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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1706.01249 | Lior Noy | Yuval Hart, Avraham E Mayo, Ruth Mayo, Liron Rozenkrantz, Avichai
Tendler, Uri Alon and Lior Noy | Creative Foraging: A Quantitative Paradigm for Studying Creative
Exploration | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Creative exploration is central to science, art and cognitive development.
However, research on creative exploration is limited by a lack of
high-resolution automated paradigms. To address this, we present such an
automated paradigm, the creative foraging game, in which people search for
novel and valuable solutions in a large and well-defined space made of all
possible shapes made of ten connected squares. Players discovered shape
categories such as digits, letters, and airplanes. They exploited each
category, then dropped it to explore once again, and so on. Aligned with a
prediction of optimal foraging theory (OFT) prediction, during exploration
phases, people moved along meandering paths that are about three times longer
than the minimal paths between shapes, when exploiting a category of related
shapes, they moved along the minimal paths. The moment of discovery of a new
category was usually done at a nonprototypical and ambiguous shape, which can
serve as an experimental proxy for creative leaps. People showed individual
differences in their search patterns, along a continuum between two strategies:
a mercurial quick-to-discover/quick-to-drop strategy and a thorough
slow-to-discover/slow-to-drop strategy. Contrary to optimal foraging theory,
players leave exploitation to explore again far before categories are depleted.
This paradigm opens the way for automated high-resolution study of creative
exploration.
| [
{
"created": "Mon, 5 Jun 2017 09:26:23 GMT",
"version": "v1"
}
] | 2017-06-06 | [
[
"Hart",
"Yuval",
""
],
[
"Mayo",
"Avraham E",
""
],
[
"Mayo",
"Ruth",
""
],
[
"Rozenkrantz",
"Liron",
""
],
[
"Tendler",
"Avichai",
""
],
[
"Alon",
"Uri",
""
],
[
"Noy",
"Lior",
""
]
] | Creative exploration is central to science, art and cognitive development. However, research on creative exploration is limited by a lack of high-resolution automated paradigms. To address this, we present such an automated paradigm, the creative foraging game, in which people search for novel and valuable solutions in a large and well-defined space made of all possible shapes made of ten connected squares. Players discovered shape categories such as digits, letters, and airplanes. They exploited each category, then dropped it to explore once again, and so on. Aligned with a prediction of optimal foraging theory (OFT) prediction, during exploration phases, people moved along meandering paths that are about three times longer than the minimal paths between shapes, when exploiting a category of related shapes, they moved along the minimal paths. The moment of discovery of a new category was usually done at a nonprototypical and ambiguous shape, which can serve as an experimental proxy for creative leaps. People showed individual differences in their search patterns, along a continuum between two strategies: a mercurial quick-to-discover/quick-to-drop strategy and a thorough slow-to-discover/slow-to-drop strategy. Contrary to optimal foraging theory, players leave exploitation to explore again far before categories are depleted. This paradigm opens the way for automated high-resolution study of creative exploration. |
1907.00950 | Romuald A. Janik | Romuald A. Janik | Explaining the Human Visual Brain Challenge 2019 -- receptive fields and
surrogate features | 6 pages, 5 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper I review the submission to the Explaining the Human Visual
Brain Challenge 2019 in both the fMRI and MEG tracks. The goal was to construct
neural network features which generate the so-called representational
dissimilarity matrix (RDM) which is most similar to the one extracted from fMRI
and MEG data upon viewing a set of images. I review exploring the optimal
granularity of the receptive field, a construction of intermediate surrogate
features using Multidimensional Scaling and modelling them using neural network
features. I also point out some peculiarities of the RDM construction which
have to be taken into account.
| [
{
"created": "Mon, 1 Jul 2019 17:39:48 GMT",
"version": "v1"
}
] | 2019-07-02 | [
[
"Janik",
"Romuald A.",
""
]
] | In this paper I review the submission to the Explaining the Human Visual Brain Challenge 2019 in both the fMRI and MEG tracks. The goal was to construct neural network features which generate the so-called representational dissimilarity matrix (RDM) which is most similar to the one extracted from fMRI and MEG data upon viewing a set of images. I review exploring the optimal granularity of the receptive field, a construction of intermediate surrogate features using Multidimensional Scaling and modelling them using neural network features. I also point out some peculiarities of the RDM construction which have to be taken into account. |
1004.3951 | Christian Mulder PhD | Christian Mulder, A. Jan Hendriks | Scaling Population Cycles of Herbivores and Carnivores | This research was partly supported by a Research Network Programme of
the European Science Foundation on body size and ecosystem dynamics (SIZEMIC) | null | null | null | q-bio.QM physics.bio-ph q-bio.PE | http://creativecommons.org/licenses/by/3.0/ | Periodicity in population dynamics is a fundamental issue. In addition to
current species-specific analyses, allometry facilitates understanding of limit
cycles amongst different species. So far, body-size regressions have been
derived for the oscillation period of the population densities of warm-blooded
species, in particular herbivores. Here, we extend the allometric analysis to
other clades, allowing for a comparison between the obtained slopes and
intercepts. The oscillation periods were derived from databases and original
studies to cover a broad range of conditions and species. Then, values were
related to specific body size by regression analysis. For different groups of
herbivorous species, the oscillation period increased as a function of
individual mass as a power law with exponents of 0.11-0.27. The intercepts of
the resulting linear regressions indicated that cycle times for equally-sized
species increased from homeotherms up to invertebrates. Overall, cycle times
for predators did not scale to body size. Implications for these differences
were addressed in the light of intra- and interspecific delays.
| [
{
"created": "Thu, 22 Apr 2010 15:55:30 GMT",
"version": "v1"
}
] | 2010-04-23 | [
[
"Mulder",
"Christian",
""
],
[
"Hendriks",
"A. Jan",
""
]
] | Periodicity in population dynamics is a fundamental issue. In addition to current species-specific analyses, allometry facilitates understanding of limit cycles amongst different species. So far, body-size regressions have been derived for the oscillation period of the population densities of warm-blooded species, in particular herbivores. Here, we extend the allometric analysis to other clades, allowing for a comparison between the obtained slopes and intercepts. The oscillation periods were derived from databases and original studies to cover a broad range of conditions and species. Then, values were related to specific body size by regression analysis. For different groups of herbivorous species, the oscillation period increased as a function of individual mass as a power law with exponents of 0.11-0.27. The intercepts of the resulting linear regressions indicated that cycle times for equally-sized species increased from homeotherms up to invertebrates. Overall, cycle times for predators did not scale to body size. Implications for these differences were addressed in the light of intra- and interspecific delays. |
q-bio/0510013 | Yury A. Koksharov | Olga A. Koksharova, Johan Klint, and Ulla Rasmussen | The protein map of Synechococcus sp. PCC 7942 - the first overlook | null | null | null | null | q-bio.GN | null | The unicellular cyanobacterium Synechococcus PCC 7942 has been used as a
model organism for studies of prokaryotic circadian rhythms,
carbon-concentrating mechanisms, response to a variety of nutrient and
environmental stresses, and cell division. This paper presents the results of
the first proteomic exploratory study of Synechococcus PCC 7942. The proteome
was analyzed using two-dimensional gel electrophoresis followed by MALDI-TOF
mass spectroscopy, and database searching. Of 140 analyzed protein spots, 110
were successfully identified as 62 different proteins, many of which occurred
as multiple spots on the gel. The identified proteins were organized into 18
different functional categories reflecting the major metabolic and cellular
processes occurring in the cyanobacterial cells in the exponential growth
phase. Among the identified proteins, 14 previously unknown or considered to be
hypothetical are here shown to be true gene products in Synechococcus sp. PCC
7942, and may be helpful for annotation of the newly sequenced genome.
| [
{
"created": "Thu, 6 Oct 2005 11:41:56 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Koksharova",
"Olga A.",
""
],
[
"Klint",
"Johan",
""
],
[
"Rasmussen",
"Ulla",
""
]
] | The unicellular cyanobacterium Synechococcus PCC 7942 has been used as a model organism for studies of prokaryotic circadian rhythms, carbon-concentrating mechanisms, response to a variety of nutrient and environmental stresses, and cell division. This paper presents the results of the first proteomic exploratory study of Synechococcus PCC 7942. The proteome was analyzed using two-dimensional gel electrophoresis followed by MALDI-TOF mass spectroscopy, and database searching. Of 140 analyzed protein spots, 110 were successfully identified as 62 different proteins, many of which occurred as multiple spots on the gel. The identified proteins were organized into 18 different functional categories reflecting the major metabolic and cellular processes occurring in the cyanobacterial cells in the exponential growth phase. Among the identified proteins, 14 previously unknown or considered to be hypothetical are here shown to be true gene products in Synechococcus sp. PCC 7942, and may be helpful for annotation of the newly sequenced genome. |
2210.02451 | Anne Modat | Alberto Mart\'inez-Ort\'i, Sonia Adam, Giovanni Garippa (UNISS),
J\'er\^ome Boissier (IHPE), M Dolores Bargues, Santiago Mas-Coma | Morpho-anatomical characterization of the urogenital schistosmiasis
vector Bulinus truncatus (Audouin, 1827) (Heterobranchia : Bulinidae) from
Southwestern Europe | null | Journal of Conchology, CONCHOLOGICAL SOC GREAT BRITAIN & IRELAND
2022, 44 (4), pp.355-372 | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Urogenital schistosomiasis has been present naturally in the South of Europe
since the beginning of the 20 th century and nowadays its presence is also
known, at least imported by Sub-Saharan emigrants and tourists, in France,
Italy, Portugal and Spain. One of the intermediate hosts of this trematode
present in Europe is the bulinid mollusc Bulinus truncatus, non-native species
that can be reached to Europe by humans and birds. In order to know this
mollusc better, we carried out a morpho-anatomical study, of the shell, the
reproductive system, radula, the respiratory organs and pseudobranch of several
populations from Italy, France and Spain. Spanish conchological material
studied comes from different populations, from material deposited in the "Museo
Nacional de Ciencias Naturales" of Madrid and the "Museu de Ci{\`e}ncies
Naturals" of Barcelona, as well as from its own material deposited in the
"Museu Valenci{\`a} d'Hist{\`o}ria Natural" of Alginet (Valencia). The shell
growth in captivity and the estimation of the population age of B. truncatus
from El Ejido (Almer{\'i}a, Spain), has also been studied. Finally, the finding
of aphallic and euphallic specimens in the different populations of southern
Europe studied is presented and taxonomic and ecological data of the genus
Bulinus are shown.
| [
{
"created": "Wed, 5 Oct 2022 12:50:07 GMT",
"version": "v1"
}
] | 2022-10-07 | [
[
"Martínez-Ortí",
"Alberto",
"",
"UNISS"
],
[
"Adam",
"Sonia",
"",
"UNISS"
],
[
"Garippa",
"Giovanni",
"",
"UNISS"
],
[
"Boissier",
"Jérôme",
"",
"IHPE"
],
[
"Bargues",
"M Dolores",
""
],
[
"Mas-Coma",
"Santiago",
""
]
] | Urogenital schistosomiasis has been present naturally in the South of Europe since the beginning of the 20 th century and nowadays its presence is also known, at least imported by Sub-Saharan emigrants and tourists, in France, Italy, Portugal and Spain. One of the intermediate hosts of this trematode present in Europe is the bulinid mollusc Bulinus truncatus, non-native species that can be reached to Europe by humans and birds. In order to know this mollusc better, we carried out a morpho-anatomical study, of the shell, the reproductive system, radula, the respiratory organs and pseudobranch of several populations from Italy, France and Spain. Spanish conchological material studied comes from different populations, from material deposited in the "Museo Nacional de Ciencias Naturales" of Madrid and the "Museu de Ci{\`e}ncies Naturals" of Barcelona, as well as from its own material deposited in the "Museu Valenci{\`a} d'Hist{\`o}ria Natural" of Alginet (Valencia). The shell growth in captivity and the estimation of the population age of B. truncatus from El Ejido (Almer{\'i}a, Spain), has also been studied. Finally, the finding of aphallic and euphallic specimens in the different populations of southern Europe studied is presented and taxonomic and ecological data of the genus Bulinus are shown. |
q-bio/0610022 | Rafael F. Pont-Lezica | Leila Feiz (SCSV), Muhammad Irshad (SCSV), Rafael F Pont-Lezica
(SCSV), Herv\'e Canut (SCSV), Elisabeth Jamet (SCSV) | Evaluation of cell wall preparations for proteomics: a new procedure for
purifying cell walls from Arabidopsis hypocotyls | null | Plant Methods 2 (2006) 10 | 10.1186/1746-4811-2-10 | null | q-bio.GN | null | The ultimate goal of proteomic analysis of a cell compartment should be the
exhaustive identification of resident proteins; excluding proteins from other
cell compartments. Plant cell walls possess specific difficulties. Several
reported procedures to isolate cell walls for proteomic analyses led to the
isolation of a high proportion (more than 50%) of predicted intracellular
proteins. The rationales of several published procedures to isolate cell walls
for proteomics were analyzed, with regard to the bioinformatic-predicted
subcellular localization of the identified proteins. A new procedure was
developed to prepare cell walls from etiolated hypocotyls of Arabidopsis
thaliana. After salt extraction, a high proportion of proteins predicted to be
secreted was released (73%), belonging to the same functional classes as
proteins identified using previously described protocols. The new cell wall
preparation described in this paper gives the lowest proportion of proteins
predicted to be intracellular when compared to available protocols. The
application of its principles should lead to a more realistic view of the cell
wall proteome, at least for the weakly bound CWP extractable by salts. In
addition, it offers a clean cell wall preparation for subsequent extraction of
strongly bound CWP.
| [
{
"created": "Thu, 12 Oct 2006 08:22:38 GMT",
"version": "v1"
},
{
"created": "Wed, 18 Oct 2006 14:42:30 GMT",
"version": "v2"
}
] | 2016-08-16 | [
[
"Feiz",
"Leila",
"",
"SCSV"
],
[
"Irshad",
"Muhammad",
"",
"SCSV"
],
[
"Pont-Lezica",
"Rafael F",
"",
"SCSV"
],
[
"Canut",
"Hervé",
"",
"SCSV"
],
[
"Jamet",
"Elisabeth",
"",
"SCSV"
]
] | The ultimate goal of proteomic analysis of a cell compartment should be the exhaustive identification of resident proteins; excluding proteins from other cell compartments. Plant cell walls possess specific difficulties. Several reported procedures to isolate cell walls for proteomic analyses led to the isolation of a high proportion (more than 50%) of predicted intracellular proteins. The rationales of several published procedures to isolate cell walls for proteomics were analyzed, with regard to the bioinformatic-predicted subcellular localization of the identified proteins. A new procedure was developed to prepare cell walls from etiolated hypocotyls of Arabidopsis thaliana. After salt extraction, a high proportion of proteins predicted to be secreted was released (73%), belonging to the same functional classes as proteins identified using previously described protocols. The new cell wall preparation described in this paper gives the lowest proportion of proteins predicted to be intracellular when compared to available protocols. The application of its principles should lead to a more realistic view of the cell wall proteome, at least for the weakly bound CWP extractable by salts. In addition, it offers a clean cell wall preparation for subsequent extraction of strongly bound CWP. |
2204.12550 | Jose E Amaro | J. E. Amaro | Systematic description of COVID-19 pandemic using exact SIR solutions
and Gumbel distributions | null | Nonlinear Dynamics 111, 1947--1969 (2023) | 10.1007/s11071-022-07907-4 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | An epidemiological study of deaths is carried out in a dozen countries by
analyzing the first wave of the COVID-19 pandemic. These countries are among
those most affected by the first wave, i.e. where daily-death data series may
closely resemble a solution of the basic SIR equations. The SIR equations are
solved parametrically using the proper time as parameter. Some general
properties of the SIR solutions are studied such as time-scaling and asymmetry.
Additionally, we use approximations to the SIR solutions through Gumbel
functions, which present a very similar behavior. The parameters of the SIR
model and the Gumbel function are extracted from the data and compared for the
different countries. It is found that ten of the selected countries are very
well described by the solutions of the SIR model, with a basic reproduction
number between 3 and 8.
| [
{
"created": "Tue, 26 Apr 2022 19:14:35 GMT",
"version": "v1"
}
] | 2023-06-22 | [
[
"Amaro",
"J. E.",
""
]
] | An epidemiological study of deaths is carried out in a dozen countries by analyzing the first wave of the COVID-19 pandemic. These countries are among those most affected by the first wave, i.e. where daily-death data series may closely resemble a solution of the basic SIR equations. The SIR equations are solved parametrically using the proper time as parameter. Some general properties of the SIR solutions are studied such as time-scaling and asymmetry. Additionally, we use approximations to the SIR solutions through Gumbel functions, which present a very similar behavior. The parameters of the SIR model and the Gumbel function are extracted from the data and compared for the different countries. It is found that ten of the selected countries are very well described by the solutions of the SIR model, with a basic reproduction number between 3 and 8. |
1505.04774 | Bo Li | Banghe Li, Bo Li, Yuefeng Shen | A Much better replacement of the Michaelis-Menten equation and its
application | null | null | null | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Michaelis-Menten equation is a basic equation of enzyme kinetics and gives an
acceptable approximation of real chemical reaction processes. Analyzing the
derivation of this equation yields the fact that its good performance of
approximating real reaction processes is due to Michaelis-Menten curve (15).
This curve is derived from Quasi-Steady-State Assumption(QSSA), which has been
proved always true and called Quasi-Steady-State Law by Banghe Li et al [19].
Here, we found a quartic equation A(S,E)=0 (22), which gives more accurate
approximation of the reaction process in two aspects: during the quasi-steady
state of a reaction, Michaelis-Menten curve approximates the reaction well,
while our quartic equation $A(S,E)=0$ gives better approximation; near the end
of the reaction, our equation approaches the end of the reaction with a tangent
line same to that of the reaction, while Michaelis-Menten curve does not. In
addition, our quartic equation A(S,E)=0 differs to Michaelis-Menten curve less
than the order of $1/S^3$ as S approaches $+\infty$.
By considering the above merits of A(S,E)=0, we suggest it as a replacement
of Michaelis-Menten curve. Intuitively, this new equation is more complex and
harder to understand. But, just because its complexity, it provides more
information about the rate constants than Michaelis-Menten curve does.
Finally, we get a better replacement of the Michaelis-Menten equation by
combing A(S,E)=0 and the equation $dP/dt=k_2C(t)$.
| [
{
"created": "Thu, 14 May 2015 17:33:29 GMT",
"version": "v1"
}
] | 2015-05-19 | [
[
"Li",
"Banghe",
""
],
[
"Li",
"Bo",
""
],
[
"Shen",
"Yuefeng",
""
]
] | Michaelis-Menten equation is a basic equation of enzyme kinetics and gives an acceptable approximation of real chemical reaction processes. Analyzing the derivation of this equation yields the fact that its good performance of approximating real reaction processes is due to Michaelis-Menten curve (15). This curve is derived from Quasi-Steady-State Assumption(QSSA), which has been proved always true and called Quasi-Steady-State Law by Banghe Li et al [19]. Here, we found a quartic equation A(S,E)=0 (22), which gives more accurate approximation of the reaction process in two aspects: during the quasi-steady state of a reaction, Michaelis-Menten curve approximates the reaction well, while our quartic equation $A(S,E)=0$ gives better approximation; near the end of the reaction, our equation approaches the end of the reaction with a tangent line same to that of the reaction, while Michaelis-Menten curve does not. In addition, our quartic equation A(S,E)=0 differs to Michaelis-Menten curve less than the order of $1/S^3$ as S approaches $+\infty$. By considering the above merits of A(S,E)=0, we suggest it as a replacement of Michaelis-Menten curve. Intuitively, this new equation is more complex and harder to understand. But, just because its complexity, it provides more information about the rate constants than Michaelis-Menten curve does. Finally, we get a better replacement of the Michaelis-Menten equation by combing A(S,E)=0 and the equation $dP/dt=k_2C(t)$. |
1609.00441 | Antonio Rueda-Toicen | Allan A. Zea and Antonio Rueda-Toicen | Characterizing the structure of protein-protein interaction networks | 10 pages, 3 figures. Conference: CIMENICS XIII at Caracas, Venezuela,
2016 | null | 10.13140/RG.2.2.13286.63043 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network theorists have developed methods to characterize the complex
interactions in natural phenomena. The structure of the network of interactions
between proteins is important in the field of proteomics, and has been subject
to intensive research in recent years, as scientists have become increasingly
capable and interested in describing the underlying structure of interactions
in both normal and pathological biological processes. In this paper, we survey
the graph-theoretic characterization of protein-protein interaction networks
(PINs) in terms of structural features, and discuss its possible applications
in biomedical research. We also perform a brief revision of network theory's
classical literature and discuss modern statistical and computational
techniques to describe the structure of PINs
| [
{
"created": "Fri, 2 Sep 2016 01:02:57 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Sep 2016 22:53:36 GMT",
"version": "v2"
}
] | 2016-09-07 | [
[
"Zea",
"Allan A.",
""
],
[
"Rueda-Toicen",
"Antonio",
""
]
] | Network theorists have developed methods to characterize the complex interactions in natural phenomena. The structure of the network of interactions between proteins is important in the field of proteomics, and has been subject to intensive research in recent years, as scientists have become increasingly capable and interested in describing the underlying structure of interactions in both normal and pathological biological processes. In this paper, we survey the graph-theoretic characterization of protein-protein interaction networks (PINs) in terms of structural features, and discuss its possible applications in biomedical research. We also perform a brief revision of network theory's classical literature and discuss modern statistical and computational techniques to describe the structure of PINs |
1411.7348 | Anand Banerjee | Anand Banerjee, Alexander Berzhkovskii and Ralph Nossal | Efficiency of cellular uptake of nanoparticles via receptor-mediated
endocytosis | 21 pages, 9 figures | null | null | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experiments show that cellular uptake of nanoparticles, via receptor-mediated
endocytosis, strongly depends on nanoparticle size. There is an optimal size,
approximately 50 nm in diameter, at which cellular uptake is the highest. In
addition, there is a maximum size, approximately 200 nm, beyond which uptake
via receptor-mediated endocytosis does not occur. By comparing results from
different experiments, we found that these sizes weakly depend on the type of
cells, nanoparticles, and ligands used in the experiments. Here, we argue that
these observations are consequences of the energetics and assembly dynamics of
the protein coat that forms on the cytoplasmic side of the outer cell membrane
during receptor-mediated endocytosis. Specifically, we show that the energetics
of coat formation imposes an upper bound on the size of the nanoparticles that
can be internalized, whereas the nanoparticle-size-dependent dynamics of coat
assembly results in the optimal nanoparticle size. The weak dependence of the
optimal and maximum sizes on cell-nanoparticle-ligand type also follows
naturally from our analysis.
| [
{
"created": "Mon, 27 Oct 2014 17:14:12 GMT",
"version": "v1"
}
] | 2014-11-27 | [
[
"Banerjee",
"Anand",
""
],
[
"Berzhkovskii",
"Alexander",
""
],
[
"Nossal",
"Ralph",
""
]
] | Experiments show that cellular uptake of nanoparticles, via receptor-mediated endocytosis, strongly depends on nanoparticle size. There is an optimal size, approximately 50 nm in diameter, at which cellular uptake is the highest. In addition, there is a maximum size, approximately 200 nm, beyond which uptake via receptor-mediated endocytosis does not occur. By comparing results from different experiments, we found that these sizes weakly depend on the type of cells, nanoparticles, and ligands used in the experiments. Here, we argue that these observations are consequences of the energetics and assembly dynamics of the protein coat that forms on the cytoplasmic side of the outer cell membrane during receptor-mediated endocytosis. Specifically, we show that the energetics of coat formation imposes an upper bound on the size of the nanoparticles that can be internalized, whereas the nanoparticle-size-dependent dynamics of coat assembly results in the optimal nanoparticle size. The weak dependence of the optimal and maximum sizes on cell-nanoparticle-ligand type also follows naturally from our analysis. |
q-bio/0611043 | Daniel Remondini | D. Remondini, N. Neretti, J. M. Sedivy, C. Franceschi, L. Milanesi, P.
Tieri, G. C. Castellani | Networks from gene expression time series: characterization of
correlation patterns | 10 pages, 3 BMP figures, 1 Table. To appear in Int. J. Bif. Chaos,
July 2007, Volume 17, Issue 7 | null | 10.1142/S0218127407018543 | null | q-bio.GN | null | This paper describes characteristic features of networks reconstructed from
gene expression time series data. Several null models are considered in order
to discriminate between informations embedded in the network that are related
to real data, and features that are due to the method used for network
reconstruction (time correlation).
| [
{
"created": "Tue, 14 Nov 2006 15:07:48 GMT",
"version": "v1"
}
] | 2015-06-26 | [
[
"Remondini",
"D.",
""
],
[
"Neretti",
"N.",
""
],
[
"Sedivy",
"J. M.",
""
],
[
"Franceschi",
"C.",
""
],
[
"Milanesi",
"L.",
""
],
[
"Tieri",
"P.",
""
],
[
"Castellani",
"G. C.",
""
]
] | This paper describes characteristic features of networks reconstructed from gene expression time series data. Several null models are considered in order to discriminate between informations embedded in the network that are related to real data, and features that are due to the method used for network reconstruction (time correlation). |
1805.12253 | Mahdi Imani | Mahdi Imani, Roozbeh Dehghannasiri, Ulisses M. Braga-Neto, Edward R.
Dougherty | Sequential Experimental Design for Optimal Structural Intervention in
Gene Regulatory Networks Based on the Mean Objective Cost of Uncertainty | null | null | null | null | q-bio.MN cs.SY stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Scientists are attempting to use models of ever increasing complexity,
especially in medicine, where gene-based diseases such as cancer require better
modeling of cell regulation. Complex models suffer from uncertainty and
experiments are needed to reduce this uncertainty. Because experiments can be
costly and time-consuming it is desirable to determine experiments providing
the most useful information. If a sequence of experiments is to be performed,
experimental design is needed to determine the order. A classical approach is
to maximally reduce the overall uncertainty in the model, meaning maximal
entropy reduction. A recently proposed method takes into account both model
uncertainty and the translational objective, for instance, optimal structural
intervention in gene regulatory networks, where the aim is to alter the
regulatory logic to maximally reduce the long-run likelihood of being in a
cancerous state. The mean objective cost of uncertainty (MOCU) quantifies
uncertainty based on the degree to which model uncertainty affects the
objective. Experimental design involves choosing the experiment that yields the
greatest reduction in MOCU. This paper introduces finite-horizon dynamic
programming for MOCU-based sequential experimental design and compares it to
the greedy approach, which selects one experiment at a time without
consideration of the full horizon of experiments. A salient aspect of the paper
is that it demonstrates the advantage of MOCU-based design over the widely used
entropy-based design for both greedy and dynamic-programming strategies and
investigates the effect of model conditions on the comparative performances.
| [
{
"created": "Wed, 30 May 2018 22:53:22 GMT",
"version": "v1"
}
] | 2018-06-01 | [
[
"Imani",
"Mahdi",
""
],
[
"Dehghannasiri",
"Roozbeh",
""
],
[
"Braga-Neto",
"Ulisses M.",
""
],
[
"Dougherty",
"Edward R.",
""
]
] | Scientists are attempting to use models of ever increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-consuming it is desirable to determine experiments providing the most useful information. If a sequence of experiments is to be performed, experimental design is needed to determine the order. A classical approach is to maximally reduce the overall uncertainty in the model, meaning maximal entropy reduction. A recently proposed method takes into account both model uncertainty and the translational objective, for instance, optimal structural intervention in gene regulatory networks, where the aim is to alter the regulatory logic to maximally reduce the long-run likelihood of being in a cancerous state. The mean objective cost of uncertainty (MOCU) quantifies uncertainty based on the degree to which model uncertainty affects the objective. Experimental design involves choosing the experiment that yields the greatest reduction in MOCU. This paper introduces finite-horizon dynamic programming for MOCU-based sequential experimental design and compares it to the greedy approach, which selects one experiment at a time without consideration of the full horizon of experiments. A salient aspect of the paper is that it demonstrates the advantage of MOCU-based design over the widely used entropy-based design for both greedy and dynamic-programming strategies and investigates the effect of model conditions on the comparative performances. |
1903.10968 | \'Elie Besserer-Offroy Ph.D. | David St-Pierre, J\'er\^ome Cabana, Brian J. Holleran, \'Elie
Besserer-Offroy, Emanuel Escher, Ga\'etan Guillemette, Pierre Lavigne, and
Richard Leduc | Angiotensin II cyclic analogs as tools to investigate AT1R biased
signaling mechanisms | This is the preprint version of the following article: St-Pierre D,
et al. (2018), Biochem Pharmacol. doi: 10.1016/j.bcp.2018.04.021, which has
been accepted and published in final form at
https://www.sciencedirect.com/science/article/pii/S0006295218301643.
Supplementary information are freely available at doi:
10.6084/m9.figshare.6108440 | St-Pierre D, et al. (2018), Biochem Pharmacol. 154:104-17 | 10.1016/j.bcp.2018.04.021 | null | q-bio.MN q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | G protein coupled receptors (GPCRs) produce pleiotropic effects by their
capacity to engage numerous signaling pathways once activated. Functional
selectivity (also called biased signaling), where specific compounds can bring
GPCRs to adopt conformations that enable selective receptor coupling to
distinct signaling pathways, continues to be significantly investigated.
However, an important but often overlooked aspect of functional selectivity is
the capability of ligands such as angiotensin II (AngII) to adopt specific
conformations that may preferentially bind to selective GPCRs structures.
Understanding both receptor and ligand conformation is of the utmost importance
for the design of new drugs targeting GPCRs. In this study, we examined the
properties of AngII cyclic analogs to impart biased agonism on the angiotensin
type 1 receptor (AT1R). Positions 3 and 5 of AngII were substituted for
cysteine and homocysteine residues ([Sar1Hcy3,5]AngII, [Sar1Cys3Hcy5]AngII and
[Sar1Cys3,5]AngII) and the resulting analogs were evaluated for their capacity
to activate the Gq/11, G12, Gi2, Gi3, Gz, ERK and \b{eta}-arrestin (\b{eta}arr)
signaling pathways via AT1R. Interestingly, [Sar1Hcy3,5]AngII exhibited potency
and full efficacy on all pathways tested with the exception of the Gq pathway.
Molecular dynamic simulations showed that the energy barrier associated with
the insertion of residue Phe8 of AngII within the hydrophobic core of AT1R,
associated with Gq/11 activation, is increased with [Sar1Hcy3,5]AngII. These
results suggest that constraining the movements of molecular determinants
within a given ligand by introducing cyclic structures may lead to the
generation of novel ligands providing more efficient biased agonism.
| [
{
"created": "Tue, 26 Mar 2019 15:52:25 GMT",
"version": "v1"
}
] | 2019-03-27 | [
[
"St-Pierre",
"David",
""
],
[
"Cabana",
"Jérôme",
""
],
[
"Holleran",
"Brian J.",
""
],
[
"Besserer-Offroy",
"Élie",
""
],
[
"Escher",
"Emanuel",
""
],
[
"Guillemette",
"Gaétan",
""
],
[
"Lavigne",
"Pierre",
""
],
[
"Leduc",
"Richard",
""
]
] | G protein coupled receptors (GPCRs) produce pleiotropic effects by their capacity to engage numerous signaling pathways once activated. Functional selectivity (also called biased signaling), where specific compounds can bring GPCRs to adopt conformations that enable selective receptor coupling to distinct signaling pathways, continues to be significantly investigated. However, an important but often overlooked aspect of functional selectivity is the capability of ligands such as angiotensin II (AngII) to adopt specific conformations that may preferentially bind to selective GPCRs structures. Understanding both receptor and ligand conformation is of the utmost importance for the design of new drugs targeting GPCRs. In this study, we examined the properties of AngII cyclic analogs to impart biased agonism on the angiotensin type 1 receptor (AT1R). Positions 3 and 5 of AngII were substituted for cysteine and homocysteine residues ([Sar1Hcy3,5]AngII, [Sar1Cys3Hcy5]AngII and [Sar1Cys3,5]AngII) and the resulting analogs were evaluated for their capacity to activate the Gq/11, G12, Gi2, Gi3, Gz, ERK and \b{eta}-arrestin (\b{eta}arr) signaling pathways via AT1R. Interestingly, [Sar1Hcy3,5]AngII exhibited potency and full efficacy on all pathways tested with the exception of the Gq pathway. Molecular dynamic simulations showed that the energy barrier associated with the insertion of residue Phe8 of AngII within the hydrophobic core of AT1R, associated with Gq/11 activation, is increased with [Sar1Hcy3,5]AngII. These results suggest that constraining the movements of molecular determinants within a given ligand by introducing cyclic structures may lead to the generation of novel ligands providing more efficient biased agonism. |
1711.09560 | Ivan Sudakov | Sergey A. Vakulenko, Ivan Sudakov, and Luke Mander | Minor climatic fluctuations lead to species extinction in a conceptual
ecosystem model | 10 pages, 2 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The extinction of species is a core process that affects the diversity of
life on Earth. One way of investigating the causes and consequences of
extinctions is to build conceptual ecological models, and to use the dynamical
outcomes of such models to provide quantitative formalization of changes to
Earth's biosphere. In this paper we propose and study a conceptual resource
model that describes a simple and easily understandable mechanism for resource
competition, generalizes the well-known Huisman and Weissing model, and takes
into account species self-regulation, extinctions, and time dependence of
resources. We use analytical investigations and numerical simulations to study
the dynamics of our model under chaotic and periodic climate oscillations, and
show that the stochastic dynamics of our model exhibit strong dependence on
initial parameters. We also demonstrate that extinctions in our model are
inevitable if an ecosystem has the maximal possible biodiversity and uses the
maximal amount of resources. Our conceptual modeling provides theoretical
support for suggestions that non-linear processes were important during major
extinction events in Earth history.
| [
{
"created": "Mon, 27 Nov 2017 06:46:44 GMT",
"version": "v1"
}
] | 2017-11-28 | [
[
"Vakulenko",
"Sergey A.",
""
],
[
"Sudakov",
"Ivan",
""
],
[
"Mander",
"Luke",
""
]
] | The extinction of species is a core process that affects the diversity of life on Earth. One way of investigating the causes and consequences of extinctions is to build conceptual ecological models, and to use the dynamical outcomes of such models to provide quantitative formalization of changes to Earth's biosphere. In this paper we propose and study a conceptual resource model that describes a simple and easily understandable mechanism for resource competition, generalizes the well-known Huisman and Weissing model, and takes into account species self-regulation, extinctions, and time dependence of resources. We use analytical investigations and numerical simulations to study the dynamics of our model under chaotic and periodic climate oscillations, and show that the stochastic dynamics of our model exhibit strong dependence on initial parameters. We also demonstrate that extinctions in our model are inevitable if an ecosystem has the maximal possible biodiversity and uses the maximal amount of resources. Our conceptual modeling provides theoretical support for suggestions that non-linear processes were important during major extinction events in Earth history. |
1401.5383 | Rayan Chikhi | Rayan Chikhi, Antoine Limasset, Shaun Jackman, Jared Simpson and Paul
Medvedev | On the representation of de Bruijn graphs | Journal version (JCB). A preliminary version of this article was
published in the proceedings of RECOMB 2014 | null | null | null | q-bio.QM cs.DS q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The de Bruijn graph plays an important role in bioinformatics, especially in
the context of de novo assembly. However, the representation of the de Bruijn
graph in memory is a computational bottleneck for many assemblers. Recent
papers proposed a navigational data structure approach in order to improve
memory usage. We prove several theoretical space lower bounds to show the
limitation of these types of approaches. We further design and implement a
general data structure (DBGFM) and demonstrate its use on a human whole-genome
dataset, achieving space usage of 1.5 GB and a 46% improvement over previous
approaches. As part of DBGFM, we develop the notion of frequency-based
minimizers and show how it can be used to enumerate all maximal simple paths of
the de Bruijn graph using only 43 MB of memory. Finally, we demonstrate that
our approach can be integrated into an existing assembler by modifying the
ABySS software to use DBGFM.
| [
{
"created": "Tue, 21 Jan 2014 16:55:02 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Jan 2014 16:53:37 GMT",
"version": "v2"
},
{
"created": "Fri, 14 Feb 2014 22:55:09 GMT",
"version": "v3"
},
{
"created": "Mon, 6 Oct 2014 12:39:56 GMT",
"version": "v4"
}
] | 2014-10-07 | [
[
"Chikhi",
"Rayan",
""
],
[
"Limasset",
"Antoine",
""
],
[
"Jackman",
"Shaun",
""
],
[
"Simpson",
"Jared",
""
],
[
"Medvedev",
"Paul",
""
]
] | The de Bruijn graph plays an important role in bioinformatics, especially in the context of de novo assembly. However, the representation of the de Bruijn graph in memory is a computational bottleneck for many assemblers. Recent papers proposed a navigational data structure approach in order to improve memory usage. We prove several theoretical space lower bounds to show the limitation of these types of approaches. We further design and implement a general data structure (DBGFM) and demonstrate its use on a human whole-genome dataset, achieving space usage of 1.5 GB and a 46% improvement over previous approaches. As part of DBGFM, we develop the notion of frequency-based minimizers and show how it can be used to enumerate all maximal simple paths of the de Bruijn graph using only 43 MB of memory. Finally, we demonstrate that our approach can be integrated into an existing assembler by modifying the ABySS software to use DBGFM. |
1605.03726 | Atsushi Miyauchi | Atsushi Miyauchi, Kazunari Iwamoto, Satya Nanda Vel Arjunan, Koichi
Takahashi | pSpatiocyte: A Parallel Stochastic Method for Particle
Reaction-Diffusion Systems | 19 pages, 17 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational systems biology has provided plenty of insights into cell
biology. Early on, the focus was on reaction networks between molecular
species. Spatial distribution only began to be considered mostly within the
last decade. However, calculations were restricted to small systems because of
tremendously high computational workloads. To date, application to the cell of
typical size with molecular resolution is still far from realization. In this
article, we present a new parallel stochastic method for particle
reaction-diffusion systems. The program called pSpatiocyte was created bearing
in mind reaction networks in biological cells operating in crowded
intracellular environments as the primary simulation target. pSpatiocyte
employs unique discretization and parallelization algorithms based on a
hexagonal close-packed lattice for efficient execution particularly on large
distributed memory parallel computers. For two-level parallelization, we
introduced isolated subdomain and tri-stage lockstep communication for
process-level, and voxel-locking techniques for thread-level. We performed a
series of parallel runs on RIKEN's K computer. For a fine lattice that had
relatively low occupancy, pSpatiocyte achieved 7686 times speedup with 663552
cores relative to 64 cores from the viewpoint of strong scaling and exhibited
74\% parallel efficiency. As for weak scaling, efficiencies at least 60% were
observed up to 663552 cores. In addition to computational performance,
diffusion and reaction rates were validated by theory and another
well-validated program and had good agreement. Lastly, as a preliminary example
of real-world applications, we present a calculation of the MAPK model, a
typical reaction network motif in cell signaling pathways.
| [
{
"created": "Thu, 12 May 2016 08:47:45 GMT",
"version": "v1"
}
] | 2016-05-13 | [
[
"Miyauchi",
"Atsushi",
""
],
[
"Iwamoto",
"Kazunari",
""
],
[
"Arjunan",
"Satya Nanda Vel",
""
],
[
"Takahashi",
"Koichi",
""
]
] | Computational systems biology has provided plenty of insights into cell biology. Early on, the focus was on reaction networks between molecular species. Spatial distribution only began to be considered mostly within the last decade. However, calculations were restricted to small systems because of tremendously high computational workloads. To date, application to the cell of typical size with molecular resolution is still far from realization. In this article, we present a new parallel stochastic method for particle reaction-diffusion systems. The program called pSpatiocyte was created bearing in mind reaction networks in biological cells operating in crowded intracellular environments as the primary simulation target. pSpatiocyte employs unique discretization and parallelization algorithms based on a hexagonal close-packed lattice for efficient execution particularly on large distributed memory parallel computers. For two-level parallelization, we introduced isolated subdomain and tri-stage lockstep communication for process-level, and voxel-locking techniques for thread-level. We performed a series of parallel runs on RIKEN's K computer. For a fine lattice that had relatively low occupancy, pSpatiocyte achieved 7686 times speedup with 663552 cores relative to 64 cores from the viewpoint of strong scaling and exhibited 74\% parallel efficiency. As for weak scaling, efficiencies at least 60% were observed up to 663552 cores. In addition to computational performance, diffusion and reaction rates were validated by theory and another well-validated program and had good agreement. Lastly, as a preliminary example of real-world applications, we present a calculation of the MAPK model, a typical reaction network motif in cell signaling pathways. |
1705.07214 | Patrick De Leenheer | Martin Schuster, Eric Foxall, David Finch, Hal Smith, Patrick De
Leenheer | Tragedy of the Commons in the Chemostat | 23 pages, 4 figures | null | 10.1371/journal.pone.0186119 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a proof of principle for the phenomenon of the tragedy of the
commons that is at the center of many theories on the evolution of cooperation.
We establish the tragedy in the context of a general chemostat model with two
species, the cooperator and the cheater. Both species have the same growth rate
function and yield constant, but the cooperator allocates a portion of the
nutrient uptake towards the production of a public good -the "Commons" in the
Tragedy- which is needed to digest the externally supplied nutrient. The
cheater on the other hand does not produce this enzyme, and allocates all
nutrient uptake towards its own growth. We prove that when the cheater is
present initially, both the cooperator and the cheater will eventually go
extinct, hereby confirming the occurrence of the tragedy. We also show that
without the cheater, the cooperator can survive indefinitely, provided that at
least a low level of public good or processed nutrient is available initially.
Our results provide a predictive framework for the analysis of
cooperator-cheater dynamics in a powerful model system of experimental
evolution.
| [
{
"created": "Fri, 19 May 2017 22:40:20 GMT",
"version": "v1"
}
] | 2018-02-07 | [
[
"Schuster",
"Martin",
""
],
[
"Foxall",
"Eric",
""
],
[
"Finch",
"David",
""
],
[
"Smith",
"Hal",
""
],
[
"De Leenheer",
"Patrick",
""
]
] | We present a proof of principle for the phenomenon of the tragedy of the commons that is at the center of many theories on the evolution of cooperation. We establish the tragedy in the context of a general chemostat model with two species, the cooperator and the cheater. Both species have the same growth rate function and yield constant, but the cooperator allocates a portion of the nutrient uptake towards the production of a public good -the "Commons" in the Tragedy- which is needed to digest the externally supplied nutrient. The cheater on the other hand does not produce this enzyme, and allocates all nutrient uptake towards its own growth. We prove that when the cheater is present initially, both the cooperator and the cheater will eventually go extinct, hereby confirming the occurrence of the tragedy. We also show that without the cheater, the cooperator can survive indefinitely, provided that at least a low level of public good or processed nutrient is available initially. Our results provide a predictive framework for the analysis of cooperator-cheater dynamics in a powerful model system of experimental evolution. |
1401.0071 | Sarabjeet Singh | Sarabjeet Singh, Christopher R. Myers | Outbreak statistics and scaling laws for externally driven epidemics | 12 pages, 8 figures | null | 10.1103/PhysRevE.89.042108 | null | q-bio.PE physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Power-law scalings are ubiquitous to physical phenomena undergoing a
continuous phase transition. The classic Susceptible-Infectious-Recovered (SIR)
model of epidemics is one such example where the scaling behavior near a
critical point has been studied extensively. In this system the distribution of
outbreak sizes scales as $P(n) \sim n^{-3/2}$ at the critical point as the
system size $N$ becomes infinite. The finite-size scaling laws for the outbreak
size and duration are also well understood and characterized. In this work, we
report scaling laws for a model with SIR structure coupled with a constant
force of infection per susceptible, akin to a `reservoir forcing'. We find that
the statistics of outbreaks in this system are fundamentally different than
those in a simple SIR model. Instead of fixed exponents, all scaling laws
exhibit tunable exponents parameterized by the dimensionless rate of external
forcing. As the external driving rate approaches a critical value, the scale of
the average outbreak size converges to that of the maximal size, and above the
critical point, the scaling laws bifurcate into two regimes. Whereas a simple
SIR process can only exhibit outbreaks of size $\mathcal{O}(N^{1/3})$ and
$\mathcal{O}(N)$ depending on whether the system is at or above the epidemic
threshold, a driven SIR process can exhibit a richer spectrum of outbreak sizes
that scale as $O(N^{\xi})$ where $\xi \in (0,1] \backslash \{2/3\}$ and
$\mathcal{O}((N/\log N)^{2/3})$ at the multi-critical point.
| [
{
"created": "Tue, 31 Dec 2013 02:06:22 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Singh",
"Sarabjeet",
""
],
[
"Myers",
"Christopher R.",
""
]
] | Power-law scalings are ubiquitous to physical phenomena undergoing a continuous phase transition. The classic Susceptible-Infectious-Recovered (SIR) model of epidemics is one such example where the scaling behavior near a critical point has been studied extensively. In this system the distribution of outbreak sizes scales as $P(n) \sim n^{-3/2}$ at the critical point as the system size $N$ becomes infinite. The finite-size scaling laws for the outbreak size and duration are also well understood and characterized. In this work, we report scaling laws for a model with SIR structure coupled with a constant force of infection per susceptible, akin to a `reservoir forcing'. We find that the statistics of outbreaks in this system are fundamentally different than those in a simple SIR model. Instead of fixed exponents, all scaling laws exhibit tunable exponents parameterized by the dimensionless rate of external forcing. As the external driving rate approaches a critical value, the scale of the average outbreak size converges to that of the maximal size, and above the critical point, the scaling laws bifurcate into two regimes. Whereas a simple SIR process can only exhibit outbreaks of size $\mathcal{O}(N^{1/3})$ and $\mathcal{O}(N)$ depending on whether the system is at or above the epidemic threshold, a driven SIR process can exhibit a richer spectrum of outbreak sizes that scale as $O(N^{\xi})$ where $\xi \in (0,1] \backslash \{2/3\}$ and $\mathcal{O}((N/\log N)^{2/3})$ at the multi-critical point. |
1204.4046 | David Lukatsky | Ariel Afek and David B. Lukatsky | Nonspecific Protein-DNA Binding Is Widespread in the Yeast Genome | null | Biophysical Journal 102(8), 1881-1888 (2012) | 10.1016/j.bpj.2012.03.044 | null | q-bio.BM q-bio.GN q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent genome-wide measurements of binding preferences of ~200 transcription
regulators in the vicinity of transcription start sites in yeast, have provided
a unique insight into the cis- regulatory code of a eukaryotic genome (Venters
et al., Mol. Cell 41, 480 (2011)). Here, we show that nonspecific transcription
factor (TF)-DNA binding significantly influences binding preferences of the
majority of transcription regulators in promoter regions of the yeast genome.
We show that promoters of SAGA-dominated and TFIID-dominated genes can be
statistically distinguished based on the landscape of nonspecific protein-DNA
binding free energy. In particular, we predict that promoters of SAGA-dominated
genes possess wider regions of reduced free energy compared to promoters of
TFIID-dominated genes. We also show that specific and nonspecific TF-DNA
binding are functionally linked and cooperatively influence gene expression in
yeast. Our results suggest that nonspecific TF-DNA binding is intrinsically
encoded into the yeast genome, and it may play a more important role in
transcriptional regulation than previously thought.
| [
{
"created": "Wed, 18 Apr 2012 11:14:10 GMT",
"version": "v1"
}
] | 2012-04-19 | [
[
"Afek",
"Ariel",
""
],
[
"Lukatsky",
"David B.",
""
]
] | Recent genome-wide measurements of binding preferences of ~200 transcription regulators in the vicinity of transcription start sites in yeast, have provided a unique insight into the cis- regulatory code of a eukaryotic genome (Venters et al., Mol. Cell 41, 480 (2011)). Here, we show that nonspecific transcription factor (TF)-DNA binding significantly influences binding preferences of the majority of transcription regulators in promoter regions of the yeast genome. We show that promoters of SAGA-dominated and TFIID-dominated genes can be statistically distinguished based on the landscape of nonspecific protein-DNA binding free energy. In particular, we predict that promoters of SAGA-dominated genes possess wider regions of reduced free energy compared to promoters of TFIID-dominated genes. We also show that specific and nonspecific TF-DNA binding are functionally linked and cooperatively influence gene expression in yeast. Our results suggest that nonspecific TF-DNA binding is intrinsically encoded into the yeast genome, and it may play a more important role in transcriptional regulation than previously thought. |
q-bio/0702027 | Eben Kenah | Eben Kenah, James M. Robins | Network-based analysis of stochastic SIR epidemic models with random and
proportionate mixing | 40 pages, 9 figures | Journal of Theoretical Biology 249: 706-722, December 2007 | 10.1016/j.jtbi.2007.09.011 | null | q-bio.QM cond-mat.stat-mech math.PR | null | In this paper, we outline the theory of epidemic percolation networks and
their use in the analysis of stochastic SIR epidemic models on undirected
contact networks. We then show how the same theory can be used to analyze
stochastic SIR models with random and proportionate mixing. The epidemic
percolation networks for these models are purely directed because undirected
edges disappear in the limit of a large population. In a series of simulations,
we show that epidemic percolation networks accurately predict the mean outbreak
size and probability and final size of an epidemic for a variety of epidemic
models in homogeneous and heterogeneous populations. Finally, we show that
epidemic percolation networks can be used to re-derive classical results from
several different areas of infectious disease epidemiology. In an appendix, we
show that an epidemic percolation network can be defined for any
time-homogeneous stochastic SIR model in a closed population and prove that the
distribution of outbreak sizes given the infection of any given node in the SIR
model is identical to the distribution of its out-component sizes in the
corresponding probability space of epidemic percolation networks. We conclude
that the theory of percolation on semi-directed networks provides a very
general framework for the analysis of stochastic SIR models in closed
populations.
| [
{
"created": "Mon, 12 Feb 2007 02:21:23 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Jul 2007 20:46:17 GMT",
"version": "v2"
},
{
"created": "Fri, 11 Jan 2008 04:39:37 GMT",
"version": "v3"
}
] | 2023-10-24 | [
[
"Kenah",
"Eben",
""
],
[
"Robins",
"James M.",
""
]
] | In this paper, we outline the theory of epidemic percolation networks and their use in the analysis of stochastic SIR epidemic models on undirected contact networks. We then show how the same theory can be used to analyze stochastic SIR models with random and proportionate mixing. The epidemic percolation networks for these models are purely directed because undirected edges disappear in the limit of a large population. In a series of simulations, we show that epidemic percolation networks accurately predict the mean outbreak size and probability and final size of an epidemic for a variety of epidemic models in homogeneous and heterogeneous populations. Finally, we show that epidemic percolation networks can be used to re-derive classical results from several different areas of infectious disease epidemiology. In an appendix, we show that an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model in a closed population and prove that the distribution of outbreak sizes given the infection of any given node in the SIR model is identical to the distribution of its out-component sizes in the corresponding probability space of epidemic percolation networks. We conclude that the theory of percolation on semi-directed networks provides a very general framework for the analysis of stochastic SIR models in closed populations. |
1209.3820 | Sean Stromberg | Sean P Stromberg, Rustom Antia, and Ilya Nemenman | Population-expression models of immune response | Revised manuscript with an additional included Supplemental. The
Supplemental contains two contrasting derivations of the
population-expression PDE formulation, one from a fluid-dynamics perspective
using the divergence theorem, the other from a statistical
physics/systems-biology perspective using a chemical master equation | Physical biology 10 (3), 035010, 2013 | 10.1088/1478-3975/10/3/035010 | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The immune response to a pathogen has two basic features. The first is the
expansion of a few pathogen-specific cells to form a population large enough to
control the pathogen. The second is the process of differentiation of cells
from an initial naive phenotype to an effector phenotype which controls the
pathogen, and subsequently to a memory phenotype that is maintained and
responsible for long-term protection. The expansion and the differentiation
have been considered largely independently. Changes in cell populations are
typically described using ecologically based ordinary differential equation
models. In contrast, differentiation of single cells is studied within systems
biology and is frequently modeled by considering changes in gene and protein
expression in individual cells. Recent advances in experimental systems biology
make available for the first time data to allow the coupling of population and
high dimensional expression data of immune cells during infections. Here we
describe and develop population-expression models which integrate these two
processes into systems biology on the multicellular level. When translated into
mathematical equations, these models result in non-conservative, non-local
advection-diffusion equations. We describe situations where the
population-expression approach can make correct inference from data while
previous modeling approaches based on common simplifying assumptions would
fail. We also explore how model reduction techniques can be used to build
population-expression models, minimizing the complexity of the model while
keeping the essential features of the system. While we consider problems in
immunology in this paper, we expect population-expression models to be more
broadly applicable.
| [
{
"created": "Tue, 18 Sep 2012 00:46:26 GMT",
"version": "v1"
},
{
"created": "Sat, 8 Dec 2012 05:08:46 GMT",
"version": "v2"
}
] | 2014-02-04 | [
[
"Stromberg",
"Sean P",
""
],
[
"Antia",
"Rustom",
""
],
[
"Nemenman",
"Ilya",
""
]
] | The immune response to a pathogen has two basic features. The first is the expansion of a few pathogen-specific cells to form a population large enough to control the pathogen. The second is the process of differentiation of cells from an initial naive phenotype to an effector phenotype which controls the pathogen, and subsequently to a memory phenotype that is maintained and responsible for long-term protection. The expansion and the differentiation have been considered largely independently. Changes in cell populations are typically described using ecologically based ordinary differential equation models. In contrast, differentiation of single cells is studied within systems biology and is frequently modeled by considering changes in gene and protein expression in individual cells. Recent advances in experimental systems biology make available for the first time data to allow the coupling of population and high dimensional expression data of immune cells during infections. Here we describe and develop population-expression models which integrate these two processes into systems biology on the multicellular level. When translated into mathematical equations, these models result in non-conservative, non-local advection-diffusion equations. We describe situations where the population-expression approach can make correct inference from data while previous modeling approaches based on common simplifying assumptions would fail. We also explore how model reduction techniques can be used to build population-expression models, minimizing the complexity of the model while keeping the essential features of the system. While we consider problems in immunology in this paper, we expect population-expression models to be more broadly applicable. |
1809.05254 | Tatsuya Haga | Tatsuya Haga, Tomoki Fukai | Extended temporal association memory by inhibitory Hebbian learning | 4 pages, 4 figures | Phys. Rev. Lett. 123, 078101 (2019) | 10.1103/PhysRevLett.123.078101 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hebbian learning of excitatory synapses plays a central role in storing
activity patterns in associative memory models. Furthermore, interstimulus
Hebbian learning associates multiple items in the brain by converting temporal
correlation to spatial correlation between attractors. However, growing
experimental evidence suggests that learning of inhibitory synapses creates
"inhibitory engrams", which presumably balance with the patterns encoded in the
excitatory network. Controlling inhibitory engrams may modify the behavior of
associative memory in neural networks, but the consequence of such control has
not been theoretically understood. Noting that Hebbian learning of inhibitory
synapses yields an anti-Hebbian effect, we show that the combination of Hebbian
and anti-Hebbian learning can increase the span of temporal association between
the correlated attractors. The balance of targetted and global inhibition
regulates this span of association in the network. Our results suggest a
nontrivial role of anti-Hebbian learning and inhibitory engrams in associative
memory.
| [
{
"created": "Fri, 14 Sep 2018 04:57:16 GMT",
"version": "v1"
}
] | 2019-08-21 | [
[
"Haga",
"Tatsuya",
""
],
[
"Fukai",
"Tomoki",
""
]
] | Hebbian learning of excitatory synapses plays a central role in storing activity patterns in associative memory models. Furthermore, interstimulus Hebbian learning associates multiple items in the brain by converting temporal correlation to spatial correlation between attractors. However, growing experimental evidence suggests that learning of inhibitory synapses creates "inhibitory engrams", which presumably balance with the patterns encoded in the excitatory network. Controlling inhibitory engrams may modify the behavior of associative memory in neural networks, but the consequence of such control has not been theoretically understood. Noting that Hebbian learning of inhibitory synapses yields an anti-Hebbian effect, we show that the combination of Hebbian and anti-Hebbian learning can increase the span of temporal association between the correlated attractors. The balance of targetted and global inhibition regulates this span of association in the network. Our results suggest a nontrivial role of anti-Hebbian learning and inhibitory engrams in associative memory. |
1812.03971 | Niv DeMalach | Niv DeMalach, Nadav Shnerb and Tadashi Fukami | Alternative states in plant communities driven by a life-history
tradeoff and demographic stochasticity | null | American Naturalist 2021 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Life-history tradeoffs among species are major drivers of community assembly.
Most studies investigate how tradeoffs promote deterministic coexistence of
species. It remains unclear how tradeoffs may instead promote historically
contingent exclusion of species, where species dominance is affected by initial
abundances, causing alternative community states. Focusing on the
establishment-longevity tradeoff, we study the transient dynamics and
equilibrium outcomes of competitive interactions in a simulation model of plant
community assembly. We show that, in this model, the establishment-longevity
tradeoff is a necessary but not sufficient condition for alternative stable
equilibria that require also low fecundity for both species. An analytical
approximation of our simulation model demonstrates that alternative stable
equilibria are driven by demographic stochasticity in the number of seeds
arriving at each establishment site. This site-scale stochasticity is only
affected by fecundity and therefore occurs even in infinitely large
communities. In many cases where the establishment-longevity tradeoff does not
cause alternative stable equilibria, it still decreases the rate of convergence
toward the single equilibrium, resulting in decades of transient dynamics that
can appear indistinguishable from alternative stable equilibria in empirical
studies.
| [
{
"created": "Mon, 10 Dec 2018 18:44:01 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Jan 2019 22:35:53 GMT",
"version": "v2"
},
{
"created": "Mon, 1 Apr 2019 16:46:21 GMT",
"version": "v3"
},
{
"created": "Thu, 25 Jul 2019 18:43:59 GMT",
"version": "v4"
},
{
"created": "Thu, 29 Oct 2020 15:53:51 GMT",
"version": "v5"
}
] | 2021-03-09 | [
[
"DeMalach",
"Niv",
""
],
[
"Shnerb",
"Nadav",
""
],
[
"Fukami",
"Tadashi",
""
]
] | Life-history tradeoffs among species are major drivers of community assembly. Most studies investigate how tradeoffs promote deterministic coexistence of species. It remains unclear how tradeoffs may instead promote historically contingent exclusion of species, where species dominance is affected by initial abundances, causing alternative community states. Focusing on the establishment-longevity tradeoff, we study the transient dynamics and equilibrium outcomes of competitive interactions in a simulation model of plant community assembly. We show that, in this model, the establishment-longevity tradeoff is a necessary but not sufficient condition for alternative stable equilibria that require also low fecundity for both species. An analytical approximation of our simulation model demonstrates that alternative stable equilibria are driven by demographic stochasticity in the number of seeds arriving at each establishment site. This site-scale stochasticity is only affected by fecundity and therefore occurs even in infinitely large communities. In many cases where the establishment-longevity tradeoff does not cause alternative stable equilibria, it still decreases the rate of convergence toward the single equilibrium, resulting in decades of transient dynamics that can appear indistinguishable from alternative stable equilibria in empirical studies. |
2212.10595 | Fan Zhang | Fan Zhang | Memory recall by controlling chaos | 6 pages | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | By incorporating feedback loops, that engender amplification and damping so
that output is not proportional to input, the biological neural networks become
highly nonlinear and thus very likely chaotic in nature. Research in control
theory reveals that strange attractors can be approximated by collection of
cycles, and be collapsed into a more coherent state centered on one of them if
we exert control. We speculate that human memories are encoded by such cycles,
and can be retrieved once sensory or virtual cues, acting as references, enable
feedback controls that nucleates the otherwise chaotic wandering mind.
| [
{
"created": "Thu, 10 Nov 2022 08:09:41 GMT",
"version": "v1"
}
] | 2022-12-22 | [
[
"Zhang",
"Fan",
""
]
] | By incorporating feedback loops, that engender amplification and damping so that output is not proportional to input, the biological neural networks become highly nonlinear and thus very likely chaotic in nature. Research in control theory reveals that strange attractors can be approximated by collection of cycles, and be collapsed into a more coherent state centered on one of them if we exert control. We speculate that human memories are encoded by such cycles, and can be retrieved once sensory or virtual cues, acting as references, enable feedback controls that nucleates the otherwise chaotic wandering mind. |
2301.08742 | Mahendra Samarawickrama | Mahendra Samarawickrama | Unifying Consciousness and Time to Enhance Artificial Intelligence | This discussion paper has been submitted to Cognitive Neuroscience of
Routledge, part of the Taylor & Francis publications | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | Consciousness is a sequential process of awareness which can focus on one
piece of information at a time. This process of awareness experiences causation
which underpins the notion of time while it interplays with matter and energy,
forming reality. The study of Consciousness, time and reality is complex and
evolving fast in many fields, including metaphysics and fundamental physics.
Reality composes patterns in human Consciousness in response to the
regularities in nature. These regularities could be physical (e.g.,
astronomical, environmental), biological, chemical, mental, social, etc. The
patterns that emerged in Consciousness were correlated to the environment, life
and social behaviours followed by constructed frameworks, systems and
structures. The complex constructs evolved as cultures, customs, norms and
values, which created a diverse society. In the evolution of responsible AI, it
is important to be attuned to the evolved cultural, ethical and moral values
through Consciousness. This requires the advocated design of self-learning AI
aware of time perception and human ethics.
| [
{
"created": "Tue, 10 Jan 2023 11:15:41 GMT",
"version": "v1"
}
] | 2023-01-24 | [
[
"Samarawickrama",
"Mahendra",
""
]
] | Consciousness is a sequential process of awareness which can focus on one piece of information at a time. This process of awareness experiences causation which underpins the notion of time while it interplays with matter and energy, forming reality. The study of Consciousness, time and reality is complex and evolving fast in many fields, including metaphysics and fundamental physics. Reality composes patterns in human Consciousness in response to the regularities in nature. These regularities could be physical (e.g., astronomical, environmental), biological, chemical, mental, social, etc. The patterns that emerged in Consciousness were correlated to the environment, life and social behaviours followed by constructed frameworks, systems and structures. The complex constructs evolved as cultures, customs, norms and values, which created a diverse society. In the evolution of responsible AI, it is important to be attuned to the evolved cultural, ethical and moral values through Consciousness. This requires the advocated design of self-learning AI aware of time perception and human ethics. |
1303.1374 | Reinhard B\"urger | Ada Akerman, Reinhard B\"urger | The consequences of gene flow for local adaptation and differentiation:
A two-locus two-deme model | null | J. Math. Biol. 68, 1135-1198 (2014) | 10.1007/s00285-013-0660-z | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a population subdivided into two demes connected by migration in
which selection acts in opposite direction. We explore the effects of
recombination and migration on the maintenance of multilocus polymorphism, on
local adaptation, and on differentiation by employing a deterministic model
with genic selection on two linked diallelic loci (i.e., no dominance or
epistasis). For the following cases, we characterize explicitly the possible
equilibrium configurations: weak, strong, highly asymmetric, and
super-symmetric migration, no or weak recombination, and independent or
strongly recombining loci. For independent loci (linkage equilibrium) and for
completely linked loci, we derive the possible bifurcation patterns as
functions of the total migration rate, assuming all other parameters are fixed
but arbitrary. For these and other cases, we determine analytically the maximum
migration rate below which a stable fully polymorphic equilibrium exists. In
this case, differentiation and local adaptation are maintained. Their degree is
quantified by a new multilocus version of $\Fst$ and by the migration load,
respectively. In addition, we investigate the invasion conditions of locally
beneficial mutants and show that linkage to a locus that is already in
migration-selection balance facilitates invasion. Hence, loci of much smaller
effect can invade than predicted by one-locus theory if linkage is sufficiently
tight. We study how this minimum amount of linkage admitting invasion depends
on the migration pattern. This suggests the emergence of clusters of locally
beneficial mutations, which may form `genomic islands of divergence'. Finally,
the influence of linkage and two-way migration on the effective migration rate
at a linked neutral locus is explored. Numerical work complements our
analytical results.
| [
{
"created": "Wed, 6 Mar 2013 16:30:03 GMT",
"version": "v1"
}
] | 2014-07-21 | [
[
"Akerman",
"Ada",
""
],
[
"Bürger",
"Reinhard",
""
]
] | We consider a population subdivided into two demes connected by migration in which selection acts in opposite direction. We explore the effects of recombination and migration on the maintenance of multilocus polymorphism, on local adaptation, and on differentiation by employing a deterministic model with genic selection on two linked diallelic loci (i.e., no dominance or epistasis). For the following cases, we characterize explicitly the possible equilibrium configurations: weak, strong, highly asymmetric, and super-symmetric migration, no or weak recombination, and independent or strongly recombining loci. For independent loci (linkage equilibrium) and for completely linked loci, we derive the possible bifurcation patterns as functions of the total migration rate, assuming all other parameters are fixed but arbitrary. For these and other cases, we determine analytically the maximum migration rate below which a stable fully polymorphic equilibrium exists. In this case, differentiation and local adaptation are maintained. Their degree is quantified by a new multilocus version of $\Fst$ and by the migration load, respectively. In addition, we investigate the invasion conditions of locally beneficial mutants and show that linkage to a locus that is already in migration-selection balance facilitates invasion. Hence, loci of much smaller effect can invade than predicted by one-locus theory if linkage is sufficiently tight. We study how this minimum amount of linkage admitting invasion depends on the migration pattern. This suggests the emergence of clusters of locally beneficial mutations, which may form `genomic islands of divergence'. Finally, the influence of linkage and two-way migration on the effective migration rate at a linked neutral locus is explored. Numerical work complements our analytical results. |
2307.00932 | Tianye Wang | Tianye Wang, Haoxuan Yao, Tai Sing Lee, Jiayi Hong, Yang Li, Hongfei
Jiang, Ian Max Andolina, Shiming Tang | A large calcium-imaging dataset reveals a systematic V4 organization for
natural scenes | 39 pages, 14 figures | null | null | null | q-bio.NC cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The visual system evolved to process natural scenes, yet most of our
understanding of the topology and function of visual cortex derives from
studies using artificial stimuli. To gain deeper insights into visual
processing of natural scenes, we utilized widefield calcium-imaging of primate
V4 in response to many natural images, generating a large dataset of
columnar-scale responses. We used this dataset to build a digital twin of V4
via deep learning, generating a detailed topographical map of natural image
preferences at each cortical position. The map revealed clustered functional
domains for specific classes of natural image features. These ranged from
surface-related attributes like color and texture to shape-related features
such as edges, curvature, and facial features. We validated the model-predicted
domains with additional widefield calcium-imaging and single-cell resolution
two-photon imaging. Our study illuminates the detailed topological organization
and neural codes in V4 that represent natural scenes.
| [
{
"created": "Mon, 3 Jul 2023 11:13:28 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Jul 2023 01:57:52 GMT",
"version": "v2"
}
] | 2023-07-25 | [
[
"Wang",
"Tianye",
""
],
[
"Yao",
"Haoxuan",
""
],
[
"Lee",
"Tai Sing",
""
],
[
"Hong",
"Jiayi",
""
],
[
"Li",
"Yang",
""
],
[
"Jiang",
"Hongfei",
""
],
[
"Andolina",
"Ian Max",
""
],
[
"Tang",
"Shiming",
""
]
] | The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses. We used this dataset to build a digital twin of V4 via deep learning, generating a detailed topographical map of natural image preferences at each cortical position. The map revealed clustered functional domains for specific classes of natural image features. These ranged from surface-related attributes like color and texture to shape-related features such as edges, curvature, and facial features. We validated the model-predicted domains with additional widefield calcium-imaging and single-cell resolution two-photon imaging. Our study illuminates the detailed topological organization and neural codes in V4 that represent natural scenes. |
2407.18952 | Stefanie Winkelmann | Nathalie Wehlitz, Mohsen Sadeghi, Alberto Montefusco, Christof
Sch\"utte, Grigorios A. Pavliotis, Stefanie Winkelmann | Approximating particle-based clustering dynamics by stochastic PDEs | null | null | null | null | q-bio.QM math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work proposes stochastic partial differential equations (SPDEs) as a
practical tool to replicate clustering effects of more detailed particle-based
dynamics. Inspired by membrane-mediated receptor dynamics on cell surfaces, we
formulate a stochastic particle-based model for diffusion and pairwise
interaction of particles, leading to intriguing clustering phenomena. Employing
numerical simulation and cluster detection methods, we explore the
approximation of the particle-based clustering dynamics through mean-field
approaches. We find that SPDEs successfully reproduce spatiotemporal clustering
dynamics, not only in the initial cluster formation period, but also on longer
time scales where the successive merging of clusters cannot be tracked by
deterministic mean-field models. The computational efficiency of the SPDE
approach allows us to generate extensive statistical data for parameter
estimation in a simpler model that uses a Markov jump process to capture the
temporal evolution of the cluster number.
| [
{
"created": "Fri, 12 Jul 2024 13:20:06 GMT",
"version": "v1"
}
] | 2024-07-30 | [
[
"Wehlitz",
"Nathalie",
""
],
[
"Sadeghi",
"Mohsen",
""
],
[
"Montefusco",
"Alberto",
""
],
[
"Schütte",
"Christof",
""
],
[
"Pavliotis",
"Grigorios A.",
""
],
[
"Winkelmann",
"Stefanie",
""
]
] | This work proposes stochastic partial differential equations (SPDEs) as a practical tool to replicate clustering effects of more detailed particle-based dynamics. Inspired by membrane-mediated receptor dynamics on cell surfaces, we formulate a stochastic particle-based model for diffusion and pairwise interaction of particles, leading to intriguing clustering phenomena. Employing numerical simulation and cluster detection methods, we explore the approximation of the particle-based clustering dynamics through mean-field approaches. We find that SPDEs successfully reproduce spatiotemporal clustering dynamics, not only in the initial cluster formation period, but also on longer time scales where the successive merging of clusters cannot be tracked by deterministic mean-field models. The computational efficiency of the SPDE approach allows us to generate extensive statistical data for parameter estimation in a simpler model that uses a Markov jump process to capture the temporal evolution of the cluster number. |
2011.03759 | Vince Grolmusz | Laszlo Keresztes and Evelin Szogi and Balint Varga and Viktor Farkas
and Andras Perczel and Vince Grolmusz | The Budapest Amyloid Predictor and its Applications | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The amyloid state of proteins is widely studied with relevancy in neurology,
biochemistry, and biotechnology. In contrast with amorphous aggregation, the
amyloid state has a well-defined structure, consisting of parallel and
anti-parallel $\beta$-sheets in a periodically repeated formation. The
understanding of the amyloid state is growing with the development of novel
molecular imaging tools, like cryogenic electron microscopy. Sequence-based
amyloid predictors were developed by using mostly artificial neural networks
(ANNs) as the underlying computational techniques. From a good neural
network-based predictor, it is a very difficult task to identify those
attributes of the input amino acid sequence, which implied the decision of the
network. Here we present a Support Vector Machine (SVM)-based predictor for
hexapeptides with correctness higher than 84\%, i.e., it is at least as good as
the published ANN-based tools. Unlike the artificial neural networks, the
decision of the SVMs are much easier to analyze, and from a good predictor, we
can infer rich biochemical knowledge.
Availability and Implementation: The Budapest Amyloid Predictor webserver is
freely available at https://pitgroup.org/bap.
| [
{
"created": "Sat, 7 Nov 2020 12:11:26 GMT",
"version": "v1"
}
] | 2020-11-10 | [
[
"Keresztes",
"Laszlo",
""
],
[
"Szogi",
"Evelin",
""
],
[
"Varga",
"Balint",
""
],
[
"Farkas",
"Viktor",
""
],
[
"Perczel",
"Andras",
""
],
[
"Grolmusz",
"Vince",
""
]
] | The amyloid state of proteins is widely studied with relevancy in neurology, biochemistry, and biotechnology. In contrast with amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and anti-parallel $\beta$-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed by using mostly artificial neural networks (ANNs) as the underlying computational techniques. From a good neural network-based predictor, it is a very difficult task to identify those attributes of the input amino acid sequence, which implied the decision of the network. Here we present a Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84\%, i.e., it is at least as good as the published ANN-based tools. Unlike the artificial neural networks, the decision of the SVMs are much easier to analyze, and from a good predictor, we can infer rich biochemical knowledge. Availability and Implementation: The Budapest Amyloid Predictor webserver is freely available at https://pitgroup.org/bap. |
2205.08902 | \`Alex Gim\'enez-Romero | \`Alex Gim\'enez-Romero, Federico Vazquez, Crist\'obal L\'opez and
Manuel A. Mat\'ias | Spatial effects in parasite induced marine diseases of immobile hosts | 11 pages, 6 figures, 1 table | R. Soc. open sci. 9, 212023 (2022) | 10.1098/rsos.212023 | null | q-bio.PE physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Emerging marine infectious diseases pose a substantial threat to marine
ecosystems and the conservation of their biodiversity. Compartmental models of
epidemic transmission in marine sessile organisms, available only recently, are
based on non-spatial descriptions in which space is homogenised and parasite
mobility is not explicitly accounted for. However, in realistic scenarios
epidemic transmission is conditioned by the spatial distribution of hosts and
the parasites mobility patterns, calling for a explicit description of space.
In this work we develop a spatially-explicit individual-based model to study
disease transmission by waterborne parasites in sessile marine populations. We
investigate the impact of spatial disease transmission through extensive
numerical simulations and theoretical analysis. Specifically, the effects of
parasite mobility into the epidemic threshold and the temporal progression of
the epidemic are assessed. We show that larger values of pathogen mobility
imply more severe epidemics, as the number of infections increases, and shorter
time-scales to extinction. An analytical expression for the basic reproduction
number of the spatial model is derived as function of the non-spatial
counterpart, which characterises a transition between a disease-free and a
propagation phase, in which the disease propagates over a large fraction of the
system.
| [
{
"created": "Wed, 18 May 2022 12:46:20 GMT",
"version": "v1"
}
] | 2022-11-03 | [
[
"Giménez-Romero",
"Àlex",
""
],
[
"Vazquez",
"Federico",
""
],
[
"López",
"Cristóbal",
""
],
[
"Matías",
"Manuel A.",
""
]
] | Emerging marine infectious diseases pose a substantial threat to marine ecosystems and the conservation of their biodiversity. Compartmental models of epidemic transmission in marine sessile organisms, available only recently, are based on non-spatial descriptions in which space is homogenised and parasite mobility is not explicitly accounted for. However, in realistic scenarios epidemic transmission is conditioned by the spatial distribution of hosts and the parasites mobility patterns, calling for a explicit description of space. In this work we develop a spatially-explicit individual-based model to study disease transmission by waterborne parasites in sessile marine populations. We investigate the impact of spatial disease transmission through extensive numerical simulations and theoretical analysis. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal progression of the epidemic are assessed. We show that larger values of pathogen mobility imply more severe epidemics, as the number of infections increases, and shorter time-scales to extinction. An analytical expression for the basic reproduction number of the spatial model is derived as function of the non-spatial counterpart, which characterises a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system. |
2204.08578 | Yannick Roy | Yannick Roy, Jocelyn Faubert | Is the Contralateral Delay Activity (CDA) a robust neural correlate for
Visual Working Memory (VWM) tasks? A reproducibility study | null | Psychophysiology, 2022 | 10.1111/psyp.14180 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Visual working memory (VWM) allows us to actively store, update and
manipulate visual information surrounding us. While the underlying neural
mechanisms of VWM remain unclear, contralateral delay activity (CDA), a
sustained negativity over the hemisphere contralateral to the positions of
visual items to be remembered, is often used to study VWM. To investigate if
the CDA is a robust neural correlate for VWM tasks, we reproduced eight
CDA-related studies with a publicly accessible EEG dataset. We used the raw EEG
data from these eight studies and analyzed all of them with the same basic
pipeline to extract CDA. We were able to reproduce the results from all the
studies and show that with a basic automated EEG pipeline we can extract a
clear CDA signal. We share insights from the trends observed across the studies
and raise some questions about the CDA decay and the CDA during the recall
phase, which surprisingly, none of the eight studies did address. Finally, we
also provide reproducibility recommendations based on our experience and
challenges in reproducing these studies.
| [
{
"created": "Mon, 18 Apr 2022 22:21:22 GMT",
"version": "v1"
}
] | 2022-09-21 | [
[
"Roy",
"Yannick",
""
],
[
"Faubert",
"Jocelyn",
""
]
] | Visual working memory (VWM) allows us to actively store, update and manipulate visual information surrounding us. While the underlying neural mechanisms of VWM remain unclear, contralateral delay activity (CDA), a sustained negativity over the hemisphere contralateral to the positions of visual items to be remembered, is often used to study VWM. To investigate if the CDA is a robust neural correlate for VWM tasks, we reproduced eight CDA-related studies with a publicly accessible EEG dataset. We used the raw EEG data from these eight studies and analyzed all of them with the same basic pipeline to extract CDA. We were able to reproduce the results from all the studies and show that with a basic automated EEG pipeline we can extract a clear CDA signal. We share insights from the trends observed across the studies and raise some questions about the CDA decay and the CDA during the recall phase, which surprisingly, none of the eight studies did address. Finally, we also provide reproducibility recommendations based on our experience and challenges in reproducing these studies. |
1910.06217 | \"Ozg\"ur G\"ultekin | \c{C}a\u{g}atay Eskin, \"Ozg\"ur G\"ultekin | Effect of Harvest on MTE Calculated by Single Step Process for
Stochastic Population Model Under Allee Effect | 5 pages | null | 10.1063/1.5135465 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study, first we expand the cubic population model under Allee effect
with a quadratic harvest function that represents harvest effect. Then, using
four reaction equations representing the micro-interactions within the
population under the influence of demographic noise and harvest, we obtain the
mean field equations containing the effect of the harvest from the solution of
the master equation. In this way, a relationship is established between the
micro and macro parameters of the population. As a result, we calculate Mean
Time to Extinction (MTE) by using WKB approximation for single step processes
and observe the effect of harvesting.
| [
{
"created": "Mon, 14 Oct 2019 15:41:51 GMT",
"version": "v1"
}
] | 2020-01-08 | [
[
"Eskin",
"Çağatay",
""
],
[
"Gültekin",
"Özgür",
""
]
] | In this study, first we expand the cubic population model under Allee effect with a quadratic harvest function that represents harvest effect. Then, using four reaction equations representing the micro-interactions within the population under the influence of demographic noise and harvest, we obtain the mean field equations containing the effect of the harvest from the solution of the master equation. In this way, a relationship is established between the micro and macro parameters of the population. As a result, we calculate Mean Time to Extinction (MTE) by using WKB approximation for single step processes and observe the effect of harvesting. |
2006.15162 | Vikram Singh | Vikram Singh and Vikram Singh | C19-TraNet: an empirical, global index-case transmission network of
SARS-CoV-2 | 28 pages, 4 figures, 4 tables | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Originating in Wuhan, the novel coronavirus, severe acute respiratory
syndrome 2 (SARS-CoV-2), has astonished health-care systems across globe due to
its rapid and simultaneous spread to the neighboring and distantly located
countries. To gain the systems level understanding of the role of global
transmission routes in the COVID-19 spread, in this study, we have developed
the first, empirical, global, index-case transmission network of SARS-CoV-2
termed as C19-TraNet. We manually curated the travel history of country wise
index-cases using government press releases, their official social media
handles and online news reports to construct this C19-TraNet that is a
spatio-temporal, sparse, growing network comprising of 187 nodes and 199 edges
and follows a power-law degree distribution. To model the growing C19-TraNet, a
novel stochastic scale free (SSF) algorithm is proposed that accounts for
stochastic addition of both nodes as well as edges at each time step. A
peculiar connectivity pattern in C19-TraNet is observed, characterized by a
fourth degree polynomial growth curve, that significantly diverges from the
average random connectivity pattern obtained from an ensemble of its 1,000 SSF
realizations. Partitioning the C19-TraNet, using edge betweenness, it is found
that most of the large communities are comprised of a heterogeneous mixture of
countries belonging to different world regions suggesting that there are no
spatial constraints on the spread of disease. This work characterizes the
superspreaders that have very quickly transported the virus, through multiple
transmission routes, to long range geographical locations alongwith their local
neighborhoods.
| [
{
"created": "Fri, 26 Jun 2020 18:20:48 GMT",
"version": "v1"
}
] | 2020-06-30 | [
[
"Singh",
"Vikram",
""
],
[
"Singh",
"Vikram",
""
]
] | Originating in Wuhan, the novel coronavirus, severe acute respiratory syndrome 2 (SARS-CoV-2), has astonished health-care systems across globe due to its rapid and simultaneous spread to the neighboring and distantly located countries. To gain the systems level understanding of the role of global transmission routes in the COVID-19 spread, in this study, we have developed the first, empirical, global, index-case transmission network of SARS-CoV-2 termed as C19-TraNet. We manually curated the travel history of country wise index-cases using government press releases, their official social media handles and online news reports to construct this C19-TraNet that is a spatio-temporal, sparse, growing network comprising of 187 nodes and 199 edges and follows a power-law degree distribution. To model the growing C19-TraNet, a novel stochastic scale free (SSF) algorithm is proposed that accounts for stochastic addition of both nodes as well as edges at each time step. A peculiar connectivity pattern in C19-TraNet is observed, characterized by a fourth degree polynomial growth curve, that significantly diverges from the average random connectivity pattern obtained from an ensemble of its 1,000 SSF realizations. Partitioning the C19-TraNet, using edge betweenness, it is found that most of the large communities are comprised of a heterogeneous mixture of countries belonging to different world regions suggesting that there are no spatial constraints on the spread of disease. This work characterizes the superspreaders that have very quickly transported the virus, through multiple transmission routes, to long range geographical locations alongwith their local neighborhoods. |
1910.07414 | Jan Karbowski | Jan Karbowski | Metabolic constraints on synaptic learning and memory | brain, synapses, energy cost of learning and memory, synaptic
plasticity, model and estimates | Journal of Neurophysiology 122: 1473-1490 (2019) | 10.1152/jn.00092.2019 | null | q-bio.NC q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dendritic spines, the carriers of long-term memory, occupy a small fraction
of cortical space, and yet they are the major consumers of brain metabolic
energy. What fraction of this energy goes for synaptic plasticity, correlated
with learning and memory? It is estimated here based on neurophysiological and
proteomic data for rat brain that, depending on the level of protein
phosphorylation, the energy cost of synaptic plasticity constitutes a small
fraction of the energy used for fast excitatory synaptic transmission,
typically $4.0-11.2 \%$. Next, this study analyzes a metabolic cost of a new
learning and its memory trace in relation to the cost of prior memories, using
a class of cascade models of synaptic plasticity. It is argued that these
models must contain bidirectional cyclic motifs, related to protein
phosphorylation, to be compatible with basic thermodynamic principles. For most
investigated parameters longer memories generally require proportionally more
energy to store. The exception are the parameters controlling the speed of
molecular transitions (e.g. ATP driven phosphorylation rate), for which memory
lifetime per invested energy can increase progressively for longer memories.
Furthermore, in general, a memory trace decouples dynamically from a
corresponding synaptic metabolic rate such that the energy expended on a new
learning and its memory trace constitutes in most cases only a small fraction
of the baseline energy associated with prior memories. Taken together, these
empirical and theoretical results suggest a metabolic efficiency of
synaptically stored information.
| [
{
"created": "Wed, 16 Oct 2019 15:30:52 GMT",
"version": "v1"
}
] | 2019-10-17 | [
[
"Karbowski",
"Jan",
""
]
] | Dendritic spines, the carriers of long-term memory, occupy a small fraction of cortical space, and yet they are the major consumers of brain metabolic energy. What fraction of this energy goes for synaptic plasticity, correlated with learning and memory? It is estimated here based on neurophysiological and proteomic data for rat brain that, depending on the level of protein phosphorylation, the energy cost of synaptic plasticity constitutes a small fraction of the energy used for fast excitatory synaptic transmission, typically $4.0-11.2 \%$. Next, this study analyzes a metabolic cost of a new learning and its memory trace in relation to the cost of prior memories, using a class of cascade models of synaptic plasticity. It is argued that these models must contain bidirectional cyclic motifs, related to protein phosphorylation, to be compatible with basic thermodynamic principles. For most investigated parameters longer memories generally require proportionally more energy to store. The exception are the parameters controlling the speed of molecular transitions (e.g. ATP driven phosphorylation rate), for which memory lifetime per invested energy can increase progressively for longer memories. Furthermore, in general, a memory trace decouples dynamically from a corresponding synaptic metabolic rate such that the energy expended on a new learning and its memory trace constitutes in most cases only a small fraction of the baseline energy associated with prior memories. Taken together, these empirical and theoretical results suggest a metabolic efficiency of synaptically stored information. |
1504.00431 | R.K. Brojen Singh | Md. Jahoor Alam and R.K. Brojen Singh | Phase transition in p53 states induced by glucose | null | null | null | null | q-bio.SC q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present p53-MDM2-Glucose model to study spatio-temporal properties of the
system induced by glucose. The variation in glucose concentration level
triggers the system at different states, namely, oscillation death
(stabilized), sustain and damped oscillations which correspond to various
cellular states. The transition of these states induced by glucose is phase
transition like behaviour. We also found that the intrinsic noise in stochastic
system helps the system to stabilize more effectively. Further, the amplitude
of $p53$ dynamics with the variation of glucose concentration level follows
power law behaviour, $A_s(k)\sim k^\gamma$, where, $\gamma$ is a constant.
| [
{
"created": "Thu, 2 Apr 2015 02:40:23 GMT",
"version": "v1"
}
] | 2015-04-03 | [
[
"Alam",
"Md. Jahoor",
""
],
[
"Singh",
"R. K. Brojen",
""
]
] | We present p53-MDM2-Glucose model to study spatio-temporal properties of the system induced by glucose. The variation in glucose concentration level triggers the system at different states, namely, oscillation death (stabilized), sustain and damped oscillations which correspond to various cellular states. The transition of these states induced by glucose is phase transition like behaviour. We also found that the intrinsic noise in stochastic system helps the system to stabilize more effectively. Further, the amplitude of $p53$ dynamics with the variation of glucose concentration level follows power law behaviour, $A_s(k)\sim k^\gamma$, where, $\gamma$ is a constant. |
1701.01744 | Tolutola Oyetunde | Tolutola Oyetunde, Jeffrey Czajka, Gang Wu, Cynthia Lo, and Yinjie
Tang | Metabolite patterns reveal regulatory responses to genetic perturbations | 15 pages, 6 figures intended for Nucleic Acids Research | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genetic and environmental perturbation experiments have been used to study
microbes in a bid to gain insight into transcriptional regulation, adaptive
evolution, and other cellular dynamics. These studies have potential in
enabling rational strain design. Unfortunately, experimentally determined
intracellular flux distribution are often inconsistent or incomparable due to
different experimental conditions and methodologies. Computational strain
design relies on constraint-based reconstruction and analysis (COBRA)
techniques to predict the effect of gene knockouts such as flux balance
analysis (FBA), regulatory on/off minimization(ROOM), minimization of metabolic
adjustment (MOMA), relative optimality in metabolic networks (RELATCH). Most of
these knock-out prediction methods are based on conserving inherent flux
patterns (between wild type and mutant) that are thought to be representative
of the cellular regulatory structure. However, it has been recently
demonstrated that these methods show poor agreement with experimental data. To
improve the fidelity of knockout predictions and subsequent computational
strain design, we developed REMEP, a metabolite-centric method. We demonstrate
the improved performance of REMEP by comparing the different methods on
experimental knockout data of E. coli, and S. cerevisiae grown in batch
cultures. REMEP retains most of the features of earlier algorithms but is much
more accurate in capturing cellular responses to genetic perturbations. A
primary reason for this is that REMEP relies on the assumption that cellular
regulatory structure leaves a signature on metabolite patterns and not just
flux patterns. REMEP will also prove useful in uncovering novel insights into
cellular regulation and control.
| [
{
"created": "Fri, 6 Jan 2017 19:51:11 GMT",
"version": "v1"
},
{
"created": "Tue, 10 Jan 2017 03:59:21 GMT",
"version": "v2"
}
] | 2017-01-11 | [
[
"Oyetunde",
"Tolutola",
""
],
[
"Czajka",
"Jeffrey",
""
],
[
"Wu",
"Gang",
""
],
[
"Lo",
"Cynthia",
""
],
[
"Tang",
"Yinjie",
""
]
] | Genetic and environmental perturbation experiments have been used to study microbes in a bid to gain insight into transcriptional regulation, adaptive evolution, and other cellular dynamics. These studies have potential in enabling rational strain design. Unfortunately, experimentally determined intracellular flux distribution are often inconsistent or incomparable due to different experimental conditions and methodologies. Computational strain design relies on constraint-based reconstruction and analysis (COBRA) techniques to predict the effect of gene knockouts such as flux balance analysis (FBA), regulatory on/off minimization(ROOM), minimization of metabolic adjustment (MOMA), relative optimality in metabolic networks (RELATCH). Most of these knock-out prediction methods are based on conserving inherent flux patterns (between wild type and mutant) that are thought to be representative of the cellular regulatory structure. However, it has been recently demonstrated that these methods show poor agreement with experimental data. To improve the fidelity of knockout predictions and subsequent computational strain design, we developed REMEP, a metabolite-centric method. We demonstrate the improved performance of REMEP by comparing the different methods on experimental knockout data of E. coli, and S. cerevisiae grown in batch cultures. REMEP retains most of the features of earlier algorithms but is much more accurate in capturing cellular responses to genetic perturbations. A primary reason for this is that REMEP relies on the assumption that cellular regulatory structure leaves a signature on metabolite patterns and not just flux patterns. REMEP will also prove useful in uncovering novel insights into cellular regulation and control. |
1605.09535 | Peter Klimek | Peter Klimek, Silke Aichberger, Stefan Thurner | Disentangling genetic and environmental risk factors for individual
diseases from multiplex comorbidity networks | null | null | null | null | q-bio.MN physics.soc-ph stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most disorders are caused by a combination of multiple genetic and/or
environmental factors. If two diseases are caused by the same molecular
mechanism, they tend to co-occur in patients. Here we provide a quantitative
method to disentangle how much genetic or environmental risk factors contribute
to the pathogenesis of 358 individual diseases, respectively. We pool data on
genetic, pathway-based, and toxicogenomic disease-causing mechanisms with
disease co-occurrence data obtained from almost two million patients. From this
data we construct a multilayer network where nodes represent disorders that are
connected by links that either represent phenotypic comorbidity of the patients
or the involvement of a certain molecular mechanism. From the similarity of
phenotypic and mechanism-based networks for each disorder we derive measure
that allows us to quantify the relative importance of various molecular
mechanisms for a given disease. We find that most diseases are dominated by
genetic risk factors, while environmental influences prevail for disorders such
as depressions, cancers, or dermatitis. Almost never we find that more than one
type of mechanisms is involved in the pathogenesis of diseases.
| [
{
"created": "Tue, 31 May 2016 09:05:07 GMT",
"version": "v1"
}
] | 2016-06-01 | [
[
"Klimek",
"Peter",
""
],
[
"Aichberger",
"Silke",
""
],
[
"Thurner",
"Stefan",
""
]
] | Most disorders are caused by a combination of multiple genetic and/or environmental factors. If two diseases are caused by the same molecular mechanism, they tend to co-occur in patients. Here we provide a quantitative method to disentangle how much genetic or environmental risk factors contribute to the pathogenesis of 358 individual diseases, respectively. We pool data on genetic, pathway-based, and toxicogenomic disease-causing mechanisms with disease co-occurrence data obtained from almost two million patients. From this data we construct a multilayer network where nodes represent disorders that are connected by links that either represent phenotypic comorbidity of the patients or the involvement of a certain molecular mechanism. From the similarity of phenotypic and mechanism-based networks for each disorder we derive measure that allows us to quantify the relative importance of various molecular mechanisms for a given disease. We find that most diseases are dominated by genetic risk factors, while environmental influences prevail for disorders such as depressions, cancers, or dermatitis. Almost never we find that more than one type of mechanisms is involved in the pathogenesis of diseases. |
1908.10791 | Feng Fu | Katherine P. Royce, Feng Fu | Mathematically Modeling Spillover Dynamics of Emerging Zoonoses with
Intermediate Hosts | Comments are welcome | null | 10.1371/journal.pone.0237780 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The World Health Organization describes zoonotic diseases as a major pandemic
threat, and modeling the behavior of such diseases is a key component of their
control. Many emerging zoonoses, such as SARS, Nipah, and Hendra, mutated from
their wild type while circulating in an intermediate host population, usually a
domestic species, to become more transmissible among humans, and moreover, this
transmission route will only become more likely as agriculture and trade
intensifies around the world. Passage through an intermediate host enables many
otherwise rare diseases to become better adapted to humans, and so
understanding this process with mathematical epidemiological models is
necessary to prevent epidemics of emerging zoonoses, guide policy interventions
in public health, and predict the behavior of an epidemic. In this paper, we
account for spillovers of a zoonotic disease mutating in an intermediate host
by means of modeling transmission dynamics within and between three host
species, namely, wild reservoir, intermediate domestic animals, and humans. We
calculate the basic reproductive number of the pathogen, present critical
conditions for the emergence dynamics of zoonosis, and perform stability
analysis of admissible disease equilibria. Our analytical results agree well
with long-term simulations of the system. We find that in the presence of
biologically realistic interspecies transmission parameters, a zoonotic disease
can establish itself in humans even if it fails to persist in its reservoir and
intermediate host species. Our model and results can be used to understand the
dynamic behavior of any zoonosis with intermediate hosts and assist efforts to
protect public health.
| [
{
"created": "Wed, 28 Aug 2019 15:47:22 GMT",
"version": "v1"
}
] | 2021-01-27 | [
[
"Royce",
"Katherine P.",
""
],
[
"Fu",
"Feng",
""
]
] | The World Health Organization describes zoonotic diseases as a major pandemic threat, and modeling the behavior of such diseases is a key component of their control. Many emerging zoonoses, such as SARS, Nipah, and Hendra, mutated from their wild type while circulating in an intermediate host population, usually a domestic species, to become more transmissible among humans, and moreover, this transmission route will only become more likely as agriculture and trade intensifies around the world. Passage through an intermediate host enables many otherwise rare diseases to become better adapted to humans, and so understanding this process with mathematical epidemiological models is necessary to prevent epidemics of emerging zoonoses, guide policy interventions in public health, and predict the behavior of an epidemic. In this paper, we account for spillovers of a zoonotic disease mutating in an intermediate host by means of modeling transmission dynamics within and between three host species, namely, wild reservoir, intermediate domestic animals, and humans. We calculate the basic reproductive number of the pathogen, present critical conditions for the emergence dynamics of zoonosis, and perform stability analysis of admissible disease equilibria. Our analytical results agree well with long-term simulations of the system. We find that in the presence of biologically realistic interspecies transmission parameters, a zoonotic disease can establish itself in humans even if it fails to persist in its reservoir and intermediate host species. Our model and results can be used to understand the dynamic behavior of any zoonosis with intermediate hosts and assist efforts to protect public health. |
1611.02116 | Zachary Kilpatrick PhD | Daniel B. Poll and Zachary P. Kilpatrick | Velocity integration in a multilayer neural field model of spatial
working memory | 37 pages, 9 figures | null | null | null | q-bio.NC nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyze a multilayer neural field model of spatial working memory,
focusing on the impact of interlaminar connectivity, spatial heterogeneity, and
velocity inputs. Models of spatial working memory typically employ networks
that generate persistent activity via a combination of local excitation and
lateral inhibition. Our model is comprised of a multilayer set of equations
that describes connectivity between neurons in the same and different layers
using an integral term. The kernel of this integral term then captures the
impact of different interlaminar connection strengths, spatial heterogeneity,
and velocity input. We begin our analysis by focusing on how interlaminar
connectivity shapes the form and stability of (persistent) bump attractor
solutions to the model. Subsequently, we derive a low-dimensional approximation
that describes how spatial heterogeneity, velocity input, and noise combine to
determine the position of bump solutions. The main impact of spatial
heterogeneity is to break the translation symmetry of the network, so bumps
prefer to reside at one of a finite number of local attractors in the domain.
With the reduced model in hand, we can then approximate the dynamics of the
bump position using a continuous time Markov chain model that describes bump
motion between local attractors. While heterogeneity reduces the effective
diffusion of the bumps, it also disrupts the processing of velocity inputs by
slowing the velocity-induced propagation of bumps. However, we demonstrate that
noise can play a constructive role by promoting bump motion transitions,
restoring a mean bump velocity that is close to the input velocity.
| [
{
"created": "Mon, 7 Nov 2016 15:34:56 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Jan 2017 16:42:41 GMT",
"version": "v2"
}
] | 2017-01-17 | [
[
"Poll",
"Daniel B.",
""
],
[
"Kilpatrick",
"Zachary P.",
""
]
] | We analyze a multilayer neural field model of spatial working memory, focusing on the impact of interlaminar connectivity, spatial heterogeneity, and velocity inputs. Models of spatial working memory typically employ networks that generate persistent activity via a combination of local excitation and lateral inhibition. Our model is comprised of a multilayer set of equations that describes connectivity between neurons in the same and different layers using an integral term. The kernel of this integral term then captures the impact of different interlaminar connection strengths, spatial heterogeneity, and velocity input. We begin our analysis by focusing on how interlaminar connectivity shapes the form and stability of (persistent) bump attractor solutions to the model. Subsequently, we derive a low-dimensional approximation that describes how spatial heterogeneity, velocity input, and noise combine to determine the position of bump solutions. The main impact of spatial heterogeneity is to break the translation symmetry of the network, so bumps prefer to reside at one of a finite number of local attractors in the domain. With the reduced model in hand, we can then approximate the dynamics of the bump position using a continuous time Markov chain model that describes bump motion between local attractors. While heterogeneity reduces the effective diffusion of the bumps, it also disrupts the processing of velocity inputs by slowing the velocity-induced propagation of bumps. However, we demonstrate that noise can play a constructive role by promoting bump motion transitions, restoring a mean bump velocity that is close to the input velocity. |
1903.00095 | Sebastiano Barbieri | Sebastiano Barbieri, Oliver J. Gurney-Champion, Remy Klaassen, Harriet
C. Thoeny | Deep Learning How to Fit an Intravoxel Incoherent Motion Model to
Diffusion-Weighted MRI | null | Magnetic Resonance in Medicine 2019 | 10.1002/mrm.27910 | null | q-bio.QM cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: This prospective clinical study assesses the feasibility of training
a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model
fitting to diffusion-weighted magnetic resonance imaging (DW-MRI) data and
evaluates its performance. Methods: In May 2011, ten male volunteers (age
range: 29 to 53 years, mean: 37 years) underwent DW-MRI of the upper abdomen on
1.5T and 3.0T magnetic resonance scanners. Regions of interest in the left and
right liver lobe, pancreas, spleen, renal cortex, and renal medulla were
delineated independently by two readers. DNNs were trained for IVIM model
fitting using these data; results were compared to least-squares and Bayesian
approaches to IVIM fitting. Intraclass Correlation Coefficients (ICC) were used
to assess consistency of measurements between readers. Intersubject variability
was evaluated using Coefficients of Variation (CV). The fitting error was
calculated based on simulated data and the average fitting time of each method
was recorded. Results: DNNs were trained successfully for IVIM parameter
estimation. This approach was associated with high consistency between the two
readers (ICCs between 50 and 97%), low intersubject variability of estimated
parameter values (CVs between 9.2 and 28.4), and the lowest error when compared
with least-squares and Bayesian approaches. Fitting by DNNs was several orders
of magnitude quicker than the other methods but the networks may need to be
re-trained for different acquisition protocols or imaged anatomical regions.
Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to
DW-MRI data. Suitable software is available at (1).
| [
{
"created": "Thu, 28 Feb 2019 22:42:02 GMT",
"version": "v1"
},
{
"created": "Thu, 23 May 2019 08:17:04 GMT",
"version": "v2"
}
] | 2020-01-08 | [
[
"Barbieri",
"Sebastiano",
""
],
[
"Gurney-Champion",
"Oliver J.",
""
],
[
"Klaassen",
"Remy",
""
],
[
"Thoeny",
"Harriet C.",
""
]
] | Purpose: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted magnetic resonance imaging (DW-MRI) data and evaluates its performance. Methods: In May 2011, ten male volunteers (age range: 29 to 53 years, mean: 37 years) underwent DW-MRI of the upper abdomen on 1.5T and 3.0T magnetic resonance scanners. Regions of interest in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla were delineated independently by two readers. DNNs were trained for IVIM model fitting using these data; results were compared to least-squares and Bayesian approaches to IVIM fitting. Intraclass Correlation Coefficients (ICC) were used to assess consistency of measurements between readers. Intersubject variability was evaluated using Coefficients of Variation (CV). The fitting error was calculated based on simulated data and the average fitting time of each method was recorded. Results: DNNs were trained successfully for IVIM parameter estimation. This approach was associated with high consistency between the two readers (ICCs between 50 and 97%), low intersubject variability of estimated parameter values (CVs between 9.2 and 28.4), and the lowest error when compared with least-squares and Bayesian approaches. Fitting by DNNs was several orders of magnitude quicker than the other methods but the networks may need to be re-trained for different acquisition protocols or imaged anatomical regions. Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data. Suitable software is available at (1). |
1311.6682 | Mario dos Reis Dr | Mario dos Reis | Population genetics and substitution models of adaptive evolution | 24 pages, 4 figures and 1 table. Manuscript written between January
and April 2010 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ratio of non-synonymous to synonymous substitutions
$\omega(=d_{N}/d_{S})$ has been widely used as a measure of adaptive evolution
in protein coding genes. Omega can be defined in terms of population genetics
parameters as the fixation ratio of selected vs. neutral mutants. Here it is
argued that approaches based on the infinite sites model are not appropriate to
define $\omega$ for single codon locations. Simple models of amino acid
substitution with reversible mutation and selection are analysed, and used to
define $\omega$ under several evolutionary scenarios. In most practical cases
$\omega<1$ when selection is constant throughout time. However, it is shown
that when the pattern of selection on amino acids changes, for example after an
environment shift, a temporary burst of adaptive evolution ($\omega\gg1$) can
be observed. The fixation probability of a novel mutant under frequency
dependent selection is calculated, and it is used to show why $\omega>1$ can be
sometimes expected for single locations at equilibrium. An example with
influenza data is discussed.
| [
{
"created": "Tue, 26 Nov 2013 14:21:37 GMT",
"version": "v1"
}
] | 2013-11-27 | [
[
"Reis",
"Mario dos",
""
]
] | The ratio of non-synonymous to synonymous substitutions $\omega(=d_{N}/d_{S})$ has been widely used as a measure of adaptive evolution in protein coding genes. Omega can be defined in terms of population genetics parameters as the fixation ratio of selected vs. neutral mutants. Here it is argued that approaches based on the infinite sites model are not appropriate to define $\omega$ for single codon locations. Simple models of amino acid substitution with reversible mutation and selection are analysed, and used to define $\omega$ under several evolutionary scenarios. In most practical cases $\omega<1$ when selection is constant throughout time. However, it is shown that when the pattern of selection on amino acids changes, for example after an environment shift, a temporary burst of adaptive evolution ($\omega\gg1$) can be observed. The fixation probability of a novel mutant under frequency dependent selection is calculated, and it is used to show why $\omega>1$ can be sometimes expected for single locations at equilibrium. An example with influenza data is discussed. |
2302.01076 | Adam Mielke | Adam Mielke and Lasse Engbo Christiansen | Convergence to the Equilibrium State in an Outbreak: When Can Growth
Rates Accurately be Measured? | 4 pages, 1 figure | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate sub-leading orders of the classic SEIR-model using contact
matrices from modeling of the Omicron and Delta variants of COVID-19 in
Denmark. The goal of this is to illustrate when the growth rate, and by
extension the infectiousness, can be accurately measured in a new outbreak,
e.g. after introduction of a new variant of a virus. We find that as long as
susceptible depletion is a minor effect, the transients are gone within around
4 generations.
| [
{
"created": "Thu, 2 Feb 2023 13:07:33 GMT",
"version": "v1"
}
] | 2023-02-03 | [
[
"Mielke",
"Adam",
""
],
[
"Christiansen",
"Lasse Engbo",
""
]
] | We investigate sub-leading orders of the classic SEIR-model using contact matrices from modeling of the Omicron and Delta variants of COVID-19 in Denmark. The goal of this is to illustrate when the growth rate, and by extension the infectiousness, can be accurately measured in a new outbreak, e.g. after introduction of a new variant of a virus. We find that as long as susceptible depletion is a minor effect, the transients are gone within around 4 generations. |
2309.05771 | Can Firtina | Can Firtina, Melina Soysal, Jo\"el Lindegger, Onur Mutlu | RawHash2: Mapping Raw Nanopore Signals Using Hash-Based Seeding and
Adaptive Quantization | Accepted in Bioinformatics:
https://doi.org/10.1093/bioinformatics/btae478 | Bioinformatics, 2024, btae478 | 10.1093/bioinformatics/btae478 | null | q-bio.GN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Summary: Raw nanopore signals can be analyzed while they are being generated,
a process known as real-time analysis. Real-time analysis of raw signals is
essential to utilize the unique features that nanopore sequencing provides,
enabling the early stopping of the sequencing of a read or the entire
sequencing run based on the analysis. The state-of-the-art mechanism, RawHash,
offers the first hash-based efficient and accurate similarity identification
between raw signals and a reference genome by quickly matching their hash
values. In this work, we introduce RawHash2, which provides major improvements
over RawHash, including a more sensitive quantization and chaining
implementation, weighted mapping decisions, frequency filters to reduce
ambiguous seed hits, minimizers for hash-based sketching, and support for the
R10.4 flow cell version and various data formats such as POD5 and SLOW5.
Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57%
and up to 20.25%) and better throughput (on average by 4.0x and up to 9.9x)
than RawHash. Availability and Implementation: RawHash2 is available at
https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully
reproduce our results on our GitHub page.
| [
{
"created": "Mon, 11 Sep 2023 18:56:48 GMT",
"version": "v1"
},
{
"created": "Sat, 21 Oct 2023 20:50:33 GMT",
"version": "v2"
},
{
"created": "Fri, 3 Nov 2023 15:46:20 GMT",
"version": "v3"
},
{
"created": "Wed, 1 May 2024 20:28:49 GMT",
"version": "v4"
},
{
"created": "Tue, 13 Aug 2024 08:25:02 GMT",
"version": "v5"
}
] | 2024-08-14 | [
[
"Firtina",
"Can",
""
],
[
"Soysal",
"Melina",
""
],
[
"Lindegger",
"Joël",
""
],
[
"Mutlu",
"Onur",
""
]
] | Summary: Raw nanopore signals can be analyzed while they are being generated, a process known as real-time analysis. Real-time analysis of raw signals is essential to utilize the unique features that nanopore sequencing provides, enabling the early stopping of the sequencing of a read or the entire sequencing run based on the analysis. The state-of-the-art mechanism, RawHash, offers the first hash-based efficient and accurate similarity identification between raw signals and a reference genome by quickly matching their hash values. In this work, we introduce RawHash2, which provides major improvements over RawHash, including a more sensitive quantization and chaining implementation, weighted mapping decisions, frequency filters to reduce ambiguous seed hits, minimizers for hash-based sketching, and support for the R10.4 flow cell version and various data formats such as POD5 and SLOW5. Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57% and up to 20.25%) and better throughput (on average by 4.0x and up to 9.9x) than RawHash. Availability and Implementation: RawHash2 is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page. |
1411.2441 | Karol Niena{\l}towski | Agata Charzy\'nska, Weronika Wronowska, Karol Niena{\l}towski and Anna
Gambin | Computational model of sphingolipids metabolism: a case study of
Alzheimer's disease | null | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Background: Sphingolipids - as suggested by the prefix in their name - are
mysterious molecules, which play surprisingly various roles in opposable
cellular processes, like autophagy, apoptosis, proliferation and
differentiation. Recently they have been also recognized as important
messengers in cellular signalling pathways. More importantly, sphingolipid
metabolism disorders were observed in various pathological conditions such as
cancer and neurodegeneration. Results: Existing formal models of sphingolipids
metabolism concentrates mostly on de novo ceramide synthesis or restrict their
focus to biochemical transformations of a particular subspecies. We propose
first comprehensive computational model of sphingolipid metabolism in human
tissue. In contrast to previous approaches we explicitly model
compartmentalization what allows emphasizing the differences among individual
organelles. Conclusions: Presented here model was validated by means of
recently proposed model analysis technics allowing for detection of most
sensitive and experimentally non-identifiable parameters and determination of
main sources of model variance. Moreover, we demonstrate the utility of the
model for the study of molecular processes underlying Alzheimer's disease.
| [
{
"created": "Mon, 10 Nov 2014 14:32:47 GMT",
"version": "v1"
}
] | 2014-11-11 | [
[
"Charzyńska",
"Agata",
""
],
[
"Wronowska",
"Weronika",
""
],
[
"Nienałtowski",
"Karol",
""
],
[
"Gambin",
"Anna",
""
]
] | Background: Sphingolipids - as suggested by the prefix in their name - are mysterious molecules, which play surprisingly various roles in opposable cellular processes, like autophagy, apoptosis, proliferation and differentiation. Recently they have been also recognized as important messengers in cellular signalling pathways. More importantly, sphingolipid metabolism disorders were observed in various pathological conditions such as cancer and neurodegeneration. Results: Existing formal models of sphingolipids metabolism concentrates mostly on de novo ceramide synthesis or restrict their focus to biochemical transformations of a particular subspecies. We propose first comprehensive computational model of sphingolipid metabolism in human tissue. In contrast to previous approaches we explicitly model compartmentalization what allows emphasizing the differences among individual organelles. Conclusions: Presented here model was validated by means of recently proposed model analysis technics allowing for detection of most sensitive and experimentally non-identifiable parameters and determination of main sources of model variance. Moreover, we demonstrate the utility of the model for the study of molecular processes underlying Alzheimer's disease. |
1910.03736 | Vladyslav Oles | Vladyslav Oles, Anton Kukushkin | BoolSi: a tool for distributed simulations and analysis of Boolean
networks | Added asynchronous BNs to the introduction, added figure showing an
attractor, updated figure node correlations, explicitly mentioned if a figure
is BoolSi output, stated correlations of CK and TDIF on proliferation in case
study | null | null | null | q-bio.MN cs.DC math.DS q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present BoolSi, an open-source cross-platform command line tool for
distributed simulations of deterministic Boolean networks with synchronous
update. It uses MPI standard to support execution on computational clusters, as
well as parallel processing on a single computer. BoolSi can be used to model
the behavior of complex dynamic networks, such as gene regulatory networks. In
particular, it allows for identification and statistical analysis of network
attractors. We perform a case study of the activity of a cambium cell to
demonstrate the capabilities of the tool.
| [
{
"created": "Wed, 9 Oct 2019 01:06:47 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Oct 2019 20:29:56 GMT",
"version": "v2"
}
] | 2019-10-22 | [
[
"Oles",
"Vladyslav",
""
],
[
"Kukushkin",
"Anton",
""
]
] | We present BoolSi, an open-source cross-platform command line tool for distributed simulations of deterministic Boolean networks with synchronous update. It uses MPI standard to support execution on computational clusters, as well as parallel processing on a single computer. BoolSi can be used to model the behavior of complex dynamic networks, such as gene regulatory networks. In particular, it allows for identification and statistical analysis of network attractors. We perform a case study of the activity of a cambium cell to demonstrate the capabilities of the tool. |
2006.05081 | Davide Faranda | Davide Faranda and Tommaso Alberti | Modelling the second wave of COVID-19 infections in France and Italy via
a Stochastic SEIR model | null | null | 10.1063/5.0015943 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | COVID-19 has forced quarantine measures in several countries across the
world. These measures have proven to be effective in significantly reducing the
prevalence of the virus. To date, no effective treatment or vaccine is
available. In the effort of preserving both public health as well as the
economical and social textures, France and Italy governments have partially
released lockdown measures. Here we extrapolate the long-term behavior of the
epidemics in both countries using a Susceptible-Exposed-Infected-Recovered
(SEIR) model where parameters are stochastically perturbed to handle the
uncertainty in the estimates of COVID-19 prevalence. Our results suggest that
uncertainties in both parameters and initial conditions rapidly propagate in
the model and can result in different outcomes of the epidemics leading or not
to a second wave of infections. Using actual knowledge, asymptotic estimates of
COVID-19 prevalence can fluctuate of order of ten millions units in both
countries.
| [
{
"created": "Tue, 9 Jun 2020 07:20:07 GMT",
"version": "v1"
}
] | 2020-12-02 | [
[
"Faranda",
"Davide",
""
],
[
"Alberti",
"Tommaso",
""
]
] | COVID-19 has forced quarantine measures in several countries across the world. These measures have proven to be effective in significantly reducing the prevalence of the virus. To date, no effective treatment or vaccine is available. In the effort of preserving both public health as well as the economical and social textures, France and Italy governments have partially released lockdown measures. Here we extrapolate the long-term behavior of the epidemics in both countries using a Susceptible-Exposed-Infected-Recovered (SEIR) model where parameters are stochastically perturbed to handle the uncertainty in the estimates of COVID-19 prevalence. Our results suggest that uncertainties in both parameters and initial conditions rapidly propagate in the model and can result in different outcomes of the epidemics leading or not to a second wave of infections. Using actual knowledge, asymptotic estimates of COVID-19 prevalence can fluctuate of order of ten millions units in both countries. |
2112.00069 | Armita Nourmohammad | Colin LaMont, Jakub Otwinowski, Kanika Vanshylla, Henning Gruell,
Florian Klein, Armita Nourmohammad | Design of an optimal combination therapy with broadly neutralizing
antibodies to suppress HIV-1 | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Broadly neutralizing antibodies (bNAbs) are promising targets for vaccination
and therapy against HIV. Passive infusions of bNAbs have shown promise in
clinical trials as a potential alternative for anti-retroviral therapy. A key
challenge for the potential clinical application of bnAbs is the suppression of
viral escape, which is more effectively achieved with a combination of bNAbs.
However, identifying an optimal bNAb cocktail is combinatorially complex. Here,
we propose a computational approach to predict the efficacy of a bNAb therapy
trial based on the population genetics of HIV escape, which we parametrize
using high-throughput HIV sequence data from a cohort of untreated bNAb-naive
patients. By quantifying the mutational target size and the fitness cost of
HIV-1 escape from bNAbs, we reliably predict the distribution of rebound times
in three clinical trials. Importantly, we show that early rebounds are
dominated by the pre-treatment standing variation of HIV-1 populations, rather
than spontaneous mutations during treatment. Lastly, we show that a cocktail of
three bNAbs is necessary to suppress the chances of viral escape below 1%, and
we predict the optimal composition of such a bNAb cocktail. Our results offer a
rational design for bNAb therapy against HIV-1, and more generally show how
genetic data could be used to predict treatment outcomes and design new
approaches to pathogenic control.
| [
{
"created": "Tue, 30 Nov 2021 19:56:50 GMT",
"version": "v1"
}
] | 2021-12-02 | [
[
"LaMont",
"Colin",
""
],
[
"Otwinowski",
"Jakub",
""
],
[
"Vanshylla",
"Kanika",
""
],
[
"Gruell",
"Henning",
""
],
[
"Klein",
"Florian",
""
],
[
"Nourmohammad",
"Armita",
""
]
] | Broadly neutralizing antibodies (bNAbs) are promising targets for vaccination and therapy against HIV. Passive infusions of bNAbs have shown promise in clinical trials as a potential alternative for anti-retroviral therapy. A key challenge for the potential clinical application of bnAbs is the suppression of viral escape, which is more effectively achieved with a combination of bNAbs. However, identifying an optimal bNAb cocktail is combinatorially complex. Here, we propose a computational approach to predict the efficacy of a bNAb therapy trial based on the population genetics of HIV escape, which we parametrize using high-throughput HIV sequence data from a cohort of untreated bNAb-naive patients. By quantifying the mutational target size and the fitness cost of HIV-1 escape from bNAbs, we reliably predict the distribution of rebound times in three clinical trials. Importantly, we show that early rebounds are dominated by the pre-treatment standing variation of HIV-1 populations, rather than spontaneous mutations during treatment. Lastly, we show that a cocktail of three bNAbs is necessary to suppress the chances of viral escape below 1%, and we predict the optimal composition of such a bNAb cocktail. Our results offer a rational design for bNAb therapy against HIV-1, and more generally show how genetic data could be used to predict treatment outcomes and design new approaches to pathogenic control. |
2101.09354 | M. Ali Vosoughi | Axel Wismuller and M. Ali Vosoughi | Large-scale Augmented Granger Causality (lsAGC) for Connectivity
Analysis in Complex Systems: From Computer Simulations to Functional MRI
(fMRI) | 15 pages, conference | null | null | null | q-bio.NC cs.LG eess.IV | http://creativecommons.org/licenses/by/4.0/ | We introduce large-scale Augmented Granger Causality (lsAGC) as a method for
connectivity analysis in complex systems. The lsAGC algorithm combines
dimension reduction with source time-series augmentation and uses predictive
time-series modeling for estimating directed causal relationships among
time-series. This method is a multivariate approach, since it is capable of
identifying the influence of each time-series on any other time-series in the
presence of all other time-series of the underlying dynamic system. We
quantitatively evaluate the performance of lsAGC on synthetic directional
time-series networks with known ground truth. As a reference method, we compare
our results with cross-correlation, which is typically used as a standard
measure of connectivity in the functional MRI (fMRI) literature. Using
extensive simulations for a wide range of time-series lengths and two different
signal-to-noise ratios of 5 and 15 dB, lsAGC consistently outperforms
cross-correlation at accurately detecting network connections, using Receiver
Operator Characteristic Curve (ROC) analysis, across all tested time-series
lengths and noise levels. In addition, as an outlook to possible clinical
application, we perform a preliminary qualitative analysis of connectivity
matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical
controls, using a subset of 59 subjects of the Autism Brain Imaging Data
Exchange II (ABIDE II) data repository. Our results suggest that lsAGC, by
extracting sparse connectivity matrices, may be useful for network analysis in
complex systems, and may be applicable to clinical fMRI analysis in future
research, such as targeting disease-related classification or regression tasks
on clinical data.
| [
{
"created": "Sun, 10 Jan 2021 01:44:48 GMT",
"version": "v1"
}
] | 2021-01-26 | [
[
"Wismuller",
"Axel",
""
],
[
"Vosoughi",
"M. Ali",
""
]
] | We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems. The lsAGC algorithm combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. We quantitatively evaluate the performance of lsAGC on synthetic directional time-series networks with known ground truth. As a reference method, we compare our results with cross-correlation, which is typically used as a standard measure of connectivity in the functional MRI (fMRI) literature. Using extensive simulations for a wide range of time-series lengths and two different signal-to-noise ratios of 5 and 15 dB, lsAGC consistently outperforms cross-correlation at accurately detecting network connections, using Receiver Operator Characteristic Curve (ROC) analysis, across all tested time-series lengths and noise levels. In addition, as an outlook to possible clinical application, we perform a preliminary qualitative analysis of connectivity matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical controls, using a subset of 59 subjects of the Autism Brain Imaging Data Exchange II (ABIDE II) data repository. Our results suggest that lsAGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such as targeting disease-related classification or regression tasks on clinical data. |
2112.11298 | Matthew Denwood | Jacob St{\ae}rk-{\O}stergaard, Carsten Kirkeby, Lasse Engbo
Christiansen, Michael Asger Andersen, Camilla Holten M{\o}ller, Marianne
Voldstedlund, Matthew J. Denwood | Evaluation of diagnostic test procedures for SARS-CoV-2 using latent
class models: comparison of antigen test kits and sampling for PCR testing
based on Danish national data registries | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Antigen test kits have been used extensively as a screening tool during the
worldwide pandemic of coronavirus (SARS-CoV-2). While it is generally expected
that taking samples for analysis with PCR testing gives more reliable results
than using antigen test kits, the overall sensitivity and specificity of the
two protocols in the field have not yet been estimated without assuming that
the PCR test constitutes a gold standard. We use latent class models to
estimate the in situ performance of both PCR and antigen testing, using data
from the Danish national registries. The results are based on 240,000 paired
tests results sub-selected from the 55 million test results that were obtained
in Denmark during the period from February 2021 until June 2021.
We found that the specificity of both tests is very high in our data sample
(>99.7%), while the sensitivity of PCR sampling was estimated to be 95.7% (95%
CI: 92.8-98.4%) and that of the antigen test kits used in Denmark over the
study period was estimated at 53.8% (95% CI: 49.8-57.9%). Our findings can be
used as supplementary information for consideration when implementing serial
testing strategies that employ a confirmatory PCR sample following a positive
result from an antigen test kit, such as the policy used in Denmark. We note
that while this strategy reduces the number of false positives associated with
antigen test screening, it also increases the false negatives. We demonstrate
that the balance of trading false positives for false negatives only favours
the use of serial testing when the expected true prevalence is low. Our results
contain substantial uncertainty in the estimates for sensitivity due to the
relatively small number of positive test results over this period: validation
of our findings in a population with higher prevalence would therefore be
highly relevant for future work.
| [
{
"created": "Tue, 21 Dec 2021 15:37:27 GMT",
"version": "v1"
}
] | 2021-12-22 | [
[
"Stærk-Østergaard",
"Jacob",
""
],
[
"Kirkeby",
"Carsten",
""
],
[
"Christiansen",
"Lasse Engbo",
""
],
[
"Andersen",
"Michael Asger",
""
],
[
"Møller",
"Camilla Holten",
""
],
[
"Voldstedlund",
"Marianne",
""
],
[
"Denwood",
"Matthew J.",
""
]
] | Antigen test kits have been used extensively as a screening tool during the worldwide pandemic of coronavirus (SARS-CoV-2). While it is generally expected that taking samples for analysis with PCR testing gives more reliable results than using antigen test kits, the overall sensitivity and specificity of the two protocols in the field have not yet been estimated without assuming that the PCR test constitutes a gold standard. We use latent class models to estimate the in situ performance of both PCR and antigen testing, using data from the Danish national registries. The results are based on 240,000 paired tests results sub-selected from the 55 million test results that were obtained in Denmark during the period from February 2021 until June 2021. We found that the specificity of both tests is very high in our data sample (>99.7%), while the sensitivity of PCR sampling was estimated to be 95.7% (95% CI: 92.8-98.4%) and that of the antigen test kits used in Denmark over the study period was estimated at 53.8% (95% CI: 49.8-57.9%). Our findings can be used as supplementary information for consideration when implementing serial testing strategies that employ a confirmatory PCR sample following a positive result from an antigen test kit, such as the policy used in Denmark. We note that while this strategy reduces the number of false positives associated with antigen test screening, it also increases the false negatives. We demonstrate that the balance of trading false positives for false negatives only favours the use of serial testing when the expected true prevalence is low. Our results contain substantial uncertainty in the estimates for sensitivity due to the relatively small number of positive test results over this period: validation of our findings in a population with higher prevalence would therefore be highly relevant for future work. |
2011.12400 | Adeel Razi | Karl J. Friston, Guillaume Flandin, Adeel Razi | Dynamic causal modelling of mitigated epidemiological outcomes | null | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | This technical report describes the rationale and technical details for the
dynamic causal modelling of mitigated epidemiological outcomes based upon a
variety of timeseries data. It details the structure of the underlying
convolution or generative model (at the time of writing on 6-Nov-20). This
report is intended for use as a reference that accompanies the predictions in
following dashboard: https://www.fil.ion.ucl.ac.uk/spm/covid-19/dashboard
| [
{
"created": "Tue, 24 Nov 2020 21:28:51 GMT",
"version": "v1"
}
] | 2020-11-26 | [
[
"Friston",
"Karl J.",
""
],
[
"Flandin",
"Guillaume",
""
],
[
"Razi",
"Adeel",
""
]
] | This technical report describes the rationale and technical details for the dynamic causal modelling of mitigated epidemiological outcomes based upon a variety of timeseries data. It details the structure of the underlying convolution or generative model (at the time of writing on 6-Nov-20). This report is intended for use as a reference that accompanies the predictions in following dashboard: https://www.fil.ion.ucl.ac.uk/spm/covid-19/dashboard |
2311.13339 | Michael Shapiro | Anna Laddach, Michael Shapiro | Non-deterministic linear thresholding systems reveal their deterministic
origins | 4 pages | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Linear thresholding systems have been used as a model of neural activation
and have more recently been proposed as a model of gene activation.
Deterministic linear thresholding systems can be turned into non-deterministic
systems by the introduction of noise. Under mild conditions on the noise, we
show that the deterministic model can be deduced from the probabilities of the
non-deterministic model.
| [
{
"created": "Wed, 22 Nov 2023 12:03:42 GMT",
"version": "v1"
}
] | 2023-11-23 | [
[
"Laddach",
"Anna",
""
],
[
"Shapiro",
"Michael",
""
]
] | Linear thresholding systems have been used as a model of neural activation and have more recently been proposed as a model of gene activation. Deterministic linear thresholding systems can be turned into non-deterministic systems by the introduction of noise. Under mild conditions on the noise, we show that the deterministic model can be deduced from the probabilities of the non-deterministic model. |
0812.2174 | Lucas Wardil | L. Wardil and J. K. L. da Silva | A discrete inhomogeneous model for the yeast cell cycle | 5 pages, 1 figure | Brazilian Journal of Physics, vol. 38, no. 3A, September, 2008 | null | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the robustness and stability of the yeast cell regulatory network by
using a general inhomogeneous discrete model. We find that inhomogeneity, on
average, enhances the stability of the biggest attractor of the dynamics and
that the large size of the basin of attraction is robust against changes in the
parameters of inhomogeneity. We find that the most frequent orbit, which
represents the cell-cycle pathway, has a better biological meaning than the one
exhibited by the homogeneous model.
| [
{
"created": "Thu, 11 Dec 2008 15:33:38 GMT",
"version": "v1"
}
] | 2008-12-12 | [
[
"Wardil",
"L.",
""
],
[
"da Silva",
"J. K. L.",
""
]
] | We study the robustness and stability of the yeast cell regulatory network by using a general inhomogeneous discrete model. We find that inhomogeneity, on average, enhances the stability of the biggest attractor of the dynamics and that the large size of the basin of attraction is robust against changes in the parameters of inhomogeneity. We find that the most frequent orbit, which represents the cell-cycle pathway, has a better biological meaning than the one exhibited by the homogeneous model. |
2007.15673 | Brian Skinner | Calvin Pozderac and Brian Skinner | Superspreading of SARS-CoV-2 in the USA | 7+9 pages pages, 3+3 figures; slightly updated numerical estimates;
published version | PLoS ONE 16(3): e0248808 (2021) | 10.1371/journal.pone.0248808 | null | q-bio.PE physics.soc-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have
been known to exhibit superspreading, in which a small fraction of infected
individuals is responsible for the majority of new infections. The existence of
superspreading implies a fat-tailed distribution of infectiousness (new
secondary infections caused per day) among different individuals. Here, we
present a simple method to estimate the variation in infectiousness by
examining the variation in early-time growth rates of new cases among different
subpopulations. We use this method to estimate the mean and variance in the
infectiousness, $\beta$, for SARS-CoV-2 transmission during the early stages of
the pandemic within the United States. We find that $\sigma_\beta/\mu_\beta
\gtrsim 3.2$, where $\mu_\beta$ is the mean infectiousness and $\sigma_\beta$
its standard deviation, which implies pervasive superspreading. This result
allows us to estimate that in the early stages of the pandemic in the USA, over
81% of new cases were a result of the top 10% of most infectious individuals.
| [
{
"created": "Thu, 30 Jul 2020 18:09:29 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Sep 2020 01:21:23 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Mar 2021 03:36:08 GMT",
"version": "v3"
}
] | 2021-03-31 | [
[
"Pozderac",
"Calvin",
""
],
[
"Skinner",
"Brian",
""
]
] | A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections. The existence of superspreading implies a fat-tailed distribution of infectiousness (new secondary infections caused per day) among different individuals. Here, we present a simple method to estimate the variation in infectiousness by examining the variation in early-time growth rates of new cases among different subpopulations. We use this method to estimate the mean and variance in the infectiousness, $\beta$, for SARS-CoV-2 transmission during the early stages of the pandemic within the United States. We find that $\sigma_\beta/\mu_\beta \gtrsim 3.2$, where $\mu_\beta$ is the mean infectiousness and $\sigma_\beta$ its standard deviation, which implies pervasive superspreading. This result allows us to estimate that in the early stages of the pandemic in the USA, over 81% of new cases were a result of the top 10% of most infectious individuals. |
q-bio/0611003 | Laurent Perrinet | Laurent Perrinet (INCM) | Feature detection using spikes: the greedy approach | This work links Matching Pursuit with bayesian inference by providing
the underlying hypotheses (linear model, uniform prior, gaussian noise
model). A parallel with the parallel and event-based nature of neural
computations is explored and we show application to modelling Primary Visual
Cortex / image processsing.
http://incm.cnrs-mrs.fr/perrinet/dynn/LaurentPerrinet/Publications/Perrinet04tauc | Journal of physiology, Paris. 98 (28/11/2005) 530--9 | 10.1016/j.jphysparis.2005.09.012 | null | q-bio.NC | null | A goal of low-level neural processes is to build an efficient code extracting
the relevant information from the sensory input. It is believed that this is
implemented in cortical areas by elementary inferential computations
dynamically extracting the most likely parameters corresponding to the sensory
signal. We explore here a neuro-mimetic feed-forward model of the primary
visual area (VI) solving this problem in the case where the signal may be
described by a robust linear generative model. This model uses an over-complete
dictionary of primitives which provides a distributed probabilistic
representation of input features. Relying on an efficiency criterion, we derive
an algorithm as an approximate solution which uses incremental greedy inference
processes. This algorithm is similar to 'Matching Pursuit' and mimics the
parallel architecture of neural computations. We propose here a simple
implementation using a network of spiking integrate-and-fire neurons which
communicate using lateral interactions. Numerical simulations show that this
Sparse Spike Coding strategy provides an efficient model for representing
visual data from a set of natural images. Even though it is simplistic, this
transformation of spatial data into a spatio-temporal pattern of binary events
provides an accurate description of some complex neural patterns observed in
the spiking activity of biological neural networks.
| [
{
"created": "Wed, 1 Nov 2006 20:42:07 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Nov 2006 11:13:51 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Perrinet",
"Laurent",
"",
"INCM"
]
] | A goal of low-level neural processes is to build an efficient code extracting the relevant information from the sensory input. It is believed that this is implemented in cortical areas by elementary inferential computations dynamically extracting the most likely parameters corresponding to the sensory signal. We explore here a neuro-mimetic feed-forward model of the primary visual area (VI) solving this problem in the case where the signal may be described by a robust linear generative model. This model uses an over-complete dictionary of primitives which provides a distributed probabilistic representation of input features. Relying on an efficiency criterion, we derive an algorithm as an approximate solution which uses incremental greedy inference processes. This algorithm is similar to 'Matching Pursuit' and mimics the parallel architecture of neural computations. We propose here a simple implementation using a network of spiking integrate-and-fire neurons which communicate using lateral interactions. Numerical simulations show that this Sparse Spike Coding strategy provides an efficient model for representing visual data from a set of natural images. Even though it is simplistic, this transformation of spatial data into a spatio-temporal pattern of binary events provides an accurate description of some complex neural patterns observed in the spiking activity of biological neural networks. |
q-bio/0408004 | Marconi Barbosa Dr | M. S. Barbosa and L. da F. Costa and E. S. Bernardes and G. Ramakers
and J. van Pelt | Characterizing neuromorphologic alterations with additive shape
functionals | null | Eur. Phys. J. B 37, 109 115 (2004) | 10.1140/epjb/e2004-00035-y | null | q-bio.QM cond-mat.stat-mech | null | The complexity of a neuronal cell shape is known to be related to its
function. Specifically, among other indicators, a decreased complexity in the
dendritic trees of cortical pyramidal neurons has been associated with mental
retardation. In this paper we develop a procedure to address the
characterization of morphological changes induced in cultured neurons by
over-expressing a gene involved in mental retardation. Measures associated with
the multiscale connectivity, an additive image functional, are found to give a
reasonable separation criterion between two categories of cells. One category
consists of a control group and two transfected groups of neurons, and the
other, a class of cat ganglionary cells. The reported framework also identified
a trend towards lower complexity in one of the transfected groups. Such results
establish the suggested measures as an effective descriptors of cell shape.
| [
{
"created": "Mon, 9 Aug 2004 18:13:22 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Barbosa",
"M. S.",
""
],
[
"Costa",
"L. da F.",
""
],
[
"Bernardes",
"E. S.",
""
],
[
"Ramakers",
"G.",
""
],
[
"van Pelt",
"J.",
""
]
] | The complexity of a neuronal cell shape is known to be related to its function. Specifically, among other indicators, a decreased complexity in the dendritic trees of cortical pyramidal neurons has been associated with mental retardation. In this paper we develop a procedure to address the characterization of morphological changes induced in cultured neurons by over-expressing a gene involved in mental retardation. Measures associated with the multiscale connectivity, an additive image functional, are found to give a reasonable separation criterion between two categories of cells. One category consists of a control group and two transfected groups of neurons, and the other, a class of cat ganglionary cells. The reported framework also identified a trend towards lower complexity in one of the transfected groups. Such results establish the suggested measures as an effective descriptors of cell shape. |
2303.17615 | Hampus Gummesson Svensson | Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, Morteza
Haghir Chehreghani | Utilizing Reinforcement Learning for de novo Drug Design | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Deep learning-based approaches for generating novel drug molecules with
specific properties have gained a lot of interest in the last few years. Recent
studies have demonstrated promising performance for string-based generation of
novel molecules utilizing reinforcement learning. In this paper, we develop a
unified framework for using reinforcement learning for de novo drug design,
wherein we systematically study various on- and off-policy reinforcement
learning algorithms and replay buffers to learn an RNN-based policy to generate
novel molecules predicted to be active against the dopamine receptor DRD2. Our
findings suggest that it is advantageous to use at least both top-scoring and
low-scoring molecules for updating the policy when structural diversity is
essential. Using all generated molecules at an iteration seems to enhance
performance stability for on-policy algorithms. In addition, when replaying
high, intermediate, and low-scoring molecules, off-policy algorithms display
the potential of improving the structural diversity and number of active
molecules generated, but possibly at the cost of a longer exploration phase.
Our work provides an open-source framework enabling researchers to investigate
various reinforcement learning methods for de novo drug design.
| [
{
"created": "Thu, 30 Mar 2023 07:40:50 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Jan 2024 21:09:48 GMT",
"version": "v2"
}
] | 2024-02-01 | [
[
"Svensson",
"Hampus Gummesson",
""
],
[
"Tyrchan",
"Christian",
""
],
[
"Engkvist",
"Ola",
""
],
[
"Chehreghani",
"Morteza Haghir",
""
]
] | Deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. Recent studies have demonstrated promising performance for string-based generation of novel molecules utilizing reinforcement learning. In this paper, we develop a unified framework for using reinforcement learning for de novo drug design, wherein we systematically study various on- and off-policy reinforcement learning algorithms and replay buffers to learn an RNN-based policy to generate novel molecules predicted to be active against the dopamine receptor DRD2. Our findings suggest that it is advantageous to use at least both top-scoring and low-scoring molecules for updating the policy when structural diversity is essential. Using all generated molecules at an iteration seems to enhance performance stability for on-policy algorithms. In addition, when replaying high, intermediate, and low-scoring molecules, off-policy algorithms display the potential of improving the structural diversity and number of active molecules generated, but possibly at the cost of a longer exploration phase. Our work provides an open-source framework enabling researchers to investigate various reinforcement learning methods for de novo drug design. |
2005.00004 | Timothy Schacker | L. Schifanella, J.L. Anderson, M. Galli, M. Corbellino, A. Lai, G.
Wieking, B. Grzywacz, N.R. Klatt, A.T. Haase and T.W. Schacker | Massive viral replication and cytopathic effects in early COVID-19
pneumonia | 19 pages, 4 figures, 3 extended data figures | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | SARS-CoV-2 is the cause of COVID-19 acute respiratory illness that like its
predecessors, MERS and SARS, can be severe and fatal 1-4. By April of 2020,
COVID-19 infections had become a worldwide pandemic with nearly 3 million
infections and over 200,000 deaths. The relative contributions of virus
replication and cytopathic effects or immunopathological host responses to the
severe and fatal outcomes of COVID-19 lung infections have as yet to be
determined. Here we show that SARS-CoV-2 replication and cytopathic effects in
type II alveolar pneumocytes causes focal lung injury in an individual with no
history of pulmonary symptoms. These findings point to the potential benefit of
early effective antiviral treatment to prevent progression to severe and fatal
COVID-19 pneumonia.
| [
{
"created": "Thu, 30 Apr 2020 12:39:22 GMT",
"version": "v1"
}
] | 2020-05-04 | [
[
"Schifanella",
"L.",
""
],
[
"Anderson",
"J. L.",
""
],
[
"Galli",
"M.",
""
],
[
"Corbellino",
"M.",
""
],
[
"Lai",
"A.",
""
],
[
"Wieking",
"G.",
""
],
[
"Grzywacz",
"B.",
""
],
[
"Klatt",
"N. R.",
""
],
[
"Haase",
"A. T.",
""
],
[
"Schacker",
"T. W.",
""
]
] | SARS-CoV-2 is the cause of COVID-19 acute respiratory illness that like its predecessors, MERS and SARS, can be severe and fatal 1-4. By April of 2020, COVID-19 infections had become a worldwide pandemic with nearly 3 million infections and over 200,000 deaths. The relative contributions of virus replication and cytopathic effects or immunopathological host responses to the severe and fatal outcomes of COVID-19 lung infections have as yet to be determined. Here we show that SARS-CoV-2 replication and cytopathic effects in type II alveolar pneumocytes causes focal lung injury in an individual with no history of pulmonary symptoms. These findings point to the potential benefit of early effective antiviral treatment to prevent progression to severe and fatal COVID-19 pneumonia. |
2111.10374 | Dipam Goswami Mr. | Dipam Goswami, Hari Om Aggrawal, Rajiv Gupta, Vinti Agarwal | Urine Microscopic Image Dataset | 7 pages, 1 image | null | null | null | q-bio.QM cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | Urinalysis is a standard diagnostic test to detect urinary system related
problems. The automation of urinalysis will reduce the overall diagnostic time.
Recent studies used urine microscopic datasets for designing deep learning
based algorithms to classify and detect urine cells. But these datasets are not
publicly available for further research. To alleviate the need for urine
datsets, we prepare our urine sediment microscopic image (UMID) dataset
comprising of around 3700 cell annotations and 3 categories of cells namely
RBC, pus and epithelial cells. We discuss the several challenges involved in
preparing the dataset and the annotations. We make the dataset publicly
available.
| [
{
"created": "Fri, 19 Nov 2021 13:11:04 GMT",
"version": "v1"
}
] | 2021-11-23 | [
[
"Goswami",
"Dipam",
""
],
[
"Aggrawal",
"Hari Om",
""
],
[
"Gupta",
"Rajiv",
""
],
[
"Agarwal",
"Vinti",
""
]
] | Urinalysis is a standard diagnostic test to detect urinary system related problems. The automation of urinalysis will reduce the overall diagnostic time. Recent studies used urine microscopic datasets for designing deep learning based algorithms to classify and detect urine cells. But these datasets are not publicly available for further research. To alleviate the need for urine datsets, we prepare our urine sediment microscopic image (UMID) dataset comprising of around 3700 cell annotations and 3 categories of cells namely RBC, pus and epithelial cells. We discuss the several challenges involved in preparing the dataset and the annotations. We make the dataset publicly available. |
1312.7774 | Tengiz Zorikov | Tengiz Zorikov | Echo-processing mechanisms in bottlenose dolphins | 10 pg. 8 fig | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The mechanisms of echo-processing were investigated in our experiments,
conducted on bottlenose dolphins. Hierarchically organized system of
independent dimensions, describing echoes in animals perception, was revealed.
The rules of discrimination and recognition of echoes in dolphins were
established.
| [
{
"created": "Mon, 30 Dec 2013 16:47:08 GMT",
"version": "v1"
}
] | 2014-01-02 | [
[
"Zorikov",
"Tengiz",
""
]
] | The mechanisms of echo-processing were investigated in our experiments, conducted on bottlenose dolphins. Hierarchically organized system of independent dimensions, describing echoes in animals perception, was revealed. The rules of discrimination and recognition of echoes in dolphins were established. |
2401.12477 | David Murrugarra | David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova, Claus Kadelka,
Matthew Wheeler, Reinhard Laubenbacher | Modular Control of Biological Networks | 16 pages, 6 figures. arXiv admin note: text overlap with
arXiv:2206.04217 | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | The concept of control is central to understanding and applications of
biological network models. Some of their key structural features relate to
control functions, through gene regulation, signaling, or metabolic mechanisms,
and computational models need to encode these. Applications of models often
focus on model-based control, such as in biomedicine or metabolic engineering.
This paper presents an approach to model-based control that exploits two common
features of biological networks, namely their modular structure and canalizing
features of their regulatory mechanisms. The paper focuses on intracellular
regulatory networks, represented by Boolean network models. A main result of
this paper is that control strategies can be identified by focusing on one
module at a time. This paper also presents a criterion based on canalizing
features of the regulatory rules to identify modules that do not contribute to
network control and can be excluded. For even moderately sized networks,
finding global control inputs is computationally very challenging. The modular
approach presented here leads to a highly efficient approach to solving this
problem. This approach is applied to a published Boolean network model of blood
cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control
set that achieves a desired control objective.
| [
{
"created": "Tue, 23 Jan 2024 04:13:31 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Jul 2024 19:56:31 GMT",
"version": "v2"
}
] | 2024-07-09 | [
[
"Murrugarra",
"David",
""
],
[
"Veliz-Cuba",
"Alan",
""
],
[
"Dimitrova",
"Elena",
""
],
[
"Kadelka",
"Claus",
""
],
[
"Wheeler",
"Matthew",
""
],
[
"Laubenbacher",
"Reinhard",
""
]
] | The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications of models often focus on model-based control, such as in biomedicine or metabolic engineering. This paper presents an approach to model-based control that exploits two common features of biological networks, namely their modular structure and canalizing features of their regulatory mechanisms. The paper focuses on intracellular regulatory networks, represented by Boolean network models. A main result of this paper is that control strategies can be identified by focusing on one module at a time. This paper also presents a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally very challenging. The modular approach presented here leads to a highly efficient approach to solving this problem. This approach is applied to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective. |
1506.00597 | Vipul Periwal | Carson C. Chow, Yanjun Li and Vipul Periwal | The Universality of Cancer | 5 pages, 1 figure | null | null | null | q-bio.TO q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cancer has been characterized as a constellation of hundreds of diseases
differing in underlying mutations and depending on cellular environments.
Carcinogenesis as a stochastic physical process has been studied for over sixty
years, but there is no accepted standard model. We show that the hazard rates
of all cancers are characterized by a simple dynamic stochastic process on a
half-line, with a universal linear restoring force balancing a universal simple
Brownian motion starting from a universal initial distribution. Only a critical
radius defining the transition from normal to tumorigenic genomes distinguishes
between different cancer types when time is measured in cell--cycle units.
Reparametrizing to chronological time units introduces two additional
parameters: the onset of cellular senescence with age and the time interval
over which this cessation in replication takes place. This universality implies
that there may exist a finite separation between normal cells and tumorigenic
cells in all tissue types that may be a viable target for both early detection
and preventive therapy.
| [
{
"created": "Mon, 1 Jun 2015 18:28:48 GMT",
"version": "v1"
}
] | 2015-06-02 | [
[
"Chow",
"Carson C.",
""
],
[
"Li",
"Yanjun",
""
],
[
"Periwal",
"Vipul",
""
]
] | Cancer has been characterized as a constellation of hundreds of diseases differing in underlying mutations and depending on cellular environments. Carcinogenesis as a stochastic physical process has been studied for over sixty years, but there is no accepted standard model. We show that the hazard rates of all cancers are characterized by a simple dynamic stochastic process on a half-line, with a universal linear restoring force balancing a universal simple Brownian motion starting from a universal initial distribution. Only a critical radius defining the transition from normal to tumorigenic genomes distinguishes between different cancer types when time is measured in cell--cycle units. Reparametrizing to chronological time units introduces two additional parameters: the onset of cellular senescence with age and the time interval over which this cessation in replication takes place. This universality implies that there may exist a finite separation between normal cells and tumorigenic cells in all tissue types that may be a viable target for both early detection and preventive therapy. |
1601.03243 | Andrea De Martino | Daniele De Martino, Fabrizio Capuani, Andrea De Martino | Growth against entropy in bacterial metabolism: the phenotypic trade-off
behind empirical growth rate distributions in E. coli | 12 pages, 5 figures | Phys. Biol. 13 (2016) 036005 | 10.1088/1478-3975/13/3/036005 | null | q-bio.MN cond-mat.dis-nn physics.bio-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The solution space of genome-scale models of cellular metabolism provides a
map between physically viable flux configurations and cellular metabolic
phenotypes described, at the most basic level, by the corresponding growth
rates. By sampling the solution space of E. coli's metabolic network, we show
that empirical growth rate distributions recently obtained in experiments at
single-cell resolution can be explained in terms of a trade-off between the
higher fitness of fast-growing phenotypes and the higher entropy of
slow-growing ones. Based on this, we propose a minimal model for the evolution
of a large bacterial population that captures this trade-off. The scaling
relationships observed in experiments encode, in such frameworks, for the same
distance from the maximum achievable growth rate, the same degree of growth
rate maximization, and/or the same rate of phenotypic change. Being grounded on
genome-scale metabolic network reconstructions, these results allow for
multiple implications and extensions in spite of the underlying conceptual
simplicity.
| [
{
"created": "Wed, 13 Jan 2016 13:54:38 GMT",
"version": "v1"
},
{
"created": "Fri, 27 May 2016 14:11:22 GMT",
"version": "v2"
}
] | 2016-05-30 | [
[
"De Martino",
"Daniele",
""
],
[
"Capuani",
"Fabrizio",
""
],
[
"De Martino",
"Andrea",
""
]
] | The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity. |
2107.02835 | Semih Kara | Semih Kara, Nuno C. Martins | Pairwise Comparison Evolutionary Dynamics with Strategy-Dependent
Revision Rates: Stability and Delta-Passivity (Expanded Version) | null | null | null | null | q-bio.PE cs.SY eess.SY math.DS math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report on new stability conditions for evolutionary dynamics in the
context of population games. We adhere to the prevailing framework consisting
of many agents, grouped into populations, that interact noncooperatively by
selecting strategies with a favorable payoff. Each agent is repeatedly allowed
to revise its strategy at a rate referred to as revision rate. Previous
stability results considered either that the payoff mechanism was a memoryless
potential game, or allowed for dynamics (in the payoff mechanism) at the
expense of precluding any explicit dependence of the agents' revision rates on
their current strategies. Allowing the dependence of revision rates on
strategies is relevant because the agents' strategies at any point in time are
generally unequal. To allow for strategy-dependent revision rates and payoff
mechanisms that are dynamic (or memoryless games that are not potential), we
focus on an evolutionary dynamics class obtained from a straightforward
modification of one that stems from the so-called impartial pairwise comparison
strategy revision protocol. Revision protocols consistent with the modified
class retain from those in the original one the advantage that the agents
operate in a fully decentralized manner and with minimal information
requirements - they need to access only the payoff values (not the mechanism)
of the available strategies. Our main results determine conditions under which
system-theoretic passivity properties are assured, which we leverage for
stability analysis.
| [
{
"created": "Tue, 6 Jul 2021 18:33:31 GMT",
"version": "v1"
}
] | 2021-07-08 | [
[
"Kara",
"Semih",
""
],
[
"Martins",
"Nuno C.",
""
]
] | We report on new stability conditions for evolutionary dynamics in the context of population games. We adhere to the prevailing framework consisting of many agents, grouped into populations, that interact noncooperatively by selecting strategies with a favorable payoff. Each agent is repeatedly allowed to revise its strategy at a rate referred to as revision rate. Previous stability results considered either that the payoff mechanism was a memoryless potential game, or allowed for dynamics (in the payoff mechanism) at the expense of precluding any explicit dependence of the agents' revision rates on their current strategies. Allowing the dependence of revision rates on strategies is relevant because the agents' strategies at any point in time are generally unequal. To allow for strategy-dependent revision rates and payoff mechanisms that are dynamic (or memoryless games that are not potential), we focus on an evolutionary dynamics class obtained from a straightforward modification of one that stems from the so-called impartial pairwise comparison strategy revision protocol. Revision protocols consistent with the modified class retain from those in the original one the advantage that the agents operate in a fully decentralized manner and with minimal information requirements - they need to access only the payoff values (not the mechanism) of the available strategies. Our main results determine conditions under which system-theoretic passivity properties are assured, which we leverage for stability analysis. |
1812.08721 | Eleni Panagiotou | E. Panagiotou and K. W. Plaxco | A topological study of protein folding kinetics | 13 pages | null | null | null | q-bio.QM cond-mat.soft math.GT physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Focusing on a small set of proteins that i) fold in a concerted, all-or-none
fashion and ii) do not contain knots or slipknots, we show that the Gauss
linking integral, the torsion and the number of sequence-distant contacts
provide information regarding the folding rate. Our results suggest that the
global topology/geometry of the proteins shifts from right-handed to
left-handed with decreasing folding rate, and that this topological change is
associated with an increase in the number of more sequence-distant contacts.
| [
{
"created": "Mon, 3 Dec 2018 13:10:58 GMT",
"version": "v1"
},
{
"created": "Sat, 28 Sep 2019 14:19:29 GMT",
"version": "v2"
}
] | 2019-10-01 | [
[
"Panagiotou",
"E.",
""
],
[
"Plaxco",
"K. W.",
""
]
] | Focusing on a small set of proteins that i) fold in a concerted, all-or-none fashion and ii) do not contain knots or slipknots, we show that the Gauss linking integral, the torsion and the number of sequence-distant contacts provide information regarding the folding rate. Our results suggest that the global topology/geometry of the proteins shifts from right-handed to left-handed with decreasing folding rate, and that this topological change is associated with an increase in the number of more sequence-distant contacts. |
2002.04484 | Robert Marsland III | Fernanda S. Valdovinos and Robert Marsland III | Niche theory for mutualism: A graphical approach to plant-pollinator
network dynamics | 41 pages, 8 figures | null | null | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Contemporary Niche Theory is a useful framework for understanding how
organisms interact with each other and with their shared environment. Its
graphical representation, popularized by Tilman's Resource Ratio Hypothesis,
facilitates the analysis of the equilibrium structure of complex dynamical
models including species coexistence. This theory has been applied primarily to
resource competition since its early beginnings. Here, we integrate mutualism
into niche theory by expanding Tilman's graphical representation to the
analysis of consumer-resource dynamics of plant-pollinator networks. We
graphically explain the qualitative phenomena previously found by numerical
simulations, including the effects on community dynamics of nestedness,
adaptive foraging, and pollinator invasions. Our graphical approach promotes
the unification of niche and network theories, and deepens the synthesis of
different types of interactions within a consumer-resource framework.
| [
{
"created": "Tue, 11 Feb 2020 15:39:13 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Jul 2020 21:35:23 GMT",
"version": "v2"
}
] | 2020-07-10 | [
[
"Valdovinos",
"Fernanda S.",
""
],
[
"Marsland",
"Robert",
"III"
]
] | Contemporary Niche Theory is a useful framework for understanding how organisms interact with each other and with their shared environment. Its graphical representation, popularized by Tilman's Resource Ratio Hypothesis, facilitates the analysis of the equilibrium structure of complex dynamical models including species coexistence. This theory has been applied primarily to resource competition since its early beginnings. Here, we integrate mutualism into niche theory by expanding Tilman's graphical representation to the analysis of consumer-resource dynamics of plant-pollinator networks. We graphically explain the qualitative phenomena previously found by numerical simulations, including the effects on community dynamics of nestedness, adaptive foraging, and pollinator invasions. Our graphical approach promotes the unification of niche and network theories, and deepens the synthesis of different types of interactions within a consumer-resource framework. |
2112.15109 | Nour Almadhoun Alserr | Nour Almadhoun Alserr, Ozgur Ulusoy, Erman Ayday, and Onur Mutlu | GenShare: Sharing Accurate Differentially-Private Statistics for Genomic
Datasets with Dependent Tuples | 8 pages, 7 figures | null | null | null | q-bio.GN cs.CR | http://creativecommons.org/licenses/by/4.0/ | Motivation: Cutting the cost of DNA sequencing technology led to a quantum
leap in the availability of genomic data. While sharing genomic data across
researchers is an essential driver of advances in health and biomedical
research, the sharing process is often infeasible due to data privacy concerns.
Differential privacy is one of the rigorous mechanisms utilized to facilitate
the sharing of aggregate statistics from genomic datasets without disclosing
any private individual-level data. However, differential privacy can still
divulge sensitive information about the dataset participants due to the
correlation between dataset tuples. Results: Here, we propose GenShare model
built upon Laplace-perturbation-mechanism-based DP to introduce a
privacy-preserving query-answering sharing model for statistical genomic
datasets that include dependency due to the inherent correlations between
genomes of individuals (i.e., family ties). We demonstrate our privacy
improvement over the state-of-the-art approaches for a range of practical
queries including cohort discovery, minor allele frequency, and chi^2
association tests. With a fine-grained analysis of sensitivity in the Laplace
perturbation mechanism and considering joint distributions, GenShare results
near-achieve the formal privacy guarantees permitted by the theory of
differential privacy as the queries that computed over independent tuples (only
up to 6% differences). GenShare ensures that query results are as accurate as
theoretically guaranteed by differential privacy. For empowering the advances
in different scientific and medical research areas, GenShare presents a path
toward an interactive genomic data sharing system when the datasets include
participants with familial relationships.
| [
{
"created": "Thu, 30 Dec 2021 16:05:26 GMT",
"version": "v1"
}
] | 2022-01-03 | [
[
"Alserr",
"Nour Almadhoun",
""
],
[
"Ulusoy",
"Ozgur",
""
],
[
"Ayday",
"Erman",
""
],
[
"Mutlu",
"Onur",
""
]
] | Motivation: Cutting the cost of DNA sequencing technology led to a quantum leap in the availability of genomic data. While sharing genomic data across researchers is an essential driver of advances in health and biomedical research, the sharing process is often infeasible due to data privacy concerns. Differential privacy is one of the rigorous mechanisms utilized to facilitate the sharing of aggregate statistics from genomic datasets without disclosing any private individual-level data. However, differential privacy can still divulge sensitive information about the dataset participants due to the correlation between dataset tuples. Results: Here, we propose GenShare model built upon Laplace-perturbation-mechanism-based DP to introduce a privacy-preserving query-answering sharing model for statistical genomic datasets that include dependency due to the inherent correlations between genomes of individuals (i.e., family ties). We demonstrate our privacy improvement over the state-of-the-art approaches for a range of practical queries including cohort discovery, minor allele frequency, and chi^2 association tests. With a fine-grained analysis of sensitivity in the Laplace perturbation mechanism and considering joint distributions, GenShare results near-achieve the formal privacy guarantees permitted by the theory of differential privacy as the queries that computed over independent tuples (only up to 6% differences). GenShare ensures that query results are as accurate as theoretically guaranteed by differential privacy. For empowering the advances in different scientific and medical research areas, GenShare presents a path toward an interactive genomic data sharing system when the datasets include participants with familial relationships. |
1901.05935 | Rocio Joo | Rocio Joo, Matthew E. Boone, Thomas A. Clay, Samantha C. Patrick,
Susana Clusella-Trullas, Mathieu Basille | Navigating through the R packages for movement | 77 pages, 4 figures | Journal of Animal Ecology, 2019 | 10.1111/1365-2656.13116 | null | q-bio.QM stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of miniaturized biologging devices has provided ecologists with
unprecedented opportunities to record animal movement across scales, and led to
the collection of ever-increasing quantities of tracking data. In parallel,
sophisticated tools have been developed to process, visualize and analyze
tracking data, however many of these tools have proliferated in isolation,
making it challenging for users to select the most appropriate method for the
question in hand. Indeed, within the R software alone, we listed 58 packages
created to deal with tracking data or 'tracking packages'. Here we reviewed and
described each tracking package based on a workflow centered around tracking
data (i.e. spatio-temporal locations (x,y,t)), broken down into three stages:
pre-processing, post-processing and analysis, the latter consisting of data
visualization, track description, path reconstruction, behavioral pattern
identification, space use characterization, trajectory simulation and others.
Supporting documentation is key to render a package accessible for users. Based
on a user survey, we reviewed the quality of packages' documentation, and
identified 11 packages with good or excellent documentation. Links between
packages were assessed through a network graph analysis. Although a large group
of packages showed some degree of connectivity (either depending on functions
or suggesting the use of another tracking package), one third of the packages
worked in isolation, reflecting a fragmentation in the R movement-ecology
programming community. Finally, we provide recommendations for users when
choosing packages, and for developers to maximize the usefulness of their
contribution and strengthen the links within the programming community.
| [
{
"created": "Thu, 17 Jan 2019 18:13:52 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Jul 2019 20:10:27 GMT",
"version": "v2"
},
{
"created": "Mon, 14 Oct 2019 18:31:49 GMT",
"version": "v3"
}
] | 2019-10-16 | [
[
"Joo",
"Rocio",
""
],
[
"Boone",
"Matthew E.",
""
],
[
"Clay",
"Thomas A.",
""
],
[
"Patrick",
"Samantha C.",
""
],
[
"Clusella-Trullas",
"Susana",
""
],
[
"Basille",
"Mathieu",
""
]
] | The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyze tracking data, however many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the R software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'. Here we reviewed and described each tracking package based on a workflow centered around tracking data (i.e. spatio-temporal locations (x,y,t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others. Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation, and identified 11 packages with good or excellent documentation. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the R movement-ecology programming community. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community. |
2312.13991 | Jeffrey West | Cristian Axenie, Oliver L\'opez-Corona, Michail A. Makridis, Meisam
Akbarzadeh, Matteo Saveriano, Alexandru Stancu, Jeffrey West | Antifragility as a complex system's response to perturbations,
volatility, and time | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Antifragility characterizes the benefit of a dynamical system derived from
the variability in environmental perturbations. Antifragility carries a precise
definition that quantifies a system's output response to input variability.
Systems may respond poorly to perturbations (fragile) or benefit from
perturbations (antifragile). In this manuscript, we review a range of
applications of antifragility theory in technical systems (e.g., traffic
control, robotics) and natural systems (e.g., cancer therapy, antibiotics).
While there is a broad overlap in methods used to quantify and apply
antifragility across disciplines, there is a need for precisely defining the
scales at which antifragility operates. Thus, we provide a brief general
introduction to the properties of antifragility in applied systems and review
relevant literature for both natural and technical systems' antifragility. We
frame this review within three scales common to technical systems: intrinsic
(input-output nonlinearity), inherited (extrinsic environmental signals), and
interventional (feedback control), with associated counterparts in biological
systems: ecological (homogeneous systems), evolutionary (heterogeneous
systems), and interventional (control). We use the common noun in designing
systems that exhibit antifragile behavior across scales and guide the reader
along the spectrum of
fragility-adaptiveness-resilience-robustness-antifragility, the principles
behind it, and its practical implications.
| [
{
"created": "Thu, 21 Dec 2023 16:27:31 GMT",
"version": "v1"
}
] | 2023-12-22 | [
[
"Axenie",
"Cristian",
""
],
[
"López-Corona",
"Oliver",
""
],
[
"Makridis",
"Michail A.",
""
],
[
"Akbarzadeh",
"Meisam",
""
],
[
"Saveriano",
"Matteo",
""
],
[
"Stancu",
"Alexandru",
""
],
[
"West",
"Jeffrey",
""
]
] | Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system's output response to input variability. Systems may respond poorly to perturbations (fragile) or benefit from perturbations (antifragile). In this manuscript, we review a range of applications of antifragility theory in technical systems (e.g., traffic control, robotics) and natural systems (e.g., cancer therapy, antibiotics). While there is a broad overlap in methods used to quantify and apply antifragility across disciplines, there is a need for precisely defining the scales at which antifragility operates. Thus, we provide a brief general introduction to the properties of antifragility in applied systems and review relevant literature for both natural and technical systems' antifragility. We frame this review within three scales common to technical systems: intrinsic (input-output nonlinearity), inherited (extrinsic environmental signals), and interventional (feedback control), with associated counterparts in biological systems: ecological (homogeneous systems), evolutionary (heterogeneous systems), and interventional (control). We use the common noun in designing systems that exhibit antifragile behavior across scales and guide the reader along the spectrum of fragility-adaptiveness-resilience-robustness-antifragility, the principles behind it, and its practical implications. |
2311.02258 | Trung Le | Lu Mi, Trung Le, Tianxing He, Eli Shlizerman, Uygar S\"umb\"ul | Learning Time-Invariant Representations for Individual Neurons from
Population Dynamics | Accepted at NeurIPS 2023 | null | null | null | q-bio.NC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neurons can display highly variable dynamics. While such variability
presumably supports the wide range of behaviors generated by the organism,
their gene expressions are relatively stable in the adult brain. This suggests
that neuronal activity is a combination of its time-invariant identity and the
inputs the neuron receives from the rest of the circuit. Here, we propose a
self-supervised learning based method to assign time-invariant representations
to individual neurons based on permutation-, and population size-invariant
summary of population recordings. We fit dynamical models to neuronal activity
to learn a representation by considering the activity of both the individual
and the neighboring population. Our self-supervised approach and use of
implicit representations enable robust inference against imperfections such as
partial overlap of neurons across sessions, trial-to-trial variability, and
limited availability of molecular (transcriptomic) labels for downstream
supervised tasks. We demonstrate our method on a public multimodal dataset of
mouse cortical neuronal activity and transcriptomic labels. We report > 35%
improvement in predicting the transcriptomic subclass identity and > 20%
improvement in predicting class identity with respect to the state-of-the-art.
| [
{
"created": "Fri, 3 Nov 2023 22:30:12 GMT",
"version": "v1"
}
] | 2023-11-07 | [
[
"Mi",
"Lu",
""
],
[
"Le",
"Trung",
""
],
[
"He",
"Tianxing",
""
],
[
"Shlizerman",
"Eli",
""
],
[
"Sümbül",
"Uygar",
""
]
] | Neurons can display highly variable dynamics. While such variability presumably supports the wide range of behaviors generated by the organism, their gene expressions are relatively stable in the adult brain. This suggests that neuronal activity is a combination of its time-invariant identity and the inputs the neuron receives from the rest of the circuit. Here, we propose a self-supervised learning based method to assign time-invariant representations to individual neurons based on permutation-, and population size-invariant summary of population recordings. We fit dynamical models to neuronal activity to learn a representation by considering the activity of both the individual and the neighboring population. Our self-supervised approach and use of implicit representations enable robust inference against imperfections such as partial overlap of neurons across sessions, trial-to-trial variability, and limited availability of molecular (transcriptomic) labels for downstream supervised tasks. We demonstrate our method on a public multimodal dataset of mouse cortical neuronal activity and transcriptomic labels. We report > 35% improvement in predicting the transcriptomic subclass identity and > 20% improvement in predicting class identity with respect to the state-of-the-art. |
q-bio/0607005 | Maurizio Serva | Maurizio Serva | Mitochondrial Dna Replacement Versus Nuclear Dna Persistence | null | null | 10.1088/1742-5468/2006/10/P10013 | null | q-bio.PE cond-mat.other q-bio.OT | null | In this paper we consider two populations whose generations are not
overlapping and whose size is large. The number of males and females in both
populations is constant. Any generation is replaced by a new one and any
individual has two parents for what concerns nuclear DNA and a single one (the
mother) for what concerns mtDNA. Moreover, at any generation some individuals
migrate from the first population to the second.
In a finite random time $T$, the mtDNA of the second population is completely
replaced by the mtDNA of the first. In the same time, the nuclear DNA is not
completely replaced and a fraction $F$ of the ancient nuclear DNA persists. We
compute both $T$ and $F$. Since this study shows that complete replacement of
mtDNA in a population is compatible with the persistence of a large fraction of
nuclear DNA, it may have some relevance for the Out of Africa/Multiregional
debate in Paleoanthropology.
| [
{
"created": "Wed, 5 Jul 2006 00:05:29 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Serva",
"Maurizio",
""
]
] | In this paper we consider two populations whose generations are not overlapping and whose size is large. The number of males and females in both populations is constant. Any generation is replaced by a new one and any individual has two parents for what concerns nuclear DNA and a single one (the mother) for what concerns mtDNA. Moreover, at any generation some individuals migrate from the first population to the second. In a finite random time $T$, the mtDNA of the second population is completely replaced by the mtDNA of the first. In the same time, the nuclear DNA is not completely replaced and a fraction $F$ of the ancient nuclear DNA persists. We compute both $T$ and $F$. Since this study shows that complete replacement of mtDNA in a population is compatible with the persistence of a large fraction of nuclear DNA, it may have some relevance for the Out of Africa/Multiregional debate in Paleoanthropology. |
2007.14922 | Jan Zrimec | Jan Zrimec | Structural representations of DNA regulatory substrates can enhance
sequence-based algorithms by associating functional sequence variants | 20 pages, 8 figures, 3 tables, conference | Proceedings of the 11th ACM International Conference on
Bioinformatics, Computational Biology and Health Informatics (BCB '20),
September 21--24, 2020, Virtual Event, USA | 10.1145/3388440.3412482 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The nucleotide sequence representation of DNA can be inadequate for resolving
protein-DNA binding sites and regulatory substrates, such as those involved in
gene expression and horizontal gene transfer. Considering that sequence-like
representations are algorithmically very useful, here we fused over 60
currently available DNA physicochemical and conformational variables into
compact structural representations that can encode single DNA binding sites to
whole regulatory regions. We find that the main structural components reflect
key properties of protein-DNA interactions and can be condensed to the amount
of information found in a single nucleotide position. The most accurate
structural representations compress functional DNA sequence variants by 30% to
50%, as each instance encodes from tens to thousands of sequences. We show that
a structural distance function discriminates among groups of DNA substrates
more accurately than nucleotide sequence-based metrics. As this opens up a
variety of implementation possibilities, we develop and test a distance-based
alignment algorithm, demonstrating the potential of using the structural
representations to enhance sequence-based algorithms. Due to the bias of most
current bioinformatic methods to nucleotide sequence representations, it is
possible that considerable performance increases might still be achievable with
such solutions.
| [
{
"created": "Wed, 29 Jul 2020 15:56:39 GMT",
"version": "v1"
}
] | 2020-07-30 | [
[
"Zrimec",
"Jan",
""
]
] | The nucleotide sequence representation of DNA can be inadequate for resolving protein-DNA binding sites and regulatory substrates, such as those involved in gene expression and horizontal gene transfer. Considering that sequence-like representations are algorithmically very useful, here we fused over 60 currently available DNA physicochemical and conformational variables into compact structural representations that can encode single DNA binding sites to whole regulatory regions. We find that the main structural components reflect key properties of protein-DNA interactions and can be condensed to the amount of information found in a single nucleotide position. The most accurate structural representations compress functional DNA sequence variants by 30% to 50%, as each instance encodes from tens to thousands of sequences. We show that a structural distance function discriminates among groups of DNA substrates more accurately than nucleotide sequence-based metrics. As this opens up a variety of implementation possibilities, we develop and test a distance-based alignment algorithm, demonstrating the potential of using the structural representations to enhance sequence-based algorithms. Due to the bias of most current bioinformatic methods to nucleotide sequence representations, it is possible that considerable performance increases might still be achievable with such solutions. |
1408.7073 | Masud Mansuripur | Masud Mansuripur | DNA, Human Memory, and the Storage Technology of the 21st Century | 29 pages, 26 figures, 40 references | Proceedings of SPIE, T. Hurst and S. Kobayashi, editors, Vol.
4342, pp 1-29 (2002) | 10.1117/12.453368 | null | q-bio.OT cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The sophisticated tools and techniques employed by Nature for purposeful
storage of information stand in stark contrast to the primitive and relatively
inefficient means used by man. We describe some impressive features of
biological data storage, and speculate on approaches to research and
development that could benefit the storage industry in the coming decades.
| [
{
"created": "Wed, 27 Aug 2014 21:51:53 GMT",
"version": "v1"
}
] | 2014-09-01 | [
[
"Mansuripur",
"Masud",
""
]
] | The sophisticated tools and techniques employed by Nature for purposeful storage of information stand in stark contrast to the primitive and relatively inefficient means used by man. We describe some impressive features of biological data storage, and speculate on approaches to research and development that could benefit the storage industry in the coming decades. |
2211.08516 | Ting Hu | Ting Hu and Gabriela Ochoa and Wolfgang Banzhaf | Phenotype Search Trajectory Networks for Linear Genetic Programming | null | null | null | null | q-bio.PE cs.AI | http://creativecommons.org/licenses/by/4.0/ | Genotype-to-phenotype mappings translate genotypic variations such as
mutations into phenotypic changes. Neutrality is the observation that some
mutations do not lead to phenotypic changes. Studying the search trajectories
in genotypic and phenotypic spaces, especially through neutral mutations, helps
us to better understand the progression of evolution and its algorithmic
behaviour. In this study, we visualise the search trajectories of a genetic
programming system as graph-based models, where nodes are genotypes/phenotypes
and edges represent their mutational transitions. We also quantitatively
measure the characteristics of phenotypes including their genotypic abundance
(the requirement for neutrality) and Kolmogorov complexity. We connect these
quantified metrics with search trajectory visualisations, and find that more
complex phenotypes are under-represented by fewer genotypes and are harder for
evolution to discover. Less complex phenotypes, on the other hand, are
over-represented by genotypes, are easier to find, and frequently serve as
stepping-stones for evolution.
| [
{
"created": "Tue, 15 Nov 2022 21:20:50 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Jun 2023 16:42:01 GMT",
"version": "v2"
}
] | 2023-06-26 | [
[
"Hu",
"Ting",
""
],
[
"Ochoa",
"Gabriela",
""
],
[
"Banzhaf",
"Wolfgang",
""
]
] | Genotype-to-phenotype mappings translate genotypic variations such as mutations into phenotypic changes. Neutrality is the observation that some mutations do not lead to phenotypic changes. Studying the search trajectories in genotypic and phenotypic spaces, especially through neutral mutations, helps us to better understand the progression of evolution and its algorithmic behaviour. In this study, we visualise the search trajectories of a genetic programming system as graph-based models, where nodes are genotypes/phenotypes and edges represent their mutational transitions. We also quantitatively measure the characteristics of phenotypes including their genotypic abundance (the requirement for neutrality) and Kolmogorov complexity. We connect these quantified metrics with search trajectory visualisations, and find that more complex phenotypes are under-represented by fewer genotypes and are harder for evolution to discover. Less complex phenotypes, on the other hand, are over-represented by genotypes, are easier to find, and frequently serve as stepping-stones for evolution. |
1008.0209 | Carlos Escudero | Carlos Escudero, Christian A. Yates, Jerome Buhl, Iain D. Couzin,
Radek Erban, Ioannis G. Kevrekidis and Philip K. Maini | Ergodic directional switching in mobile insect groups | Physical Review Focus 26, July 2010 | Phys. Rev. E 82, 011926 (2010) | 10.1103/PhysRevE.82.011926 | null | q-bio.PE cond-mat.stat-mech q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We obtain a Fokker-Planck equation describing experimental data on the
collective motion of locusts. The noise is of internal origin and due to the
discrete character and finite number of constituents of the swarm. The
stationary probability distribution shows a rich phenomenology including
non-monotonic behavior of several order/disorder transition indicators in noise
intensity. This complex behavior arises naturally as a result of the randomness
in the system. Its counterintuitive character challenges standard
interpretations of noise induced transitions and calls for an extension of this
theory in order to capture the behavior of certain classes of biologically
motivated models. Our results suggest that the collective switches of the
group's direction of motion might be due to a random ergodic effect and, as
such, they are inherent to group formation.
| [
{
"created": "Sun, 1 Aug 2010 22:58:07 GMT",
"version": "v1"
}
] | 2015-05-19 | [
[
"Escudero",
"Carlos",
""
],
[
"Yates",
"Christian A.",
""
],
[
"Buhl",
"Jerome",
""
],
[
"Couzin",
"Iain D.",
""
],
[
"Erban",
"Radek",
""
],
[
"Kevrekidis",
"Ioannis G.",
""
],
[
"Maini",
"Philip K.",
""
]
] | We obtain a Fokker-Planck equation describing experimental data on the collective motion of locusts. The noise is of internal origin and due to the discrete character and finite number of constituents of the swarm. The stationary probability distribution shows a rich phenomenology including non-monotonic behavior of several order/disorder transition indicators in noise intensity. This complex behavior arises naturally as a result of the randomness in the system. Its counterintuitive character challenges standard interpretations of noise induced transitions and calls for an extension of this theory in order to capture the behavior of certain classes of biologically motivated models. Our results suggest that the collective switches of the group's direction of motion might be due to a random ergodic effect and, as such, they are inherent to group formation. |
2403.01927 | Akhila Krishna | Akhila Krishna, Ravi Kant Gupta, Pranav Jeevan, Amit Sethi | Advancing Gene Selection in Oncology: A Fusion of Deep Learning and
Sparsity for Precision Gene Selection | null | null | null | null | q-bio.GN cs.CV q-bio.QM q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Gene selection plays a pivotal role in oncology research for improving
outcome prediction accuracy and facilitating cost-effective genomic profiling
for cancer patients. This paper introduces two gene selection strategies for
deep learning-based survival prediction models. The first strategy uses a
sparsity-inducing method while the second one uses importance based gene
selection for identifying relevant genes. Our overall approach leverages the
power of deep learning to model complex biological data structures, while
sparsity-inducing methods ensure the selection process focuses on the most
informative genes, minimizing noise and redundancy. Through comprehensive
experimentation on diverse genomic and survival datasets, we demonstrate that
our strategy not only identifies gene signatures with high predictive power for
survival outcomes but can also streamlines the process for low-cost genomic
profiling. The implications of this research are profound as it offers a
scalable and effective tool for advancing personalized medicine and targeted
cancer therapies. By pushing the boundaries of gene selection methodologies,
our work contributes significantly to the ongoing efforts in cancer genomics,
promising improved diagnostic and prognostic capabilities in clinical settings.
| [
{
"created": "Mon, 4 Mar 2024 10:44:57 GMT",
"version": "v1"
}
] | 2024-03-05 | [
[
"Krishna",
"Akhila",
""
],
[
"Gupta",
"Ravi Kant",
""
],
[
"Jeevan",
"Pranav",
""
],
[
"Sethi",
"Amit",
""
]
] | Gene selection plays a pivotal role in oncology research for improving outcome prediction accuracy and facilitating cost-effective genomic profiling for cancer patients. This paper introduces two gene selection strategies for deep learning-based survival prediction models. The first strategy uses a sparsity-inducing method while the second one uses importance based gene selection for identifying relevant genes. Our overall approach leverages the power of deep learning to model complex biological data structures, while sparsity-inducing methods ensure the selection process focuses on the most informative genes, minimizing noise and redundancy. Through comprehensive experimentation on diverse genomic and survival datasets, we demonstrate that our strategy not only identifies gene signatures with high predictive power for survival outcomes but can also streamlines the process for low-cost genomic profiling. The implications of this research are profound as it offers a scalable and effective tool for advancing personalized medicine and targeted cancer therapies. By pushing the boundaries of gene selection methodologies, our work contributes significantly to the ongoing efforts in cancer genomics, promising improved diagnostic and prognostic capabilities in clinical settings. |
2209.02780 | Youngmin Park | Youngmin Park, C\'ecile Leduc, Sandrine Etienne-Manneville,
St\'ephanie Portet | Models of Vimentin Organization Under Actin-Driven Transport | 25 pages, 8 figures | null | 10.1103/PhysRevE.107.054408 | null | q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | Intermediate filaments form an essential structural network, spread
throughout the cytoplasm and play a key role in cell mechanics, intracellular
organization and molecular signaling. The maintenance of the network and its
adaptation to the cell's dynamic behavior relies on several mechanisms
implicating cytoskeletal crosstalk which are not fully understood. Mathematical
modeling allows us to compare several biologically realistic scenarios to help
us interpret experimental data. In this study, we observe and model the
dynamics of the vimentin intermediate filaments in single glial cells seeded on
circular micropatterns following microtubule disruption by nocodazole
treatment. In these conditions, the vimentin filaments move towards the cell
center and accumulate before eventually reaching a steady-state. In absence of
microtubule-driven transport, the motion of the vimentin network is primarily
driven by actin-related mechanisms. To model these experimental findings, we
hypothesize that vimentin may exist in two states, mobile and immobile, and
switches between the states at unknown (either constant or non-constant) rates.
Mobile vimentin are assumed to advect with either constant or non-constant
velocity. We introduce several biologically realistic scenarios using this set
of assumptions. For each scenario, we use differential evolution to find the
best parameter sets resulting in a solution that most closely matches the
experimental data, then the assumptions are evaluated using the Akaike
Information Criterion. This modeling approach allows us to conclude that our
experimental data are best explained by a spatially dependent trapping of
intermediate filaments or a spatially dependent speed of actin-dependent
transport.
| [
{
"created": "Tue, 6 Sep 2022 19:02:38 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Apr 2023 19:30:08 GMT",
"version": "v2"
}
] | 2023-06-14 | [
[
"Park",
"Youngmin",
""
],
[
"Leduc",
"Cécile",
""
],
[
"Etienne-Manneville",
"Sandrine",
""
],
[
"Portet",
"Stéphanie",
""
]
] | Intermediate filaments form an essential structural network, spread throughout the cytoplasm and play a key role in cell mechanics, intracellular organization and molecular signaling. The maintenance of the network and its adaptation to the cell's dynamic behavior relies on several mechanisms implicating cytoskeletal crosstalk which are not fully understood. Mathematical modeling allows us to compare several biologically realistic scenarios to help us interpret experimental data. In this study, we observe and model the dynamics of the vimentin intermediate filaments in single glial cells seeded on circular micropatterns following microtubule disruption by nocodazole treatment. In these conditions, the vimentin filaments move towards the cell center and accumulate before eventually reaching a steady-state. In absence of microtubule-driven transport, the motion of the vimentin network is primarily driven by actin-related mechanisms. To model these experimental findings, we hypothesize that vimentin may exist in two states, mobile and immobile, and switches between the states at unknown (either constant or non-constant) rates. Mobile vimentin are assumed to advect with either constant or non-constant velocity. We introduce several biologically realistic scenarios using this set of assumptions. For each scenario, we use differential evolution to find the best parameter sets resulting in a solution that most closely matches the experimental data, then the assumptions are evaluated using the Akaike Information Criterion. This modeling approach allows us to conclude that our experimental data are best explained by a spatially dependent trapping of intermediate filaments or a spatially dependent speed of actin-dependent transport. |
1801.08950 | Kaushik Majumdar | Puneet Dheer, Sandipan Pati, Srinath Jayachandran, Kaushik Kumar
Majumdar | Ictal and Post Ictal Impaired Consciousness due to Enhanced Mutual
Information in Temporal Lobe Epilepsy | 30 pages, 5 figures, 8 tables, under review in Brain Topography | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Seizure and synchronization are related to each other in complex manner.
Altered synchrony has been implicated in loss of consciousness during partial
seizures. However, the mechanism of altered consciousness following termination
of seizures has not been studied well. In this work we used bivariate mutual
information as a measure of synchronization to understand the neural correlate
of altered consciousness during and after termination of mesial temporal lobe
onset seizures. First, we have compared discrete bivariate mutual information
(MI) measure with amplitude correlation (AC), phase synchronization (PS),
nonlinear correlation and coherence, and established MI as a robust measure of
synchronization. Next, we have extended MI to more than two signals by
principal component method. The extended MI was applied on intracranial
electroencephalogram (iEEG) before, during and after 23 temporal lobe seizures
recorded from 11 patients. The analyses were carried out in delta, theta,
alpha, beta and gamma bands. In 77% of the complex partial seizures MI was
higher towards the seizure offset than in the first half of the seizure in the
seizure onset zone (SOZ) channels in beta and gamma bands, whereas MI remained
higher in the beginning or in the middle of the seizure than towards the offset
across the least involved channels in the same bands. Synchronization seems
built up outside the SOZ, gradually spread and culminated in SOZ and remained
high beyond offset leading to impaired consciousness in 82% of the complex
partial temporal lobe seizures. Consciousness impairment was scored according
to a method previously applied to assess the same in patients with temporal
lobe epilepsy during seizure.
| [
{
"created": "Fri, 26 Jan 2018 19:27:40 GMT",
"version": "v1"
}
] | 2018-01-30 | [
[
"Dheer",
"Puneet",
""
],
[
"Pati",
"Sandipan",
""
],
[
"Jayachandran",
"Srinath",
""
],
[
"Majumdar",
"Kaushik Kumar",
""
]
] | Seizure and synchronization are related to each other in complex manner. Altered synchrony has been implicated in loss of consciousness during partial seizures. However, the mechanism of altered consciousness following termination of seizures has not been studied well. In this work we used bivariate mutual information as a measure of synchronization to understand the neural correlate of altered consciousness during and after termination of mesial temporal lobe onset seizures. First, we have compared discrete bivariate mutual information (MI) measure with amplitude correlation (AC), phase synchronization (PS), nonlinear correlation and coherence, and established MI as a robust measure of synchronization. Next, we have extended MI to more than two signals by principal component method. The extended MI was applied on intracranial electroencephalogram (iEEG) before, during and after 23 temporal lobe seizures recorded from 11 patients. The analyses were carried out in delta, theta, alpha, beta and gamma bands. In 77% of the complex partial seizures MI was higher towards the seizure offset than in the first half of the seizure in the seizure onset zone (SOZ) channels in beta and gamma bands, whereas MI remained higher in the beginning or in the middle of the seizure than towards the offset across the least involved channels in the same bands. Synchronization seems built up outside the SOZ, gradually spread and culminated in SOZ and remained high beyond offset leading to impaired consciousness in 82% of the complex partial temporal lobe seizures. Consciousness impairment was scored according to a method previously applied to assess the same in patients with temporal lobe epilepsy during seizure. |
1912.12058 | Souhil Harchaoui | Souhil Harchaoui and Petros Chatzimpiros | The nitrogen operating space of world food production | 39 pages, 15 figures, 7 tables | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Agriculture operates within a global ecosystem for which planetary boundaries
have recently been defined. Efficiency in nitrogen use is essential for
maximizing the benefits of agriculture for humanity and minimizing adverse
socio-ecological impacts. The set of variables that support global system
efficiency also determine the food production boundaries of agriculture, which
govern the maximum supportable human population. Food production boundaries,
nitrogen loss and nitrogen self-sufficiency are combined here into the nitrogen
operating space of world food production. We position world regions and the
world trajectory (1961-2013) within the nitrogen operating space and show that
the maximum supportable human population ranges from 6 to almost 17 billion
people according to the share of grain used as feed and the nitrogen
fertilization regime. All UN population projections for the 21st century can
only be conditionally achieved. We discuss the growth rate requirements in
production and efficiency to meet food production boundaries and the nitrogen
planetary boundary by 2050.
| [
{
"created": "Fri, 27 Dec 2019 10:58:33 GMT",
"version": "v1"
}
] | 2019-12-30 | [
[
"Harchaoui",
"Souhil",
""
],
[
"Chatzimpiros",
"Petros",
""
]
] | Agriculture operates within a global ecosystem for which planetary boundaries have recently been defined. Efficiency in nitrogen use is essential for maximizing the benefits of agriculture for humanity and minimizing adverse socio-ecological impacts. The set of variables that support global system efficiency also determine the food production boundaries of agriculture, which govern the maximum supportable human population. Food production boundaries, nitrogen loss and nitrogen self-sufficiency are combined here into the nitrogen operating space of world food production. We position world regions and the world trajectory (1961-2013) within the nitrogen operating space and show that the maximum supportable human population ranges from 6 to almost 17 billion people according to the share of grain used as feed and the nitrogen fertilization regime. All UN population projections for the 21st century can only be conditionally achieved. We discuss the growth rate requirements in production and efficiency to meet food production boundaries and the nitrogen planetary boundary by 2050. |
1510.08780 | John Medaglia | John D. Medaglia, Theodore D. Satterthwaite, Tyler M. Moore, Kosha
Ruparel, Ruben C. Gur, Raquel E. Gur, Danielle S. Bassett | Flexible Traversal Through Diverse Brain States Underlies Executive
Function in Normative Neurodevelopment | 14 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adolescence is marked by rapid development of executive function. Mounting
evidence suggests that executive function in adults may be driven by dynamic
control of neurophysiological processes. Yet, how these dynamics evolve over
adolescence and contribute to cognitive development is unknown. Using a novel
dynamic graph approach in which each moment in time is a node and the
similarity in brain states at two different times is an edge, we identify two
primary brain states reminiscent of intrinsic and task-evoked systems. We
demonstrate that time spent in these two states increases over development, as
does the flexibility with which the brain switches between them. Increasing
time spent in primary states and flexibility among states relates to increased
executive performance over adolescence. Indeed, flexibility is increasingly
advantageous for performance toward early adulthood. These findings demonstrate
that brain state dynamics underlie the development of executive function during
the critical period of adolescence.
| [
{
"created": "Thu, 29 Oct 2015 17:06:40 GMT",
"version": "v1"
}
] | 2015-10-30 | [
[
"Medaglia",
"John D.",
""
],
[
"Satterthwaite",
"Theodore D.",
""
],
[
"Moore",
"Tyler M.",
""
],
[
"Ruparel",
"Kosha",
""
],
[
"Gur",
"Ruben C.",
""
],
[
"Gur",
"Raquel E.",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Adolescence is marked by rapid development of executive function. Mounting evidence suggests that executive function in adults may be driven by dynamic control of neurophysiological processes. Yet, how these dynamics evolve over adolescence and contribute to cognitive development is unknown. Using a novel dynamic graph approach in which each moment in time is a node and the similarity in brain states at two different times is an edge, we identify two primary brain states reminiscent of intrinsic and task-evoked systems. We demonstrate that time spent in these two states increases over development, as does the flexibility with which the brain switches between them. Increasing time spent in primary states and flexibility among states relates to increased executive performance over adolescence. Indeed, flexibility is increasingly advantageous for performance toward early adulthood. These findings demonstrate that brain state dynamics underlie the development of executive function during the critical period of adolescence. |
1609.06021 | Thomas R. Weikl | Fabian Paul and Thomas R. Weikl | How to distinguish conformational selection and induced fit based on
chemical relaxation rates | 20 pages, 4 figures | PLoS Comp Biol 12(9): e1005067 (2016) | 10.1371/journal.pcbi.1005067 | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein binding often involves conformational changes. Important questions
are whether a conformational change occurs prior to a binding event
('conformational selection') or after a binding event ('induced fit'), and how
conformational transition rates can be obtained from experiments. In this
article, we present general results for the chemical relaxation rates of
conformational-selection and induced-fit binding processes that hold for all
concentrations of proteins and ligands and, thus, go beyond the standard
pseudo-first-order approximation of large ligand concentration. These results
allow to distinguish conformational-selection from induced-fit processes - also
in cases in which such a distinction is not possible under pseudo-first-order
conditions - and to extract conformational transition rates of proteins from
chemical relaxation data.
| [
{
"created": "Tue, 20 Sep 2016 05:22:49 GMT",
"version": "v1"
}
] | 2016-09-22 | [
[
"Paul",
"Fabian",
""
],
[
"Weikl",
"Thomas R.",
""
]
] | Protein binding often involves conformational changes. Important questions are whether a conformational change occurs prior to a binding event ('conformational selection') or after a binding event ('induced fit'), and how conformational transition rates can be obtained from experiments. In this article, we present general results for the chemical relaxation rates of conformational-selection and induced-fit binding processes that hold for all concentrations of proteins and ligands and, thus, go beyond the standard pseudo-first-order approximation of large ligand concentration. These results allow to distinguish conformational-selection from induced-fit processes - also in cases in which such a distinction is not possible under pseudo-first-order conditions - and to extract conformational transition rates of proteins from chemical relaxation data. |
q-bio/0510051 | Quang-Cuong Pham | Quang-Cuong Pham, Jean-Jacques Slotine | Stable Concurrent Synchronization in Dynamic System Networks | 32 pages, 12 figures. More detailed proofs were given in section 2.
Section 3.4 on robust synchronization was added | null | 10.1016/j.neunet.2006.07.008 | null | q-bio.NC | null | In a network of dynamical systems, concurrent synchronization is a regime
where multiple groups of fully synchronized elements coexist. In the brain,
concurrent synchronization may occur at several scales, with multiple
``rhythms'' interacting and functional assemblies combining neural oscillators
of many different types. Mathematically, stable concurrent synchronization
corresponds to convergence to a flow-invariant linear subspace of the global
state space. We derive a general condition for such convergence to occur
globally and exponentially. We also show that, under mild conditions, global
convergence to a concurrently synchronized regime is preserved under basic
system combinations such as negative feedback or hierarchies, so that stable
concurrently synchronized aggregates of arbitrary size can be constructed.
Robustnesss of stable concurrent synchronization to variations in individual
dynamics is also quantified. Simple applications of these results to classical
questions in systems neuroscience and robotics are discussed.
| [
{
"created": "Thu, 27 Oct 2005 20:56:53 GMT",
"version": "v1"
},
{
"created": "Sat, 24 Dec 2005 14:56:53 GMT",
"version": "v2"
},
{
"created": "Thu, 1 Jun 2006 09:06:11 GMT",
"version": "v3"
}
] | 2007-05-23 | [
[
"Pham",
"Quang-Cuong",
""
],
[
"Slotine",
"Jean-Jacques",
""
]
] | In a network of dynamical systems, concurrent synchronization is a regime where multiple groups of fully synchronized elements coexist. In the brain, concurrent synchronization may occur at several scales, with multiple ``rhythms'' interacting and functional assemblies combining neural oscillators of many different types. Mathematically, stable concurrent synchronization corresponds to convergence to a flow-invariant linear subspace of the global state space. We derive a general condition for such convergence to occur globally and exponentially. We also show that, under mild conditions, global convergence to a concurrently synchronized regime is preserved under basic system combinations such as negative feedback or hierarchies, so that stable concurrently synchronized aggregates of arbitrary size can be constructed. Robustnesss of stable concurrent synchronization to variations in individual dynamics is also quantified. Simple applications of these results to classical questions in systems neuroscience and robotics are discussed. |
2103.06954 | Itamar Daniel Landau | Itamar Daniel Landau and Haim Sompolinsky | Macroscopic Fluctuations Emerge in Balanced Networks with Incomplete
Recurrent Alignment | null | Phys. Rev. Research 3, 023171 (2021) | 10.1103/PhysRevResearch.3.023171 | null | q-bio.NC nlin.CD | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Networks of strongly-coupled neurons with random connectivity exhibit
chaotic, asynchronous fluctuations. In previous work, we showed that when
endowed with an additional low-rank connectivity consisting of the outer
product of orthogonal vectors, these networks generate large-scale coherent
fluctuations. Although a striking phenomenon, that result depended on a
fine-tuned choice of low-rank structure. Here we extend that work by
generalizing the theory of excitation-inhibition balance to networks with
arbitrary low-rank structure and show that low-dimensional variability emerges
intrinsically through what we call incomplete recurrent alignment. We say that
a low-rank connectivity structure exhibits incomplete alignment if its
row-space is not contained in its column-space. In the setting of incomplete
alignment, recurrent connectivity can be decomposed into a subspace-recurrent
component and an effective-feedforward component. We show that
high-dimensional, microscopic fluctuations are propagated via the
effective-feedforward component to a low-dimensional subspace where they are
dynamically balanced by macroscopic fluctuations. We present biologically
plausible examples from excitation-inhibition networks and networks with
heterogeneous degree distributions. Finally, we define the alignment matrix as
the overlap between left and right-singular vectors of the structured
connectivity, and show that the singular values of the alignment matrix
determine the amplitude of macroscopic variability, while its singular vectors
determine the structure. Our work shows how macroscopic fluctuations can emerge
generically in strongly-coupled networks with low-rank structure. Furthermore,
by generalizing excitation-inhibition balance to arbitrary low-rank structure
our work may find relevance in any setting with strongly interacting units,
whether in biological, social, or technological networks.
| [
{
"created": "Thu, 11 Mar 2021 21:09:09 GMT",
"version": "v1"
}
] | 2021-06-09 | [
[
"Landau",
"Itamar Daniel",
""
],
[
"Sompolinsky",
"Haim",
""
]
] | Networks of strongly-coupled neurons with random connectivity exhibit chaotic, asynchronous fluctuations. In previous work, we showed that when endowed with an additional low-rank connectivity consisting of the outer product of orthogonal vectors, these networks generate large-scale coherent fluctuations. Although a striking phenomenon, that result depended on a fine-tuned choice of low-rank structure. Here we extend that work by generalizing the theory of excitation-inhibition balance to networks with arbitrary low-rank structure and show that low-dimensional variability emerges intrinsically through what we call incomplete recurrent alignment. We say that a low-rank connectivity structure exhibits incomplete alignment if its row-space is not contained in its column-space. In the setting of incomplete alignment, recurrent connectivity can be decomposed into a subspace-recurrent component and an effective-feedforward component. We show that high-dimensional, microscopic fluctuations are propagated via the effective-feedforward component to a low-dimensional subspace where they are dynamically balanced by macroscopic fluctuations. We present biologically plausible examples from excitation-inhibition networks and networks with heterogeneous degree distributions. Finally, we define the alignment matrix as the overlap between left and right-singular vectors of the structured connectivity, and show that the singular values of the alignment matrix determine the amplitude of macroscopic variability, while its singular vectors determine the structure. Our work shows how macroscopic fluctuations can emerge generically in strongly-coupled networks with low-rank structure. Furthermore, by generalizing excitation-inhibition balance to arbitrary low-rank structure our work may find relevance in any setting with strongly interacting units, whether in biological, social, or technological networks. |
1907.07249 | Cecilia Berardo | Cecilia Berardo, Stefan Geritz, Mats Gyllenberg, Ga\"el Raoul | Interactions between different predator-prey states. A method for the
derivation of the functional and numerical response | 27 pages, 14 figures, 7 sections, 4 appendices | 2020, Journal of Mathematical Biology | 10.1007/s00285-020-01500-2 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we introduce a formal method for the derivation of a predator's
functional response from a system of fast state transitions of the prey or
predator on a time scale during which the total prey and predator densities
remain constant. Such derivation permits an explicit interpretation of the
structure and parameters of the functional response in terms of individual
behaviour. The same method is also used here to derive the corresponding
numerical response of the predator as well as of the prey.
| [
{
"created": "Tue, 16 Jul 2019 20:26:45 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Apr 2020 12:15:29 GMT",
"version": "v2"
}
] | 2020-05-19 | [
[
"Berardo",
"Cecilia",
""
],
[
"Geritz",
"Stefan",
""
],
[
"Gyllenberg",
"Mats",
""
],
[
"Raoul",
"Gaël",
""
]
] | In this paper we introduce a formal method for the derivation of a predator's functional response from a system of fast state transitions of the prey or predator on a time scale during which the total prey and predator densities remain constant. Such derivation permits an explicit interpretation of the structure and parameters of the functional response in terms of individual behaviour. The same method is also used here to derive the corresponding numerical response of the predator as well as of the prey. |
2201.13371 | Condell Eastmond | Condell Eastmond, Aseem Subedi, Suvranu De, Xavier Intes | Deep Learning in fNIRS: A review | 41 pages, 9 figures | null | 10.1117/1.NPh.9.4.041411 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Significance: Optical neuroimaging has become a well-established clinical and
research tool to monitor cortical activations in the human brain. It is notable
that outcomes of functional Near-InfraRed Spectroscopy (fNIRS) studies depend
heavily on the data processing pipeline and classification model employed.
Recently, Deep Learning (DL) methodologies have demonstrated fast and accurate
performances in data processing and classification tasks across many biomedical
fields. Aim: We aim to review the emerging DL applications in fNIRS studies.
Approach: We first introduce some of the commonly used DL techniques. Then the
review summarizes current DL work in some of the most active areas of this
field, including brain-computer interface, neuro-impairment diagnosis, and
neuroscience discovery. Results: Of the 63 papers considered in this review, 32
report a comparative study of deep learning techniques to traditional machine
learning techniques where 26 have been shown outperforming the latter in terms
of classification accuracy. Additionally, 8 studies also utilize deep learning
to reduce the amount of preprocessing typically done with fNIRS data or
increase the amount of data via data augmentation. Conclusions: The application
of DL techniques to fNIRS studies has shown to mitigate many of the hurdles
present in fNIRS studies such as lengthy data preprocessing or small sample
sizes while achieving comparable or improved classification accuracy.
| [
{
"created": "Mon, 31 Jan 2022 17:33:03 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Jul 2022 11:52:42 GMT",
"version": "v2"
}
] | 2023-01-03 | [
[
"Eastmond",
"Condell",
""
],
[
"Subedi",
"Aseem",
""
],
[
"De",
"Suvranu",
""
],
[
"Intes",
"Xavier",
""
]
] | Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional Near-InfraRed Spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, Deep Learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields. Aim: We aim to review the emerging DL applications in fNIRS studies. Approach: We first introduce some of the commonly used DL techniques. Then the review summarizes current DL work in some of the most active areas of this field, including brain-computer interface, neuro-impairment diagnosis, and neuroscience discovery. Results: Of the 63 papers considered in this review, 32 report a comparative study of deep learning techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of classification accuracy. Additionally, 8 studies also utilize deep learning to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation. Conclusions: The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy. |
2007.04500 | Johann H. Mart\'inez | J. Mendoza-Ruiz, C. E. Alonso-Malaver, M. Valderrama, O. A. Rosso,
J.H. Mart\'inez | Dynamics in cortical activity revealed by resting-state MEG rhythms | 16 pages, 11 figures | null | 10.1063/5.0025189 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The brain may be thought of as a many-body architecture with a
spatio-temporal dynamics described by neuronal structures. The oscillatory
nature of brain activity allows these structures (nodes) to be described as a
set of coupled oscillators forming a network where the node dynamics, and that
of the network topology can be studied. Quantifying its dynamics at various
scales is an issue that claims to be explored for several brain activities,
e.g., activity at rest. The resting-state associates the underlying brain
dynamics of healthy subjects that are not actively compromised with sensory or
cognitive processes. Studying its dynamics is highly non-trivial but opens the
door to understand the general principles of brain functioning. We hypothesize
about how could be the spatio-temporal dynamics of cortical fluctuations for
healthy subjects at resting-state. We retrieve the alphabet that reconstructs
the dynamics (entropy/complexity) of magnetoencephalograpy signals. We assemble
the cortical connectivity to elicit the network's dynamics. We depict an order
relation between entropy/complexity for frequency bands. We unveiled that the
posterior cortex conglomerates nodes with both stronger dynamics and high
clustering for {\alpha} band. The existence of these order relations suggests
an emergent phenomenon of each band. Interestingly, we find that the posterior
cortex plays a cardinal role in both the dynamics and structure regarding the
resting-state. To the best of our knowledge, this is the first study with
magnetoencephalograpy involving information theory and network science to
better understand the dynamics and structure of brain activity at rest for
different bands and scales.
| [
{
"created": "Thu, 9 Jul 2020 01:37:26 GMT",
"version": "v1"
}
] | 2021-02-03 | [
[
"Mendoza-Ruiz",
"J.",
""
],
[
"Alonso-Malaver",
"C. E.",
""
],
[
"Valderrama",
"M.",
""
],
[
"Rosso",
"O. A.",
""
],
[
"Martínez",
"J. H.",
""
]
] | The brain may be thought of as a many-body architecture with a spatio-temporal dynamics described by neuronal structures. The oscillatory nature of brain activity allows these structures (nodes) to be described as a set of coupled oscillators forming a network where the node dynamics, and that of the network topology can be studied. Quantifying its dynamics at various scales is an issue that claims to be explored for several brain activities, e.g., activity at rest. The resting-state associates the underlying brain dynamics of healthy subjects that are not actively compromised with sensory or cognitive processes. Studying its dynamics is highly non-trivial but opens the door to understand the general principles of brain functioning. We hypothesize about how could be the spatio-temporal dynamics of cortical fluctuations for healthy subjects at resting-state. We retrieve the alphabet that reconstructs the dynamics (entropy/complexity) of magnetoencephalograpy signals. We assemble the cortical connectivity to elicit the network's dynamics. We depict an order relation between entropy/complexity for frequency bands. We unveiled that the posterior cortex conglomerates nodes with both stronger dynamics and high clustering for {\alpha} band. The existence of these order relations suggests an emergent phenomenon of each band. Interestingly, we find that the posterior cortex plays a cardinal role in both the dynamics and structure regarding the resting-state. To the best of our knowledge, this is the first study with magnetoencephalograpy involving information theory and network science to better understand the dynamics and structure of brain activity at rest for different bands and scales. |
1803.00112 | Viktor Stojkoski MSc | Viktor Stojkoski, Zoran Utkovski, Lasko Basnarkov and Ljupco Kocarev | Cooperation dynamics of generalized reciprocity in state-based social
dilemmas | 29 pages, 5 figures | Phys. Rev. E 97, 052305 (2018) | 10.1103/PhysRevE.97.052305 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a framework for studying social dilemmas in networked societies
where individuals follow a simple state-based behavioral mechanism based on
generalized reciprocity, which is rooted in the principle "help anyone if
helped by someone". Within this general framework, which applies to a wide
range of social dilemmas including, among others, public goods, donation and
snowdrift games, we study the cooperation dynamics on a variety of complex
network examples. By interpreting the studied model through the lenses of
nonlinear dynamical systems, we show that cooperation through generalized
reciprocity always emerges as the unique attractor in which the overall level
of cooperation is maximized, while simultaneously exploitation of the
participating individuals is prevented. The analysis elucidates the role of the
network structure, here captured by a local centrality measure which uniquely
quantifies the propensity of the network structure to cooperation, by dictating
the degree of cooperation displayed both at microscopic and macroscopic level.
We demonstrate the applicability of the analysis on a practical example by
considering an interaction structure that couples a donation process with a
public goods game.
| [
{
"created": "Wed, 28 Feb 2018 22:15:32 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Apr 2018 09:00:16 GMT",
"version": "v2"
},
{
"created": "Thu, 28 Feb 2019 12:57:22 GMT",
"version": "v3"
}
] | 2019-03-01 | [
[
"Stojkoski",
"Viktor",
""
],
[
"Utkovski",
"Zoran",
""
],
[
"Basnarkov",
"Lasko",
""
],
[
"Kocarev",
"Ljupco",
""
]
] | We introduce a framework for studying social dilemmas in networked societies where individuals follow a simple state-based behavioral mechanism based on generalized reciprocity, which is rooted in the principle "help anyone if helped by someone". Within this general framework, which applies to a wide range of social dilemmas including, among others, public goods, donation and snowdrift games, we study the cooperation dynamics on a variety of complex network examples. By interpreting the studied model through the lenses of nonlinear dynamical systems, we show that cooperation through generalized reciprocity always emerges as the unique attractor in which the overall level of cooperation is maximized, while simultaneously exploitation of the participating individuals is prevented. The analysis elucidates the role of the network structure, here captured by a local centrality measure which uniquely quantifies the propensity of the network structure to cooperation, by dictating the degree of cooperation displayed both at microscopic and macroscopic level. We demonstrate the applicability of the analysis on a practical example by considering an interaction structure that couples a donation process with a public goods game. |
1407.5946 | William Bialek | Gasper Tkacik, Thierry Mora, Olivier Marre, Dario Amodei, Michael J.
Berry II, and William Bialek | Thermodynamics for a network of neurons: Signatures of criticality | null | null | null | null | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The activity of a neural network is defined by patterns of spiking and
silence from the individual neurons. Because spikes are (relatively) sparse,
patterns of activity with increasing numbers of spikes are less probable, but
with more spikes the number of possible patterns increases. This tradeoff
between probability and numerosity is mathematically equivalent to the
relationship between entropy and energy in statistical physics. We construct
this relationship for populations of up to N=160 neurons in a small patch of
the vertebrate retina, using a combination of direct and model-based analyses
of experiments on the response of this network to naturalistic movies. We see
signs of a thermodynamic limit, where the entropy per neuron approaches a
smooth function of the energy per neuron as N increases. The form of this
function corresponds to the distribution of activity being poised near an
unusual kind of critical point. Networks with more or less correlation among
neurons would not reach this critical state. We suggest further tests of
criticality, and give a brief discussion of its functional significance.
| [
{
"created": "Tue, 22 Jul 2014 17:16:12 GMT",
"version": "v1"
}
] | 2014-07-23 | [
[
"Tkacik",
"Gasper",
""
],
[
"Mora",
"Thierry",
""
],
[
"Marre",
"Olivier",
""
],
[
"Amodei",
"Dario",
""
],
[
"Berry",
"Michael J.",
"II"
],
[
"Bialek",
"William",
""
]
] | The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but with more spikes the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N=160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. Networks with more or less correlation among neurons would not reach this critical state. We suggest further tests of criticality, and give a brief discussion of its functional significance. |
2006.14933 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Influence of Various Temporal Recoding on Pavlovian Eyeblink
Conditioning in The Cerebellum | arXiv admin note: substantial text overlap with arXiv:2003.11325 | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the Pavlovian eyeblink conditioning (EBC) via repeated
presentation of paired conditioned stimulus (tone) and unconditioned stimulus
(airpuff). The influence of various temporal recoding of granule cells on the
EBC is investigated in a cerebellar network where the connection probability
$p_c$ from Golgi to granule cells is changed. In an optimal case of
$p_c^*~(=0.029)$, individual granule cells show various well- and ill-matched
firing patterns relative to the unconditioned stimulus. Then, these
variously-recoded signals are fed into the Purkinje cells (PCs) through
parallel-fibers (PFs). In the case of well-matched PF-PC synapses, their
synaptic weights are strongly depressed through strong long-term depression
(LTD). On the other hand, practically no LTD occurs for the ill-matched PF-PC
synapses. This type of "effective" depression at the PF-PC synapses coordinates
firings of PCs effectively, which then make effective inhibitory coordination
on cerebellar nucleus neuron [which elicits conditioned response (CR;
eyeblink)]. When the learning trial passes a threshold, acquisition of CR
begins. In this case, the timing degree ${\cal T}_d$ of CR becomes good due to
presence of the ill-matched firing group which plays a role of protection
barrier for the timing. With further increase in the trial, strength $\cal S$
of CR (corresponding to the amplitude of eyelid closure) increases due to
strong LTD in the well-matched firing group. Thus, with increasing the learning
trial, the (overall) learning efficiency degree ${\cal L}_e$ (taking into
consideration both timing and strength of CR) for the CR is increased, and
eventually it becomes saturated. By changing $p_c$ from $p_c^*$, we also
investigate the influence of various temporal recoding on the EBC. It is thus
found that, the more various in temporal recoding, the more effective in
learning for the Pavlovian EBC.
| [
{
"created": "Thu, 25 Jun 2020 02:39:48 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Jul 2020 04:02:15 GMT",
"version": "v2"
}
] | 2020-07-09 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | We consider the Pavlovian eyeblink conditioning (EBC) via repeated presentation of paired conditioned stimulus (tone) and unconditioned stimulus (airpuff). The influence of various temporal recoding of granule cells on the EBC is investigated in a cerebellar network where the connection probability $p_c$ from Golgi to granule cells is changed. In an optimal case of $p_c^*~(=0.029)$, individual granule cells show various well- and ill-matched firing patterns relative to the unconditioned stimulus. Then, these variously-recoded signals are fed into the Purkinje cells (PCs) through parallel-fibers (PFs). In the case of well-matched PF-PC synapses, their synaptic weights are strongly depressed through strong long-term depression (LTD). On the other hand, practically no LTD occurs for the ill-matched PF-PC synapses. This type of "effective" depression at the PF-PC synapses coordinates firings of PCs effectively, which then make effective inhibitory coordination on cerebellar nucleus neuron [which elicits conditioned response (CR; eyeblink)]. When the learning trial passes a threshold, acquisition of CR begins. In this case, the timing degree ${\cal T}_d$ of CR becomes good due to presence of the ill-matched firing group which plays a role of protection barrier for the timing. With further increase in the trial, strength $\cal S$ of CR (corresponding to the amplitude of eyelid closure) increases due to strong LTD in the well-matched firing group. Thus, with increasing the learning trial, the (overall) learning efficiency degree ${\cal L}_e$ (taking into consideration both timing and strength of CR) for the CR is increased, and eventually it becomes saturated. By changing $p_c$ from $p_c^*$, we also investigate the influence of various temporal recoding on the EBC. It is thus found that, the more various in temporal recoding, the more effective in learning for the Pavlovian EBC. |
1401.7589 | Ziyue Gao | Ziyue Gao, Molly Przeworski, Guy Sella | Footprints of ancient balanced polymorphisms in genetic variation data | 5 Figures, 4 Supplementary Figures, 3 Supplementary Tables | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When long-lived, balancing selection can lead to trans-species polymorphisms
that are shared by two or more species identical by descent. In this case, the
gene genealogies at the selected sites cluster by allele instead of by species
and, because of linkage, nearby neutral sites also have unusual genealogies.
Although it is clear that this scenario should lead to discernible footprints
in genetic variation data, notably the presence of additional neutral
polymorphisms shared between species and the absence of fixed differences, the
effects remain poorly characterized. We focus on the case of a single site
under long-lived balancing selection and derive approximations for summaries of
the data that are sensitive to a trans-species polymorphism: the length of the
segment that carries most of the signals, the expected number of shared neutral
SNPs within the segment and the patterns of allelic associations among them.
Coalescent simulations of ancient balancing selection confirm the accuracy of
our approximations. We further show that for humans and chimpanzees, and more
generally for pairs of species with low genetic diversity levels, the patterns
of genetic variation on which we focus are highly unlikely to be generated by
neutral recurrent mutations, so these statistics are specific as well as
sensitive. We discuss the implications of our results for the design and
interpretation of genome scans for ancient balancing selection in apes and
other taxa.
| [
{
"created": "Wed, 29 Jan 2014 17:05:45 GMT",
"version": "v1"
}
] | 2014-01-30 | [
[
"Gao",
"Ziyue",
""
],
[
"Przeworski",
"Molly",
""
],
[
"Sella",
"Guy",
""
]
] | When long-lived, balancing selection can lead to trans-species polymorphisms that are shared by two or more species identical by descent. In this case, the gene genealogies at the selected sites cluster by allele instead of by species and, because of linkage, nearby neutral sites also have unusual genealogies. Although it is clear that this scenario should lead to discernible footprints in genetic variation data, notably the presence of additional neutral polymorphisms shared between species and the absence of fixed differences, the effects remain poorly characterized. We focus on the case of a single site under long-lived balancing selection and derive approximations for summaries of the data that are sensitive to a trans-species polymorphism: the length of the segment that carries most of the signals, the expected number of shared neutral SNPs within the segment and the patterns of allelic associations among them. Coalescent simulations of ancient balancing selection confirm the accuracy of our approximations. We further show that for humans and chimpanzees, and more generally for pairs of species with low genetic diversity levels, the patterns of genetic variation on which we focus are highly unlikely to be generated by neutral recurrent mutations, so these statistics are specific as well as sensitive. We discuss the implications of our results for the design and interpretation of genome scans for ancient balancing selection in apes and other taxa. |
1206.6366 | Mahnaz Kazemipoor | Mahnaz Kazemipoor (Corresponding author), Che Wan Jasimah Wan Mohamed
Radzi, Khyrunnisa Begum, Iman Yaze | Screening of antibacterial activity of lactic acid bacteria isolated
from fermented vegetables against food borne pathogens | 10 pages | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by/3.0/ | This study aims to screen the antibacterial activity of lactic acid bacteria
(LAB) isolated from home-made fermented vegetables against common food borne
pathogens. The antagonistic properties of these isolates against Escherichia
coli, Staphylococcus aureus, Yersinia enterocolitica and Bacillus cereus were
examined using agar well diffusion method. Four LAB namely MF6, MF10, MF13, and
MF15 identified as Lactobacillus animalis, Lactobacillus rhamnosus,
Lactobacillus fermentum and Lactobacillus reuteri, respectively were effective
against all selected pathogenic strains. Amongst the four isolates, MF6
exhibited the highest antibacterial activity, against all the indicator
pathogens tested except Y. enterocolitic. Its activity was maximum against
E.coli with a Zone of Inhibition (ZOI) ranging from 18.7 to 21.3 mm and least
for Y. enterocolitica (10 \pm 1.1 mm). Isolate MF13 also showed antimicrobial
property against all tested pathogens showing highest activity against Y.
enterocolitica (14 \pm 1.7 mm) and least against E.coli (8 \pm 1.4 mm), which
was in direct contrast to isolate MF6. Isolate MF15 showed greater activity
against E.coli (12 \pm 0.8 mm) and least against S. aureus (8 \pm 1.7 mm).
Least antimicrobial property was observed in isolate MF10, with a ZOI in the
range of 2.5-7 mm. The degree of antimicrobial property among the isolates was
in the order of MF6>MF13>MF15>MF10. Overall, the isolated LAB showed the
remarkable inhibitory effect against both Gram positive and Gram negative
pathogenic strains. However, the spectrum of inhibition was different for the
isolates tested. These results suggest that this potent isolates could be used
as a natural biopreservatives in different food products.
| [
{
"created": "Mon, 25 Jun 2012 10:13:46 GMT",
"version": "v1"
}
] | 2012-06-28 | [
[
"Kazemipoor",
"Mahnaz",
"",
"Corresponding author"
],
[
"Radzi",
"Che Wan Jasimah Wan Mohamed",
""
],
[
"Begum",
"Khyrunnisa",
""
],
[
"Yaze",
"Iman",
""
]
] | This study aims to screen the antibacterial activity of lactic acid bacteria (LAB) isolated from home-made fermented vegetables against common food borne pathogens. The antagonistic properties of these isolates against Escherichia coli, Staphylococcus aureus, Yersinia enterocolitica and Bacillus cereus were examined using agar well diffusion method. Four LAB namely MF6, MF10, MF13, and MF15 identified as Lactobacillus animalis, Lactobacillus rhamnosus, Lactobacillus fermentum and Lactobacillus reuteri, respectively were effective against all selected pathogenic strains. Amongst the four isolates, MF6 exhibited the highest antibacterial activity, against all the indicator pathogens tested except Y. enterocolitic. Its activity was maximum against E.coli with a Zone of Inhibition (ZOI) ranging from 18.7 to 21.3 mm and least for Y. enterocolitica (10 \pm 1.1 mm). Isolate MF13 also showed antimicrobial property against all tested pathogens showing highest activity against Y. enterocolitica (14 \pm 1.7 mm) and least against E.coli (8 \pm 1.4 mm), which was in direct contrast to isolate MF6. Isolate MF15 showed greater activity against E.coli (12 \pm 0.8 mm) and least against S. aureus (8 \pm 1.7 mm). Least antimicrobial property was observed in isolate MF10, with a ZOI in the range of 2.5-7 mm. The degree of antimicrobial property among the isolates was in the order of MF6>MF13>MF15>MF10. Overall, the isolated LAB showed the remarkable inhibitory effect against both Gram positive and Gram negative pathogenic strains. However, the spectrum of inhibition was different for the isolates tested. These results suggest that this potent isolates could be used as a natural biopreservatives in different food products. |
1401.3452 | Amir Toor | Juliana K. Sampson, Nihar U. Sheth, Vishal N. Koparde, Allison F.
Scalora, Myrna G. Serrano, Vladimir Lee, Catherine H. Roberts, Maximilian
Jameson-Lee, Andrea Ferriera-Gonzalez, Masoud H. Manjili, Gregory A. Buck,
Michael C. Neale, Amir A. Toor | Whole Exome Sequencing to Estimate Alloreactivity Potential Between
Donors and Recipients in Stem Cell Transplantation | 12 pages- main article, 29 pages total, 5 figures, 1 supplementary
figure | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Whole exome sequencing was performed on HLA-matched stem cell donors and
transplant recipients to measure sequence variation contributing to minor
histocompatibility antigen differences between the two. A large number of
nonsynonymous single nucleotide polymorphisms were identified in each of the
nine unique donor-recipient pairs tested. This variation was greater in
magnitude in unrelated donors as compared with matched related donors.
Knowledge of the magnitude of exome variation between stem cell transplant
recipients and donors may allow more accurate titration of immunosuppressive
therapy following stem cell transplantation.
| [
{
"created": "Wed, 15 Jan 2014 05:12:48 GMT",
"version": "v1"
}
] | 2014-01-16 | [
[
"Sampson",
"Juliana K.",
""
],
[
"Sheth",
"Nihar U.",
""
],
[
"Koparde",
"Vishal N.",
""
],
[
"Scalora",
"Allison F.",
""
],
[
"Serrano",
"Myrna G.",
""
],
[
"Lee",
"Vladimir",
""
],
[
"Roberts",
"Catherine H.",
""
],
[
"Jameson-Lee",
"Maximilian",
""
],
[
"Ferriera-Gonzalez",
"Andrea",
""
],
[
"Manjili",
"Masoud H.",
""
],
[
"Buck",
"Gregory A.",
""
],
[
"Neale",
"Michael C.",
""
],
[
"Toor",
"Amir A.",
""
]
] | Whole exome sequencing was performed on HLA-matched stem cell donors and transplant recipients to measure sequence variation contributing to minor histocompatibility antigen differences between the two. A large number of nonsynonymous single nucleotide polymorphisms were identified in each of the nine unique donor-recipient pairs tested. This variation was greater in magnitude in unrelated donors as compared with matched related donors. Knowledge of the magnitude of exome variation between stem cell transplant recipients and donors may allow more accurate titration of immunosuppressive therapy following stem cell transplantation. |
2105.06811 | Gianluca Truda | Gianluca Truda | Quantified Sleep: Machine learning techniques for observational n-of-1
studies | Source code: https://github.com/gianlucatruda/quantified-sleep | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper applies statistical learning techniques to an observational
Quantified-Self (QS) study to build a descriptive model of sleep quality. A
total of 472 days of my sleep data was collected with an Oura ring and combined
with lifestyle, environmental, and psychological data. Such n-of-1 QS projects
pose a number of challenges: heterogeneous data sources; missing values; high
dimensionality; dynamic feedback loops; human biases. This paper directly
addresses these challenges with an end-to-end QS pipeline that produces robust
descriptive models. Sleep quality is one of the most difficult modelling
targets in QS research, due to high noise and a large number of
weakly-contributing factors. Sleep quality was selected so that approaches from
this paper would generalise to most other n-of-1 QS projects. Techniques are
presented for combining and engineering features for the different classes of
data types, sample frequencies, and schema - including event logs, weather, and
geo-spatial data. Statistical analyses for outliers, normality,
(auto)correlation, stationarity, and missing data are detailed, along with a
proposed method for hierarchical clustering to identify correlated groups of
features. The missing data was overcome using a combination of knowledge-based
and statistical techniques, including several multivariate imputation
algorithms. "Markov unfolding" is presented for collapsing the time series into
a collection of independent observations, whilst incorporating historical
information. The final model was interpreted in two ways: by inspecting the
internal $\beta$-parameters, and using the SHAP framework. These two
interpretation techniques were combined to produce a list of the 16
most-predictive features, demonstrating that an observational study can greatly
narrow down the number of features that need to be considered when designing
interventional QS studies.
| [
{
"created": "Fri, 14 May 2021 13:13:17 GMT",
"version": "v1"
}
] | 2021-05-17 | [
[
"Truda",
"Gianluca",
""
]
] | This paper applies statistical learning techniques to an observational Quantified-Self (QS) study to build a descriptive model of sleep quality. A total of 472 days of my sleep data was collected with an Oura ring and combined with lifestyle, environmental, and psychological data. Such n-of-1 QS projects pose a number of challenges: heterogeneous data sources; missing values; high dimensionality; dynamic feedback loops; human biases. This paper directly addresses these challenges with an end-to-end QS pipeline that produces robust descriptive models. Sleep quality is one of the most difficult modelling targets in QS research, due to high noise and a large number of weakly-contributing factors. Sleep quality was selected so that approaches from this paper would generalise to most other n-of-1 QS projects. Techniques are presented for combining and engineering features for the different classes of data types, sample frequencies, and schema - including event logs, weather, and geo-spatial data. Statistical analyses for outliers, normality, (auto)correlation, stationarity, and missing data are detailed, along with a proposed method for hierarchical clustering to identify correlated groups of features. The missing data was overcome using a combination of knowledge-based and statistical techniques, including several multivariate imputation algorithms. "Markov unfolding" is presented for collapsing the time series into a collection of independent observations, whilst incorporating historical information. The final model was interpreted in two ways: by inspecting the internal $\beta$-parameters, and using the SHAP framework. These two interpretation techniques were combined to produce a list of the 16 most-predictive features, demonstrating that an observational study can greatly narrow down the number of features that need to be considered when designing interventional QS studies. |
1410.5851 | Yong Kong | Yong Kong | Calculating complexity of large randomized libraries | 14 pages, 4 figures | Journal of Theoretical Biology 259 (3), 641-645, 2009 | 10.1016/j.jtbi.2009.04.008 | null | q-bio.QM stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Randomized libraries are increasingly popular in protein engineering and
other biomedical research fields. Statistics of the libraries are useful to
guide and evaluate randomized library construction. Previous works only give
the mean of the number of unique sequences in the library, and they can only
handle equal molar ratio of the four nucleotides at a small number of mutation
sites. We derive formulas to calculate the mean and variance of the number of
unique sequences in libraries generated by cassette mutagenesis with mixtures
of arbitrary nucleotide ratios. Computer program was developed which utilizes
arbitrary numerical precision software package to calculate the statistics of
large libraries. The statistics of library with mutations in more than $20$
amino acids can be calculated easily. Results show that the nucleotide ratios
have significant effects on these statistics. The more skewed the ratio, the
larger the library size is needed to obtain the same expected number of unique
sequences. The program is freely available at
\url{http://graphics.med.yale.edu/cgi-bin/lib_comp.pl}.
| [
{
"created": "Tue, 21 Oct 2014 20:43:32 GMT",
"version": "v1"
}
] | 2024-05-28 | [
[
"Kong",
"Yong",
""
]
] | Randomized libraries are increasingly popular in protein engineering and other biomedical research fields. Statistics of the libraries are useful to guide and evaluate randomized library construction. Previous works only give the mean of the number of unique sequences in the library, and they can only handle equal molar ratio of the four nucleotides at a small number of mutation sites. We derive formulas to calculate the mean and variance of the number of unique sequences in libraries generated by cassette mutagenesis with mixtures of arbitrary nucleotide ratios. Computer program was developed which utilizes arbitrary numerical precision software package to calculate the statistics of large libraries. The statistics of library with mutations in more than $20$ amino acids can be calculated easily. Results show that the nucleotide ratios have significant effects on these statistics. The more skewed the ratio, the larger the library size is needed to obtain the same expected number of unique sequences. The program is freely available at \url{http://graphics.med.yale.edu/cgi-bin/lib_comp.pl}. |
q-bio/0703004 | Mauro Copelli | Mauro Copelli and Paulo R. A. Campos | Excitable Scale Free Networks | 6 pages, 4 figures | Eur. Phys. J. B 56, 273-278 (2007) | 10.1140/epjb/e2007-00114-7 | null | q-bio.NC cond-mat.dis-nn nlin.CG physics.bio-ph | null | When a simple excitable system is continuously stimulated by a Poissonian
external source, the response function (mean activity versus stimulus rate)
generally shows a linear saturating shape. This is experimentally verified in
some classes of sensory neurons, which accordingly present a small dynamic
range (defined as the interval of stimulus intensity which can be appropriately
coded by the mean activity of the excitable element), usually about one or two
decades only. The brain, on the other hand, can handle a significantly broader
range of stimulus intensity, and a collective phenomenon involving the
interaction among excitable neurons has been suggested to account for the
enhancement of the dynamic range. Since the role of the pattern of such
interactions is still unclear, here we investigate the performance of a
scale-free (SF) network topology in this dynamic range problem. Specifically,
we study the transfer function of disordered SF networks of excitable
Greenberg-Hastings cellular automata. We observe that the dynamic range is
maximum when the coupling among the elements is critical, corroborating a
general reasoning recently proposed. Although the maximum dynamic range yielded
by general SF networks is slightly worse than that of random networks, for
special SF networks which lack loops the enhancement of the dynamic range can
be dramatic, reaching nearly five decades. In order to understand the role of
loops on the transfer function we propose a simple model in which the density
of loops in the network can be gradually increased, and show that this is
accompanied by a gradual decrease of dynamic range.
| [
{
"created": "Thu, 1 Mar 2007 20:03:21 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Apr 2007 21:41:25 GMT",
"version": "v2"
},
{
"created": "Fri, 11 May 2007 12:44:45 GMT",
"version": "v3"
}
] | 2007-05-23 | [
[
"Copelli",
"Mauro",
""
],
[
"Campos",
"Paulo R. A.",
""
]
] | When a simple excitable system is continuously stimulated by a Poissonian external source, the response function (mean activity versus stimulus rate) generally shows a linear saturating shape. This is experimentally verified in some classes of sensory neurons, which accordingly present a small dynamic range (defined as the interval of stimulus intensity which can be appropriately coded by the mean activity of the excitable element), usually about one or two decades only. The brain, on the other hand, can handle a significantly broader range of stimulus intensity, and a collective phenomenon involving the interaction among excitable neurons has been suggested to account for the enhancement of the dynamic range. Since the role of the pattern of such interactions is still unclear, here we investigate the performance of a scale-free (SF) network topology in this dynamic range problem. Specifically, we study the transfer function of disordered SF networks of excitable Greenberg-Hastings cellular automata. We observe that the dynamic range is maximum when the coupling among the elements is critical, corroborating a general reasoning recently proposed. Although the maximum dynamic range yielded by general SF networks is slightly worse than that of random networks, for special SF networks which lack loops the enhancement of the dynamic range can be dramatic, reaching nearly five decades. In order to understand the role of loops on the transfer function we propose a simple model in which the density of loops in the network can be gradually increased, and show that this is accompanied by a gradual decrease of dynamic range. |
2011.01873 | J. C. Phillips | J. C. Phillips | Self-Organized Networks: Darwinian Evolution of Myosin-1 | 20 pages, 9 figures | null | null | null | q-bio.OT cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cytoskeletons are self-organized networks based on polymerized proteins:
actin, tubulin, and driven by motor proteins, such as myosin, kinesin and
dynein. Their positive Darwinian evolution enables them to approach optimized
functionality (self-organized criticality). The principal features of the
eukaryotic evolution of the cytoskeleton motor protein myosin-1 parallel those
of actin and tubulin, but also show striking differences connected to its
dynamical function. Optimized (long) hydropathic waves characterize the
molecular level Darwinian evolution towards optimized functionality
(self-organized criticality). The N-terminal and central domains of myosin-1
have evolved in eukaryotes at different rates, with the central domain
hydropathic extrema being optimally active in humans. A test shows that
hydropathic scaling can yield accuracies of better than 1% near optimized
functionality. Evolution towards synchronized level extrema is connected to a
special function of Mys-1 in humans involving Golgi complexes.
| [
{
"created": "Wed, 28 Oct 2020 19:06:54 GMT",
"version": "v1"
}
] | 2020-11-04 | [
[
"Phillips",
"J. C.",
""
]
] | Cytoskeletons are self-organized networks based on polymerized proteins: actin, tubulin, and driven by motor proteins, such as myosin, kinesin and dynein. Their positive Darwinian evolution enables them to approach optimized functionality (self-organized criticality). The principal features of the eukaryotic evolution of the cytoskeleton motor protein myosin-1 parallel those of actin and tubulin, but also show striking differences connected to its dynamical function. Optimized (long) hydropathic waves characterize the molecular level Darwinian evolution towards optimized functionality (self-organized criticality). The N-terminal and central domains of myosin-1 have evolved in eukaryotes at different rates, with the central domain hydropathic extrema being optimally active in humans. A test shows that hydropathic scaling can yield accuracies of better than 1% near optimized functionality. Evolution towards synchronized level extrema is connected to a special function of Mys-1 in humans involving Golgi complexes. |
0807.0122 | Tobias Reichenbach | Tobias Reichenbach and Erwin Frey | Instability of spatial patterns and its ambiguous impact on species
diversity | 4 pages, 3 figures and supplementary information. To appear in Phys.
Rev. Lett. | Phys. Rev. Lett. 101, 058102 (2008) | 10.1103/PhysRevLett.101.058102 | LMU-ASC 12/08 | q-bio.PE cond-mat.stat-mech nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-arrangement of individuals into spatial patterns often accompanies and
promotes species diversity in ecological systems. Here, we investigate pattern
formation arising from cyclic dominance of three species, operating near a
bifurcation point. In its vicinity, an Eckhaus instability occurs, leading to
convectively unstable "blurred" patterns. At the bifurcation point, stochastic
effects dominate and induce counterintuitive effects on diversity: Large
patterns, emerging for medium values of individuals' mobility, lead to rapid
species extinction, while small patterns (low mobility) promote diversity, and
high mobilities render spatial structures irrelevant. We provide a quantitative
analysis of these phenomena, employing a complex Ginzburg-Landau equation.
| [
{
"created": "Tue, 1 Jul 2008 11:29:30 GMT",
"version": "v1"
}
] | 2008-08-31 | [
[
"Reichenbach",
"Tobias",
""
],
[
"Frey",
"Erwin",
""
]
] | Self-arrangement of individuals into spatial patterns often accompanies and promotes species diversity in ecological systems. Here, we investigate pattern formation arising from cyclic dominance of three species, operating near a bifurcation point. In its vicinity, an Eckhaus instability occurs, leading to convectively unstable "blurred" patterns. At the bifurcation point, stochastic effects dominate and induce counterintuitive effects on diversity: Large patterns, emerging for medium values of individuals' mobility, lead to rapid species extinction, while small patterns (low mobility) promote diversity, and high mobilities render spatial structures irrelevant. We provide a quantitative analysis of these phenomena, employing a complex Ginzburg-Landau equation. |
1703.05490 | Sergei Vakulenko | V. Kozlov, S. Vakulenko and U. Wennergren | Biodiversity, extinctions and evolution of ecosystems with shared
resources | null | null | 10.1103/PhysRevE.95.032413 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the formation of stable ecological networks where many species
share the same resource. We show that such stable ecosystem naturally occurs as
a result of extinctions. We obtain an analytical relation for the number of
coexisting species and find a relation describing how many species that may go
extinct as a result of a sharp environmental change. We introduce a special
parameter that is a combination of species traits and resource characteristics
used in the model formulation. This parameter describes the pressure on system
to converge, by extinctions. When that stress parameter is large we obtain that
the species traits concentrate at some values. This stress parameter is thereby
a parameter that determines the level of final biodiversity of the system.
Moreover, we show that dynamics of this limit system can be described by simple
differential equations.
| [
{
"created": "Thu, 16 Mar 2017 07:07:43 GMT",
"version": "v1"
}
] | 2017-04-05 | [
[
"Kozlov",
"V.",
""
],
[
"Vakulenko",
"S.",
""
],
[
"Wennergren",
"U.",
""
]
] | We investigate the formation of stable ecological networks where many species share the same resource. We show that such stable ecosystem naturally occurs as a result of extinctions. We obtain an analytical relation for the number of coexisting species and find a relation describing how many species that may go extinct as a result of a sharp environmental change. We introduce a special parameter that is a combination of species traits and resource characteristics used in the model formulation. This parameter describes the pressure on system to converge, by extinctions. When that stress parameter is large we obtain that the species traits concentrate at some values. This stress parameter is thereby a parameter that determines the level of final biodiversity of the system. Moreover, we show that dynamics of this limit system can be described by simple differential equations. |
1611.00833 | Osman Kahraman | Osman Kahraman, Peter D. Koch, William S. Klug, Christoph A.
Haselwandter | Architecture and Function of Mechanosensitive Membrane Protein Lattices | null | Sci. Rep. 6, 19214 (2016) | 10.1038/srep19214 | null | q-bio.BM cond-mat.soft physics.bio-ph q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experiments have revealed that membrane proteins can form two-dimensional
clusters with regular translational and orientational protein arrangements,
which may allow cells to modulate protein function. However, the physical
mechanisms yielding supramolecular organization and collective function of
membrane proteins remain largely unknown. Here we show that bilayer-mediated
elastic interactions between membrane proteins can yield regular and
distinctive lattice architectures of protein clusters, and may provide a link
between lattice architecture and lattice function. Using the mechanosensitive
channel of large conductance (MscL) as a model system, we obtain relations
between the shape of MscL and the supramolecular architecture of MscL lattices.
We predict that the tetrameric and pentameric MscL symmetries observed in
previous structural studies yield distinct lattice architectures of MscL
clusters and that, in turn, these distinct MscL lattice architectures yield
distinct lattice activation barriers. Our results suggest general physical
mechanisms linking protein symmetry, the lattice architecture of membrane
protein clusters, and the collective function of membrane protein lattices.
| [
{
"created": "Wed, 2 Nov 2016 22:50:55 GMT",
"version": "v1"
}
] | 2016-11-04 | [
[
"Kahraman",
"Osman",
""
],
[
"Koch",
"Peter D.",
""
],
[
"Klug",
"William S.",
""
],
[
"Haselwandter",
"Christoph A.",
""
]
] | Experiments have revealed that membrane proteins can form two-dimensional clusters with regular translational and orientational protein arrangements, which may allow cells to modulate protein function. However, the physical mechanisms yielding supramolecular organization and collective function of membrane proteins remain largely unknown. Here we show that bilayer-mediated elastic interactions between membrane proteins can yield regular and distinctive lattice architectures of protein clusters, and may provide a link between lattice architecture and lattice function. Using the mechanosensitive channel of large conductance (MscL) as a model system, we obtain relations between the shape of MscL and the supramolecular architecture of MscL lattices. We predict that the tetrameric and pentameric MscL symmetries observed in previous structural studies yield distinct lattice architectures of MscL clusters and that, in turn, these distinct MscL lattice architectures yield distinct lattice activation barriers. Our results suggest general physical mechanisms linking protein symmetry, the lattice architecture of membrane protein clusters, and the collective function of membrane protein lattices. |
2111.13537 | Sen Cheng | Zahra Fayyaz, Aya Altamimi, Sen Cheng, Laurenz Wiskott | A model of semantic completion in generative episodic memory | 15 pages, 9 figures, 58 references | null | null | null | q-bio.NC cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Many different studies have suggested that episodic memory is a generative
process, but most computational models adopt a storage view. In this work, we
propose a computational model for generative episodic memory. It is based on
the central hypothesis that the hippocampus stores and retrieves selected
aspects of an episode as a memory trace, which is necessarily incomplete. At
recall, the neocortex reasonably fills in the missing information based on
general semantic information in a process we call semantic completion.
As episodes we use images of digits (MNIST) augmented by different
backgrounds representing context. Our model is based on a VQ-VAE which
generates a compressed latent representation in form of an index matrix, which
still has some spatial resolution. We assume that attention selects some part
of the index matrix while others are discarded, this then represents the gist
of the episode and is stored as a memory trace. At recall the missing parts are
filled in by a PixelCNN, modeling semantic completion, and the completed index
matrix is then decoded into a full image by the VQ-VAE.
The model is able to complete missing parts of a memory trace in a
semantically plausible way up to the point where it can generate plausible
images from scratch. Due to the combinatorics in the index matrix, the model
generalizes well to images not trained on. Compression as well as semantic
completion contribute to a strong reduction in memory requirements and
robustness to noise. Finally we also model an episodic memory experiment and
can reproduce that semantically congruent contexts are always recalled better
than incongruent ones, high attention levels improve memory accuracy in both
cases, and contexts that are not remembered correctly are more often remembered
semantically congruently than completely wrong.
| [
{
"created": "Fri, 26 Nov 2021 15:14:17 GMT",
"version": "v1"
}
] | 2021-11-29 | [
[
"Fayyaz",
"Zahra",
""
],
[
"Altamimi",
"Aya",
""
],
[
"Cheng",
"Sen",
""
],
[
"Wiskott",
"Laurenz",
""
]
] | Many different studies have suggested that episodic memory is a generative process, but most computational models adopt a storage view. In this work, we propose a computational model for generative episodic memory. It is based on the central hypothesis that the hippocampus stores and retrieves selected aspects of an episode as a memory trace, which is necessarily incomplete. At recall, the neocortex reasonably fills in the missing information based on general semantic information in a process we call semantic completion. As episodes we use images of digits (MNIST) augmented by different backgrounds representing context. Our model is based on a VQ-VAE which generates a compressed latent representation in form of an index matrix, which still has some spatial resolution. We assume that attention selects some part of the index matrix while others are discarded, this then represents the gist of the episode and is stored as a memory trace. At recall the missing parts are filled in by a PixelCNN, modeling semantic completion, and the completed index matrix is then decoded into a full image by the VQ-VAE. The model is able to complete missing parts of a memory trace in a semantically plausible way up to the point where it can generate plausible images from scratch. Due to the combinatorics in the index matrix, the model generalizes well to images not trained on. Compression as well as semantic completion contribute to a strong reduction in memory requirements and robustness to noise. Finally we also model an episodic memory experiment and can reproduce that semantically congruent contexts are always recalled better than incongruent ones, high attention levels improve memory accuracy in both cases, and contexts that are not remembered correctly are more often remembered semantically congruently than completely wrong. |
1103.1653 | Adilson Enio Motter | Sagar Sahasrabudhe and Adilson E. Motter | Rescuing ecosystems from extinction cascades through compensatory
perturbations | The supplementary information file can be downloaded from here:
http://dyn.phys.northwestern.edu/ncomms1163-s1.pdf. The published version of
the article is also available here:
http://dyn.phys.northwestern.edu/ncomms1163.pdf | Nature Communications 2, 170 (2011) | 10.1038/ncomms1163 | null | q-bio.PE cond-mat.dis-nn nlin.AO nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Food-web perturbations stemming from climate change, overexploitation,
invasive species, and habitat degradation often cause an initial loss of
species that results in a cascade of secondary extinctions, posing considerable
challenges to ecosystem conservation efforts. Here we devise a systematic
network-based approach to reduce the number of secondary extinctions using a
predictive modeling framework. We show that the extinction of one species can
often be compensated by the concurrent removal or population suppression of
other specific species, which is a counterintuitive effect not previously
tested in complex food webs. These compensatory perturbations frequently
involve long-range interactions that are not evident from local predator-prey
relationships. In numerous cases, even the early removal of a species that
would eventually be extinct by the cascade is found to significantly reduce the
number of cascading extinctions. These compensatory perturbations only exploit
resources available in the system, and illustrate the potential of human
intervention combined with predictive modeling for ecosystem management.
| [
{
"created": "Tue, 8 Mar 2011 22:00:38 GMT",
"version": "v1"
}
] | 2011-03-10 | [
[
"Sahasrabudhe",
"Sagar",
""
],
[
"Motter",
"Adilson E.",
""
]
] | Food-web perturbations stemming from climate change, overexploitation, invasive species, and habitat degradation often cause an initial loss of species that results in a cascade of secondary extinctions, posing considerable challenges to ecosystem conservation efforts. Here we devise a systematic network-based approach to reduce the number of secondary extinctions using a predictive modeling framework. We show that the extinction of one species can often be compensated by the concurrent removal or population suppression of other specific species, which is a counterintuitive effect not previously tested in complex food webs. These compensatory perturbations frequently involve long-range interactions that are not evident from local predator-prey relationships. In numerous cases, even the early removal of a species that would eventually be extinct by the cascade is found to significantly reduce the number of cascading extinctions. These compensatory perturbations only exploit resources available in the system, and illustrate the potential of human intervention combined with predictive modeling for ecosystem management. |
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