating a mechanistic view of evolution. |
2107.11192 | Antoine Chambaz | Thi Thanh Yen Nguyen (MAP5 - UMR 8145), Warith Harchaoui (MAP5 - UMR
8145, DERAISON.ai), Lucile M\'egret (Brain-C), Cloe Mendoza (B2A), Olivier
Bouaziz (MAP5 - UMR 8145), Christian Neri (B2A), Antoine Chambaz (MAP5 - UMR
8145) | Optimal transport-based machine learning to match specific patterns:
application to the detection of molecular regulation patterns in omics data | null | null | null | null | q-bio.GN math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present several algorithms designed to learn a pattern of correspondence
between two data sets in situations where it is desirable to match elements
that exhibit a relationship belonging to a known parametric model. In the
motivating case study, the challenge is to better understand micro-RNA
regulation in the striatum of Huntington's disease model mice. The algorithms
unfold in two stages. First, an optimal transport plan P and an optimal affine
transformation are learned, using the Sinkhorn-Knopp algorithm and a mini-batch
gradient descent. Second, P is exploited to derive either several co-clusters
or several sets of matched elements. A simulation study illustrates how the
algorithms work and perform. The real data application further illustrates
their applicability and interest.
| [
{
"created": "Wed, 21 Jul 2021 12:02:16 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Jan 2022 08:38:33 GMT",
"version": "v2"
},
{
"created": "Thu, 2 Mar 2023 15:57:09 GMT",
"version": "v3"
}
] | 2023-03-03 | [
[
"Nguyen",
"Thi Thanh Yen",
"",
"MAP5 - UMR 8145"
],
[
"Harchaoui",
"Warith",
"",
"MAP5 - UMR\n 8145, DERAISON.ai"
],
[
"Mégret",
"Lucile",
"",
"Brain-C"
],
[
"Mendoza",
"Cloe",
"",
"B2A"
],
[
"Bouaziz",
"Olivier",
"",
"MAP5 - UMR 8145"
],
[
"Neri",
"Christian",
"",
"B2A"
],
[
"Chambaz",
"Antoine",
"",
"MAP5 - UMR\n 8145"
]
] | We present several algorithms designed to learn a pattern of correspondence between two data sets in situations where it is desirable to match elements that exhibit a relationship belonging to a known parametric model. In the motivating case study, the challenge is to better understand micro-RNA regulation in the striatum of Huntington's disease model mice. The algorithms unfold in two stages. First, an optimal transport plan P and an optimal affine transformation are learned, using the Sinkhorn-Knopp algorithm and a mini-batch gradient descent. Second, P is exploited to derive either several co-clusters or several sets of matched elements. A simulation study illustrates how the algorithms work and perform. The real data application further illustrates their applicability and interest. |
q-bio/0703055 | Haret Rosu | H.C. Rosu | The microtubule transistor | 4 pages | null | null | null | q-bio.SC | null | I point out the similarity between the microtubule experiment reported by
Priel et al [Biophys. J. 90, 4639 (2006)] and the ZnO nanowire experiment of
Wang et al [Nanolett. 6, 2768 (2006)]. It is quite possible that MTs are
similar to a piezoelectric field effect transistor for which the role of the
control gate electrode is played by the piezo-induced electric field across the
width of the MT walls and their elastic bending features
| [
{
"created": "Mon, 26 Mar 2007 19:11:45 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Rosu",
"H. C.",
""
]
] | I point out the similarity between the microtubule experiment reported by Priel et al [Biophys. J. 90, 4639 (2006)] and the ZnO nanowire experiment of Wang et al [Nanolett. 6, 2768 (2006)]. It is quite possible that MTs are similar to a piezoelectric field effect transistor for which the role of the control gate electrode is played by the piezo-induced electric field across the width of the MT walls and their elastic bending features |
1407.2414 | Matthew Turner | Daniel J. G. Pearce and A. M. Miller and George Rowlands and Matthew
S. Turner | The Role of Projection in the Control of Bird Flocks | PNAS early edition published online at
http://www.pnas.org/cgi/doi/10.1073/pnas.1402202111 | null | 10.1073/pnas.1402202111 | null | q-bio.QM cond-mat.stat-mech nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Swarming is a conspicuous behavioural trait observed in bird flocks, fish
shoals, insect swarms and mammal herds. It is thought to improve collective
awareness and offer protection from predators. Many current models involve the
hypothesis that information coordinating motion is exchanged between neighbors.
We argue that such local interactions alone are insufficient to explain the
organization of large flocks of birds and that the mechanism for the exchange
of long-ranged information necessary to control their density remains unknown.
We show that large flocks self-organize to the maximum density at which a
typical individual is still just able to see out of the flock in many
directions. Such flocks are marginally opaque - an external observer can also
just still see a substantial fraction of sky through the flock. Although
seemingly intuitive we show that this need not be the case; flocks could easily
be highly diffuse or entirely opaque. The emergence of marginal opacity
strongly constrains how individuals interact with each other within large
swarms. It also provides a mechanism for global interactions: An individual can
respond to the projection of the flock that it sees. This provides for faster
information transfer and hence rapid flock dynamics, another advantage over
local models. From a behavioural perspective it optimizes the information
available to each bird while maintaining the protection of a dense, coherent
flock.
| [
{
"created": "Wed, 9 Jul 2014 10:04:54 GMT",
"version": "v1"
}
] | 2015-06-22 | [
[
"Pearce",
"Daniel J. G.",
""
],
[
"Miller",
"A. M.",
""
],
[
"Rowlands",
"George",
""
],
[
"Turner",
"Matthew S.",
""
]
] | Swarming is a conspicuous behavioural trait observed in bird flocks, fish shoals, insect swarms and mammal herds. It is thought to improve collective awareness and offer protection from predators. Many current models involve the hypothesis that information coordinating motion is exchanged between neighbors. We argue that such local interactions alone are insufficient to explain the organization of large flocks of birds and that the mechanism for the exchange of long-ranged information necessary to control their density remains unknown. We show that large flocks self-organize to the maximum density at which a typical individual is still just able to see out of the flock in many directions. Such flocks are marginally opaque - an external observer can also just still see a substantial fraction of sky through the flock. Although seemingly intuitive we show that this need not be the case; flocks could easily be highly diffuse or entirely opaque. The emergence of marginal opacity strongly constrains how individuals interact with each other within large swarms. It also provides a mechanism for global interactions: An individual can respond to the projection of the flock that it sees. This provides for faster information transfer and hence rapid flock dynamics, another advantage over local models. From a behavioural perspective it optimizes the information available to each bird while maintaining the protection of a dense, coherent flock. |
2406.19611 | Huajun Zhou | Huajun Zhou, Fengtao Zhou, Chenyu Zhao, Yingxue Xu, Luyang Luo, Hao
Chen | Multimodal Data Integration for Precision Oncology: Challenges and
Future Directions | 15 pages, 4 figures | null | null | null | q-bio.QM cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The essence of precision oncology lies in its commitment to tailor targeted
treatments and care measures to each patient based on the individual
characteristics of the tumor. The inherent heterogeneity of tumors necessitates
gathering information from diverse data sources to provide valuable insights
from various perspectives, fostering a holistic comprehension of the tumor.
Over the past decade, multimodal data integration technology for precision
oncology has made significant strides, showcasing remarkable progress in
understanding the intricate details within heterogeneous data modalities. These
strides have exhibited tremendous potential for improving clinical
decision-making and model interpretation, contributing to the advancement of
cancer care and treatment. Given the rapid progress that has been achieved, we
provide a comprehensive overview of about 300 papers detailing cutting-edge
multimodal data integration techniques in precision oncology. In addition, we
conclude the primary clinical applications that have reaped significant
benefits, including early assessment, diagnosis, prognosis, and biomarker
discovery. Finally, derived from the findings of this survey, we present an
in-depth analysis that explores the pivotal challenges and reveals essential
pathways for future research in the field of multimodal data integration for
precision oncology.
| [
{
"created": "Fri, 28 Jun 2024 02:35:05 GMT",
"version": "v1"
}
] | 2024-07-01 | [
[
"Zhou",
"Huajun",
""
],
[
"Zhou",
"Fengtao",
""
],
[
"Zhao",
"Chenyu",
""
],
[
"Xu",
"Yingxue",
""
],
[
"Luo",
"Luyang",
""
],
[
"Chen",
"Hao",
""
]
] | The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor. The inherent heterogeneity of tumors necessitates gathering information from diverse data sources to provide valuable insights from various perspectives, fostering a holistic comprehension of the tumor. Over the past decade, multimodal data integration technology for precision oncology has made significant strides, showcasing remarkable progress in understanding the intricate details within heterogeneous data modalities. These strides have exhibited tremendous potential for improving clinical decision-making and model interpretation, contributing to the advancement of cancer care and treatment. Given the rapid progress that has been achieved, we provide a comprehensive overview of about 300 papers detailing cutting-edge multimodal data integration techniques in precision oncology. In addition, we conclude the primary clinical applications that have reaped significant benefits, including early assessment, diagnosis, prognosis, and biomarker discovery. Finally, derived from the findings of this survey, we present an in-depth analysis that explores the pivotal challenges and reveals essential pathways for future research in the field of multimodal data integration for precision oncology. |
1710.08784 | Mirna Kramar | Felix B\"auerle, Mirna Kramar, Karen Alim | Spatial mapping reveals multi-step pattern of wound healing in Physarum
polycephalum | Felix B\"auerle and Mirna Kramar contributed equally to this work | Journal of Physics D: Applied Physics (2017) Volume 50, Number 43 | 10.1088/1361-6463/aa8a21 | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wounding is a severe impairment of function, especially for an exposed
organism like the network-forming true slime mould Physarum polycephalum. The
tubular network making up the organism's body plan is entirely interconnected
and shares a common cytoplasm. Oscillatory contractions of the enclosing tube
walls drive the shuttle streaming of the cytoplasm. Cytoplasmic flows underlie
the reorganization of the network for example by movement toward attractive
stimuli or away from repellants. Here, we follow the reorganization of Physarum
polycephalum networks after severe wounding. Spatial mapping of the contraction
changes in response to wounding reveal a multi-step pattern. Phases of
increased activity alternate with cessation of contractions and stalling of
flows, giving rise to coordinated transport and growth at the severing site.
Overall, severing surprisingly acts like an attractive stimulus enabling
healing of severed tubes. The reproducible cessation of contractions arising
during this wound-healing response may open up new venues to investigate the
biochemical wiring underlying Physarum polycephalum's complex behaviours.
| [
{
"created": "Tue, 24 Oct 2017 14:06:41 GMT",
"version": "v1"
}
] | 2017-10-25 | [
[
"Bäuerle",
"Felix",
""
],
[
"Kramar",
"Mirna",
""
],
[
"Alim",
"Karen",
""
]
] | Wounding is a severe impairment of function, especially for an exposed organism like the network-forming true slime mould Physarum polycephalum. The tubular network making up the organism's body plan is entirely interconnected and shares a common cytoplasm. Oscillatory contractions of the enclosing tube walls drive the shuttle streaming of the cytoplasm. Cytoplasmic flows underlie the reorganization of the network for example by movement toward attractive stimuli or away from repellants. Here, we follow the reorganization of Physarum polycephalum networks after severe wounding. Spatial mapping of the contraction changes in response to wounding reveal a multi-step pattern. Phases of increased activity alternate with cessation of contractions and stalling of flows, giving rise to coordinated transport and growth at the severing site. Overall, severing surprisingly acts like an attractive stimulus enabling healing of severed tubes. The reproducible cessation of contractions arising during this wound-healing response may open up new venues to investigate the biochemical wiring underlying Physarum polycephalum's complex behaviours. |
2206.09818 | Lijun Wu | Qizhi Pei, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin,
Haiguang Liu, Tie-Yan Liu, Rui Yan | SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity
Prediction | Accepted by Briefings in Bioinformatics 2023 | null | null | null | q-bio.BM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in
early-stage drug discovery, facilitating the identification of drugs that can
effectively interact with specific targets and regulate their activities. While
wet experiments remain the most reliable method, they are time-consuming and
resource-intensive, resulting in limited data availability that poses
challenges for deep learning approaches. Existing methods have primarily
focused on developing techniques based on the available DTA data, without
adequately addressing the data scarcity issue. To overcome this challenge, we
present the SSM-DTA framework, which incorporates three simple yet highly
effective strategies: (1) A multi-task training approach that combines DTA
prediction with masked language modeling (MLM) using paired drug-target data.
(2) A semi-supervised training method that leverages large-scale unpaired
molecules and proteins to enhance drug and target representations. This
approach differs from previous methods that only employed molecules or proteins
in pre-training. (3) The integration of a lightweight cross-attention module to
improve the interaction between drugs and targets, further enhancing prediction
accuracy. Through extensive experiments on benchmark datasets such as
BindingDB, DAVIS, and KIBA, we demonstrate the superior performance of our
framework. Additionally, we conduct case studies on specific drug-target
binding activities, virtual screening experiments, drug feature visualizations,
and real-world applications, all of which showcase the significant potential of
our work. In conclusion, our proposed SSM-DTA framework addresses the data
limitation challenge in DTA prediction and yields promising results, paving the
way for more efficient and accurate drug discovery processes. Our code is
available at $\href{https://github.com/QizhiPei/SSM-DTA}{Github}$.
| [
{
"created": "Mon, 20 Jun 2022 14:53:25 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Jun 2022 02:45:34 GMT",
"version": "v2"
},
{
"created": "Tue, 17 Oct 2023 14:06:07 GMT",
"version": "v3"
}
] | 2023-10-18 | [
[
"Pei",
"Qizhi",
""
],
[
"Wu",
"Lijun",
""
],
[
"Zhu",
"Jinhua",
""
],
[
"Xia",
"Yingce",
""
],
[
"Xie",
"Shufang",
""
],
[
"Qin",
"Tao",
""
],
[
"Liu",
"Haiguang",
""
],
[
"Liu",
"Tie-Yan",
""
],
[
"Yan",
"Rui",
""
]
] | Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the SSM-DTA framework, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling (MLM) using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS, and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations, and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes. Our code is available at $\href{https://github.com/QizhiPei/SSM-DTA}{Github}$. |
0808.2232 | Stephen Quake | Richard A. White III, Paul Blainey, H. Christina Fan, and Stephen R.
Quake | Digital PCR provides sensitive and absolute calibration for high
throughput sequencing | null | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several of the next generation sequencers are limited in their sample
preparation process by the need to make an absolute measurement of the number
of template molecules in the library to be sequenced. As currently practiced,
the practical effects of this requirement compromise sequencing performance,
both by requiring large amounts of sample DNA and by requiring extra sequencing
runs to be performed. We used digital PCR to quantitate sequencing libraries,
and demonstrated its sensitivity and robustness by preparing and sequencing
libraries from subnanogram amounts of bacterial and human DNA on the 454 and
Solexa sequencing platforms. This assay allows absolute quantitation and
eliminates uncertainties associated with the construction and application of
standard curves. The digital PCR platform consumes subfemptogram amounts of the
sequencing library and gives highly accurate results, allowing the optimal DNA
concentration to be used in setting up sequencing runs without costly and
time-consuming titration techniques. This approach also reduces the input
sample requirement more than 1000-fold: from micrograms of DNA to less than a
nanogram.
| [
{
"created": "Sat, 16 Aug 2008 02:53:08 GMT",
"version": "v1"
}
] | 2008-08-19 | [
[
"White",
"Richard A.",
"III"
],
[
"Blainey",
"Paul",
""
],
[
"Fan",
"H. Christina",
""
],
[
"Quake",
"Stephen R.",
""
]
] | Several of the next generation sequencers are limited in their sample preparation process by the need to make an absolute measurement of the number of template molecules in the library to be sequenced. As currently practiced, the practical effects of this requirement compromise sequencing performance, both by requiring large amounts of sample DNA and by requiring extra sequencing runs to be performed. We used digital PCR to quantitate sequencing libraries, and demonstrated its sensitivity and robustness by preparing and sequencing libraries from subnanogram amounts of bacterial and human DNA on the 454 and Solexa sequencing platforms. This assay allows absolute quantitation and eliminates uncertainties associated with the construction and application of standard curves. The digital PCR platform consumes subfemptogram amounts of the sequencing library and gives highly accurate results, allowing the optimal DNA concentration to be used in setting up sequencing runs without costly and time-consuming titration techniques. This approach also reduces the input sample requirement more than 1000-fold: from micrograms of DNA to less than a nanogram. |
1310.2415 | Gerardo Aquino | Sophie V. Pageon, Gerardo Aquino, Kathryn Lagrue, Karsten K\"ohler,
Robert G. Endres and Daniel M. Davis | Dynamics of Natural Killer cell receptor revealed by quantitative
analysis of photoswitchable protein | 25 pages, 5 figures | Biophysical Journal, Volume 105, 1-10, (2013) | 10.1016/j.bpj.2013.09.025 | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural Killer (NK) cell activation is dynamically regulated by numerous
activating and inhibitory surface receptors that accumulate at the immune
synapse. Quantitative analysis of receptor dynamics has been limited by
methodologies which rely on indirect measurements such as fluorescence recovery
after photobleaching. Here, we report a novel approach to study how proteins
traffic to and from the immune synapse using NK cell receptors tagged with the
photoswitchable fluorescent protein tdEosFP, which can be irreversibly
photoswitched from a green to red fluorescent state by ultraviolet light. Thus,
following a localized switching event, the movement of the photoswitched
molecules can be temporally and spatially resolved by monitoring fluorescence
in two regions of interest. By comparing images with mathematical models, we
evaluated the diffusion coefficient of the receptor KIR2DL1 (0.23 +- 0.06
micron^2/s) and assessed how synapse formation affects receptor dynamics. Our
data conclude that the inhibitory NK cell receptor KIR2DL1 is continually
trafficked into the synapse and remains surprisingly stable there. Unexpectedly
however, in NK cells forming synapses with multiple target cells
simultaneously, KIR2DL1 at one synapse can relocate to another synapse. Thus,
our results reveal a previously undetected inter-synaptic exchange of protein.
| [
{
"created": "Wed, 9 Oct 2013 09:51:43 GMT",
"version": "v1"
}
] | 2015-06-17 | [
[
"Pageon",
"Sophie V.",
""
],
[
"Aquino",
"Gerardo",
""
],
[
"Lagrue",
"Kathryn",
""
],
[
"Köhler",
"Karsten",
""
],
[
"Endres",
"Robert G.",
""
],
[
"Davis",
"Daniel M.",
""
]
] | Natural Killer (NK) cell activation is dynamically regulated by numerous activating and inhibitory surface receptors that accumulate at the immune synapse. Quantitative analysis of receptor dynamics has been limited by methodologies which rely on indirect measurements such as fluorescence recovery after photobleaching. Here, we report a novel approach to study how proteins traffic to and from the immune synapse using NK cell receptors tagged with the photoswitchable fluorescent protein tdEosFP, which can be irreversibly photoswitched from a green to red fluorescent state by ultraviolet light. Thus, following a localized switching event, the movement of the photoswitched molecules can be temporally and spatially resolved by monitoring fluorescence in two regions of interest. By comparing images with mathematical models, we evaluated the diffusion coefficient of the receptor KIR2DL1 (0.23 +- 0.06 micron^2/s) and assessed how synapse formation affects receptor dynamics. Our data conclude that the inhibitory NK cell receptor KIR2DL1 is continually trafficked into the synapse and remains surprisingly stable there. Unexpectedly however, in NK cells forming synapses with multiple target cells simultaneously, KIR2DL1 at one synapse can relocate to another synapse. Thus, our results reveal a previously undetected inter-synaptic exchange of protein. |
2107.12838 | Fuad Noman | Fuad Noman, Chee-Ming Ting, Hakmook Kang, Raphael C.-W. Phan, Brian D.
Boyd, Warren D. Taylor, and Hernando Ombao | Graph Autoencoders for Embedding Learning in Brain Networks and Major
Depressive Disorder Identification | null | null | 10.1109/JBHI.2024.3351177 | null | q-bio.NC cs.AI cs.LG | http://creativecommons.org/publicdomain/zero/1.0/ | Brain functional connectivity (FC) reveals biomarkers for identification of
various neuropsychiatric disorders. Recent application of deep neural networks
(DNNs) to connectome-based classification mostly relies on traditional
convolutional neural networks using input connectivity matrices on a regular
Euclidean grid. We propose a graph deep learning framework to incorporate the
non-Euclidean information about graph structure for classifying functional
magnetic resonance imaging (fMRI)-derived brain networks in major depressive
disorder (MDD). We design a novel graph autoencoder (GAE) architecture based on
the graph convolutional networks (GCNs) to embed the topological structure and
node content of large-sized fMRI networks into low-dimensional latent
representations. In network construction, we employ the Ledoit-Wolf (LDW)
shrinkage method to estimate the high-dimensional FC metrics efficiently from
fMRI data. We consider both supervised and unsupervised approaches for the
graph embedding learning. The learned embeddings are then used as feature
inputs for a deep fully-connected neural network (FCNN) to discriminate MDD
from healthy controls. Evaluated on two resting-state fMRI (rs-fMRI) MDD
datasets, results show that the proposed GAE-FCNN model significantly
outperforms several state-of-the-art methods for brain connectome
classification, achieving the best accuracy using the LDW-FC edges as node
features. The graph embeddings of fMRI FC networks learned by the GAE also
reveal apparent group differences between MDD and HC. Our new framework
demonstrates feasibility of learning graph embeddings on brain networks to
provide discriminative information for diagnosis of brain disorders.
| [
{
"created": "Tue, 27 Jul 2021 14:12:39 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Jun 2022 13:34:00 GMT",
"version": "v2"
}
] | 2024-01-31 | [
[
"Noman",
"Fuad",
""
],
[
"Ting",
"Chee-Ming",
""
],
[
"Kang",
"Hakmook",
""
],
[
"Phan",
"Raphael C. -W.",
""
],
[
"Boyd",
"Brian D.",
""
],
[
"Taylor",
"Warren D.",
""
],
[
"Ombao",
"Hernando",
""
]
] | Brain functional connectivity (FC) reveals biomarkers for identification of various neuropsychiatric disorders. Recent application of deep neural networks (DNNs) to connectome-based classification mostly relies on traditional convolutional neural networks using input connectivity matrices on a regular Euclidean grid. We propose a graph deep learning framework to incorporate the non-Euclidean information about graph structure for classifying functional magnetic resonance imaging (fMRI)-derived brain networks in major depressive disorder (MDD). We design a novel graph autoencoder (GAE) architecture based on the graph convolutional networks (GCNs) to embed the topological structure and node content of large-sized fMRI networks into low-dimensional latent representations. In network construction, we employ the Ledoit-Wolf (LDW) shrinkage method to estimate the high-dimensional FC metrics efficiently from fMRI data. We consider both supervised and unsupervised approaches for the graph embedding learning. The learned embeddings are then used as feature inputs for a deep fully-connected neural network (FCNN) to discriminate MDD from healthy controls. Evaluated on two resting-state fMRI (rs-fMRI) MDD datasets, results show that the proposed GAE-FCNN model significantly outperforms several state-of-the-art methods for brain connectome classification, achieving the best accuracy using the LDW-FC edges as node features. The graph embeddings of fMRI FC networks learned by the GAE also reveal apparent group differences between MDD and HC. Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders. |
2208.10304 | Cecilia Jarne Dr | Cecilia Jarne and Mariano Caruso | Effect in the spectra of eigenvalues and dynamics of RNNs trained with
Excitatory-Inhibitory constraint | null | null | 10.1007/s11571-023-09956-w | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to comprehend and enhance models that describes various brain
regions is important to study the dynamics of trained recurrent neural
networks. Including Dales law in such models usually presents several
challenges. However, this is an important aspect that allows computational
models to better capture the characteristics of the brain. Here we present a
framework to train networks using such constraint. Then we have used it to
train them in simple decision making tasks. We characterized the eigenvalue
distributions of the recurrent weight matrices of such networks. Interestingly,
we discovered that the non-dominant eigenvalues of the recurrent weight matrix
are distributed in a circle with a radius less than 1 for those whose initial
condition before training was random normal and in a ring for those whose
initial condition was random orthogonal. In both cases, the radius does not
depend on the fraction of excitatory and inhibitory units nor the size of the
network. Diminution of the radius, compared to networks trained without the
constraint, has implications on the activity and dynamics that we discussed
here.
| [
{
"created": "Mon, 22 Aug 2022 13:33:56 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Aug 2022 11:46:15 GMT",
"version": "v2"
},
{
"created": "Mon, 9 Jan 2023 15:20:13 GMT",
"version": "v3"
}
] | 2023-04-11 | [
[
"Jarne",
"Cecilia",
""
],
[
"Caruso",
"Mariano",
""
]
] | In order to comprehend and enhance models that describes various brain regions is important to study the dynamics of trained recurrent neural networks. Including Dales law in such models usually presents several challenges. However, this is an important aspect that allows computational models to better capture the characteristics of the brain. Here we present a framework to train networks using such constraint. Then we have used it to train them in simple decision making tasks. We characterized the eigenvalue distributions of the recurrent weight matrices of such networks. Interestingly, we discovered that the non-dominant eigenvalues of the recurrent weight matrix are distributed in a circle with a radius less than 1 for those whose initial condition before training was random normal and in a ring for those whose initial condition was random orthogonal. In both cases, the radius does not depend on the fraction of excitatory and inhibitory units nor the size of the network. Diminution of the radius, compared to networks trained without the constraint, has implications on the activity and dynamics that we discussed here. |
1705.00944 | Helene Leman | Charline Smadi and Helene Leman and Violaine Llaurens | Looking for the right mate in diploid species: how dominance
relationships affect population differentiation in sexual trait? | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Divergence between populations for a given trait can be driven by natural or
sexual selection, interacting with migration behaviour. Mating preference for
different phenotypes can lead to the emergence and persistence of
differentiated populations. Dominance between alleles encoding for divergent
phenotypes can interfere in such processes. Using a diploid model of trait
determining both mating success and migration rate, we explored differentiation
between two connected populations, assuming either co-dominance or strict
dominance between alleles. The model assumes that individuals prefer mating
with partners displaying the same phenotype and therefore tend to move to the
other population when their own phenotype is rare. We show that the emergence
of differentiated populations in this diploid model is limited as compared to
results obtained with the same model assuming haploidy. When assuming
co-dominance, differentiation arises only when migration is limited as compared
to preference. Such differentiation is less dependent on migration when
assuming strict dominance between haplotypes. Dominant alleles frequently
invade populations because their phenotype is more frequently expressed,
resulting in higher mating success and rapid decrease in migration. However,
depending on the initial distribution of alleles, this advantage associated
with dominance (i.e. Haldane's sieve) may lead to fixation of the dominant
allele throughout both populations. Depending on the initial distribution of
heterozygotes, persistence of polymorphisms within populations can also occur
because heterozygotes displaying the predominant phenotype benefit from mating
preferences. Altogether, our results highlight that heterozygotes' behaviour
has a strong impact on population differentiation and stress out the need of
diploid models of differentiation and speciation driven by natural and sexual
selection.
| [
{
"created": "Tue, 2 May 2017 12:51:52 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Jan 2018 20:45:01 GMT",
"version": "v2"
}
] | 2018-01-25 | [
[
"Smadi",
"Charline",
""
],
[
"Leman",
"Helene",
""
],
[
"Llaurens",
"Violaine",
""
]
] | Divergence between populations for a given trait can be driven by natural or sexual selection, interacting with migration behaviour. Mating preference for different phenotypes can lead to the emergence and persistence of differentiated populations. Dominance between alleles encoding for divergent phenotypes can interfere in such processes. Using a diploid model of trait determining both mating success and migration rate, we explored differentiation between two connected populations, assuming either co-dominance or strict dominance between alleles. The model assumes that individuals prefer mating with partners displaying the same phenotype and therefore tend to move to the other population when their own phenotype is rare. We show that the emergence of differentiated populations in this diploid model is limited as compared to results obtained with the same model assuming haploidy. When assuming co-dominance, differentiation arises only when migration is limited as compared to preference. Such differentiation is less dependent on migration when assuming strict dominance between haplotypes. Dominant alleles frequently invade populations because their phenotype is more frequently expressed, resulting in higher mating success and rapid decrease in migration. However, depending on the initial distribution of alleles, this advantage associated with dominance (i.e. Haldane's sieve) may lead to fixation of the dominant allele throughout both populations. Depending on the initial distribution of heterozygotes, persistence of polymorphisms within populations can also occur because heterozygotes displaying the predominant phenotype benefit from mating preferences. Altogether, our results highlight that heterozygotes' behaviour has a strong impact on population differentiation and stress out the need of diploid models of differentiation and speciation driven by natural and sexual selection. |
0812.2787 | Brigitte Gaillard | Joseph Hughes, Francois Criscuolo (DEPE-IPHC) | Evolutionary history of the UCP gene family: gene duplication and
selection | null | BMC Evol. Biol. 8 (2008) 306 | 10.1186/1471-2148-8-306 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | BACKGROUND: The uncoupling protein (UCP) genes belong to the superfamily of
electron transport carriers of the mitochondrial inner membrane. Members of the
uncoupling protein family are involved in thermogenesis and determining the
functional evolution of UCP genes is important to understand the evolution of
thermo-regulation in vertebrates. RESULTS: Sequence similarity searches of
genome and scaffold data identified homologues of UCP in eutherians, teleosts
and the first squamates uncoupling proteins. Phylogenetic analysis was used to
characterize the family evolutionary history by identifying two duplications
early in vertebrate evolution and two losses in the avian lineage (excluding
duplications within a species, excluding the losses due to incompletely
sequenced taxa and excluding the losses and duplications inferred through
mismatch of species and gene trees). Estimates of synonymous and nonsynonymous
substitution rates (dN/dS) and more complex branch and site models suggest that
the duplication events were not associated with positive Darwinian selection
and that the UCP is constrained by strong purifying selection except for a
single site which has undergone positive Darwinian selection, demonstrating
that the UCP gene family must be highly conserved. CONCLUSION: We present a
phylogeny describing the evolutionary history of the UCP gene family and show
that the genes have evolved through duplications followed by purifying
selection except for a single site in the mitochondrial matrix between the 5th
and 6th alpha-helices which has undergone positive selection.
| [
{
"created": "Mon, 15 Dec 2008 12:41:47 GMT",
"version": "v1"
}
] | 2008-12-16 | [
[
"Hughes",
"Joseph",
"",
"DEPE-IPHC"
],
[
"Criscuolo",
"Francois",
"",
"DEPE-IPHC"
]
] | BACKGROUND: The uncoupling protein (UCP) genes belong to the superfamily of electron transport carriers of the mitochondrial inner membrane. Members of the uncoupling protein family are involved in thermogenesis and determining the functional evolution of UCP genes is important to understand the evolution of thermo-regulation in vertebrates. RESULTS: Sequence similarity searches of genome and scaffold data identified homologues of UCP in eutherians, teleosts and the first squamates uncoupling proteins. Phylogenetic analysis was used to characterize the family evolutionary history by identifying two duplications early in vertebrate evolution and two losses in the avian lineage (excluding duplications within a species, excluding the losses due to incompletely sequenced taxa and excluding the losses and duplications inferred through mismatch of species and gene trees). Estimates of synonymous and nonsynonymous substitution rates (dN/dS) and more complex branch and site models suggest that the duplication events were not associated with positive Darwinian selection and that the UCP is constrained by strong purifying selection except for a single site which has undergone positive Darwinian selection, demonstrating that the UCP gene family must be highly conserved. CONCLUSION: We present a phylogeny describing the evolutionary history of the UCP gene family and show that the genes have evolved through duplications followed by purifying selection except for a single site in the mitochondrial matrix between the 5th and 6th alpha-helices which has undergone positive selection. |
1806.07156 | Rub\'en Ahijado-Guzm\'an | Weixiang Ye, Markus G\"otz, Sirin Celiksoy, Laura T\"uting, Christoph
Ratzke, Janak Prasad, Rub\'en Ahijado-Guzm\'an, Thorsten Hugel, Carsten
S\"onnichsen | Conformational dynamics of a single protein monitored for 24 hours at
video rate | null | null | 10.1021/acs.nanolett.8b03342 | null | q-bio.QM physics.bio-ph q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We use plasmon rulers to follow the conformational dynamics of a single
protein for up to 24 h at a video rate. The plasmon ruler consists of two gold
nanospheres connected by a single protein linker. In our experiment, we follow
the dynamics of the molecular chaperone heat shock protein 90, which is known
to show open and closed conformations. Our measurements confirm the previously
known conformational dynamics with transition times in the second to minute
time scale and reveals new dynamics on the time scale of minutes to hours.
Plasmon rulers thus extend the observation bandwidth 3/4 orders of magnitude
with respect to single-molecule fluorescence resonance energy transfer and
enable the study of molecular dynamics with unprecedented precision.
| [
{
"created": "Tue, 19 Jun 2018 11:22:19 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Oct 2018 07:44:29 GMT",
"version": "v2"
}
] | 2018-10-08 | [
[
"Ye",
"Weixiang",
""
],
[
"Götz",
"Markus",
""
],
[
"Celiksoy",
"Sirin",
""
],
[
"Tüting",
"Laura",
""
],
[
"Ratzke",
"Christoph",
""
],
[
"Prasad",
"Janak",
""
],
[
"Ahijado-Guzmán",
"Rubén",
""
],
[
"Hugel",
"Thorsten",
""
],
[
"Sönnichsen",
"Carsten",
""
]
] | We use plasmon rulers to follow the conformational dynamics of a single protein for up to 24 h at a video rate. The plasmon ruler consists of two gold nanospheres connected by a single protein linker. In our experiment, we follow the dynamics of the molecular chaperone heat shock protein 90, which is known to show open and closed conformations. Our measurements confirm the previously known conformational dynamics with transition times in the second to minute time scale and reveals new dynamics on the time scale of minutes to hours. Plasmon rulers thus extend the observation bandwidth 3/4 orders of magnitude with respect to single-molecule fluorescence resonance energy transfer and enable the study of molecular dynamics with unprecedented precision. |
1404.5111 | Susmita Roy | Susmita Roy, Krishna Shrinivas and Biman Bagchi | A stochastic chemical dynamic approach to correlate autoimmunity and
optimal vitamin-D range | arXiv admin note: substantial text overlap with arXiv:1304.7193 | null | 10.1371/journal.pone.0100635 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by several recent experimental observations that vitamin-D could
interact with antigen presenting cells (APCs) and T-lymphocyte cells (T-cells)
to promote and to regulate different stages of immune response, we developed a
coarse grained kinetic model in an attempt to quantify the role of vitamin-D in
immunomodulatory responses. Our kinetic model, developed using the ideas of
chemical network theory, leads to a system of nine coupled equations that we
solve both by direct and by stochastic (Gillespie) methods. Both the analyses
consistently provide detail information on the dependence of immune response to
the variation of critical rate parameters. We find that although vitamin-D
plays a negligible role in the initial immune response, it exerts a profound
influence in the long term, especially in helping the system to achieve a new,
stable steady state. The study explores the role of vitamin-D in preserving an
observed bistability in the phase diagram (spanned by system parameters) of
immune regulation, thus allowing the response to tolerate a wide range of
pathogenic stimulation which could help in resisting autoimmune diseases. We
also study how vitamin-D affects the time dependent population of dendritic
cells that connect between innate and adaptive immune responses. Variations in
dose dependent response in anti-inflammatory and pro-inflammatory T-cell
populations to vitamin-D correlate well with recent experimental results. Our
kinetic model allows for an estimation of the range of optimum level of
vitamin-D required for smooth functioning of the immune system and for control
of both hyper-regulation and inflammation. Most importantly, the present study
reveals that an overdose or toxic level of vitamin-D or any steroid analogue
could give rise to too large a tolerant response, leading to an inefficacy in
adaptive immune function.
| [
{
"created": "Mon, 21 Apr 2014 05:19:47 GMT",
"version": "v1"
}
] | 2020-07-01 | [
[
"Roy",
"Susmita",
""
],
[
"Shrinivas",
"Krishna",
""
],
[
"Bagchi",
"Biman",
""
]
] | Motivated by several recent experimental observations that vitamin-D could interact with antigen presenting cells (APCs) and T-lymphocyte cells (T-cells) to promote and to regulate different stages of immune response, we developed a coarse grained kinetic model in an attempt to quantify the role of vitamin-D in immunomodulatory responses. Our kinetic model, developed using the ideas of chemical network theory, leads to a system of nine coupled equations that we solve both by direct and by stochastic (Gillespie) methods. Both the analyses consistently provide detail information on the dependence of immune response to the variation of critical rate parameters. We find that although vitamin-D plays a negligible role in the initial immune response, it exerts a profound influence in the long term, especially in helping the system to achieve a new, stable steady state. The study explores the role of vitamin-D in preserving an observed bistability in the phase diagram (spanned by system parameters) of immune regulation, thus allowing the response to tolerate a wide range of pathogenic stimulation which could help in resisting autoimmune diseases. We also study how vitamin-D affects the time dependent population of dendritic cells that connect between innate and adaptive immune responses. Variations in dose dependent response in anti-inflammatory and pro-inflammatory T-cell populations to vitamin-D correlate well with recent experimental results. Our kinetic model allows for an estimation of the range of optimum level of vitamin-D required for smooth functioning of the immune system and for control of both hyper-regulation and inflammation. Most importantly, the present study reveals that an overdose or toxic level of vitamin-D or any steroid analogue could give rise to too large a tolerant response, leading to an inefficacy in adaptive immune function. |
2405.17066 | Jeff Guo | Jeff Guo, Philippe Schwaller | Saturn: Sample-efficient Generative Molecular Design using Memory
Manipulation | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Generative molecular design for drug discovery has very recently achieved a
wave of experimental validation, with language-based backbones being the most
common architectures employed. The most important factor for downstream success
is whether an in silico oracle is well correlated with the desired end-point.
To this end, current methods use cheaper proxy oracles with higher throughput
before evaluating the most promising subset with high-fidelity oracles. The
ability to directly optimize high-fidelity oracles would greatly enhance
generative design and be expected to improve hit rates. However, current models
are not efficient enough to consider such a prospect, exemplifying the sample
efficiency problem. In this work, we introduce Saturn, which leverages the
Augmented Memory algorithm and demonstrates the first application of the Mamba
architecture for generative molecular design. We elucidate how experience
replay with data augmentation improves sample efficiency and how Mamba
synergistically exploits this mechanism. Saturn outperforms 22 models on
multi-parameter optimization tasks relevant to drug discovery and may possess
sufficient sample efficiency to consider the prospect of directly optimizing
high-fidelity oracles.
| [
{
"created": "Mon, 27 May 2024 11:37:36 GMT",
"version": "v1"
}
] | 2024-05-28 | [
[
"Guo",
"Jeff",
""
],
[
"Schwaller",
"Philippe",
""
]
] | Generative molecular design for drug discovery has very recently achieved a wave of experimental validation, with language-based backbones being the most common architectures employed. The most important factor for downstream success is whether an in silico oracle is well correlated with the desired end-point. To this end, current methods use cheaper proxy oracles with higher throughput before evaluating the most promising subset with high-fidelity oracles. The ability to directly optimize high-fidelity oracles would greatly enhance generative design and be expected to improve hit rates. However, current models are not efficient enough to consider such a prospect, exemplifying the sample efficiency problem. In this work, we introduce Saturn, which leverages the Augmented Memory algorithm and demonstrates the first application of the Mamba architecture for generative molecular design. We elucidate how experience replay with data augmentation improves sample efficiency and how Mamba synergistically exploits this mechanism. Saturn outperforms 22 models on multi-parameter optimization tasks relevant to drug discovery and may possess sufficient sample efficiency to consider the prospect of directly optimizing high-fidelity oracles. |
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