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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1309.0801 | Susan Khor | Susan Khor | Piggy-backing protein domains with Formal Concept Analysis | null | PLoS ONE 2014 9(2): e88943 | 10.1371/journal.pone.0088943 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying reliable domain-domain interactions (DDIs) will increase our
ability to predict novel protein-protein interactions (PPIs), to unravel
interactions in protein complexes, and thus gain more information about the
function and behavior of genes. One of the challenges of identifying reliable
DDIs is domain promiscuity. Promiscuous domains are domains that can occur in
many domain architectures and are therefore found in many proteins. This
becomes a problem for a method where the score of a domain-pair is the ratio
between observed and expected frequencies because the PPI network is sparse. As
such, many protein-pairs will be non-interacting and domain-pairs with
promiscuous domains will be penalized. This domain promiscuity challenge to the
problem of inferring reliable DDIs from PPIs has been recognized, and a number
of work-arounds have been proposed. In this paper, we report an application of
Formal Concept Analysis (FCA) to this problem. We find that the relationship
between formal concepts provide a natural way for rare domains to elevate the
rank of promiscuous domains, and enrich highly ranked domain-pairs with
reliable DDIs. This piggy-backing of promiscuous domains onto rare domains is
possible due to the domain architecture of proteins which mixes promiscuous
with rare domains.
| [
{
"created": "Tue, 3 Sep 2013 19:45:12 GMT",
"version": "v1"
}
] | 2014-02-21 | [
[
"Khor",
"Susan",
""
]
] | Identifying reliable domain-domain interactions (DDIs) will increase our ability to predict novel protein-protein interactions (PPIs), to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable DDIs is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the PPI network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable DDIs from PPIs has been recognized, and a number of work-arounds have been proposed. In this paper, we report an application of Formal Concept Analysis (FCA) to this problem. We find that the relationship between formal concepts provide a natural way for rare domains to elevate the rank of promiscuous domains, and enrich highly ranked domain-pairs with reliable DDIs. This piggy-backing of promiscuous domains onto rare domains is possible due to the domain architecture of proteins which mixes promiscuous with rare domains. |
2007.10823 | Andreas Kamilaris | Andreas Kamilaris, Immaculada Funes Mesa, Robert Sav\'e, Felicidad De
Herralde and Francesc X. Prenafeta-Bold\'u | Can animal manure be used to increase soil organic carbon stocks in the
Mediterranean as a mitigation climate change strategy? | Proc. of EnviroInfo 2020, Nicosia, Cyprus, September 2020. arXiv
admin note: text overlap with arXiv:2006.09122 | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Soil organic carbon (SOC) plays an important role on improving soil
conditions and soil functions. Increasing land use changes have induced an
important decline of SOC content at global scale. Increasing SOC in
agricultural soils has been proposed as a strategy to mitigate climate change.
Animal manure has the characteristic of enriching SOC, when applied to crop
fields, while, in parallel, it could constitute a natural fertilizer for the
crops. In this paper, a simulation is performed using the area of Catalonia,
Spain as a case study for the characteristic low SOC in the Mediterranean, to
examine whether animal manure can improve substantially the SOC of agricultural
fields, when applied as organic fertilizers. Our results show that the policy
goals of the 4x1000 strategy can be achieved only partially by using manure
transported to the fields. This implies that the proposed approach needs to be
combined with other strategies.
| [
{
"created": "Fri, 17 Jul 2020 11:30:56 GMT",
"version": "v1"
}
] | 2020-07-22 | [
[
"Kamilaris",
"Andreas",
""
],
[
"Mesa",
"Immaculada Funes",
""
],
[
"Savé",
"Robert",
""
],
[
"De Herralde",
"Felicidad",
""
],
[
"Prenafeta-Boldú",
"Francesc X.",
""
]
] | Soil organic carbon (SOC) plays an important role on improving soil conditions and soil functions. Increasing land use changes have induced an important decline of SOC content at global scale. Increasing SOC in agricultural soils has been proposed as a strategy to mitigate climate change. Animal manure has the characteristic of enriching SOC, when applied to crop fields, while, in parallel, it could constitute a natural fertilizer for the crops. In this paper, a simulation is performed using the area of Catalonia, Spain as a case study for the characteristic low SOC in the Mediterranean, to examine whether animal manure can improve substantially the SOC of agricultural fields, when applied as organic fertilizers. Our results show that the policy goals of the 4x1000 strategy can be achieved only partially by using manure transported to the fields. This implies that the proposed approach needs to be combined with other strategies. |
1802.08024 | Omer Markovitch | Omer Markovitch, Olaf Witkowski and Nathaniel Virgo | Chemical Heredity as Group Selection at the Molecular Level | Keywords: Composomes, Evolution, Cooperation, Price equation, Group
selection, Compositional information, Population dynamics | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many examples of cooperation exist in biology. In chemical systems however,
which can sometimes be quite complex, we do not appear to observe intricate
cooperative interactions. A key question for the origin of life, is then how
can molecular cooperation first arise in an abiotic system prior to the
emergence of biological replication. We postulate that selection at the
molecular level is a driving force behind the complexification of chemical
systems, particularly during the origins of life. In the theory of multilevel
selection the two selective forces are: within-group and between-group, where
the former tends to favor "selfish" replication of individuals and the latter
favor cooperation between individuals enhancing the replication of the group as
a whole. These forces can be quantified using the Price equation, which is a
standard tool used in evolutionary biology to quantify evolutionary change. Our
central claim is that replication and heredity in chemical systems are subject
to selection, and quantifiable using the multilevel Price equation. We
demonstrate this using the Graded Autocatalysis Replication Domain computer
model, describing simple protocell composed out of molecules and its
replication, which respectively analogue to the group and the individuals. In
contrast to previous treatments of this model, we treat the lipid molecules
themselves as replicating individuals and the protocells they form as groups of
individuals. Our goal is to demonstrate how evolutionary biology tools and
concepts can be applied in chemistry and we suggest that molecular cooperation
may arise as a result of group selection. Further, the biological relation of
parent-progeny is proposed to be analogue to the reactant-product relation in
chemistry, thus allowing for tools from evolutionary biology to be applied to
chemistry and would deepen the connection between chemistry and biology.
| [
{
"created": "Thu, 22 Feb 2018 13:07:36 GMT",
"version": "v1"
}
] | 2018-02-23 | [
[
"Markovitch",
"Omer",
""
],
[
"Witkowski",
"Olaf",
""
],
[
"Virgo",
"Nathaniel",
""
]
] | Many examples of cooperation exist in biology. In chemical systems however, which can sometimes be quite complex, we do not appear to observe intricate cooperative interactions. A key question for the origin of life, is then how can molecular cooperation first arise in an abiotic system prior to the emergence of biological replication. We postulate that selection at the molecular level is a driving force behind the complexification of chemical systems, particularly during the origins of life. In the theory of multilevel selection the two selective forces are: within-group and between-group, where the former tends to favor "selfish" replication of individuals and the latter favor cooperation between individuals enhancing the replication of the group as a whole. These forces can be quantified using the Price equation, which is a standard tool used in evolutionary biology to quantify evolutionary change. Our central claim is that replication and heredity in chemical systems are subject to selection, and quantifiable using the multilevel Price equation. We demonstrate this using the Graded Autocatalysis Replication Domain computer model, describing simple protocell composed out of molecules and its replication, which respectively analogue to the group and the individuals. In contrast to previous treatments of this model, we treat the lipid molecules themselves as replicating individuals and the protocells they form as groups of individuals. Our goal is to demonstrate how evolutionary biology tools and concepts can be applied in chemistry and we suggest that molecular cooperation may arise as a result of group selection. Further, the biological relation of parent-progeny is proposed to be analogue to the reactant-product relation in chemistry, thus allowing for tools from evolutionary biology to be applied to chemistry and would deepen the connection between chemistry and biology. |
1903.07537 | Hossein A. Rahmani | Hossein A. Rahmani, AliReza Khanteymoori, and Mohammad Olyaee | Comparison of Tumor and Normal Cells Protein-Protein Interaction Network
Parameters | null | null | null | null | q-bio.MN cs.CE | http://creativecommons.org/publicdomain/zero/1.0/ | In this paper, we compared cancerous and normal cell according to their
protein-protein interaction network. Cancer is one of the complicated diseases
and experimental investigations have been showed that protein interactions have
an important role in the growth of cancer. We calculated some graph related
parameters such as Number of Vertices, Number of Edges, Closeness, Graph
Diameter, Graph Radius, Index of Aggregation, Connectivity, Number of Edges
divided by the Number of Vertices, Degree, Cluster Coefficient, Subgraph
Centrality, and Betweenness. Furthermore, the number of motifs and hubs in
these networks have been measured. In this paper bone, breast, colon, kidney
and liver benchmark datasets have been used for experiments. The experimental
results show that Graph Degree Mean, Subgraph Centrality, Betweenness, and Hubs
have higher values in the cancer cells and can be used as a measure to
distinguish between normal and cancerous networks. The cancerous tissues of the
five studied samples are denser in the interaction networks.
| [
{
"created": "Fri, 15 Mar 2019 17:28:38 GMT",
"version": "v1"
}
] | 2019-03-19 | [
[
"Rahmani",
"Hossein A.",
""
],
[
"Khanteymoori",
"AliReza",
""
],
[
"Olyaee",
"Mohammad",
""
]
] | In this paper, we compared cancerous and normal cell according to their protein-protein interaction network. Cancer is one of the complicated diseases and experimental investigations have been showed that protein interactions have an important role in the growth of cancer. We calculated some graph related parameters such as Number of Vertices, Number of Edges, Closeness, Graph Diameter, Graph Radius, Index of Aggregation, Connectivity, Number of Edges divided by the Number of Vertices, Degree, Cluster Coefficient, Subgraph Centrality, and Betweenness. Furthermore, the number of motifs and hubs in these networks have been measured. In this paper bone, breast, colon, kidney and liver benchmark datasets have been used for experiments. The experimental results show that Graph Degree Mean, Subgraph Centrality, Betweenness, and Hubs have higher values in the cancer cells and can be used as a measure to distinguish between normal and cancerous networks. The cancerous tissues of the five studied samples are denser in the interaction networks. |
1310.7522 | David Caballero | David Caballero, Raphael Voituriez, Daniel Riveline | Protrusion fluctuations direct cell motion | Supplementary movies available upon request | Biophysical Journal, 107(1), 34-42 (2014) | 10.1016/j.bpj.2014.05.002 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many physiological phenomena involve directional cell migration. It is
usually attributed to chemical gradients in vivo. Recently, other cues have
been shown to guide cells in vitro, including stiffness/adhesion gradients or
micro-patterned adhesive motifs. However, the cellular mechanism leading to
these biased migrations remains unknown, and, often, even the direction of
motion is unpredictable. In this study, we show the key role of fluctuating
protrusions on ratchet-like structures in driving NIH3T3 cell migration. We
identified the concept of efficient protrusion and an associated direction
index. Our analysis of the protrusion statistics facilitated the quantitative
prediction of cell trajectories in all investigated conditions. We varied the
external cues by changing the adhesive patterns. We also modified the internal
cues using drug treatments, which modified the protrusion activity.
Stochasticity affects the short- and long-term steps. We developed a
theoretical model showing that an asymmetry in the protrusion fluctuations is
sufficient for predicting all measures associated with the long-term motion,
which can be described as a biased persistent random walk.
| [
{
"created": "Mon, 28 Oct 2013 18:18:18 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Jul 2014 17:10:02 GMT",
"version": "v2"
}
] | 2014-07-21 | [
[
"Caballero",
"David",
""
],
[
"Voituriez",
"Raphael",
""
],
[
"Riveline",
"Daniel",
""
]
] | Many physiological phenomena involve directional cell migration. It is usually attributed to chemical gradients in vivo. Recently, other cues have been shown to guide cells in vitro, including stiffness/adhesion gradients or micro-patterned adhesive motifs. However, the cellular mechanism leading to these biased migrations remains unknown, and, often, even the direction of motion is unpredictable. In this study, we show the key role of fluctuating protrusions on ratchet-like structures in driving NIH3T3 cell migration. We identified the concept of efficient protrusion and an associated direction index. Our analysis of the protrusion statistics facilitated the quantitative prediction of cell trajectories in all investigated conditions. We varied the external cues by changing the adhesive patterns. We also modified the internal cues using drug treatments, which modified the protrusion activity. Stochasticity affects the short- and long-term steps. We developed a theoretical model showing that an asymmetry in the protrusion fluctuations is sufficient for predicting all measures associated with the long-term motion, which can be described as a biased persistent random walk. |
2405.10078 | Yoshihiro Nagano | Ken Shirakawa, Yoshihiro Nagano, Misato Tanaka, Shuntaro C. Aoki, Kei
Majima, Yusuke Muraki, Yukiyasu Kamitani | Spurious reconstruction from brain activity | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Advances in brain decoding, particularly visual image reconstruction, have
sparked discussions about the societal implications and ethical considerations
of neurotechnology. As these methods aim to recover visual experiences from
brain activity and achieve prediction beyond training samples (zero-shot
prediction), it is crucial to assess their capabilities and limitations to
inform public expectations and regulations. Our case study of recent
text-guided reconstruction methods, which leverage a large-scale dataset (NSD)
and text-to-image diffusion models, reveals limitations in their
generalizability. We found decreased performance when applying these methods to
a different dataset designed to prevent category overlaps between training and
test sets. UMAP visualization of the text features with NSD images showed
limited diversity of semantic and visual clusters, with overlap between
training and test sets. Formal analysis and simulations demonstrated that
clustered training samples can lead to "output dimension collapse," restricting
predictable output feature dimensions. Diversifying the training set improved
generalizability. However, text features alone are insufficient for mapping to
the visual space. We argue that recent photo-like reconstructions may primarily
be a blend of classification into trained categories and generation of
inauthentic images through text-to-image diffusion (hallucination). Diverse
datasets and compositional representations spanning the image space are
essential for genuine zero-shot prediction. Interdisciplinary discussions
grounded in understanding the current capabilities and limitations, as well as
ethical considerations, of the technology are crucial for its responsible
development.
| [
{
"created": "Thu, 16 May 2024 13:20:01 GMT",
"version": "v1"
}
] | 2024-05-17 | [
[
"Shirakawa",
"Ken",
""
],
[
"Nagano",
"Yoshihiro",
""
],
[
"Tanaka",
"Misato",
""
],
[
"Aoki",
"Shuntaro C.",
""
],
[
"Majima",
"Kei",
""
],
[
"Muraki",
"Yusuke",
""
],
[
"Kamitani",
"Yukiyasu",
""
]
] | Advances in brain decoding, particularly visual image reconstruction, have sparked discussions about the societal implications and ethical considerations of neurotechnology. As these methods aim to recover visual experiences from brain activity and achieve prediction beyond training samples (zero-shot prediction), it is crucial to assess their capabilities and limitations to inform public expectations and regulations. Our case study of recent text-guided reconstruction methods, which leverage a large-scale dataset (NSD) and text-to-image diffusion models, reveals limitations in their generalizability. We found decreased performance when applying these methods to a different dataset designed to prevent category overlaps between training and test sets. UMAP visualization of the text features with NSD images showed limited diversity of semantic and visual clusters, with overlap between training and test sets. Formal analysis and simulations demonstrated that clustered training samples can lead to "output dimension collapse," restricting predictable output feature dimensions. Diversifying the training set improved generalizability. However, text features alone are insufficient for mapping to the visual space. We argue that recent photo-like reconstructions may primarily be a blend of classification into trained categories and generation of inauthentic images through text-to-image diffusion (hallucination). Diverse datasets and compositional representations spanning the image space are essential for genuine zero-shot prediction. Interdisciplinary discussions grounded in understanding the current capabilities and limitations, as well as ethical considerations, of the technology are crucial for its responsible development. |
1506.00453 | Fernando Puente-S\'anchez | Fernando Puente-S\'anchez, Jacobo Aguirre, V\'ictor Parro | A read-filtering algorithm for high-throughput marker-gene studies that
greatly improves OTU accuracy | null | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adequate read filtering is critical when processing high-throughput data in
marker-gene-based studies. Sequencing errors can cause the mis-clustering of
otherwise similar reads, artificially increasing the number of retrieved
Operational Taxonomic Units (OTUs) and therefore leading to the overestimation
of microbial diversity. Sequencing errors will also result in OTUs that are not
accurate reconstructions of the original biological sequences. Herein we
present a novel and sensitive sequence filtering algorithm that minimizes both
problems by calculating the exact error-probability distribution of a sequence
from its quality scores. In order to validate our method, we quality-filtered
thirty-seven publicly available datasets obtained by sequencing mock and
environmental microbial communities with the Roche 454, Illumina MiSeq and
IonTorrent PGM platforms, and compared our results to those obtained with
previous approaches such as the ones included in mothur, QIIME and UPARSE. Our
algorithm retained substantially more reads than its predecessors, while
resulting in fewer and more accurate OTUs. This improved sensitiveness produced
more faithful representations, both quantitatively and qualitatively, of the
true microbial diversity present in the studied samples. Furthermore, the
method introduced in this work is computationally inexpensive and can be
readily applied in conjunction with any existent analysis pipeline.
| [
{
"created": "Mon, 1 Jun 2015 11:44:19 GMT",
"version": "v1"
}
] | 2015-06-02 | [
[
"Puente-Sánchez",
"Fernando",
""
],
[
"Aguirre",
"Jacobo",
""
],
[
"Parro",
"Víctor",
""
]
] | Adequate read filtering is critical when processing high-throughput data in marker-gene-based studies. Sequencing errors can cause the mis-clustering of otherwise similar reads, artificially increasing the number of retrieved Operational Taxonomic Units (OTUs) and therefore leading to the overestimation of microbial diversity. Sequencing errors will also result in OTUs that are not accurate reconstructions of the original biological sequences. Herein we present a novel and sensitive sequence filtering algorithm that minimizes both problems by calculating the exact error-probability distribution of a sequence from its quality scores. In order to validate our method, we quality-filtered thirty-seven publicly available datasets obtained by sequencing mock and environmental microbial communities with the Roche 454, Illumina MiSeq and IonTorrent PGM platforms, and compared our results to those obtained with previous approaches such as the ones included in mothur, QIIME and UPARSE. Our algorithm retained substantially more reads than its predecessors, while resulting in fewer and more accurate OTUs. This improved sensitiveness produced more faithful representations, both quantitatively and qualitatively, of the true microbial diversity present in the studied samples. Furthermore, the method introduced in this work is computationally inexpensive and can be readily applied in conjunction with any existent analysis pipeline. |
1401.5035 | Quentin Grimonprez | Quentin Grimonprez (INRIA Lille - Nord Europe), Alain Celisse (INRIA
Lille - Nord Europe), Meyling Cheok, Martin Figeac, Guillemette Marot (INRIA
Lille - Nord Europe, CERIM) | MPAgenomics : An R package for multi-patients analysis of genomic
markers | null | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MPAgenomics, standing for multi-patients analysis (MPA) of genomic markers,
is an R-package devoted to: (i) efficient segmentation, and (ii) genomic marker
selection from multi-patient copy number and SNP data profiles. It provides
wrappers from commonly used packages to facilitate their repeated (sometimes
difficult) use, offering an easy-to-use pipeline for beginners in R. The
segmentation of successive multiple profiles (finding losses and gains) is
based on a new automatic choice of influential parameters since default ones
were misleading in the original packages. Considering multiple profiles in the
same time, MPAgenomics wraps efficient penalized regression methods to select
relevant markers associated with a given response.
| [
{
"created": "Mon, 20 Jan 2014 19:58:38 GMT",
"version": "v1"
}
] | 2014-01-21 | [
[
"Grimonprez",
"Quentin",
"",
"INRIA Lille - Nord Europe"
],
[
"Celisse",
"Alain",
"",
"INRIA\n Lille - Nord Europe"
],
[
"Cheok",
"Meyling",
"",
"INRIA\n Lille - Nord Europe, CERIM"
],
[
"Figeac",
"Martin",
"",
"INRIA\n Lille - Nord Europe, CERIM"
],
[
"Marot",
"Guillemette",
"",
"INRIA\n Lille - Nord Europe, CERIM"
]
] | MPAgenomics, standing for multi-patients analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation, and (ii) genomic marker selection from multi-patient copy number and SNP data profiles. It provides wrappers from commonly used packages to facilitate their repeated (sometimes difficult) use, offering an easy-to-use pipeline for beginners in R. The segmentation of successive multiple profiles (finding losses and gains) is based on a new automatic choice of influential parameters since default ones were misleading in the original packages. Considering multiple profiles in the same time, MPAgenomics wraps efficient penalized regression methods to select relevant markers associated with a given response. |
1710.02098 | Michele Monti | Michele Monti, David K Lubensky and Pieter Rein ten Wolde | Robustness of clocks to input noise | 28 Pages, 16 figures | Phys. Rev. Lett. 121, 078101 (2018) | 10.1103/PhysRevLett.121.078101 | null | q-bio.MN cond-mat.stat-mech physics.bio-ph q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To estimate the time, many organisms, ranging from cyanobacteria to animals,
employ a circadian clock which is based on a limit-cycle oscillator that can
tick autonomously with a nearly 24h period. Yet, a limit-cycle oscillator is
not essential for knowing the time, as exemplified by bacteria that possess an
'hourglass': a system that when forced by an oscillatory light input exhibits
robust oscillations from which the organism can infer the time, but that in the
absence of driving relaxes to a stable fixed point. Here, using models of the
Kai system of cyanobacteria, we compare a limit- cycle oscillator with two
hourglass models, one that without driving relaxes exponentially and one that
does so in an oscillatory fashion. In the limit of low input-noise, all three
systems are equally informative on time, yet in the regime of high input-noise
the limit-cycle oscillator is far superior. The same behavior is found in the
Stuart-Landau model, indicating that our result is universal.
| [
{
"created": "Wed, 27 Sep 2017 12:44:54 GMT",
"version": "v1"
},
{
"created": "Wed, 9 May 2018 13:44:26 GMT",
"version": "v2"
},
{
"created": "Fri, 11 May 2018 13:21:50 GMT",
"version": "v3"
}
] | 2018-08-22 | [
[
"Monti",
"Michele",
""
],
[
"Lubensky",
"David K",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | To estimate the time, many organisms, ranging from cyanobacteria to animals, employ a circadian clock which is based on a limit-cycle oscillator that can tick autonomously with a nearly 24h period. Yet, a limit-cycle oscillator is not essential for knowing the time, as exemplified by bacteria that possess an 'hourglass': a system that when forced by an oscillatory light input exhibits robust oscillations from which the organism can infer the time, but that in the absence of driving relaxes to a stable fixed point. Here, using models of the Kai system of cyanobacteria, we compare a limit- cycle oscillator with two hourglass models, one that without driving relaxes exponentially and one that does so in an oscillatory fashion. In the limit of low input-noise, all three systems are equally informative on time, yet in the regime of high input-noise the limit-cycle oscillator is far superior. The same behavior is found in the Stuart-Landau model, indicating that our result is universal. |
2308.13633 | Lyndon Duong | Lyndon R. Duong, Eero P. Simoncelli, Dmitri B. Chklovskii, David
Lipshutz | Adaptive whitening with fast gain modulation and slow synaptic
plasticity | NeurIPS 2023 Spotlight; 18 pages, 8 figures | null | null | null | q-bio.NC cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Neurons in early sensory areas rapidly adapt to changing sensory statistics,
both by normalizing the variance of their individual responses and by reducing
correlations between their responses. Together, these transformations may be
viewed as an adaptive form of statistical whitening. Existing mechanistic
models of adaptive whitening exclusively use either synaptic plasticity or gain
modulation as the biological substrate for adaptation; however, on their own,
each of these models has significant limitations. In this work, we unify these
approaches in a normative multi-timescale mechanistic model that adaptively
whitens its responses with complementary computational roles for synaptic
plasticity and gain modulation. Gains are modified on a fast timescale to adapt
to the current statistical context, whereas synapses are modified on a slow
timescale to match structural properties of the input statistics that are
invariant across contexts. Our model is derived from a novel multi-timescale
whitening objective that factorizes the inverse whitening matrix into basis
vectors, which correspond to synaptic weights, and a diagonal matrix, which
corresponds to neuronal gains. We test our model on synthetic and natural
datasets and find that the synapses learn optimal configurations over long
timescales that enable adaptive whitening on short timescales using gain
modulation.
| [
{
"created": "Fri, 25 Aug 2023 18:58:53 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Oct 2023 15:52:44 GMT",
"version": "v2"
}
] | 2023-10-27 | [
[
"Duong",
"Lyndon R.",
""
],
[
"Simoncelli",
"Eero P.",
""
],
[
"Chklovskii",
"Dmitri B.",
""
],
[
"Lipshutz",
"David",
""
]
] | Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between their responses. Together, these transformations may be viewed as an adaptive form of statistical whitening. Existing mechanistic models of adaptive whitening exclusively use either synaptic plasticity or gain modulation as the biological substrate for adaptation; however, on their own, each of these models has significant limitations. In this work, we unify these approaches in a normative multi-timescale mechanistic model that adaptively whitens its responses with complementary computational roles for synaptic plasticity and gain modulation. Gains are modified on a fast timescale to adapt to the current statistical context, whereas synapses are modified on a slow timescale to match structural properties of the input statistics that are invariant across contexts. Our model is derived from a novel multi-timescale whitening objective that factorizes the inverse whitening matrix into basis vectors, which correspond to synaptic weights, and a diagonal matrix, which corresponds to neuronal gains. We test our model on synthetic and natural datasets and find that the synapses learn optimal configurations over long timescales that enable adaptive whitening on short timescales using gain modulation. |
1901.01309 | Mireille Broucke | Mireille E. Broucke | Adaptive Internal Models: Explaining the Oculomotor System and the
Cerebellum | null | null | null | null | q-bio.NC math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new model of the oculomotor system, particularly the
vestibulo-ocular reflex, gaze fixation, and smooth pursuit. Our key insight is
to exploit recent developments on adaptive internal models. The outcome is a
simple model that includes the interactions between the brainstem and the
cerebellum and that recovers behaviors from more than 15 oculomotor
experiments. In addition, we put forward a thesis that the cerebellum embodies
internal models of all persistent, exogenous reference and disturbance signals
acting on the body and observable through the error signals it receives.
| [
{
"created": "Fri, 4 Jan 2019 20:55:38 GMT",
"version": "v1"
},
{
"created": "Sat, 6 Feb 2021 20:38:42 GMT",
"version": "v2"
}
] | 2021-02-09 | [
[
"Broucke",
"Mireille E.",
""
]
] | We propose a new model of the oculomotor system, particularly the vestibulo-ocular reflex, gaze fixation, and smooth pursuit. Our key insight is to exploit recent developments on adaptive internal models. The outcome is a simple model that includes the interactions between the brainstem and the cerebellum and that recovers behaviors from more than 15 oculomotor experiments. In addition, we put forward a thesis that the cerebellum embodies internal models of all persistent, exogenous reference and disturbance signals acting on the body and observable through the error signals it receives. |
1708.01772 | Nikolai Slavov | Dmitry Malioutov, Tianchi Chen, Jacob Jaffe, Edoardo Airoldi, Steven
Carr, Bogdan Budnik and Nikolai Slavov | Quantifying homologous proteins and proteoforms | null | Molecular & Cellular Proteomics, 2018 | 10.1074/mcp.TIR118.000947 | mcp.TIR118.000947 | q-bio.QM q-bio.GN stat.AP stat.ME stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Many proteoforms - arising from alternative splicing, post-translational
modifications (PTMs), or paralogous genes - have distinct biological functions,
such as histone PTM proteoforms. However, their quantification by existing
bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific
biases. To avoid these biases, we developed and implemented a first-principles
model (HIquant) for quantifying proteoform stoichiometries. We characterized
when MS data allow inferring proteoform stoichiometries by HIquant, derived an
algorithm for optimal inference, and demonstrated experimentally high accuracy
in quantifying fractional PTM occupancy without using external standards, even
in the challenging case of the histone modification code.
HIquant server is implemented at:
https://web.northeastern.edu/slavov/2014_HIquant/
| [
{
"created": "Sat, 5 Aug 2017 13:52:12 GMT",
"version": "v1"
}
] | 2018-10-29 | [
[
"Malioutov",
"Dmitry",
""
],
[
"Chen",
"Tianchi",
""
],
[
"Jaffe",
"Jacob",
""
],
[
"Airoldi",
"Edoardo",
""
],
[
"Carr",
"Steven",
""
],
[
"Budnik",
"Bogdan",
""
],
[
"Slavov",
"Nikolai",
""
]
] | Many proteoforms - arising from alternative splicing, post-translational modifications (PTMs), or paralogous genes - have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (HIquant) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform stoichiometries by HIquant, derived an algorithm for optimal inference, and demonstrated experimentally high accuracy in quantifying fractional PTM occupancy without using external standards, even in the challenging case of the histone modification code. HIquant server is implemented at: https://web.northeastern.edu/slavov/2014_HIquant/ |
2306.00944 | Alice Berger | Phoebe C. R. Parrish, Daniel J. Groso, James D. Thomas, Robert K.
Bradley, Alice H. Berger | pgMAP: a pipeline to enable guide RNA read mapping from dual-targeting
CRISPR screens | 6 pages, 1 figure, code available at
https://github.com/fredhutch/pgmap_pipeline | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We developed pgMAP, an analysis pipeline to map gRNA sequencing reads from
dual-targeting CRISPR screens. pgMAP output includes a dual gRNA read counts
table and quality control metrics including the proportion of correctly-paired
reads and CRISPR library sequencing coverage across all time points and
samples. pgMAP is implemented using Snakemake and is available open-source
under the MIT license at https://github.com/fredhutch/pgmap_pipeline.
| [
{
"created": "Thu, 1 Jun 2023 17:44:03 GMT",
"version": "v1"
}
] | 2023-06-02 | [
[
"Parrish",
"Phoebe C. R.",
""
],
[
"Groso",
"Daniel J.",
""
],
[
"Thomas",
"James D.",
""
],
[
"Bradley",
"Robert K.",
""
],
[
"Berger",
"Alice H.",
""
]
] | We developed pgMAP, an analysis pipeline to map gRNA sequencing reads from dual-targeting CRISPR screens. pgMAP output includes a dual gRNA read counts table and quality control metrics including the proportion of correctly-paired reads and CRISPR library sequencing coverage across all time points and samples. pgMAP is implemented using Snakemake and is available open-source under the MIT license at https://github.com/fredhutch/pgmap_pipeline. |
q-bio/0402012 | Catherine Beauchemin | Catherine Beauchemin, John Samuel and Jack Tuszynski | A Simple Cellular Automaton Model for Influenza A Viral Infections | LaTeX, 12 pages, 18 EPS figures, uses document class ReTeX4, and
packages amsmath and SIunits | Journal of Theoretical Biology, 232(2), 21 January 2005, pp.
223-234 | 10.1016/j.jtbi.2004.08.001 | null | q-bio.CB q-bio.QM | null | Viral kinetics have been extensively studied in the past through the use of
spatially homogeneous ordinary differential equations describing the time
evolution of the diseased state. However, spatial characteristics such as
localized populations of dead cells might adversely affect the spread of
infection, similar to the manner in which a counter-fire can stop a forest fire
from spreading. In order to investigate the influence of spatial
heterogeneities on viral spread, a simple 2-D cellular automaton (CA) model of
a viral infection has been developed. In this initial phase of the
investigation, the CA model is validated against clinical immunological data
for uncomplicated influenza A infections. Our results will be shown and
discussed.
| [
{
"created": "Fri, 6 Feb 2004 20:42:04 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Beauchemin",
"Catherine",
""
],
[
"Samuel",
"John",
""
],
[
"Tuszynski",
"Jack",
""
]
] | Viral kinetics have been extensively studied in the past through the use of spatially homogeneous ordinary differential equations describing the time evolution of the diseased state. However, spatial characteristics such as localized populations of dead cells might adversely affect the spread of infection, similar to the manner in which a counter-fire can stop a forest fire from spreading. In order to investigate the influence of spatial heterogeneities on viral spread, a simple 2-D cellular automaton (CA) model of a viral infection has been developed. In this initial phase of the investigation, the CA model is validated against clinical immunological data for uncomplicated influenza A infections. Our results will be shown and discussed. |
2104.10795 | Connor Courtney | Connor D. Courtney, Arin Pamukcu, C. Savio Chan | The external pallidum: think locally, act globally | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The globus pallidus (GPe) of the basal ganglia has been underappreciated for
decades due to poor understanding of its cells and circuits. The advent of
molecular tools has sparked a resurgence in interest in the GPe. Here, we
review a recent flurry of publications that has unveiled the complexity of the
molecular landscape and cellular composition within the GPe. These discoveries
have revealed that GPe neurons display a number of circuit features that do not
conform to the traditional views of the basal ganglia. Consistent with its
broad interconnectivity across the brain, emerging evidence suggests that the
GPe plays multifaceted roles in both motor and non-motor functions. Altogether,
recent data highlight cellular and functional diversity within the GPe and
prompt new proposals for computational rules of the basal ganglia.
| [
{
"created": "Wed, 21 Apr 2021 23:34:56 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Mar 2022 01:17:48 GMT",
"version": "v2"
}
] | 2022-03-09 | [
[
"Courtney",
"Connor D.",
""
],
[
"Pamukcu",
"Arin",
""
],
[
"Chan",
"C. Savio",
""
]
] | The globus pallidus (GPe) of the basal ganglia has been underappreciated for decades due to poor understanding of its cells and circuits. The advent of molecular tools has sparked a resurgence in interest in the GPe. Here, we review a recent flurry of publications that has unveiled the complexity of the molecular landscape and cellular composition within the GPe. These discoveries have revealed that GPe neurons display a number of circuit features that do not conform to the traditional views of the basal ganglia. Consistent with its broad interconnectivity across the brain, emerging evidence suggests that the GPe plays multifaceted roles in both motor and non-motor functions. Altogether, recent data highlight cellular and functional diversity within the GPe and prompt new proposals for computational rules of the basal ganglia. |
1703.00445 | Antonino Sciarrino | A. Sciarrino | Metrics and Harmonic Analysis on DNA | 21 pages, 10 figures | null | null | null | q-bio.OT cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A mathematical algorithm to describe DNA or RNA sequences of $N$ nucleotides
by a string of $2N$ integers numbers is presented in the framework of the so
called crystal basis model of the genetic code. The description allows to
define a not trivial distance between two sequences and to perform the Discrete
Fourier Transform on a sequence. Using the definition of distance the sequence
of $\beta$-globin for Homo, Gallus and Opossum are compared. The Discrete
Fourier Transform for some simple examples is computed.
| [
{
"created": "Wed, 1 Mar 2017 15:22:03 GMT",
"version": "v1"
}
] | 2017-03-06 | [
[
"Sciarrino",
"A.",
""
]
] | A mathematical algorithm to describe DNA or RNA sequences of $N$ nucleotides by a string of $2N$ integers numbers is presented in the framework of the so called crystal basis model of the genetic code. The description allows to define a not trivial distance between two sequences and to perform the Discrete Fourier Transform on a sequence. Using the definition of distance the sequence of $\beta$-globin for Homo, Gallus and Opossum are compared. The Discrete Fourier Transform for some simple examples is computed. |
1909.04560 | Jo\~ao Paulo Mendon\c{c}a | Jo\~ao Paulo Almeida de Mendon\c{c}a and Leonardo da Motta de
Vasconcellos Teixeira and Fernando Sato | Digital Beings as an option to study gut flora evolution and adaptation | 13 pages, 8 figures | null | null | null | q-bio.PE math.DS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we introduce a computational model for the study of the
host-bacteria interaction and the influence of the intestinal microbiota on the
behavior and feeding pattern of an individual. The model is based on digital
entities, which we've called Digital Beings (DBs), modeled using dynamic
systems and genetic algorithms. We have successfully tested the use of the DBs
by reproducing observation in previously made studies using rats and humans.
Among these studies, we highlight those on how the bacteria in an individual's
stomach could influence their eating behavior and how a controlled and
continuous diet can affect the longevity of a certain population. Our results
point that the Digital Beings can be used as a tool for supporting the devising
of experiments and corroborating with theoretical hypotheses, reducing the
number of in vivo tests.
| [
{
"created": "Tue, 10 Sep 2019 15:11:42 GMT",
"version": "v1"
}
] | 2019-09-11 | [
[
"de Mendonça",
"João Paulo Almeida",
""
],
[
"Teixeira",
"Leonardo da Motta de Vasconcellos",
""
],
[
"Sato",
"Fernando",
""
]
] | In this work, we introduce a computational model for the study of the host-bacteria interaction and the influence of the intestinal microbiota on the behavior and feeding pattern of an individual. The model is based on digital entities, which we've called Digital Beings (DBs), modeled using dynamic systems and genetic algorithms. We have successfully tested the use of the DBs by reproducing observation in previously made studies using rats and humans. Among these studies, we highlight those on how the bacteria in an individual's stomach could influence their eating behavior and how a controlled and continuous diet can affect the longevity of a certain population. Our results point that the Digital Beings can be used as a tool for supporting the devising of experiments and corroborating with theoretical hypotheses, reducing the number of in vivo tests. |
1007.0876 | Andrew Teschendorff | Andrew Teschendorff and Simone Severini | Increased entropy of signal transduction in the cancer metastasis
phenotype | 5 figures, 2 Supplementary Figures and Tables | BMC Systems Biology 2010, 4:104 | null | null | q-bio.MN q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Studies into the statistical properties of biological networks have led to
important biological insights, such as the presence of hubs and hierarchical
modularity. There is also a growing interest in studying the statistical
properties of networks in the context of cancer genomics. However, relatively
little is known as to what network features differ between the cancer and
normal cell physiologies, or between different cancer cell phenotypes. Based on
the observation that frequent genomic alterations underlie a more aggressive
cancer phenotype, we asked if such an effect could be detectable as an increase
in the randomness of local gene expression patterns. Using a breast cancer gene
expression data set and a model network of protein interactions we derive
constrained weighted networks defined by a stochastic information flux matrix
reflecting expression correlations between interacting proteins. Based on this
stochastic matrix we propose and compute an entropy measure that quantifies the
degree of randomness in the local pattern of information flux around single
genes. By comparing the local entropies in the non-metastatic versus metastatic
breast cancer networks, we here show that breast cancers that metastasize are
characterised by a small yet significant increase in the degree of randomness
of local expression patterns. We validate this result in three additional
breast cancer expression data sets and demonstrate that local entropy better
characterises the metastatic phenotype than other non-entropy based measures.
We show that increases in entropy can be used to identify genes and signalling
pathways implicated in breast cancer metastasis. Further exploration of such
integrated cancer expression and protein interaction networks will therefore be
a fruitful endeavour.
| [
{
"created": "Tue, 6 Jul 2010 12:42:20 GMT",
"version": "v1"
},
{
"created": "Sun, 1 Aug 2010 13:37:24 GMT",
"version": "v2"
}
] | 2010-08-03 | [
[
"Teschendorff",
"Andrew",
""
],
[
"Severini",
"Simone",
""
]
] | Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour. |
1702.08617 | Kun-Han Lu | Kun-Han Lu, Jun Young Jeong, Haiguang Wen, Zhongming Liu | Spontaneous Activity in the Visual Cortex is Organized by Visual Streams | 33 pages, 10 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large-scale functional networks have been extensively studied using resting
state functional magnetic resonance imaging. However, the pattern,
organization, and function of fine-scale network activity remain largely
unknown. Here we characterized the spontaneously emerging visual cortical
activity by applying independent component analysis to resting state fMRI
signals exclusively within the visual cortex. In this sub-system scale, we
observed about 50 spatially independent components that were reproducible
within and across subjects, and analyzed their spatial patterns and temporal
relationships to reveal the intrinsic parcellation and organization of the
visual cortex. We found that the visual cortical parcels were aligned with the
steepest gradient of cortical myelination, and organized into functional
modules segregated along the dorsal/ventral pathways and foveal/peripheral
early visual areas. In contrast, cortical retinotopy, folding, and
cytoarchitecture impose limited constraints to the organization of resting
state activity. From these findings, we conclude that spontaneous activity
patterns in the visual cortex are primarily organized by visual streams, likely
reflecting feedback network interactions.
| [
{
"created": "Tue, 28 Feb 2017 03:03:40 GMT",
"version": "v1"
}
] | 2017-03-01 | [
[
"Lu",
"Kun-Han",
""
],
[
"Jeong",
"Jun Young",
""
],
[
"Wen",
"Haiguang",
""
],
[
"Liu",
"Zhongming",
""
]
] | Large-scale functional networks have been extensively studied using resting state functional magnetic resonance imaging. However, the pattern, organization, and function of fine-scale network activity remain largely unknown. Here we characterized the spontaneously emerging visual cortical activity by applying independent component analysis to resting state fMRI signals exclusively within the visual cortex. In this sub-system scale, we observed about 50 spatially independent components that were reproducible within and across subjects, and analyzed their spatial patterns and temporal relationships to reveal the intrinsic parcellation and organization of the visual cortex. We found that the visual cortical parcels were aligned with the steepest gradient of cortical myelination, and organized into functional modules segregated along the dorsal/ventral pathways and foveal/peripheral early visual areas. In contrast, cortical retinotopy, folding, and cytoarchitecture impose limited constraints to the organization of resting state activity. From these findings, we conclude that spontaneous activity patterns in the visual cortex are primarily organized by visual streams, likely reflecting feedback network interactions. |
2008.10276 | Mohammad Mahdi Dehshibi Dr. | Mohammad Mahdi Dehshibi and Andrew Adamatzky | Electrical activity of fungi: Spikes detection and complexity analysis | null | null | null | null | q-bio.NC cs.ET | http://creativecommons.org/licenses/by/4.0/ | Oyster fungi \emph{Pleurotus djamor} generate actin potential like spikes of
electrical potential. The trains of spikes might manifest propagation of
growing mycelium in a substrate, transportation of nutrients and metabolites
and communication processes in the mycelium network. The spiking activity of
the mycelium networks is highly variable compared to neural activity and
therefore can not be analysed by standard tools from neuroscience. We propose
original techniques for detecting and classifying the spiking activity of
fungi. Using these techniques, we analyse the information-theoretic complexity
of the fungal electrical activity. The results can pave ways for future
research on sensorial fusion and decision making of fungi.
| [
{
"created": "Mon, 24 Aug 2020 09:11:56 GMT",
"version": "v1"
}
] | 2020-08-25 | [
[
"Dehshibi",
"Mohammad Mahdi",
""
],
[
"Adamatzky",
"Andrew",
""
]
] | Oyster fungi \emph{Pleurotus djamor} generate actin potential like spikes of electrical potential. The trains of spikes might manifest propagation of growing mycelium in a substrate, transportation of nutrients and metabolites and communication processes in the mycelium network. The spiking activity of the mycelium networks is highly variable compared to neural activity and therefore can not be analysed by standard tools from neuroscience. We propose original techniques for detecting and classifying the spiking activity of fungi. Using these techniques, we analyse the information-theoretic complexity of the fungal electrical activity. The results can pave ways for future research on sensorial fusion and decision making of fungi. |
1810.09520 | Matthieu Gilson | Matthieu Gilson and Jean-Pascal Pfister | Propagation of spiking moments in linear Hawkes networks | null | null | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The present paper provides exact mathematical expressions for the high-order
moments of spiking activity in a recurrently-connected network of linear Hawkes
processes. It extends previous studies that have explored the case of a
(linear) Hawkes network driven by deterministic intensity functions to the case
of a stimulation by external inputs (rate functions or spike trains) with
arbitrary correlation structure. Our approach describes the spatio-temporal
filtering induced by the afferent and recurrent connectivities (with arbitrary
synaptic response kernels) using operators acting on the input moments. This
algebraic viewpoint provides intuition about how the network ingredients shape
the input-output mapping for moments, as well as cumulants. We also show using
numerical simulation that our results hold for neurons with refractoriness
implemented by self-inhibition, provided the corresponding negative feedback
for each neuron only mildly alters its mean firing probability.
| [
{
"created": "Tue, 18 Sep 2018 17:03:49 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Sep 2019 14:57:48 GMT",
"version": "v2"
},
{
"created": "Mon, 16 Dec 2019 16:12:09 GMT",
"version": "v3"
}
] | 2019-12-17 | [
[
"Gilson",
"Matthieu",
""
],
[
"Pfister",
"Jean-Pascal",
""
]
] | The present paper provides exact mathematical expressions for the high-order moments of spiking activity in a recurrently-connected network of linear Hawkes processes. It extends previous studies that have explored the case of a (linear) Hawkes network driven by deterministic intensity functions to the case of a stimulation by external inputs (rate functions or spike trains) with arbitrary correlation structure. Our approach describes the spatio-temporal filtering induced by the afferent and recurrent connectivities (with arbitrary synaptic response kernels) using operators acting on the input moments. This algebraic viewpoint provides intuition about how the network ingredients shape the input-output mapping for moments, as well as cumulants. We also show using numerical simulation that our results hold for neurons with refractoriness implemented by self-inhibition, provided the corresponding negative feedback for each neuron only mildly alters its mean firing probability. |
1106.0929 | Shiladitya Banerjee | Shiladitya Banerjee and M. Cristina Marchetti | Substrate rigidity deforms and polarizes active gels | 6 pages, 4 figures, EPL format | EPL, 96 (2011) 28003 | 10.1209/0295-5075/96/28003 | null | q-bio.CB cond-mat.soft | http://creativecommons.org/licenses/by/3.0/ | We present a continuum model of the coupling between cells and substrate that
accounts for some of the observed substrate-stiffness dependence of cell
properties. The cell is modeled as an elastic active gel, adapting recently
developed continuum theories of active viscoelastic fluids. The coupling to the
substrate enters as a boundary condition that relates the cell's deformation
field to local stress gradients. In the presence of activity, the coupling to
the substrate yields spatially inhomogeneous contractile stresses and
deformations in the cell and can enhance polarization, breaking the cell's
front-rear symmetry.
| [
{
"created": "Sun, 5 Jun 2011 20:23:56 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Aug 2011 13:57:32 GMT",
"version": "v2"
}
] | 2015-05-28 | [
[
"Banerjee",
"Shiladitya",
""
],
[
"Marchetti",
"M. Cristina",
""
]
] | We present a continuum model of the coupling between cells and substrate that accounts for some of the observed substrate-stiffness dependence of cell properties. The cell is modeled as an elastic active gel, adapting recently developed continuum theories of active viscoelastic fluids. The coupling to the substrate enters as a boundary condition that relates the cell's deformation field to local stress gradients. In the presence of activity, the coupling to the substrate yields spatially inhomogeneous contractile stresses and deformations in the cell and can enhance polarization, breaking the cell's front-rear symmetry. |
2311.04369 | C. Brandon Ogbunu | Sudam Surasinghe, Ketty Kabengele, Paul E. Turner, and C. Brandon
Ogbunugafor | The context-specificity of virulence evolution revealed through
evolutionary invasion analysis | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Models are often employed to integrate knowledge about epidemics across
scales and simulate disease dynamics. While these approaches have played a
central role in studying the mechanics underlying epidemics, we lack ways to
reliably predict how the relationship between virulence (the harm to hosts
caused by an infection) and transmission will evolve in certain virus-host
contexts. In this study, we invoke evolutionary invasion analysis -- a method
used to identify the evolution of uninvadable strategies in dynamical systems
-- to examine how the virulence-transmission dichotomy can evolve in models of
virus infections defined by different natural histories. We reveal that
peculiar ecologies drive different evolved relationships between virulence and
transmission. Specifically, we discover patterns of virulence evolution between
epidemics of various kinds (SARS-CoV-2 and hepatitis C virus) and that varying
definitions of virulence alter our predictions for how viruses will evolve. We
discuss the findings in light of contemporary conversations in the public
health sector around the possibility of predicting virus evolution and in more
extensive theoretical discussions involving virulence evolution in emerging
infectious diseases.
| [
{
"created": "Tue, 7 Nov 2023 22:29:25 GMT",
"version": "v1"
}
] | 2023-11-09 | [
[
"Surasinghe",
"Sudam",
""
],
[
"Kabengele",
"Ketty",
""
],
[
"Turner",
"Paul E.",
""
],
[
"Ogbunugafor",
"C. Brandon",
""
]
] | Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the relationship between virulence (the harm to hosts caused by an infection) and transmission will evolve in certain virus-host contexts. In this study, we invoke evolutionary invasion analysis -- a method used to identify the evolution of uninvadable strategies in dynamical systems -- to examine how the virulence-transmission dichotomy can evolve in models of virus infections defined by different natural histories. We reveal that peculiar ecologies drive different evolved relationships between virulence and transmission. Specifically, we discover patterns of virulence evolution between epidemics of various kinds (SARS-CoV-2 and hepatitis C virus) and that varying definitions of virulence alter our predictions for how viruses will evolve. We discuss the findings in light of contemporary conversations in the public health sector around the possibility of predicting virus evolution and in more extensive theoretical discussions involving virulence evolution in emerging infectious diseases. |
1301.5899 | Stephen Tennenbaum | Stephen Tennenbaum, Caroline Freitag, and Svetlana Roudenko | Modeling the Influence of Environment and Intervention on Cholera in
Haiti | null | null | null | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a simple model with two infective classes in order to model the
cholera epidemic in Haiti. We include the impact of environmental events
(rainfall, temperature and tidal range) on the epidemic in the Artibonite and
Ouest regions by introducing terms in the transmission rate that vary with
environmental conditions. We fit the model on weekly data from the beginning of
the epidemic until December 2013, including the vaccination programs that were
recently undertaken in the Ouest and Artibonite regions. We then modified these
projections excluding vaccination to assess the programs' effectiveness. Using
real-time daily rainfall, we found lag times between precipitation events and
new cases that range from 3.4 to 8.4 weeks in Artibonite and 5.1 to 7.4 in
Ouest. In addition, it appears that, in the Ouest region, tidal influences play
a significant role in the dynamics of the disease. Intervention efforts of all
types have reduced case numbers in both regions; however, persistent outbreaks
continue. In Ouest, where the population at risk seems particularly besieged
and the overall population is larger, vaccination efforts seem to be taking
hold more slowly than in Artibonite, where a smaller core population was
vaccinated. The models including the vaccination programs predicted that a year
and six months later, the mean number of cases in Artibonite would be reduced
by about two thousand cases, and in Ouest by twenty four hundred cases below
that predicted by the models without vaccination. We also found that
vaccination is best when done in the early spring, and as early as possible in
the epidemic. Comparing vaccination between the first spring and the second,
there is a drop of about 40% in the case reduction due to the vaccine and about
10% per year after that.
| [
{
"created": "Fri, 25 Jan 2013 17:00:27 GMT",
"version": "v1"
},
{
"created": "Sun, 21 Apr 2013 04:12:06 GMT",
"version": "v2"
},
{
"created": "Fri, 7 Jun 2013 03:03:40 GMT",
"version": "v3"
},
{
"created": "Thu, 13 Jun 2013 17:21:17 GMT",
"version": "v4"
},
{
"created": "Tue, 16 Sep 2014 21:16:46 GMT",
"version": "v5"
}
] | 2014-09-18 | [
[
"Tennenbaum",
"Stephen",
""
],
[
"Freitag",
"Caroline",
""
],
[
"Roudenko",
"Svetlana",
""
]
] | We propose a simple model with two infective classes in order to model the cholera epidemic in Haiti. We include the impact of environmental events (rainfall, temperature and tidal range) on the epidemic in the Artibonite and Ouest regions by introducing terms in the transmission rate that vary with environmental conditions. We fit the model on weekly data from the beginning of the epidemic until December 2013, including the vaccination programs that were recently undertaken in the Ouest and Artibonite regions. We then modified these projections excluding vaccination to assess the programs' effectiveness. Using real-time daily rainfall, we found lag times between precipitation events and new cases that range from 3.4 to 8.4 weeks in Artibonite and 5.1 to 7.4 in Ouest. In addition, it appears that, in the Ouest region, tidal influences play a significant role in the dynamics of the disease. Intervention efforts of all types have reduced case numbers in both regions; however, persistent outbreaks continue. In Ouest, where the population at risk seems particularly besieged and the overall population is larger, vaccination efforts seem to be taking hold more slowly than in Artibonite, where a smaller core population was vaccinated. The models including the vaccination programs predicted that a year and six months later, the mean number of cases in Artibonite would be reduced by about two thousand cases, and in Ouest by twenty four hundred cases below that predicted by the models without vaccination. We also found that vaccination is best when done in the early spring, and as early as possible in the epidemic. Comparing vaccination between the first spring and the second, there is a drop of about 40% in the case reduction due to the vaccine and about 10% per year after that. |
1201.3232 | Mustafa Barasa | Barasa Mustafa, Maamun Jenneby, Kagasi Ambogo Esther, Ozwara Suba
Hastings and Gicheru Muita Michael | Immunity to Plasmodium knowlesi H strain malaria in olive baboons | 6 Pages, 8 Figures; This study was funded by the research capability
strengthening World Health Organisation (WHO) grant (Grant Number: A 50075)
for malaria research in Africa under the Multilateral Initiative on
Malaria/Special Programme for Research and Training in Tropical Diseases
(WHO-MIM/TDR). Blue highlighting on previous abstract removed, title font
increased | Int. J. Integ. Biol. Volume 10, Issue No. 3: 147 -- 152 (2010) | null | null | q-bio.CB q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Malaria disease is a major global health and economic development threat. It
results in approximately 2.7 million deaths annually. There is currently no
vaccine that has been licensed for use against malaria. Studies in animal
models, especially non-human primates can lead to the revelation of possible
immunological mechanisms that can lead to protection or predisposition of the
host to malaria. Plasmodium knowlesi, a simian and human malaria parasite, is
an attractive experimental parasite for malaria research since it can infect
olive baboons (Papio anubis), non-human primates that have similar
host-pathogen interactions to humans. This study was carried out to determine
host immunological profiles provoked in olive baboons during the course of an
infection with Plasmodium knowlesi. A total of eight adult baboons were
intravenously inoculated with overnight cultured blood stage P. knowlesi H
strain parasites. Five of these baboons became acutely infected while the other
three became chronically infected. The immunological basis of this dual outcome
of the infection was determined by measuring circulating cytokine (T helper 1
and T helper 2) and antibody (immunoglobulin G and immunoglobulin M) responses
elicited in the infected baboons on a weekly basis by Enzyme Linked
Immunosorbent Assay (ELISA) for up to six weeks post infection. Generated data
for the first time indicated that acute P. knowlesi malaria is accompanied by
increased concentrations of interferon gamma (IFN gamma), tumour necrosis
factor alpha (TNF alpha) and IL 6 and reduced levels of circulating interleukin
10 (IL 10), IL 4, IL 12, immunoglobulin G (IgG) and IgM in the baboon host.
These results are largely agreeable with data from human studies, thereby
increasing the relevance of the olive baboon - P. knowlesi experimental
infection system for future malaria studies.
| [
{
"created": "Mon, 16 Jan 2012 12:30:32 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Jan 2012 15:21:50 GMT",
"version": "v2"
},
{
"created": "Sun, 19 Feb 2012 18:48:16 GMT",
"version": "v3"
},
{
"created": "Fri, 13 Apr 2012 23:02:36 GMT",
"version": "v4"
}
] | 2012-04-17 | [
[
"Mustafa",
"Barasa",
""
],
[
"Jenneby",
"Maamun",
""
],
[
"Esther",
"Kagasi Ambogo",
""
],
[
"Hastings",
"Ozwara Suba",
""
],
[
"Michael",
"Gicheru Muita",
""
]
] | Malaria disease is a major global health and economic development threat. It results in approximately 2.7 million deaths annually. There is currently no vaccine that has been licensed for use against malaria. Studies in animal models, especially non-human primates can lead to the revelation of possible immunological mechanisms that can lead to protection or predisposition of the host to malaria. Plasmodium knowlesi, a simian and human malaria parasite, is an attractive experimental parasite for malaria research since it can infect olive baboons (Papio anubis), non-human primates that have similar host-pathogen interactions to humans. This study was carried out to determine host immunological profiles provoked in olive baboons during the course of an infection with Plasmodium knowlesi. A total of eight adult baboons were intravenously inoculated with overnight cultured blood stage P. knowlesi H strain parasites. Five of these baboons became acutely infected while the other three became chronically infected. The immunological basis of this dual outcome of the infection was determined by measuring circulating cytokine (T helper 1 and T helper 2) and antibody (immunoglobulin G and immunoglobulin M) responses elicited in the infected baboons on a weekly basis by Enzyme Linked Immunosorbent Assay (ELISA) for up to six weeks post infection. Generated data for the first time indicated that acute P. knowlesi malaria is accompanied by increased concentrations of interferon gamma (IFN gamma), tumour necrosis factor alpha (TNF alpha) and IL 6 and reduced levels of circulating interleukin 10 (IL 10), IL 4, IL 12, immunoglobulin G (IgG) and IgM in the baboon host. These results are largely agreeable with data from human studies, thereby increasing the relevance of the olive baboon - P. knowlesi experimental infection system for future malaria studies. |
2111.06279 | Karna Gowda | Chandana Gopalakrishnappa, Karna Gowda, Kaumudi Prabhakara, Seppe
Kuehn | An ensemble approach to the structure-function problem in microbial
communities | 77 pages, 5 figures | null | 10.1016/j.isci.2022.103761 | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The metabolic activity of microbes has played an essential role in the
evolution and persistence of life on Earth. Microbial metabolism plays a
primary role in the flow of carbon, nitrogen and other elements through the
biosphere on a global scale. Microbes perform these metabolic activities in the
context of complex communities comprised of many species that interact in
dynamic and spatially-structured environments. Molecular genetics has revealed
many of the metabolic pathways microbes utilize to generate energy and biomass.
However, most of this knowledge is derived from model organisms, so we have a
limited view of role of the massive genomic diversity in the wild on metabolic
phenotypes. Further, we are only beginning to glimpse the principles governing
how the metabolism of a community emerges from the collective action of its
constituent members. As a result, one of the biggest challenges in the field is
to understand how the metabolic activity of a community emerges from the
genomic structure of the constituents. Here we propose an approach to this
problem that rests on the quantitative analysis of metabolic activity in
ensembles of microbial communities. We propose quantifying metabolic fluxes in
diverse communities, either in the laboratory or the wild. We suggest that
using sequencing data to quantify the genomic, taxonomic or transcriptional
variation across an ensemble of communities can reveal low-dimensional
descriptions of community structure that can explain or predict their emergent
metabolic activity. We survey the types of communities for which this approach
might be best suited and then review the analytical techniques available for
quantifying metabolite dynamics in communities. Finally, we discuss what types
of data analysis approaches might be lucrative for learning the
structure-function mapping in communities from these data.
| [
{
"created": "Thu, 11 Nov 2021 15:39:54 GMT",
"version": "v1"
}
] | 2022-01-20 | [
[
"Gopalakrishnappa",
"Chandana",
""
],
[
"Gowda",
"Karna",
""
],
[
"Prabhakara",
"Kaumudi",
""
],
[
"Kuehn",
"Seppe",
""
]
] | The metabolic activity of microbes has played an essential role in the evolution and persistence of life on Earth. Microbial metabolism plays a primary role in the flow of carbon, nitrogen and other elements through the biosphere on a global scale. Microbes perform these metabolic activities in the context of complex communities comprised of many species that interact in dynamic and spatially-structured environments. Molecular genetics has revealed many of the metabolic pathways microbes utilize to generate energy and biomass. However, most of this knowledge is derived from model organisms, so we have a limited view of role of the massive genomic diversity in the wild on metabolic phenotypes. Further, we are only beginning to glimpse the principles governing how the metabolism of a community emerges from the collective action of its constituent members. As a result, one of the biggest challenges in the field is to understand how the metabolic activity of a community emerges from the genomic structure of the constituents. Here we propose an approach to this problem that rests on the quantitative analysis of metabolic activity in ensembles of microbial communities. We propose quantifying metabolic fluxes in diverse communities, either in the laboratory or the wild. We suggest that using sequencing data to quantify the genomic, taxonomic or transcriptional variation across an ensemble of communities can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited and then review the analytical techniques available for quantifying metabolite dynamics in communities. Finally, we discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data. |
1601.03037 | J. C. Phillips | V. Sachdeva and J. C. Phillips | Hidden Thermodynamic Information in Protein Amino Acid Mutation Tables | 14 pages, 4 figures, 4 tables | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We combine the standard 1992 20x20 substitution matrix based on block
alignment, BLOSUM62, with the standard 1982 amino acid hydropathicity scale KD
as well as the modern 2007 hydropathicity scale MZ, and compare the results.
The 20-parameter KD and MZ hydropathicity scales have different thermodynamic
character, corresponding to first and second order transitions. The KD and MZ
comparisons show that the mutation rates reflect quantitative iteration of
qualitative amino acid phobic and philic binary 2x10 properties that define
quaternary 4x5 subgroups (but not quinary 5x4 subgroups), with the modern MZ
bioinformatic scale giving much better results. The quaternary 5 mer MZ 4x5
subgroups are called mutons (Mu5).
| [
{
"created": "Mon, 11 Jan 2016 22:37:21 GMT",
"version": "v1"
}
] | 2016-01-14 | [
[
"Sachdeva",
"V.",
""
],
[
"Phillips",
"J. C.",
""
]
] | We combine the standard 1992 20x20 substitution matrix based on block alignment, BLOSUM62, with the standard 1982 amino acid hydropathicity scale KD as well as the modern 2007 hydropathicity scale MZ, and compare the results. The 20-parameter KD and MZ hydropathicity scales have different thermodynamic character, corresponding to first and second order transitions. The KD and MZ comparisons show that the mutation rates reflect quantitative iteration of qualitative amino acid phobic and philic binary 2x10 properties that define quaternary 4x5 subgroups (but not quinary 5x4 subgroups), with the modern MZ bioinformatic scale giving much better results. The quaternary 5 mer MZ 4x5 subgroups are called mutons (Mu5). |
1601.04972 | Robert Rosenbaum | Ryan Pyle and Robert Rosenbaum | Highly connected neurons spike less frequently in balanced networks | null | Phys. Rev. E 93, 040302 (2016) | 10.1103/PhysRevE.93.040302 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many biological neuronal networks exhibit highly variable spiking activity.
Balanced networks offer a parsimonious model of this variability. In balanced
networks, strong excitatory synaptic inputs are canceled by strong inhibitory
inputs on average and spiking activity is driven by transient breaks in this
balance. Most previous studies of balanced networks assume a homogeneous or
distance-dependent connectivity structure, but connectivity in biological
cortical networks is more intricate. We use a heterogeneous mean-field theory
of balanced networks to show that heterogeneous in-degrees can break balance,
but balance can be restored by heterogeneous out-degrees that are correlated
with in-degrees. In all examples considered, we find that highly connected
neurons spike less frequently, consistent with recent experimental
observations.
| [
{
"created": "Tue, 19 Jan 2016 16:09:10 GMT",
"version": "v1"
}
] | 2016-05-04 | [
[
"Pyle",
"Ryan",
""
],
[
"Rosenbaum",
"Robert",
""
]
] | Many biological neuronal networks exhibit highly variable spiking activity. Balanced networks offer a parsimonious model of this variability. In balanced networks, strong excitatory synaptic inputs are canceled by strong inhibitory inputs on average and spiking activity is driven by transient breaks in this balance. Most previous studies of balanced networks assume a homogeneous or distance-dependent connectivity structure, but connectivity in biological cortical networks is more intricate. We use a heterogeneous mean-field theory of balanced networks to show that heterogeneous in-degrees can break balance, but balance can be restored by heterogeneous out-degrees that are correlated with in-degrees. In all examples considered, we find that highly connected neurons spike less frequently, consistent with recent experimental observations. |
2305.11107 | Eugene R. Rush | Eugene R. Rush, Kaushik Jayaram, J. Sean Humbert | From Data-Fitting to Discovery: Interpreting the Neural Dynamics of
Motor Control through Reinforcement Learning | null | null | null | null | q-bio.NC cs.AI cs.LG cs.NE cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In motor neuroscience, artificial recurrent neural networks models often
complement animal studies. However, most modeling efforts are limited to
data-fitting, and the few that examine virtual embodied agents in a
reinforcement learning context, do not draw direct comparisons to their
biological counterparts. Our study addressing this gap, by uncovering
structured neural activity of a virtual robot performing legged locomotion that
directly support experimental findings of primate walking and cycling. We find
that embodied agents trained to walk exhibit smooth dynamics that avoid
tangling -- or opposing neural trajectories in neighboring neural space -- a
core principle in computational neuroscience. Specifically, across a wide suite
of gaits, the agent displays neural trajectories in the recurrent layers are
less tangled than those in the input-driven actuation layers. To better
interpret the neural separation of these elliptical-shaped trajectories, we
identify speed axes that maximizes variance of mean activity across different
forward, lateral, and rotational speed conditions.
| [
{
"created": "Thu, 18 May 2023 16:52:27 GMT",
"version": "v1"
}
] | 2023-05-19 | [
[
"Rush",
"Eugene R.",
""
],
[
"Jayaram",
"Kaushik",
""
],
[
"Humbert",
"J. Sean",
""
]
] | In motor neuroscience, artificial recurrent neural networks models often complement animal studies. However, most modeling efforts are limited to data-fitting, and the few that examine virtual embodied agents in a reinforcement learning context, do not draw direct comparisons to their biological counterparts. Our study addressing this gap, by uncovering structured neural activity of a virtual robot performing legged locomotion that directly support experimental findings of primate walking and cycling. We find that embodied agents trained to walk exhibit smooth dynamics that avoid tangling -- or opposing neural trajectories in neighboring neural space -- a core principle in computational neuroscience. Specifically, across a wide suite of gaits, the agent displays neural trajectories in the recurrent layers are less tangled than those in the input-driven actuation layers. To better interpret the neural separation of these elliptical-shaped trajectories, we identify speed axes that maximizes variance of mean activity across different forward, lateral, and rotational speed conditions. |
q-bio/0512002 | Stuart Samuel | Stuart Samuel (Lawrence Berkeley National Laboratory) Gezhi Weng
(Mount Sinai School of Medicine in New York) | Characterization of a Branch of the Phylogenetic Tree | LaTeX file, 10-page publication, 7 figures in jpeg format | J. Theor. Biol. 220, 457 (2003) | null | LBNL-47547 | q-bio.PE | null | We use a combination of analytic models and computer simulations to gain
insight into the dynamics of evolution. Our results suggest that certain
interesting phenomena should eventually emerge from the fossil record. For
example, there should be a ``tortoise and hare effect'': Those genera with the
smallest species death rate are likely to survive much longer than genera with
large species birth and death rates. A complete characterization of the
behavior of a branch of the phylogenetic tree corresponding to a genus and
accurate mathematical representations of the various stages are obtained. We
apply our results to address certain controversial issues that have arisen in
paleontology such as the importance of punctuated equilibrium and whether
unique Cambrian phyla have survived to the present.
| [
{
"created": "Thu, 1 Dec 2005 01:00:57 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Samuel",
"Stuart",
"",
"Lawrence Berkeley National Laboratory"
],
[
"Weng",
"Gezhi",
"",
"Mount Sinai School of Medicine in New York"
]
] | We use a combination of analytic models and computer simulations to gain insight into the dynamics of evolution. Our results suggest that certain interesting phenomena should eventually emerge from the fossil record. For example, there should be a ``tortoise and hare effect'': Those genera with the smallest species death rate are likely to survive much longer than genera with large species birth and death rates. A complete characterization of the behavior of a branch of the phylogenetic tree corresponding to a genus and accurate mathematical representations of the various stages are obtained. We apply our results to address certain controversial issues that have arisen in paleontology such as the importance of punctuated equilibrium and whether unique Cambrian phyla have survived to the present. |
1612.06313 | Jacek Urbanek PhD | Jacek K. Urbanek, Vadim Zipunnikov, Tamara Harris, Ciprian
Crainiceanu, Jaroslaw Harezlak, Nancy W. Glynn | Validation of gait characteristics extracted from raw accelerometry
during walking against measures of physical function, mobility, fatigability,
and fitness | null | null | 10.1093/gerona/glx174 | glx174 | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background. Wearable accelerometry devices allow collection of high-density
activity data in large epidemiological studies both in-the-lab as well as
in-the-wild (free-living). Such data can be used to detect and identify periods
of sustained harmonic walking. This report aims to establish whether the micro-
and macro-features of walking identified in the laboratory and free-living
environments are associated with measures of physical function, mobility,
fatigability, and fitness.
Methods. Fifty-one older adults (median age 77.5) enrolled in the
Developmental Epidemiologic Cohort Study in Pittsburgh, Pennsylvania were
included in the analyses. The study included an in-the-lab component as well as
7 days of monitoring in-the-wild. Participants were equipped with hip-worn
Actigraph GT3X+ activity monitors, which collect high-density raw accelerometry
data. We applied a walking identification algorithm to the data and defined
features of walking, such as participant-specific walking acceleration and
cadence. The association between these walking features and physical function,
mobility, fatigability, and fitness was quantified using linear regression
analysis.
Results. Micro-scale features of walking (acceleration and cadence) estimated
from in-the-lab and in-the-wild data were associated with measures of physical
function, mobility, fatigability, and fitness. In-the-lab median walking
acceleration was strongly inversely associated with physical function,
mobility, fatigability and fitness. Additionally, in-the-wild daily walking
time was inversely associated with usual- and fast-paced 400m walking time.
Conclusions. The proposed accelerometry-derived walking features are
significantly associated with measures of physical function, mobility,
fatigability, and fitness, which provides evidence of convergent validity.
| [
{
"created": "Fri, 16 Dec 2016 00:10:04 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Nov 2017 21:16:57 GMT",
"version": "v2"
}
] | 2017-11-20 | [
[
"Urbanek",
"Jacek K.",
""
],
[
"Zipunnikov",
"Vadim",
""
],
[
"Harris",
"Tamara",
""
],
[
"Crainiceanu",
"Ciprian",
""
],
[
"Harezlak",
"Jaroslaw",
""
],
[
"Glynn",
"Nancy W.",
""
]
] | Background. Wearable accelerometry devices allow collection of high-density activity data in large epidemiological studies both in-the-lab as well as in-the-wild (free-living). Such data can be used to detect and identify periods of sustained harmonic walking. This report aims to establish whether the micro- and macro-features of walking identified in the laboratory and free-living environments are associated with measures of physical function, mobility, fatigability, and fitness. Methods. Fifty-one older adults (median age 77.5) enrolled in the Developmental Epidemiologic Cohort Study in Pittsburgh, Pennsylvania were included in the analyses. The study included an in-the-lab component as well as 7 days of monitoring in-the-wild. Participants were equipped with hip-worn Actigraph GT3X+ activity monitors, which collect high-density raw accelerometry data. We applied a walking identification algorithm to the data and defined features of walking, such as participant-specific walking acceleration and cadence. The association between these walking features and physical function, mobility, fatigability, and fitness was quantified using linear regression analysis. Results. Micro-scale features of walking (acceleration and cadence) estimated from in-the-lab and in-the-wild data were associated with measures of physical function, mobility, fatigability, and fitness. In-the-lab median walking acceleration was strongly inversely associated with physical function, mobility, fatigability and fitness. Additionally, in-the-wild daily walking time was inversely associated with usual- and fast-paced 400m walking time. Conclusions. The proposed accelerometry-derived walking features are significantly associated with measures of physical function, mobility, fatigability, and fitness, which provides evidence of convergent validity. |
2001.02441 | Ramon Grima | Chen Jia, Ramon Grima | Small protein number effects in stochastic models of autoregulated
bursty gene expression | 30 pages, 10 figures | null | 10.1063/1.5144578 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A stochastic model of autoregulated bursty gene expression by Kumar et al.
[Phys. Rev. Lett. 113, 268105 (2014)] has been exactly solved in steady-state
conditions under the implicit assumption that protein numbers are sufficiently
large such that fluctuations in protein numbers due to reversible
protein-promoter binding can be ignored. Here we derive an alternative model
that takes into account these fluctuations and hence can be used to study low
protein number effects. The exact steady-state protein number distributions is
derived as a sum of Gaussian hypergeometric functions. We use the theory to
study how promoter switching rates and the type of feedback influence the size
of protein noise and noise-induced bistability. Furthermore we show that our
model predictions for the protein number distribution are significantly
different from those of Kumar et al. when the protein mean is small, gene
switching is fast, and protein binding is faster than unbinding.
| [
{
"created": "Wed, 8 Jan 2020 10:37:59 GMT",
"version": "v1"
},
{
"created": "Thu, 13 Feb 2020 19:01:30 GMT",
"version": "v2"
}
] | 2020-03-18 | [
[
"Jia",
"Chen",
""
],
[
"Grima",
"Ramon",
""
]
] | A stochastic model of autoregulated bursty gene expression by Kumar et al. [Phys. Rev. Lett. 113, 268105 (2014)] has been exactly solved in steady-state conditions under the implicit assumption that protein numbers are sufficiently large such that fluctuations in protein numbers due to reversible protein-promoter binding can be ignored. Here we derive an alternative model that takes into account these fluctuations and hence can be used to study low protein number effects. The exact steady-state protein number distributions is derived as a sum of Gaussian hypergeometric functions. We use the theory to study how promoter switching rates and the type of feedback influence the size of protein noise and noise-induced bistability. Furthermore we show that our model predictions for the protein number distribution are significantly different from those of Kumar et al. when the protein mean is small, gene switching is fast, and protein binding is faster than unbinding. |
1907.11178 | Andr\'es Dolinko | Maria Fernanda DJonsiles, Gustavo Ernesto Galizzi, Andres Ezequiel
Dolinko, Mar\'ia Victoria Novas, Esteban Ceriani Nakamurakare and Cecilia
Cristina Carmar\'an | Optical study of laser biospeckle activity in leaves of Jatropha curcas
L. A noninvasive analysis of foliar endophyte colonization | null | null | 10.1007/s11557-020-01563-x | null | q-bio.TO physics.bio-ph physics.optics | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Currently, the detection of endophytic fungi is determined mostly by invasive
methods, including direct isolation of fungal organismsfrom plant tissue in
growth media, molecular detection of endophyticfungi DNA from plant material by
PCR, or evaluation under microscopy techniques.In this work we explore the
potential of laser biospeckle activity (LBSA) to be usedfor the detection of
endophytic colonization of leaves of a promising energy crop, Jatropha curcas
L. We compared the laser biospeckle activityof endophyte infected and
uninfected J. curcas leaves. The differences between blade and veins (including
midrib) of the studied leaves was validated and growth parameters of the
studied plants were also analyzed using the normalized weigthed generalized
differencescoefficient (nWGD). The obtained results showeda relationship
between the endophytic burden of leaves and the LBS, suggesting that LSBA is a
useful tools to detect endophytic colonization in situ.Also, the increasedwater
movements inside leaves promoted by endophytic colonizationcould be explainby
the obtained data.
| [
{
"created": "Thu, 25 Jul 2019 16:38:54 GMT",
"version": "v1"
}
] | 2022-06-28 | [
[
"DJonsiles",
"Maria Fernanda",
""
],
[
"Galizzi",
"Gustavo Ernesto",
""
],
[
"Dolinko",
"Andres Ezequiel",
""
],
[
"Novas",
"María Victoria",
""
],
[
"Nakamurakare",
"Esteban Ceriani",
""
],
[
"Carmarán",
"Cecilia Cristina",
""
]
] | Currently, the detection of endophytic fungi is determined mostly by invasive methods, including direct isolation of fungal organismsfrom plant tissue in growth media, molecular detection of endophyticfungi DNA from plant material by PCR, or evaluation under microscopy techniques.In this work we explore the potential of laser biospeckle activity (LBSA) to be usedfor the detection of endophytic colonization of leaves of a promising energy crop, Jatropha curcas L. We compared the laser biospeckle activityof endophyte infected and uninfected J. curcas leaves. The differences between blade and veins (including midrib) of the studied leaves was validated and growth parameters of the studied plants were also analyzed using the normalized weigthed generalized differencescoefficient (nWGD). The obtained results showeda relationship between the endophytic burden of leaves and the LBS, suggesting that LSBA is a useful tools to detect endophytic colonization in situ.Also, the increasedwater movements inside leaves promoted by endophytic colonizationcould be explainby the obtained data. |
2109.15313 | Paolo Bocchini | Sena Mursel, Nathaniel Alter, Lindsay Slavit, Anna Smith, Paolo
Bocchini, Javier Buceta | Estimation of Ebola's spillover infection exposure in Sierra Leone based
on sociodemographic and economic factors | null | null | 10.1371/journal.pone.0271886 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Zoonotic diseases spread through pathogens-infected animal carriers. In the
case of Ebola Virus Disease (EVD), evidence supports that the main carriers are
fruit bats and non-human primates. Further, EVD spread is a multi-factorial
problem that depends on sociodemographic and economic (SDE) factors. Here we
inquire into this phenomenon and aim at determining, quantitatively, the Ebola
spillover infection exposure map and try to link it to SDE factors. To that
end, we designed and conducted a survey in Sierra Leone and implement a
pipeline to analyze data using regression and machine learning techniques. Our
methodology is able (1) to identify the features that are best predictors of an
individual's tendency to partake in behaviors that can expose them to Ebola
infection, (2) to develop a predictive model about the spillover risk
statistics that can be calibrated for different regions and future times, and
(3) to compute a spillover exposure map for Sierra Leone. Our results and
conclusions are relevant to identify the regions in Sierra Leone at risk of EVD
spillover and, consequently, to design and implement policies for an effective
deployment of resources (e.g., drug supplies) and other preventative measures
(e.g., educational campaigns).
| [
{
"created": "Thu, 30 Sep 2021 17:54:48 GMT",
"version": "v1"
}
] | 2022-09-26 | [
[
"Mursel",
"Sena",
""
],
[
"Alter",
"Nathaniel",
""
],
[
"Slavit",
"Lindsay",
""
],
[
"Smith",
"Anna",
""
],
[
"Bocchini",
"Paolo",
""
],
[
"Buceta",
"Javier",
""
]
] | Zoonotic diseases spread through pathogens-infected animal carriers. In the case of Ebola Virus Disease (EVD), evidence supports that the main carriers are fruit bats and non-human primates. Further, EVD spread is a multi-factorial problem that depends on sociodemographic and economic (SDE) factors. Here we inquire into this phenomenon and aim at determining, quantitatively, the Ebola spillover infection exposure map and try to link it to SDE factors. To that end, we designed and conducted a survey in Sierra Leone and implement a pipeline to analyze data using regression and machine learning techniques. Our methodology is able (1) to identify the features that are best predictors of an individual's tendency to partake in behaviors that can expose them to Ebola infection, (2) to develop a predictive model about the spillover risk statistics that can be calibrated for different regions and future times, and (3) to compute a spillover exposure map for Sierra Leone. Our results and conclusions are relevant to identify the regions in Sierra Leone at risk of EVD spillover and, consequently, to design and implement policies for an effective deployment of resources (e.g., drug supplies) and other preventative measures (e.g., educational campaigns). |
2207.01572 | Hadeel Elayan | Hadeel Elayan, Andrew W. Eckford, and Raviraj Adve | Selectivity of Protein Interactions Stimulated by Terahertz Signals | Accepted for publication in IEEE Transactions on Nanobioscience | null | null | null | q-bio.MN cs.ET | http://creativecommons.org/licenses/by/4.0/ | It has been established that Terahertz (THz) band signals can interact with
biomolecules through resonant modes. Specifically, of interest here, protein
activation. Our research goal is to show how directing the mechanical signaling
inside protein molecules using THz signals can control changes in their
structure and activate associated biochemical and biomechanical events. To
establish that, we formulate a selectivity metric that quantifies the system
performance and captures the capability of the nanoantenna to induce a
conformational change in the desired protein molecule/population. The metric
provides a score between -1 and 1 that indicates the degree of control we have
over the system to achieve targeted protein interactions. To develop the
selectivity measure, we first use the Langevin stochastic equation driven by an
external force to model the protein behavior. We then determine the probability
of protein folding by computing the steady-state energy of the driven protein
and then generalize our model to account for protein populations. Our numerical
analysis results indicate that a maximum selectivity score is attained when
only the targeted population experiences a folding behavior due to the
impinging THz signal. From the achieved selectivity values, we conclude that
the system response not only depends on the resonant frequency but also on the
system controlling parameters namely, the nanoantenna force, the damping
constant, and the abundance of each protein population. The presented work
sheds light on the potential associated with the electromagnetic-based control
of protein networks, which could lead to a plethora of applications in the
medical field ranging from bio-sensing to targeted therapy.
| [
{
"created": "Mon, 4 Jul 2022 16:42:21 GMT",
"version": "v1"
}
] | 2022-07-05 | [
[
"Elayan",
"Hadeel",
""
],
[
"Eckford",
"Andrew W.",
""
],
[
"Adve",
"Raviraj",
""
]
] | It has been established that Terahertz (THz) band signals can interact with biomolecules through resonant modes. Specifically, of interest here, protein activation. Our research goal is to show how directing the mechanical signaling inside protein molecules using THz signals can control changes in their structure and activate associated biochemical and biomechanical events. To establish that, we formulate a selectivity metric that quantifies the system performance and captures the capability of the nanoantenna to induce a conformational change in the desired protein molecule/population. The metric provides a score between -1 and 1 that indicates the degree of control we have over the system to achieve targeted protein interactions. To develop the selectivity measure, we first use the Langevin stochastic equation driven by an external force to model the protein behavior. We then determine the probability of protein folding by computing the steady-state energy of the driven protein and then generalize our model to account for protein populations. Our numerical analysis results indicate that a maximum selectivity score is attained when only the targeted population experiences a folding behavior due to the impinging THz signal. From the achieved selectivity values, we conclude that the system response not only depends on the resonant frequency but also on the system controlling parameters namely, the nanoantenna force, the damping constant, and the abundance of each protein population. The presented work sheds light on the potential associated with the electromagnetic-based control of protein networks, which could lead to a plethora of applications in the medical field ranging from bio-sensing to targeted therapy. |
2010.06225 | Didier Pinault | Caroline Lahogue (NCPS, FMTS, UNISTRA), Didier Pinault (NCPS, FMTS,
UNISTRA) | Transcranial Bipolar Direct Current Stimulation of the Frontoparietal
Cortex Reduces Ketamine-Induced Oscillopathies: A Pilot Study in the Sedated
Rat | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Running title: Frontoparietal anodal tDCS reduces ketamine-induced
oscillopathies.Abstract: During the prodromal phase of schizophrenia with its
complex and insidious clinical picture, electroencephalographic recordings
detect widespread oscillation disturbances (or oscillopathies). Neural
oscillations are electro-biomarkers of the connectivity state within systems. A
single systemic administration of ketamine, a non-competitive NMDA glutamate
receptor antagonist, transiently reproduces the oscillopathies with a clinical
picture reminiscent of the psychosis prodrome. This acute pharmacological model
may help the research and development of innovative treatments against the
psychotic transition. Transcranial electrical stimulation is recognized as an
appropriate non-invasive therapeutic modality since it can increase cognitive
performance and modulate neural oscillations with little or no side effects.
Therefore, our objective was to set up, in the sedated adult rat, a stimulation
method able to normalize the ketamine-induced oscillopathies. Unilateral
transcranial frontoparietal anodal stimulation by direct current (<+1 mA) was
applied in ketamine-treated rats. A concomitant electroencephalographic
recording of the parietal cortex measured the stimulation effects on its
spontaneously-occurring oscillations. A 5-min bipolar anodal tDCS immediately
and quickly reduced, significantly with an intensity-effect relationship, the
ketamine-induced oscillopathies at least in the bilateral parietal cortex. A
duration effect was also recorded. These preliminary neurophysiological
findings are promising for developing a therapeutic proof-of-concept against
neuropsychiatric disorders.
| [
{
"created": "Tue, 13 Oct 2020 08:14:53 GMT",
"version": "v1"
}
] | 2020-10-14 | [
[
"Lahogue",
"Caroline",
"",
"NCPS, FMTS, UNISTRA"
],
[
"Pinault",
"Didier",
"",
"NCPS, FMTS,\n UNISTRA"
]
] | Running title: Frontoparietal anodal tDCS reduces ketamine-induced oscillopathies.Abstract: During the prodromal phase of schizophrenia with its complex and insidious clinical picture, electroencephalographic recordings detect widespread oscillation disturbances (or oscillopathies). Neural oscillations are electro-biomarkers of the connectivity state within systems. A single systemic administration of ketamine, a non-competitive NMDA glutamate receptor antagonist, transiently reproduces the oscillopathies with a clinical picture reminiscent of the psychosis prodrome. This acute pharmacological model may help the research and development of innovative treatments against the psychotic transition. Transcranial electrical stimulation is recognized as an appropriate non-invasive therapeutic modality since it can increase cognitive performance and modulate neural oscillations with little or no side effects. Therefore, our objective was to set up, in the sedated adult rat, a stimulation method able to normalize the ketamine-induced oscillopathies. Unilateral transcranial frontoparietal anodal stimulation by direct current (<+1 mA) was applied in ketamine-treated rats. A concomitant electroencephalographic recording of the parietal cortex measured the stimulation effects on its spontaneously-occurring oscillations. A 5-min bipolar anodal tDCS immediately and quickly reduced, significantly with an intensity-effect relationship, the ketamine-induced oscillopathies at least in the bilateral parietal cortex. A duration effect was also recorded. These preliminary neurophysiological findings are promising for developing a therapeutic proof-of-concept against neuropsychiatric disorders. |
1310.1676 | Liane Gabora | Douglas Hofstadter and Liane Gabora | Synopsis of the workshop on humor and cognition | null | (1989). Synopsis of the workshop on humor and cognition. Humor,
2(4), 417-440 | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We summarize the proceedings of the Workshop on Humor and. The principal type
of humor considered, slippage humor, is defined and contrasted with
aggression-based humor. A variety of slippage humor, based on Hofstadter's
notion of a frame blend, is presented. Given that a frame is a small coherent
cluster of concepts pertaining to a single topic (similar to a script), a frame
blend results when elements extracted from two distinct frames are spliced
together to yield a new hybrid frame. Diverse ways of blending two frames can
produce varying degrees and types of humor. Studies of this phenomenon are
presented. We discuss the connection between frame blends and analogies,
focusing on the Copycat domain: an idealized microworld in which analogy making
can be studied and modeled on computer. It is shown how jokes can be mapped
into that domain, giving rise to a kind of abstract microworld humor. This
highlights the relationships amongst good jokes, defective analogies, and frame
blends. We discuss the notion that many jokes can share the same abstract
structure, and propose the term ur-joke for the most abstract level of a joke.
Several specific ur-jokes are presented, each with a set of fully fleshed-out
jokes based on it. We recount efforts at translating two jokes from one subject
matter to another in an attempt to determine whether a joke's funniness is due
more to its underlying ur-joke or its subject matter. We discuss the
relationship between the Raskin's overlapping-script theory of humor,
Hofstadter's frame-blend theory, and a theory that incorporates multiple-level
analysis of jokes. Finally, a speculative theory about the adaptive value of
humor is presented.
| [
{
"created": "Mon, 7 Oct 2013 05:07:44 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Oct 2013 00:32:27 GMT",
"version": "v2"
},
{
"created": "Thu, 4 Jul 2019 21:04:15 GMT",
"version": "v3"
}
] | 2019-07-08 | [
[
"Hofstadter",
"Douglas",
""
],
[
"Gabora",
"Liane",
""
]
] | We summarize the proceedings of the Workshop on Humor and. The principal type of humor considered, slippage humor, is defined and contrasted with aggression-based humor. A variety of slippage humor, based on Hofstadter's notion of a frame blend, is presented. Given that a frame is a small coherent cluster of concepts pertaining to a single topic (similar to a script), a frame blend results when elements extracted from two distinct frames are spliced together to yield a new hybrid frame. Diverse ways of blending two frames can produce varying degrees and types of humor. Studies of this phenomenon are presented. We discuss the connection between frame blends and analogies, focusing on the Copycat domain: an idealized microworld in which analogy making can be studied and modeled on computer. It is shown how jokes can be mapped into that domain, giving rise to a kind of abstract microworld humor. This highlights the relationships amongst good jokes, defective analogies, and frame blends. We discuss the notion that many jokes can share the same abstract structure, and propose the term ur-joke for the most abstract level of a joke. Several specific ur-jokes are presented, each with a set of fully fleshed-out jokes based on it. We recount efforts at translating two jokes from one subject matter to another in an attempt to determine whether a joke's funniness is due more to its underlying ur-joke or its subject matter. We discuss the relationship between the Raskin's overlapping-script theory of humor, Hofstadter's frame-blend theory, and a theory that incorporates multiple-level analysis of jokes. Finally, a speculative theory about the adaptive value of humor is presented. |
2003.11221 | Alexis Akira Toda | Alexis Akira Toda | Susceptible-Infected-Recovered (SIR) Dynamics of COVID-19 and Economic
Impact | null | null | null | null | q-bio.PE econ.GN q-fin.EC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I estimate the Susceptible-Infected-Recovered (SIR) epidemic model for
Coronavirus Disease 2019 (COVID-19). The transmission rate is heterogeneous
across countries and far exceeds the recovery rate, which enables a fast
spread. In the benchmark model, 28% of the population may be simultaneously
infected at the peak, potentially overwhelming the healthcare system. The peak
reduces to 6.2% under the optimal mitigation policy that controls the timing
and intensity of social distancing. A stylized asset pricing model suggests
that the stock price temporarily decreases by 50% in the benchmark case but
shows a W-shaped, moderate but longer bear market under the optimal policy.
| [
{
"created": "Wed, 25 Mar 2020 05:00:17 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Mar 2020 05:30:16 GMT",
"version": "v2"
}
] | 2020-03-27 | [
[
"Toda",
"Alexis Akira",
""
]
] | I estimate the Susceptible-Infected-Recovered (SIR) epidemic model for Coronavirus Disease 2019 (COVID-19). The transmission rate is heterogeneous across countries and far exceeds the recovery rate, which enables a fast spread. In the benchmark model, 28% of the population may be simultaneously infected at the peak, potentially overwhelming the healthcare system. The peak reduces to 6.2% under the optimal mitigation policy that controls the timing and intensity of social distancing. A stylized asset pricing model suggests that the stock price temporarily decreases by 50% in the benchmark case but shows a W-shaped, moderate but longer bear market under the optimal policy. |
2202.00322 | Thomas G\"otz | Thomas G\"otz | Analysis of an SIR--model with global and local infections | null | null | null | null | q-bio.PE math.DS | http://creativecommons.org/licenses/by/4.0/ | An epidemic model where disease transmission can occur either through global
contacts or through local, nearest neighbor interactions is considered. The
classical SIR--model describing the global interactions is extended by adding
additional equations for the density of local pairs in different epidemic
states. A locality parameter $p\in [0,1]$ characterizes the probability of
global or local infections. The equilibria of the resulting model are analyzed
in dependence of the locality parameter and the transmission rate of the
pathogen. An explicit expression for the reproduction number in terms of the
locality parameter and the disease parameters is obtained. Transient
simulations confirm these findings. Neighboring pairs of one infected and one
susceptible can be considered as active pairs, since local transmission of the
disease can only occur in that situation. Our analysis shows, that the fraction
of active pairs is minimal for intermediate values of the locality parameter.
| [
{
"created": "Tue, 1 Feb 2022 10:45:29 GMT",
"version": "v1"
}
] | 2022-02-02 | [
[
"Götz",
"Thomas",
""
]
] | An epidemic model where disease transmission can occur either through global contacts or through local, nearest neighbor interactions is considered. The classical SIR--model describing the global interactions is extended by adding additional equations for the density of local pairs in different epidemic states. A locality parameter $p\in [0,1]$ characterizes the probability of global or local infections. The equilibria of the resulting model are analyzed in dependence of the locality parameter and the transmission rate of the pathogen. An explicit expression for the reproduction number in terms of the locality parameter and the disease parameters is obtained. Transient simulations confirm these findings. Neighboring pairs of one infected and one susceptible can be considered as active pairs, since local transmission of the disease can only occur in that situation. Our analysis shows, that the fraction of active pairs is minimal for intermediate values of the locality parameter. |
1904.00986 | Daniel Toker | Daniel Toker, Friedrich T. Sommer, Mark D'Esposito | A simple method for detecting chaos in nature | null | null | null | null | q-bio.QM nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chaos, or exponential sensitivity to small perturbations, appears everywhere
in nature. Moreover, chaos is predicted to play diverse functional roles in
living systems. A method for detecting chaos from empirical measurements should
therefore be a key component of the biologist's toolkit. But, classic
chaos-detection tools are highly sensitive to measurement noise and break down
for common edge cases, making it difficult to detect chaos in domains, like
biology, where measurements are noisy. However, newer tools promise to overcome
these limitations. Here, we combine several such tools into an automated
processing pipeline, and show that our pipeline can detect the presence (or
absence) of chaos in noisy recordings, even for difficult edge cases. As a
first-pass application of our pipeline, we show that heart rate variability is
not chaotic as some have proposed, and instead reflects a stochastic process in
both health and disease. Our tool is easy-to-use and freely available.
| [
{
"created": "Tue, 26 Mar 2019 19:09:57 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Jun 2019 20:07:46 GMT",
"version": "v2"
},
{
"created": "Fri, 10 Jan 2020 00:57:50 GMT",
"version": "v3"
}
] | 2020-01-13 | [
[
"Toker",
"Daniel",
""
],
[
"Sommer",
"Friedrich T.",
""
],
[
"D'Esposito",
"Mark",
""
]
] | Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems. A method for detecting chaos from empirical measurements should therefore be a key component of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed, and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available. |
1504.01813 | Birte Kehr | Birte Kehr and P\'all Melsted and Bjarni V. Halld\'orsson | PopIns: population-scale detection of novel sequence insertions | Presented at RECOMB-SEQ 2015 | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The detection of genomic structural variation (SV) has advanced tremendously
in recent years due to progress in high-throughput sequencing technologies.
Novel sequence insertions, insertions without similarity to a human reference
genome, have received less attention than other types of SVs due to the
computational challenges in their detection from short read sequencing data,
which inherently involves de novo assembly. De novo assembly is not only
computationally challenging, but also requires high-quality data. While the
reads from a single individual may not always meet this requirement, using
reads from multiple individuals can increase power to detect novel insertions.
We have developed the program PopIns, which can discover and characterize
non-reference insertions of 100 bp or longer on a population scale. In this
paper, we describe the approach we implemented in PopIns. It takes as input a
reads-to-reference alignment, assembles unaligned reads using a standard
assembly tool, merges the contigs of different individuals into high-confidence
sequences, anchors the merged sequences into the reference genome, and finally
genotypes all individuals for the discovered insertions. Our tests on simulated
data indicate that the merging step greatly improves the quality and
reliability of predicted insertions and that PopIns shows significantly better
recall and precision than the recent tool MindTheGap. Preliminary results on a
data set of 305 Icelanders demonstrate the practicality of the new approach.
The source code of PopIns is available from http://github.com/bkehr/popins.
| [
{
"created": "Wed, 8 Apr 2015 03:04:04 GMT",
"version": "v1"
}
] | 2015-04-09 | [
[
"Kehr",
"Birte",
""
],
[
"Melsted",
"Páll",
""
],
[
"Halldórsson",
"Bjarni V.",
""
]
] | The detection of genomic structural variation (SV) has advanced tremendously in recent years due to progress in high-throughput sequencing technologies. Novel sequence insertions, insertions without similarity to a human reference genome, have received less attention than other types of SVs due to the computational challenges in their detection from short read sequencing data, which inherently involves de novo assembly. De novo assembly is not only computationally challenging, but also requires high-quality data. While the reads from a single individual may not always meet this requirement, using reads from multiple individuals can increase power to detect novel insertions. We have developed the program PopIns, which can discover and characterize non-reference insertions of 100 bp or longer on a population scale. In this paper, we describe the approach we implemented in PopIns. It takes as input a reads-to-reference alignment, assembles unaligned reads using a standard assembly tool, merges the contigs of different individuals into high-confidence sequences, anchors the merged sequences into the reference genome, and finally genotypes all individuals for the discovered insertions. Our tests on simulated data indicate that the merging step greatly improves the quality and reliability of predicted insertions and that PopIns shows significantly better recall and precision than the recent tool MindTheGap. Preliminary results on a data set of 305 Icelanders demonstrate the practicality of the new approach. The source code of PopIns is available from http://github.com/bkehr/popins. |
2207.00636 | Jack Lindsey | Jack Lindsey, Ashok Litwin-Kumar | Action-modulated midbrain dopamine activity arises from distributed
control policies | null | null | null | null | q-bio.NC cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Animal behavior is driven by multiple brain regions working in parallel with
distinct control policies. We present a biologically plausible model of
off-policy reinforcement learning in the basal ganglia, which enables learning
in such an architecture. The model accounts for action-related modulation of
dopamine activity that is not captured by previous models that implement
on-policy algorithms. In particular, the model predicts that dopamine activity
signals a combination of reward prediction error (as in classic models) and
"action surprise," a measure of how unexpected an action is relative to the
basal ganglia's current policy. In the presence of the action surprise term,
the model implements an approximate form of Q-learning. On benchmark navigation
and reaching tasks, we show empirically that this model is capable of learning
from data driven completely or in part by other policies (e.g. from other brain
regions). By contrast, models without the action surprise term suffer in the
presence of additional policies, and are incapable of learning at all from
behavior that is completely externally driven. The model provides a
computational account for numerous experimental findings about dopamine
activity that cannot be explained by classic models of reinforcement learning
in the basal ganglia. These include differing levels of action surprise signals
in dorsal and ventral striatum, decreasing amounts movement-modulated dopamine
activity with practice, and representations of action initiation and kinematics
in dopamine activity. It also provides further predictions that can be tested
with recordings of striatal dopamine activity.
| [
{
"created": "Fri, 1 Jul 2022 19:24:29 GMT",
"version": "v1"
}
] | 2022-07-05 | [
[
"Lindsey",
"Jack",
""
],
[
"Litwin-Kumar",
"Ashok",
""
]
] | Animal behavior is driven by multiple brain regions working in parallel with distinct control policies. We present a biologically plausible model of off-policy reinforcement learning in the basal ganglia, which enables learning in such an architecture. The model accounts for action-related modulation of dopamine activity that is not captured by previous models that implement on-policy algorithms. In particular, the model predicts that dopamine activity signals a combination of reward prediction error (as in classic models) and "action surprise," a measure of how unexpected an action is relative to the basal ganglia's current policy. In the presence of the action surprise term, the model implements an approximate form of Q-learning. On benchmark navigation and reaching tasks, we show empirically that this model is capable of learning from data driven completely or in part by other policies (e.g. from other brain regions). By contrast, models without the action surprise term suffer in the presence of additional policies, and are incapable of learning at all from behavior that is completely externally driven. The model provides a computational account for numerous experimental findings about dopamine activity that cannot be explained by classic models of reinforcement learning in the basal ganglia. These include differing levels of action surprise signals in dorsal and ventral striatum, decreasing amounts movement-modulated dopamine activity with practice, and representations of action initiation and kinematics in dopamine activity. It also provides further predictions that can be tested with recordings of striatal dopamine activity. |
1207.2424 | Daniel Jones | Daniel C. Jones, Walter L. Ruzzo, Xinxia Peng, and Michael G. Katze | Compression of next-generation sequencing reads aided by highly
efficient de novo assembly | null | null | null | null | q-bio.QM cs.DS q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present Quip, a lossless compression algorithm for next-generation
sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing
reference-based compression, we have developed, to our knowledge, the first
assembly-based compressor, using a novel de novo assembly algorithm. A
probabilistic data structure is used to dramatically reduce the memory required
by traditional de Bruijn graph assemblers, allowing millions of reads to be
assembled very efficiently. Read sequences are then stored as positions within
the assembled contigs. This is combined with statistical compression of read
identifiers, quality scores, alignment information, and sequences, effectively
collapsing very large datasets to less than 15% of their original size with no
loss of information.
Availability: Quip is freely available under the BSD license from
http://cs.washington.edu/homes/dcjones/quip.
| [
{
"created": "Tue, 10 Jul 2012 17:49:17 GMT",
"version": "v1"
}
] | 2012-07-11 | [
[
"Jones",
"Daniel C.",
""
],
[
"Ruzzo",
"Walter L.",
""
],
[
"Peng",
"Xinxia",
""
],
[
"Katze",
"Michael G.",
""
]
] | We present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information, and sequences, effectively collapsing very large datasets to less than 15% of their original size with no loss of information. Availability: Quip is freely available under the BSD license from http://cs.washington.edu/homes/dcjones/quip. |
2306.12598 | Angelica Kaufmann | Angelica Kaufmann, Sara Parmigiani, Toshikazu Kawagoe, Elliot
Zabaroff, Barnaby Wells | Two Models of Mind Blanking | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Mind blanking is a mental state in which attention does not bring any
perceptual input into conscious awareness. As this state is still largely
unexplored, we suggest that a comprehensive understanding of mind blanking can
be achieved through a multifaceted approach combining self-assessment methods,
neuroimaging, and neuromodulation. In this article, we explain how EEG and TMS
could be combined to help determine whether mind blanking is associated with a
lack of mental content or a lack of linguistically or conceptually determinable
mental content. We also question whether mind blanking occurs spontaneously or
intentionally and whether these two forms are instantiated by the same or
different neural correlates.
| [
{
"created": "Wed, 21 Jun 2023 22:40:41 GMT",
"version": "v1"
}
] | 2023-06-23 | [
[
"Kaufmann",
"Angelica",
""
],
[
"Parmigiani",
"Sara",
""
],
[
"Kawagoe",
"Toshikazu",
""
],
[
"Zabaroff",
"Elliot",
""
],
[
"Wells",
"Barnaby",
""
]
] | Mind blanking is a mental state in which attention does not bring any perceptual input into conscious awareness. As this state is still largely unexplored, we suggest that a comprehensive understanding of mind blanking can be achieved through a multifaceted approach combining self-assessment methods, neuroimaging, and neuromodulation. In this article, we explain how EEG and TMS could be combined to help determine whether mind blanking is associated with a lack of mental content or a lack of linguistically or conceptually determinable mental content. We also question whether mind blanking occurs spontaneously or intentionally and whether these two forms are instantiated by the same or different neural correlates. |
0911.3070 | Laurent Gueguen | Laurent Gu\'eguen (LBBE) | Computing the likelihood of sequence segmentation under Markov modelling | null | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I tackle the problem of partitioning a sequence into homogeneous segments,
where homogeneity is defined by a set of Markov models. The problem is to study
the likelihood that a sequence is divided into a given number of segments.
Here, the moments of this likelihood are computed through an efficient
algorithm. Unlike methods involving Hidden Markov Models, this algorithm does
not require probability transitions between the models. Among many possible
usages of the likelihood, I present a maximum \textit{a posteriori} probability
criterion to predict the number of homogeneous segments into which a sequence
can be divided, and an application of this method to find CpG islands.
| [
{
"created": "Mon, 16 Nov 2009 16:38:22 GMT",
"version": "v1"
}
] | 2009-11-17 | [
[
"Guéguen",
"Laurent",
"",
"LBBE"
]
] | I tackle the problem of partitioning a sequence into homogeneous segments, where homogeneity is defined by a set of Markov models. The problem is to study the likelihood that a sequence is divided into a given number of segments. Here, the moments of this likelihood are computed through an efficient algorithm. Unlike methods involving Hidden Markov Models, this algorithm does not require probability transitions between the models. Among many possible usages of the likelihood, I present a maximum \textit{a posteriori} probability criterion to predict the number of homogeneous segments into which a sequence can be divided, and an application of this method to find CpG islands. |
2006.13932 | Faisal Mahmood | Ming Y. Lu, Melissa Zhao, Maha Shady, Jana Lipkova, Tiffany Y. Chen,
Drew F. K. Williamson, Faisal Mahmood | Deep Learning-based Computational Pathology Predicts Origins for Cancers
of Unknown Primary | null | null | 10.1038/s41586-021-03512-4 | null | q-bio.TO cs.LG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the
primary anatomical site of tumor origin cannot be determined. This poses a
significant challenge since modern therapeutics such as chemotherapy regimen
and immune checkpoint inhibitors are specific to the primary tumor. Recent work
has focused on using genomics and transcriptomics for identification of tumor
origins. However, genomic testing is not conducted for every patient and lacks
clinical penetration in low resource settings. Herein, to overcome these
challenges, we present a deep learning-based computational pathology
algorithm-TOAD-that can provide a differential diagnosis for CUP using
routinely acquired histology slides. We used 17,486 gigapixel whole slide
images with known primaries spread over 18 common origins to train a multi-task
deep model to simultaneously identify the tumor as primary or metastatic and
predict its site of origin. We tested our model on an internal test set of
4,932 cases with known primaries and achieved a top-1 accuracy of 0.84, a top-3
accuracy of 0.94 while on our external test set of 662 cases from 202 different
hospitals, it achieved a top-1 and top-3 accuracy of 0.79 and 0.93
respectively. We further curated a dataset of 717 CUP cases from 151 different
medical centers and identified a subset of 290 cases for which a differential
diagnosis was assigned. Our model predictions resulted in concordance for 50%
of cases (\k{appa}=0.4 when adjusted for agreement by chance) and a top-3
agreement of 75%. Our proposed method can be used as an assistive tool to
assign differential diagnosis to complicated metastatic and CUP cases and could
be used in conjunction with or in lieu of immunohistochemical analysis and
extensive diagnostic work-ups to reduce the occurrence of CUP.
| [
{
"created": "Wed, 24 Jun 2020 17:59:36 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Jun 2020 02:38:40 GMT",
"version": "v2"
}
] | 2021-07-14 | [
[
"Lu",
"Ming Y.",
""
],
[
"Zhao",
"Melissa",
""
],
[
"Shady",
"Maha",
""
],
[
"Lipkova",
"Jana",
""
],
[
"Chen",
"Tiffany Y.",
""
],
[
"Williamson",
"Drew F. K.",
""
],
[
"Mahmood",
"Faisal",
""
]
] | Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined. This poses a significant challenge since modern therapeutics such as chemotherapy regimen and immune checkpoint inhibitors are specific to the primary tumor. Recent work has focused on using genomics and transcriptomics for identification of tumor origins. However, genomic testing is not conducted for every patient and lacks clinical penetration in low resource settings. Herein, to overcome these challenges, we present a deep learning-based computational pathology algorithm-TOAD-that can provide a differential diagnosis for CUP using routinely acquired histology slides. We used 17,486 gigapixel whole slide images with known primaries spread over 18 common origins to train a multi-task deep model to simultaneously identify the tumor as primary or metastatic and predict its site of origin. We tested our model on an internal test set of 4,932 cases with known primaries and achieved a top-1 accuracy of 0.84, a top-3 accuracy of 0.94 while on our external test set of 662 cases from 202 different hospitals, it achieved a top-1 and top-3 accuracy of 0.79 and 0.93 respectively. We further curated a dataset of 717 CUP cases from 151 different medical centers and identified a subset of 290 cases for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 50% of cases (\k{appa}=0.4 when adjusted for agreement by chance) and a top-3 agreement of 75%. Our proposed method can be used as an assistive tool to assign differential diagnosis to complicated metastatic and CUP cases and could be used in conjunction with or in lieu of immunohistochemical analysis and extensive diagnostic work-ups to reduce the occurrence of CUP. |
1808.10795 | Ruiqi Zhong | Ruiqi Zhong, Tyler Joseph, Joao B Xavier, Itsik Pe'er | Latent Space Temporal Model of Microbial Abundance to Predict Domination
and Bacteremia | Experiment code available at
https://github.com/ZhongRuiqi1997/NIPS2017MLCB, software at
https://github.com/ZhongRuiqi1997/Kalman-Filter-Intestinal-Microbiota | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gut microbial composition has been linked to multiple health outcomes. Yet,
temporal analysis of this composition had been limited to deterministic models.
In this paper, we introduce a probabilistic model for the dynamics of
intestinal microbiomes that takes into account interaction among bacteria as
well as external effects such as antibiotics. The model successfully deals with
pragmatic issues such as random measurement error and varying time intervals
between measurements through latent space modeling. We demonstrate utility of
the model by using latent state features to predict the clinical events of
intestinal domination and bacteremia, improving accuracy over existing methods.
We further leverage this framework to validate known links between antibiotics
and clinical outcomes, while discovering new ones.
| [
{
"created": "Fri, 31 Aug 2018 15:00:12 GMT",
"version": "v1"
}
] | 2018-09-03 | [
[
"Zhong",
"Ruiqi",
""
],
[
"Joseph",
"Tyler",
""
],
[
"Xavier",
"Joao B",
""
],
[
"Pe'er",
"Itsik",
""
]
] | Gut microbial composition has been linked to multiple health outcomes. Yet, temporal analysis of this composition had been limited to deterministic models. In this paper, we introduce a probabilistic model for the dynamics of intestinal microbiomes that takes into account interaction among bacteria as well as external effects such as antibiotics. The model successfully deals with pragmatic issues such as random measurement error and varying time intervals between measurements through latent space modeling. We demonstrate utility of the model by using latent state features to predict the clinical events of intestinal domination and bacteremia, improving accuracy over existing methods. We further leverage this framework to validate known links between antibiotics and clinical outcomes, while discovering new ones. |
2306.08018 | Ningyu Zhang | Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo
Chen, Xiaohui Fan, Huajun Chen | Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for
Large Language Models | ICLR 2024. Project homepage:
https://github.com/zjunlp/Mol-Instructions | null | null | null | q-bio.QM cs.AI cs.CE cs.CL cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs), with their remarkable task-handling
capabilities and innovative outputs, have catalyzed significant advancements
across a spectrum of fields. However, their proficiency within specialized
domains such as biomolecular studies remains limited. To address this
challenge, we introduce Mol-Instructions, a comprehensive instruction dataset
designed for the biomolecular domain. Mol-Instructions encompasses three key
components: molecule-oriented instructions, protein-oriented instructions, and
biomolecular text instructions. Each component aims to improve the
understanding and prediction capabilities of LLMs concerning biomolecular
features and behaviors. Through extensive instruction tuning experiments on
LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large
models' performance in the intricate realm of biomolecular studies, thus
fostering progress in the biomolecular research community. Mol-Instructions is
publicly available for ongoing research and will undergo regular updates to
enhance its applicability.
| [
{
"created": "Tue, 13 Jun 2023 14:35:34 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Aug 2023 17:13:05 GMT",
"version": "v2"
},
{
"created": "Mon, 2 Oct 2023 15:27:20 GMT",
"version": "v3"
},
{
"created": "Thu, 30 Nov 2023 15:29:58 GMT",
"version": "v4"
},
{
"created": "Mon, 4 Mar 2024 12:49:31 GMT",
"version": "v5"
}
] | 2024-03-05 | [
[
"Fang",
"Yin",
""
],
[
"Liang",
"Xiaozhuan",
""
],
[
"Zhang",
"Ningyu",
""
],
[
"Liu",
"Kangwei",
""
],
[
"Huang",
"Rui",
""
],
[
"Chen",
"Zhuo",
""
],
[
"Fan",
"Xiaohui",
""
],
[
"Chen",
"Huajun",
""
]
] | Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability. |
2404.01405 | Nicholas Glykos | Olympia-Dialekti Vouzina, Alexandros Tafanidis, Nicholas M. Glykos | The curious case of A31P, a topology-switching mutant of the Repressor
of Primer protein : A molecular dynamics study of its folding and misfolding | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | The effect of mutations on protein structures is usually rather localized and
minor. Finding a mutation that can single-handedly change the fold and/or
topology of a protein structure is a rare exception. The A31P mutant of the
homodimeric Repressor of Primer (Rop) protein is one such exception: This
single mutation -- and as demonstrated by two independent crystal structure
determinations -- can convert the canonical (left-handed/all-antiparallel)
4-alpha-helical bundle of Rop, to a new form (right-handed/mixed parallel and
antiparallel bundle) displaying a previously unobserved 'bisecting U' topology.
The main problem with understanding the dramatic effect of this mutation on the
folding of Rop is to understand its very existence : Most computational methods
appear to agree that the mutation should have had no appreciable effect, with
the majority of energy minimization methods and protein structure prediction
protocols indicating that this mutation is fully consistent with the native Rop
structure, requiring only a local and minor change at the mutation site. Here
we use two long (10 us each) molecular dynamics simulations to compare the
stability and dynamics of the native Rop versus a hypothetical structure that
is identical with the native Rop but is carrying this single Alanine-31 to
Proline mutation. Comparative analysis of the two trajectories convincingly
shows that in contrast to the indications from energy minimization -- but in
agreement with the experimental data -- this hypothetical native-like A31P
structure is unstable, with its turn regions almost completely unfolding, even
under the relatively mild 320K NpT simulations that we have used for this
study. We discuss the implication of these findings for the folding of the A31P
mutant, especially with respect to the proposed model of a double-funneled
energy landscape.
| [
{
"created": "Mon, 1 Apr 2024 18:17:49 GMT",
"version": "v1"
}
] | 2024-04-03 | [
[
"Vouzina",
"Olympia-Dialekti",
""
],
[
"Tafanidis",
"Alexandros",
""
],
[
"Glykos",
"Nicholas M.",
""
]
] | The effect of mutations on protein structures is usually rather localized and minor. Finding a mutation that can single-handedly change the fold and/or topology of a protein structure is a rare exception. The A31P mutant of the homodimeric Repressor of Primer (Rop) protein is one such exception: This single mutation -- and as demonstrated by two independent crystal structure determinations -- can convert the canonical (left-handed/all-antiparallel) 4-alpha-helical bundle of Rop, to a new form (right-handed/mixed parallel and antiparallel bundle) displaying a previously unobserved 'bisecting U' topology. The main problem with understanding the dramatic effect of this mutation on the folding of Rop is to understand its very existence : Most computational methods appear to agree that the mutation should have had no appreciable effect, with the majority of energy minimization methods and protein structure prediction protocols indicating that this mutation is fully consistent with the native Rop structure, requiring only a local and minor change at the mutation site. Here we use two long (10 us each) molecular dynamics simulations to compare the stability and dynamics of the native Rop versus a hypothetical structure that is identical with the native Rop but is carrying this single Alanine-31 to Proline mutation. Comparative analysis of the two trajectories convincingly shows that in contrast to the indications from energy minimization -- but in agreement with the experimental data -- this hypothetical native-like A31P structure is unstable, with its turn regions almost completely unfolding, even under the relatively mild 320K NpT simulations that we have used for this study. We discuss the implication of these findings for the folding of the A31P mutant, especially with respect to the proposed model of a double-funneled energy landscape. |
1803.01997 | Keiko Itano | Keiko Itano | Mathematical modeling and analysis of the pathway network consisting of
symmetrical complexes with N monomers, like the activation of MMP2 | null | null | null | null | q-bio.BM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The activation of matrix metalloproteinase 2 (MMP2) is a crucial event during
tumor metastasis and invasion, and this pathway network consists of 3 monomers.
The pathway network of the activation obeys to a set of specified reaction
rules. According to the rules, the individual molecules localize in a
particular order and symmetrically around a homodimer following the formation
of that dimer. We generalized the homodimer pathway network obeying to similar
reaction rules, by changing the number of monomers involved in this pathway
from 3 to N. At the previous work, we found the molecules in the pathway
network are classified to some reaction groups. We derived the law of mass
conservation between the groups. Each group concentration converges to its
equilibrium solution. Using these results, we derive the concentrations of the
complexes theoretically and reveal that each complex concentration converges to
its equilibrium value. We can say the pathway network with homodimer symmetric
form complexes is asymptotic stable and identify the regulator parameter of the
target complex in the network. Our mathematical approach may help us understand
the mechanism of this type pathway network by knowing the background
mathematical laws which govern this type pathway network.
| [
{
"created": "Tue, 6 Mar 2018 02:55:56 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Mar 2018 06:15:34 GMT",
"version": "v2"
}
] | 2018-03-08 | [
[
"Itano",
"Keiko",
""
]
] | The activation of matrix metalloproteinase 2 (MMP2) is a crucial event during tumor metastasis and invasion, and this pathway network consists of 3 monomers. The pathway network of the activation obeys to a set of specified reaction rules. According to the rules, the individual molecules localize in a particular order and symmetrically around a homodimer following the formation of that dimer. We generalized the homodimer pathway network obeying to similar reaction rules, by changing the number of monomers involved in this pathway from 3 to N. At the previous work, we found the molecules in the pathway network are classified to some reaction groups. We derived the law of mass conservation between the groups. Each group concentration converges to its equilibrium solution. Using these results, we derive the concentrations of the complexes theoretically and reveal that each complex concentration converges to its equilibrium value. We can say the pathway network with homodimer symmetric form complexes is asymptotic stable and identify the regulator parameter of the target complex in the network. Our mathematical approach may help us understand the mechanism of this type pathway network by knowing the background mathematical laws which govern this type pathway network. |
2102.06768 | Wesley Hamilton | W. Hamilton, J.E. Borgert, T. Hamelryck, J.S. Marron | Persistent topology of protein space | 23 pages, 15 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Protein fold classification is a classic problem in structural biology and
bioinformatics. We approach this problem using persistent homology. In
particular, we use alpha shape filtrations to compare a topological
representation of the data with a different representation that makes use of
knot-theoretic ideas. We use the statistical method of Angle-based Joint and
Individual Variation Explained (AJIVE) to understand similarities and
differences between these representations.
| [
{
"created": "Fri, 12 Feb 2021 20:53:59 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Aug 2021 19:27:56 GMT",
"version": "v2"
}
] | 2021-08-23 | [
[
"Hamilton",
"W.",
""
],
[
"Borgert",
"J. E.",
""
],
[
"Hamelryck",
"T.",
""
],
[
"Marron",
"J. S.",
""
]
] | Protein fold classification is a classic problem in structural biology and bioinformatics. We approach this problem using persistent homology. In particular, we use alpha shape filtrations to compare a topological representation of the data with a different representation that makes use of knot-theoretic ideas. We use the statistical method of Angle-based Joint and Individual Variation Explained (AJIVE) to understand similarities and differences between these representations. |
1812.00278 | Pamela Douglas | Nikolaus Kriegeskorte, Pamela K. Douglas | Interpreting Encoding and Decoding Models | 19 pages, 2 figures, author preprint | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Encoding and decoding models are widely used in systems, cognitive, and
computational neuroscience to make sense of brain-activity data. However, the
interpretation of their results requires care. Decoding models can help reveal
whether particular information is present in a brain region in a format the
decoder can exploit. Encoding models make comprehensive predictions about
representational spaces. In the context of sensory systems, encoding models
enable us to test and compare brain-computational models, and thus directly
constrain computational theory. Encoding and decoding models typically include
fitted linear-model components. Sometimes the weights of the fitted linear
combinations are interpreted as reflecting, in an encoding model, the
contribution of different sensory features to the representation or, in a
decoding model, the contribution of different measured brain responses to a
decoded feature. Such interpretations can be problematic when the predictor
variables or their noise components are correlated and when priors (or
penalties) are used to regularize the fit. Encoding and decoding models are
evaluated in terms of their generalization performance. The correct
interpretation depends on the level of generalization a model achieves (e.g. to
new response measurements for the same stimuli, to new stimuli from the same
population, or to stimuli from a different population). Significant decoding or
encoding performance of a single model (at whatever level of generality) does
not provide strong constraints for theory. Many models must be tested and
inferentially compared for analyses to drive theoretical progress.
| [
{
"created": "Sat, 1 Dec 2018 22:58:55 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Apr 2019 05:13:15 GMT",
"version": "v2"
}
] | 2019-04-29 | [
[
"Kriegeskorte",
"Nikolaus",
""
],
[
"Douglas",
"Pamela K.",
""
]
] | Encoding and decoding models are widely used in systems, cognitive, and computational neuroscience to make sense of brain-activity data. However, the interpretation of their results requires care. Decoding models can help reveal whether particular information is present in a brain region in a format the decoder can exploit. Encoding models make comprehensive predictions about representational spaces. In the context of sensory systems, encoding models enable us to test and compare brain-computational models, and thus directly constrain computational theory. Encoding and decoding models typically include fitted linear-model components. Sometimes the weights of the fitted linear combinations are interpreted as reflecting, in an encoding model, the contribution of different sensory features to the representation or, in a decoding model, the contribution of different measured brain responses to a decoded feature. Such interpretations can be problematic when the predictor variables or their noise components are correlated and when priors (or penalties) are used to regularize the fit. Encoding and decoding models are evaluated in terms of their generalization performance. The correct interpretation depends on the level of generalization a model achieves (e.g. to new response measurements for the same stimuli, to new stimuli from the same population, or to stimuli from a different population). Significant decoding or encoding performance of a single model (at whatever level of generality) does not provide strong constraints for theory. Many models must be tested and inferentially compared for analyses to drive theoretical progress. |
1207.5848 | Benjamin Diament | Benjamin J. Diament, Michael J. MacCoss, William Stafford Noble | On the feasibility and utility of exploiting real time database search
to improve adaptive peak selection | null | null | null | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rationale: In a shotgun proteomics experiment with data-dependent
acquisition, real-time analysis of a precursor scan results in selection of a
handful of peaks for subsequent isolation, fragmentation and secondary
scanning. This peak selection protocol typically focuses on the most abundant
peaks in the precursor scan, while attempting to avoid re-sampling the same m/z
values in rapid succession. The protocol does not, however, incorporate
analysis of previous fragmentation scans into the peak selection procedure.
Methods: In this work, we investigate the feasibility and utility of
incorporating analysis of previous fragmentation scans into the peak selection
protocol. We demonstrate that real-time identification of fragmentation spectra
is feasible in principle, and we investigate, via simulations, several
strategies to make use of the resulting peptide identifications during peak
selection.
Results: Our simulations fail to provide evidence that peptide
identifications can provide a large improvement in the total number of peptides
identified by a shotgun proteomics experiment.
Conclusions: These results are significant because they point out the
feasibility of using peptide identifications during peak selection, and because
our experiments may provide a starting point for others working in this
direction.
| [
{
"created": "Tue, 24 Jul 2012 23:13:21 GMT",
"version": "v1"
}
] | 2012-07-26 | [
[
"Diament",
"Benjamin J.",
""
],
[
"MacCoss",
"Michael J.",
""
],
[
"Noble",
"William Stafford",
""
]
] | Rationale: In a shotgun proteomics experiment with data-dependent acquisition, real-time analysis of a precursor scan results in selection of a handful of peaks for subsequent isolation, fragmentation and secondary scanning. This peak selection protocol typically focuses on the most abundant peaks in the precursor scan, while attempting to avoid re-sampling the same m/z values in rapid succession. The protocol does not, however, incorporate analysis of previous fragmentation scans into the peak selection procedure. Methods: In this work, we investigate the feasibility and utility of incorporating analysis of previous fragmentation scans into the peak selection protocol. We demonstrate that real-time identification of fragmentation spectra is feasible in principle, and we investigate, via simulations, several strategies to make use of the resulting peptide identifications during peak selection. Results: Our simulations fail to provide evidence that peptide identifications can provide a large improvement in the total number of peptides identified by a shotgun proteomics experiment. Conclusions: These results are significant because they point out the feasibility of using peptide identifications during peak selection, and because our experiments may provide a starting point for others working in this direction. |
1609.06763 | Jaime Ashander | Jaime Ashander, Luis-Miguel Chevin, Marissa L. Baskett | Predicting evolutionary rescue via evolving plasticity in stochastic
environments | null | Proc. R. Soc. B 283:20161690 (2016) | 10.1098/rspb.2016.1690 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phenotypic plasticity and its evolution may help evolutionary rescue in a
novel and stressful environment, especially if environmental novelty reveals
cryptic genetic variation that enables the evolution of increased plasticity.
However, the environmental stochasticity ubiquitous in natural systems may
alter these predictions because high plasticity may amplify
phenotype-environment mismatches. Although previous studies have highlighted
this potential detrimental effect of plasticity in stochastic environments,
they have not investigated how it affects extinction risk in the context of
evolutionary rescue and with evolving plasticity. We investigate this question
here by integrating stochastic demography with quantitative genetic theory in a
model with simultaneous change in the mean and predictability (temporal
autocorrelation) of the environment. We develop an approximate prediction of
long-term persistence under the new pattern of environmental fluctuations, and
compare it with numerical simulations for short- and long-term extinction risk.
We find that reduced predictability increases extinction risk and reduces
persistence because it increases stochastic load during rescue. This
understanding of how stochastic demography, phenotypic plasticity, and
evolution interact when evolution acts on cryptic genetic variation revealed in
a novel environment can inform expectations for invasions, extinctions, or the
emergence of chemical resistance in pests.
| [
{
"created": "Wed, 21 Sep 2016 21:49:59 GMT",
"version": "v1"
}
] | 2016-09-23 | [
[
"Ashander",
"Jaime",
""
],
[
"Chevin",
"Luis-Miguel",
""
],
[
"Baskett",
"Marissa L.",
""
]
] | Phenotypic plasticity and its evolution may help evolutionary rescue in a novel and stressful environment, especially if environmental novelty reveals cryptic genetic variation that enables the evolution of increased plasticity. However, the environmental stochasticity ubiquitous in natural systems may alter these predictions because high plasticity may amplify phenotype-environment mismatches. Although previous studies have highlighted this potential detrimental effect of plasticity in stochastic environments, they have not investigated how it affects extinction risk in the context of evolutionary rescue and with evolving plasticity. We investigate this question here by integrating stochastic demography with quantitative genetic theory in a model with simultaneous change in the mean and predictability (temporal autocorrelation) of the environment. We develop an approximate prediction of long-term persistence under the new pattern of environmental fluctuations, and compare it with numerical simulations for short- and long-term extinction risk. We find that reduced predictability increases extinction risk and reduces persistence because it increases stochastic load during rescue. This understanding of how stochastic demography, phenotypic plasticity, and evolution interact when evolution acts on cryptic genetic variation revealed in a novel environment can inform expectations for invasions, extinctions, or the emergence of chemical resistance in pests. |
q-bio/0502028 | Fred Moller | F. Moller | Release of Brain Mitochondrial Hexokinase by Acidic Proteins and
Macromolecular Polyanions | 29 pages,3 figures. Figures and page number revised(28 pages) | null | null | null | q-bio.BM q-bio.SC | null | Preparations of arachidonic acid binding and non-binding proteins from bovine
brain, four acidic proteins (alpha-casein, phosvitin, beta-lactoglobulin A and
B), the peptide polyglutamate, and two polyanions (heparin, dextran sulfate)
enhanced both basal and glucose 6-phosphate induced solubilization of rat brain
mitochondrial hexokinase (ATP:D-hexose 6-phosphotransferase, EC 2.7.1.1). In
contrast, three other acidic proteins, had little (alpha-lactalbumin) or no
effect (bovine serum albumin, ovalbumin) and five basic proteins inhibited
release of the enzyme. Solubilizing activity therefore appears to require a net
negative charge and one or more of the following structural features: extended
conformation, random coil, and unordered or beta-structure, in the latter case,
as the beta-barrel in the fatty acid binding proteins and beta-lactoglobulins.
It is of interest that a difference of a single negative charge between
beta-lactoglobulin A and B, resulted in a statistically significant difference
in the stimulation of hexokinase release. Possible physiological and
pathological roles of this hexokinase solubilizing effect are discussed
briefly.
| [
{
"created": "Tue, 22 Feb 2005 19:46:50 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Mar 2005 19:19:29 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Moller",
"F.",
""
]
] | Preparations of arachidonic acid binding and non-binding proteins from bovine brain, four acidic proteins (alpha-casein, phosvitin, beta-lactoglobulin A and B), the peptide polyglutamate, and two polyanions (heparin, dextran sulfate) enhanced both basal and glucose 6-phosphate induced solubilization of rat brain mitochondrial hexokinase (ATP:D-hexose 6-phosphotransferase, EC 2.7.1.1). In contrast, three other acidic proteins, had little (alpha-lactalbumin) or no effect (bovine serum albumin, ovalbumin) and five basic proteins inhibited release of the enzyme. Solubilizing activity therefore appears to require a net negative charge and one or more of the following structural features: extended conformation, random coil, and unordered or beta-structure, in the latter case, as the beta-barrel in the fatty acid binding proteins and beta-lactoglobulins. It is of interest that a difference of a single negative charge between beta-lactoglobulin A and B, resulted in a statistically significant difference in the stimulation of hexokinase release. Possible physiological and pathological roles of this hexokinase solubilizing effect are discussed briefly. |
2205.05634 | Jian-Jun Shu | Zi Hian Tan, Kian Yan Yong, Jian-Jun Shu | Evolution of viral pathogens follows a linear order | null | Microbiology Spectrum, Vol. 10, No. 1, pp. e01655-21, 2022 | 10.1128/spectrum.01655-21 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although lessons have been learned from previous severe acute respiratory
syndrome (SARS) and Middle East respiratory syndrome (MERS) outbreaks, the
rapid evolution of the viruses means that future outbreaks of a much larger
scale are possible, as shown by the current coronavirus disease 2019 (COVID-19)
outbreak. Therefore, it is necessary to better understand the evolution of
coronaviruses as well as viruses in general. This study reports a comparative
analysis of the amino acid usage within several key viral families and genera
that are prone to triggering outbreaks, including coronavirus (SARS-CoV-2,
SARS-CoV, MERS-CoV, HCoV-HKU1, HCoV-OC43, HCoV-NL63, HCoV-229E), influenza A
(H1N1, H3N2), flavivirus (dengue virus serotypes 1-4, Zika) and ebolavirus
(Zaire, Sudan, Bundibugyo ebolavirus). Our analysis reveals that the
distribution of amino acid usage in the viral genome is constrained to follow a
linear order, and the distribution remains closely related to the viral species
within the family or genus. This constraint can be adapted to predict viral
mutations and future variants of concern. By studying previous SARS and MERS
outbreaks, we have adapted this naturally occurring pattern to determine that
although pangolin plays a role in the outbreak of COVID-19, it may not be the
sole agent as an intermediate animal. In addition to this study, our findings
contribute to the understanding of viral mutations for subsequent development
of vaccines and toward developing a model to determine the source of the
outbreak.
| [
{
"created": "Wed, 11 May 2022 17:07:05 GMT",
"version": "v1"
}
] | 2022-05-12 | [
[
"Tan",
"Zi Hian",
""
],
[
"Yong",
"Kian Yan",
""
],
[
"Shu",
"Jian-Jun",
""
]
] | Although lessons have been learned from previous severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) outbreaks, the rapid evolution of the viruses means that future outbreaks of a much larger scale are possible, as shown by the current coronavirus disease 2019 (COVID-19) outbreak. Therefore, it is necessary to better understand the evolution of coronaviruses as well as viruses in general. This study reports a comparative analysis of the amino acid usage within several key viral families and genera that are prone to triggering outbreaks, including coronavirus (SARS-CoV-2, SARS-CoV, MERS-CoV, HCoV-HKU1, HCoV-OC43, HCoV-NL63, HCoV-229E), influenza A (H1N1, H3N2), flavivirus (dengue virus serotypes 1-4, Zika) and ebolavirus (Zaire, Sudan, Bundibugyo ebolavirus). Our analysis reveals that the distribution of amino acid usage in the viral genome is constrained to follow a linear order, and the distribution remains closely related to the viral species within the family or genus. This constraint can be adapted to predict viral mutations and future variants of concern. By studying previous SARS and MERS outbreaks, we have adapted this naturally occurring pattern to determine that although pangolin plays a role in the outbreak of COVID-19, it may not be the sole agent as an intermediate animal. In addition to this study, our findings contribute to the understanding of viral mutations for subsequent development of vaccines and toward developing a model to determine the source of the outbreak. |
2006.06987 | Andrea Vandin | Tabea Waizmann, Luca Bortolussi, Andrea Vandin, Mirco Tribastone | Improved estimations of stochastic chemical kinetics by finite state
expansion | 33 pages, 9 figures | null | 10.1098/rspa.2020.0964 | null | q-bio.MN cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic reaction networks are a fundamental model to describe interactions
between species where random fluctuations are relevant. The master equation
provides the evolution of the probability distribution across the discrete
state space consisting of vectors of population counts for each species.
However, since its exact solution is often elusive, several analytical
approximations have been proposed. The deterministic rate equation (DRE) gives
a macroscopic approximation as a compact system of differential equations that
estimate the average populations for each species, but it may be inaccurate in
the case of nonlinear interaction dynamics. Here we propose finite state
expansion (FSE), an analytical method mediating between the microscopic and the
macroscopic interpretations of a stochastic reaction network by coupling the
master equation dynamics of a chosen subset of the discrete state space with
the mean population dynamics of the DRE. An algorithm translates a network into
an expanded one where each discrete state is represented as a further distinct
species. This translation exactly preserves the stochastic dynamics, but the
DRE of the expanded network can be interpreted as a correction to the original
one. The effectiveness of FSE is demonstrated in models that challenge
state-of-the-art techniques due to intrinsic noise, multi-scale populations,
and multi-stability.
| [
{
"created": "Fri, 12 Jun 2020 08:08:12 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Jul 2020 15:41:59 GMT",
"version": "v2"
},
{
"created": "Mon, 14 Jun 2021 14:27:07 GMT",
"version": "v3"
}
] | 2021-06-15 | [
[
"Waizmann",
"Tabea",
""
],
[
"Bortolussi",
"Luca",
""
],
[
"Vandin",
"Andrea",
""
],
[
"Tribastone",
"Mirco",
""
]
] | Stochastic reaction networks are a fundamental model to describe interactions between species where random fluctuations are relevant. The master equation provides the evolution of the probability distribution across the discrete state space consisting of vectors of population counts for each species. However, since its exact solution is often elusive, several analytical approximations have been proposed. The deterministic rate equation (DRE) gives a macroscopic approximation as a compact system of differential equations that estimate the average populations for each species, but it may be inaccurate in the case of nonlinear interaction dynamics. Here we propose finite state expansion (FSE), an analytical method mediating between the microscopic and the macroscopic interpretations of a stochastic reaction network by coupling the master equation dynamics of a chosen subset of the discrete state space with the mean population dynamics of the DRE. An algorithm translates a network into an expanded one where each discrete state is represented as a further distinct species. This translation exactly preserves the stochastic dynamics, but the DRE of the expanded network can be interpreted as a correction to the original one. The effectiveness of FSE is demonstrated in models that challenge state-of-the-art techniques due to intrinsic noise, multi-scale populations, and multi-stability. |
2308.11910 | Saiful Islam | Saiful Islam, Pitambar Khanra, Johan Nakuci, Sarah F. Muldoon,
Takamitsu Watanabe, Naoki Masuda | State-transition dynamics of resting-state functional magnetic resonance
imaging data: Model comparison and test-to-retest analysis | 27 pages, 7 figures, 9 tables | BMC Neurosci 25, 14 (2024) | 10.1186/s12868-024-00854-3 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Electroencephalogram (EEG) microstate analysis entails finding dynamics of
quasi-stable and generally recurrent discrete states in multichannel EEG time
series data and relating properties of the estimated state-transition dynamics
to observables such as cognition and behavior. While microstate analysis has
been widely employed to analyze EEG data, its use remains less prevalent in
functional magnetic resonance imaging (fMRI) data, largely due to the slower
timescale of such data. In the present study, we extend various data clustering
methods used in EEG microstate analysis to resting-state fMRI data from healthy
humans to extract their state-transition dynamics. We show that the quality of
clustering is on par with that for various microstate analyses of EEG data. We
then develop a method for examining test-retest reliability of the
discrete-state transition dynamics between fMRI sessions and show that the
within-participant test-retest reliability is higher than between-participant
test-retest reliability for different indices of state-transition dynamics,
different networks, and different data sets. This result suggests that
state-transition dynamics analysis of fMRI data could discriminate between
different individuals and is a promising tool for performing fingerprinting
analysis of individuals.
| [
{
"created": "Wed, 23 Aug 2023 04:36:38 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Mar 2024 23:34:32 GMT",
"version": "v2"
}
] | 2024-03-07 | [
[
"Islam",
"Saiful",
""
],
[
"Khanra",
"Pitambar",
""
],
[
"Nakuci",
"Johan",
""
],
[
"Muldoon",
"Sarah F.",
""
],
[
"Watanabe",
"Takamitsu",
""
],
[
"Masuda",
"Naoki",
""
]
] | Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals. |
1906.05785 | Tong Wang | Tong Wang, Haipeng Gong and Eugene I. Shakhnovich | Improved fragment-based movement with LRFragLib for all-atom Ab initio
protein folding | null | null | null | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fragment-based assembly has been widely used in Ab initio protein folding
simulation which can effectively reduce the conformational space and thus
accelerate sampling. The efficiency of fragment-based movement as well as the
quality of fragment library determine whether the folding process can lead the
free energy landscape to the global minimum and help the protein to reach
near-native folded state. We designed an improved fragment-based movement,
"fragmove", which substituted multiple backbone dihedral angles in every
simulation step. This movement strategy was derived from the fragment library
generated by LRFragLib, an effective fragment detection algorithm using
logistic regression model. We show in replica exchange Monte Carlo (REMC)
simulation that "fragmove", when compared with a set of existing movements in
REMC, shows significant improved ability at increasing secondary and tertiary
predicted model accuracy by 11.24% and 17.98%, respectively and reaching energy
minima decreased by 5.72%. Our results demonstrate that this improved movement
is more powerful to guide proteins faster to low energy regions of
conformational space and promote folding efficiency and predicted model
accuracy.
| [
{
"created": "Sun, 2 Jun 2019 07:54:23 GMT",
"version": "v1"
}
] | 2019-06-14 | [
[
"Wang",
"Tong",
""
],
[
"Gong",
"Haipeng",
""
],
[
"Shakhnovich",
"Eugene I.",
""
]
] | Fragment-based assembly has been widely used in Ab initio protein folding simulation which can effectively reduce the conformational space and thus accelerate sampling. The efficiency of fragment-based movement as well as the quality of fragment library determine whether the folding process can lead the free energy landscape to the global minimum and help the protein to reach near-native folded state. We designed an improved fragment-based movement, "fragmove", which substituted multiple backbone dihedral angles in every simulation step. This movement strategy was derived from the fragment library generated by LRFragLib, an effective fragment detection algorithm using logistic regression model. We show in replica exchange Monte Carlo (REMC) simulation that "fragmove", when compared with a set of existing movements in REMC, shows significant improved ability at increasing secondary and tertiary predicted model accuracy by 11.24% and 17.98%, respectively and reaching energy minima decreased by 5.72%. Our results demonstrate that this improved movement is more powerful to guide proteins faster to low energy regions of conformational space and promote folding efficiency and predicted model accuracy. |
q-bio/0703044 | Ping Ao | P. Ao, C. Kwon, H. Qian | On the existence of potential landscape in the evolution of complex
systems | latex, 18 pages | Complexity 12 (2007) 19-27 | null | null | q-bio.QM cond-mat.stat-mech cs.IT math.DS math.IT nlin.AO q-bio.MN | null | A recently developed treatment of stochastic processes leads to the
construction of a potential landscape for the dynamical evolution of complex
systems. Since the existence of a potential function in generic settings has
been frequently questioned in literature,herewe study several related
theoretical issues that lie at core of the construction. We showthat the novel
treatment,via a transformation,is closely related to the symplectic structure
that is central in many branches of theoretical physics. Using this insight, we
demonstrate an invariant under the transformation. We further explicitly
demonstrate, in one-dimensional case, the contradistinction among the new
treatment to those of Ito and Stratonovich, as well as others.Our results
strongly suggest that the method from statistical physics can be useful in
studying stochastic, complex systems in general.
| [
{
"created": "Tue, 20 Mar 2007 18:13:41 GMT",
"version": "v1"
}
] | 2007-07-16 | [
[
"Ao",
"P.",
""
],
[
"Kwon",
"C.",
""
],
[
"Qian",
"H.",
""
]
] | A recently developed treatment of stochastic processes leads to the construction of a potential landscape for the dynamical evolution of complex systems. Since the existence of a potential function in generic settings has been frequently questioned in literature,herewe study several related theoretical issues that lie at core of the construction. We showthat the novel treatment,via a transformation,is closely related to the symplectic structure that is central in many branches of theoretical physics. Using this insight, we demonstrate an invariant under the transformation. We further explicitly demonstrate, in one-dimensional case, the contradistinction among the new treatment to those of Ito and Stratonovich, as well as others.Our results strongly suggest that the method from statistical physics can be useful in studying stochastic, complex systems in general. |
1803.10342 | Patrick Charbonneau | Andrew E. Bruno, Patrick Charbonneau, Janet Newman, Edward H. Snell,
David R. So, Vincent Vanhoucke, Christopher J. Watkins, Shawn Williams, Julie
Wilson | Classification of crystallization outcomes using deep convolutional
neural networks | 11 pages, 4 figures, minor text and figure updates | null | 10.1371/journal.pone.0198883 | null | q-bio.BM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Machine Recognition of Crystallization Outcomes (MARCO) initiative has
assembled roughly half a million annotated images of macromolecular
crystallization experiments from various sources and setups. Here,
state-of-the-art machine learning algorithms are trained and tested on
different parts of this data set. We find that more than 94% of the test images
can be correctly labeled, irrespective of their experimental origin. Because
crystal recognition is key to high-density screening and the systematic
analysis of crystallization experiments, this approach opens the door to both
industrial and fundamental research applications.
| [
{
"created": "Tue, 27 Mar 2018 22:03:20 GMT",
"version": "v1"
},
{
"created": "Sat, 26 May 2018 02:28:35 GMT",
"version": "v2"
}
] | 2018-07-04 | [
[
"Bruno",
"Andrew E.",
""
],
[
"Charbonneau",
"Patrick",
""
],
[
"Newman",
"Janet",
""
],
[
"Snell",
"Edward H.",
""
],
[
"So",
"David R.",
""
],
[
"Vanhoucke",
"Vincent",
""
],
[
"Watkins",
"Christopher J.",
""
],
[
"Williams",
"Shawn",
""
],
[
"Wilson",
"Julie",
""
]
] | The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications. |
1904.10718 | Isabelle Landrieu | Jo\~ao Neves (UGSF), Isabelle Landrieu (UGSF), Hamida Merzougui
(UGSF), Emmanuelle Boll (UGSF), Xavier Hanoulle (UGSF), Fran\c{c}ois-Xavier
Cantrelle (UGSF) | Backbone chemical shift assignments of human 14-3-3$\sigma$ | null | Biomolecular NMR Assignments, Springer, 2019, 13 (1), pp.103-107 | 10.1007/s12104-018-9860-1 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 14-3-3 proteins are a group of seven dimeric adapter proteins that exert
their biological function by interacting with hundreds of phosphorylated
proteins, thus influencing their sub-cellular localization, activity or
stability in the cell. Due to this remarkable interaction network, 14-3-3
proteins have been associated with several pathologies and the protein-protein
interactions established with a number of partners are now considered promising
drug targets. The activity of 14-3-3 proteins is often isoform specific and to
our knowledge only one out of seven isoforms, 14-3-3$\zeta$, has been assigned.
Despite the availability of the crystal structures of all seven isoforms of
14-3-3, the additional NMR assignments of 14-3-3 proteins are important for
both biological mechanism studies and chemical biology approaches. Herein, we
present a robust backbone assignment of 14-3-3$\sigma$, which will allow
advances in the discovery of potential therapeutic compounds. This assignment
is now being applied to the discovery of both inhibitors and stabilizers of
14-3-3 protein-protein interactions.
| [
{
"created": "Wed, 24 Apr 2019 09:49:33 GMT",
"version": "v1"
}
] | 2019-04-25 | [
[
"Neves",
"João",
"",
"UGSF"
],
[
"Landrieu",
"Isabelle",
"",
"UGSF"
],
[
"Merzougui",
"Hamida",
"",
"UGSF"
],
[
"Boll",
"Emmanuelle",
"",
"UGSF"
],
[
"Hanoulle",
"Xavier",
"",
"UGSF"
],
[
"Cantrelle",
"François-Xavier",
"",
"UGSF"
]
] | 14-3-3 proteins are a group of seven dimeric adapter proteins that exert their biological function by interacting with hundreds of phosphorylated proteins, thus influencing their sub-cellular localization, activity or stability in the cell. Due to this remarkable interaction network, 14-3-3 proteins have been associated with several pathologies and the protein-protein interactions established with a number of partners are now considered promising drug targets. The activity of 14-3-3 proteins is often isoform specific and to our knowledge only one out of seven isoforms, 14-3-3$\zeta$, has been assigned. Despite the availability of the crystal structures of all seven isoforms of 14-3-3, the additional NMR assignments of 14-3-3 proteins are important for both biological mechanism studies and chemical biology approaches. Herein, we present a robust backbone assignment of 14-3-3$\sigma$, which will allow advances in the discovery of potential therapeutic compounds. This assignment is now being applied to the discovery of both inhibitors and stabilizers of 14-3-3 protein-protein interactions. |
1510.03172 | Stephen Smith | Stephen Smith, Claudia Cianci and Ramon Grima | Model reduction for stochastic chemical systems with abundant species | 25 pages, 11 figures | null | 10.1063/1.4936394 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biochemical processes typically involve many chemical species, some in
abundance and some in low molecule numbers. Here we first identify the rate
constant limits under which the concentrations of a given set of species will
tend to infinity (the abundant species) while the concentrations of all other
species remains constant (the non-abundant species). Subsequently we prove that
in this limit, the fluctuations in the molecule numbers of non-abundant species
are accurately described by a hybrid stochastic description consisting of a
chemical master equation coupled to deterministic rate equations. This is a
reduced description when compared to the conventional chemical master equation
which describes the fluctuations in both abundant and non-abundant species. We
show that the reduced master equation can be solved exactly for a number of
biochemical networks involving gene expression and enzyme catalysis, whose
conventional chemical master equation description is analytically impenetrable.
We use the linear noise approximation to obtain approximate expressions for the
difference between the variance of fluctuations in the non-abundant species as
predicted by the hybrid approach and by the conventional chemical master
equation. Furthermore we show that surprisingly, irrespective of any separation
in the mean molecule numbers of various species, the conventional and hybrid
master equations exactly agree for a class of chemical systems.
| [
{
"created": "Mon, 12 Oct 2015 07:59:29 GMT",
"version": "v1"
}
] | 2016-01-20 | [
[
"Smith",
"Stephen",
""
],
[
"Cianci",
"Claudia",
""
],
[
"Grima",
"Ramon",
""
]
] | Biochemical processes typically involve many chemical species, some in abundance and some in low molecule numbers. Here we first identify the rate constant limits under which the concentrations of a given set of species will tend to infinity (the abundant species) while the concentrations of all other species remains constant (the non-abundant species). Subsequently we prove that in this limit, the fluctuations in the molecule numbers of non-abundant species are accurately described by a hybrid stochastic description consisting of a chemical master equation coupled to deterministic rate equations. This is a reduced description when compared to the conventional chemical master equation which describes the fluctuations in both abundant and non-abundant species. We show that the reduced master equation can be solved exactly for a number of biochemical networks involving gene expression and enzyme catalysis, whose conventional chemical master equation description is analytically impenetrable. We use the linear noise approximation to obtain approximate expressions for the difference between the variance of fluctuations in the non-abundant species as predicted by the hybrid approach and by the conventional chemical master equation. Furthermore we show that surprisingly, irrespective of any separation in the mean molecule numbers of various species, the conventional and hybrid master equations exactly agree for a class of chemical systems. |
q-bio/0401010 | Eivind Almaas | E. Almaas, A.-L. Barabasi | Power laws in biological networks | Review article, to appear in "Power laws, scale-free networks and
genome biology" edited by E. Koonin | null | null | null | q-bio.MN cond-mat.dis-nn q-bio.CB | null | The rapidly developing theory of complex networks indicates that real
networks are not random, but have a highly robust large-scale architecture,
governed by strict organizational principles. Here, we focus on the properties
of biological networks, discussing their scale-free and hierarchical features.
We illustrate the major network characteristics using examples from the
metabolic network of the bacterium Escherichia coli. We also discuss the
principles of network utilization, acknowledging that the interactions in a
real network have unequal strengths. We study the interplay between topology
and reaction fluxes provided by flux-balance analysis. We find that the
cellular utilization of the metabolic network is both globally and locally
highly inhomogeneous, dominated by "hot-spots", representing connected
high-flux pathways.
| [
{
"created": "Wed, 7 Jan 2004 21:58:20 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Almaas",
"E.",
""
],
[
"Barabasi",
"A. -L.",
""
]
] | The rapidly developing theory of complex networks indicates that real networks are not random, but have a highly robust large-scale architecture, governed by strict organizational principles. Here, we focus on the properties of biological networks, discussing their scale-free and hierarchical features. We illustrate the major network characteristics using examples from the metabolic network of the bacterium Escherichia coli. We also discuss the principles of network utilization, acknowledging that the interactions in a real network have unequal strengths. We study the interplay between topology and reaction fluxes provided by flux-balance analysis. We find that the cellular utilization of the metabolic network is both globally and locally highly inhomogeneous, dominated by "hot-spots", representing connected high-flux pathways. |
2407.00004 | Ayush Noori | Ayush Noori, I\~naki Arango, William E. Byrd, Nada Amin | Multi-objective generative AI for designing novel brain-targeting small
molecules | 20 pages, 4 figures, Generative and Experimental Perspectives for
Biomolecular Design Workshop at the 12th International Conference on Learning
Representations | null | null | null | q-bio.BM cs.AI cs.LG q-bio.QM | http://creativecommons.org/licenses/by-sa/4.0/ | The strict selectivity of the blood-brain barrier (BBB) represents one of the
most formidable challenges to successful central nervous system (CNS) drug
delivery. Computational methods to generate BBB permeable drugs in silico may
be valuable tools in the CNS drug design pipeline. However, in real-world
applications, BBB penetration alone is insufficient; rather, after transiting
the BBB, molecules must bind to a specific target or receptor in the brain and
must also be safe and non-toxic. To discover small molecules that concurrently
satisfy these constraints, we use multi-objective generative AI to synthesize
drug-like BBB-permeable small molecules. Specifically, we computationally
synthesize molecules with predicted binding affinity against dopamine receptor
D2, the primary target for many clinically effective antipsychotic drugs. After
training several graph neural network-based property predictors, we adapt
SyntheMol (Swanson et al., 2024), a recently developed Monte Carlo Tree
Search-based algorithm for antibiotic design, to perform a multi-objective
guided traversal over an easily synthesizable molecular space. We design a
library of 26,581 novel and diverse small molecules containing hits with high
predicted BBB permeability and favorable predicted safety and toxicity
profiles, and that could readily be synthesized for experimental validation in
the wet lab. We also validate top scoring molecules with molecular docking
simulation against the D2 receptor and demonstrate predicted binding affinity
on par with risperidone, a clinically prescribed D2-targeting antipsychotic. In
the future, the SyntheMol-based computational approach described here may
enable the discovery of novel neurotherapeutics for currently intractable
disorders of the CNS.
| [
{
"created": "Tue, 16 Apr 2024 12:57:06 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Noori",
"Ayush",
""
],
[
"Arango",
"Iñaki",
""
],
[
"Byrd",
"William E.",
""
],
[
"Amin",
"Nada",
""
]
] | The strict selectivity of the blood-brain barrier (BBB) represents one of the most formidable challenges to successful central nervous system (CNS) drug delivery. Computational methods to generate BBB permeable drugs in silico may be valuable tools in the CNS drug design pipeline. However, in real-world applications, BBB penetration alone is insufficient; rather, after transiting the BBB, molecules must bind to a specific target or receptor in the brain and must also be safe and non-toxic. To discover small molecules that concurrently satisfy these constraints, we use multi-objective generative AI to synthesize drug-like BBB-permeable small molecules. Specifically, we computationally synthesize molecules with predicted binding affinity against dopamine receptor D2, the primary target for many clinically effective antipsychotic drugs. After training several graph neural network-based property predictors, we adapt SyntheMol (Swanson et al., 2024), a recently developed Monte Carlo Tree Search-based algorithm for antibiotic design, to perform a multi-objective guided traversal over an easily synthesizable molecular space. We design a library of 26,581 novel and diverse small molecules containing hits with high predicted BBB permeability and favorable predicted safety and toxicity profiles, and that could readily be synthesized for experimental validation in the wet lab. We also validate top scoring molecules with molecular docking simulation against the D2 receptor and demonstrate predicted binding affinity on par with risperidone, a clinically prescribed D2-targeting antipsychotic. In the future, the SyntheMol-based computational approach described here may enable the discovery of novel neurotherapeutics for currently intractable disorders of the CNS. |
2004.07391 | Calistus N. Ngonghala | Calistus N. Ngonghala, Enahoro Iboi, Steffen Eikenberry, Matthew
Scotch, Chandini Raina MacIntyre, Matthew H. Bonds and Abba B. Gumel | Mathematical assessment of the impact of non-pharmaceutical
interventions on curtailing the 2019 novel Coronavirus | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A novel Coronavirus pandemic emerged in December of 2019, causing devastating
public health impact across the world. In the absence of a safe and effective
vaccine or antiviral, strategies for mitigating the burden of the pandemic are
focused on non-pharmaceutical interventions, such as social-distancing,
contact-tracing, quarantine, isolation and the use of face-masks in public. We
develop a new mathematical model for assessing the population-level impact of
these mitigation strategies. Simulations of the model, using data relevant to
COVID-19 transmission in New York state and the entire US, show that the
pandemic will peak in mid and late April, respectively. The worst-case scenario
projections for cumulative mortality (based on the baseline levels of
anti-COVID non-pharmaceutical interventions considered in the study) in New
York State and the entire US decrease dramatically by 80% and 64%,
respectively, if the strict social-distancing measures implemented are
maintained until the end of May or June, 2020. This study shows that early
termination of strict social-distancing could trigger a devastating second wave
with burden similar to that projected before the onset of strict
social-distance. The use of efficacious face-masks (efficacy greater than 70%)
could lead to the elimination of the pandemic if at least 70% of the residents
of New York state use such masks consistently (nationwide, a compliance of at
least 80% will be required using such masks). The use of low efficacy masks,
such as cloth masks (of efficacy less than 30%), could also lead to significant
reduction of COVID-19 burden (albeit, they are not able to lead to
elimination). Combining low efficacy masks with improved levels of other
anti-COVID-19 intervention measures can lead to elimination of the pandemic.
The mask coverage needed to eliminate COVID-19 decreases if mask-use is
combined with strict social-distancing.
| [
{
"created": "Wed, 15 Apr 2020 23:30:27 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Apr 2020 14:24:05 GMT",
"version": "v2"
}
] | 2020-04-27 | [
[
"Ngonghala",
"Calistus N.",
""
],
[
"Iboi",
"Enahoro",
""
],
[
"Eikenberry",
"Steffen",
""
],
[
"Scotch",
"Matthew",
""
],
[
"MacIntyre",
"Chandini Raina",
""
],
[
"Bonds",
"Matthew H.",
""
],
[
"Gumel",
"Abba B.",
""
]
] | A novel Coronavirus pandemic emerged in December of 2019, causing devastating public health impact across the world. In the absence of a safe and effective vaccine or antiviral, strategies for mitigating the burden of the pandemic are focused on non-pharmaceutical interventions, such as social-distancing, contact-tracing, quarantine, isolation and the use of face-masks in public. We develop a new mathematical model for assessing the population-level impact of these mitigation strategies. Simulations of the model, using data relevant to COVID-19 transmission in New York state and the entire US, show that the pandemic will peak in mid and late April, respectively. The worst-case scenario projections for cumulative mortality (based on the baseline levels of anti-COVID non-pharmaceutical interventions considered in the study) in New York State and the entire US decrease dramatically by 80% and 64%, respectively, if the strict social-distancing measures implemented are maintained until the end of May or June, 2020. This study shows that early termination of strict social-distancing could trigger a devastating second wave with burden similar to that projected before the onset of strict social-distance. The use of efficacious face-masks (efficacy greater than 70%) could lead to the elimination of the pandemic if at least 70% of the residents of New York state use such masks consistently (nationwide, a compliance of at least 80% will be required using such masks). The use of low efficacy masks, such as cloth masks (of efficacy less than 30%), could also lead to significant reduction of COVID-19 burden (albeit, they are not able to lead to elimination). Combining low efficacy masks with improved levels of other anti-COVID-19 intervention measures can lead to elimination of the pandemic. The mask coverage needed to eliminate COVID-19 decreases if mask-use is combined with strict social-distancing. |
0902.2239 | Masaki Sasai | Kazuhito Itoh and Masaki Sasai | Multi-Dimensional Theory of Protein Folding | to appear in J. Chem. Phys | null | 10.1063/1.3097018 | null | q-bio.BM cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Theory of multi-dimensional representation of free energy surface of protein
folding is developed by adopting structural order parameters of multiple
regions in protein as multiple coordinates. Various scenarios of folding are
classified in terms of cooperativity within individual regions and interactions
among multiple regions and thus obtained classification is used to analyze the
folding process of several example proteins. Ribosomal protein S6, src-SH3
domain, CheY, barnase, and BBL domain are analyzed with the two-dimensional
representation by using a structure-based Hamiltonian model. Extension to the
higher dimensional representation leads to the finer description of the folding
process. Barnase, NtrC, and an ankyrin repeat protein are examined with the
three-dimensional representation. The multi-dimensional representation allows
us to directly address questions on folding pathways, intermediates, and
transition states.
| [
{
"created": "Fri, 13 Feb 2009 00:53:44 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Itoh",
"Kazuhito",
""
],
[
"Sasai",
"Masaki",
""
]
] | Theory of multi-dimensional representation of free energy surface of protein folding is developed by adopting structural order parameters of multiple regions in protein as multiple coordinates. Various scenarios of folding are classified in terms of cooperativity within individual regions and interactions among multiple regions and thus obtained classification is used to analyze the folding process of several example proteins. Ribosomal protein S6, src-SH3 domain, CheY, barnase, and BBL domain are analyzed with the two-dimensional representation by using a structure-based Hamiltonian model. Extension to the higher dimensional representation leads to the finer description of the folding process. Barnase, NtrC, and an ankyrin repeat protein are examined with the three-dimensional representation. The multi-dimensional representation allows us to directly address questions on folding pathways, intermediates, and transition states. |
2307.06974 | Jorge Ramirez Osorio | Jorge M. Ramirez, Juan M. Restrepo, Valerio Lucarini, David Weston | Probabilistic Measures for Biological Adaptation and Resilience | null | null | null | null | q-bio.QM math.PR physics.bio-ph physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | This paper introduces a novel approach to quantifying ecological resilience
in biological systems, particularly focusing on noisy systems responding to
episodic disturbances with sudden adaptations. Incorporating concepts from
non-equilibrium statistical mechanics, we propose a measure termed `ecological
resilience through adaptation,' specifically tailored to noisy, forced systems
that undergo physiological adaptation in the face of stressful environmental
changes. Randomness plays a key role, accounting for model uncertainty and the
inherent variability in the dynamical response among components of biological
systems. Our measure of resilience is rooted in the probabilistic description
of states within these systems, and is defined in terms of the dynamics of the
ensemble average of a model-specific observable quantifying success or
well-being. Our approach utilizes stochastic linear response theory to compute
how the expected success of a system, originally in statistical equilibrium,
dynamically changes in response to a environmental perturbation and a
subsequent adaptation. The resulting mathematical derivations allow for the
estimation of resilience in terms of ensemble averages of simulated or
experimental data. Finally, through a simple but clear conceptual example, we
illustrate how our resilience measure can be interpreted and compared to other
existing frameworks in the literature. The methodology is general but inspired
by applications in plant systems, with the potential for broader application to
complex biological processes.
| [
{
"created": "Thu, 13 Jul 2023 13:12:15 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Jul 2023 18:52:22 GMT",
"version": "v2"
},
{
"created": "Tue, 12 Dec 2023 19:52:54 GMT",
"version": "v3"
}
] | 2023-12-14 | [
[
"Ramirez",
"Jorge M.",
""
],
[
"Restrepo",
"Juan M.",
""
],
[
"Lucarini",
"Valerio",
""
],
[
"Weston",
"David",
""
]
] | This paper introduces a novel approach to quantifying ecological resilience in biological systems, particularly focusing on noisy systems responding to episodic disturbances with sudden adaptations. Incorporating concepts from non-equilibrium statistical mechanics, we propose a measure termed `ecological resilience through adaptation,' specifically tailored to noisy, forced systems that undergo physiological adaptation in the face of stressful environmental changes. Randomness plays a key role, accounting for model uncertainty and the inherent variability in the dynamical response among components of biological systems. Our measure of resilience is rooted in the probabilistic description of states within these systems, and is defined in terms of the dynamics of the ensemble average of a model-specific observable quantifying success or well-being. Our approach utilizes stochastic linear response theory to compute how the expected success of a system, originally in statistical equilibrium, dynamically changes in response to a environmental perturbation and a subsequent adaptation. The resulting mathematical derivations allow for the estimation of resilience in terms of ensemble averages of simulated or experimental data. Finally, through a simple but clear conceptual example, we illustrate how our resilience measure can be interpreted and compared to other existing frameworks in the literature. The methodology is general but inspired by applications in plant systems, with the potential for broader application to complex biological processes. |
1912.08144 | John Abel | John H. Abel, Marcus A. Badgeley, Taylor E. Baum, Sourish Chakravarty,
Patrick L. Purdon, Emery N. Brown | Constructing a control-ready model of EEG signal during general
anesthesia in humans | 7 pages, 6 figures. This work has been submitted to IFAC for possible
publication | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Significant effort toward the automation of general anesthesia has been made
in the past decade. One open challenge is in the development of control-ready
patient models for closed-loop anesthesia delivery. Standard
depth-of-anesthesia tracking does not readily capture inter-individual
differences in response to anesthetics, especially those due to age, and does
not aim to predict a relationship between a control input (infused anesthetic
dose) and system state (commonly, a function of electroencephalography (EEG)
signal). In this work, we developed a control-ready patient model for
closed-loop propofol-induced anesthesia using data recorded during a clinical
study of EEG during general anesthesia in ten healthy volunteers. We used
principal component analysis to identify the low-dimensional state-space in
which EEG signal evolves during anesthesia delivery. We parameterized the
response of the EEG signal to changes in propofol target-site concentration
using logistic models. We note that inter-individual differences in anesthetic
sensitivity may be captured by varying a constant cofactor of the predicted
effect-site concentration. We linked the EEG dose-response with the control
input using a pharmacokinetic model. Finally, we present a simple nonlinear
model predictive control in silico demonstration of how such a closed-loop
system would work.
| [
{
"created": "Tue, 17 Dec 2019 17:24:49 GMT",
"version": "v1"
}
] | 2019-12-18 | [
[
"Abel",
"John H.",
""
],
[
"Badgeley",
"Marcus A.",
""
],
[
"Baum",
"Taylor E.",
""
],
[
"Chakravarty",
"Sourish",
""
],
[
"Purdon",
"Patrick L.",
""
],
[
"Brown",
"Emery N.",
""
]
] | Significant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed-loop anesthesia delivery. Standard depth-of-anesthesia tracking does not readily capture inter-individual differences in response to anesthetics, especially those due to age, and does not aim to predict a relationship between a control input (infused anesthetic dose) and system state (commonly, a function of electroencephalography (EEG) signal). In this work, we developed a control-ready patient model for closed-loop propofol-induced anesthesia using data recorded during a clinical study of EEG during general anesthesia in ten healthy volunteers. We used principal component analysis to identify the low-dimensional state-space in which EEG signal evolves during anesthesia delivery. We parameterized the response of the EEG signal to changes in propofol target-site concentration using logistic models. We note that inter-individual differences in anesthetic sensitivity may be captured by varying a constant cofactor of the predicted effect-site concentration. We linked the EEG dose-response with the control input using a pharmacokinetic model. Finally, we present a simple nonlinear model predictive control in silico demonstration of how such a closed-loop system would work. |
0705.0227 | Graeme J. Ackland | Graeme J. Ackland, Richard D.L.Hanes, Morrel H. Cohen | Self assembly of a model multicellular organism resembling the
Dictyostelium slime molds | null | null | null | null | q-bio.CB q-bio.PE | null | The evolution of multicellular organisms from monocellular ancestors
represents one of the greatest advances of the history of life. The assembly of
such multicellular organisms requires signalling and response between cells:
over millions of years these signalling processes have become extremely
sophisticated and refined by evolution, such that study of modern organisms may
not be able to shed much light on the original ancient processes . Here we are
interested in determining how simple a signalling method can be, while still
achieving self-assembly. In 2D a coupled cellular automaton/differential
equation approach models organisms and chemotaxic chemicals, producing
spiralling aggregation. In 3D Lennard-Jones-like particles are used to
represent single cells, and their evolution in response to signalling is
followed by molecular dynamics. It is found that if a single cell is able to
emit a signal which induces others to move towards it, then a colony of
single-cell organisms can assemble into shapes as complex as a tower, a ball
atop a stalk, or a fast-moving slug. The similarity with the behaviour of
modern Dictyostelium slime molds signalling with cyclic adenosine monophosphate
(cAMP) is striking.
| [
{
"created": "Wed, 2 May 2007 08:45:54 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Ackland",
"Graeme J.",
""
],
[
"Hanes",
"Richard D. L.",
""
],
[
"Cohen",
"Morrel H.",
""
]
] | The evolution of multicellular organisms from monocellular ancestors represents one of the greatest advances of the history of life. The assembly of such multicellular organisms requires signalling and response between cells: over millions of years these signalling processes have become extremely sophisticated and refined by evolution, such that study of modern organisms may not be able to shed much light on the original ancient processes . Here we are interested in determining how simple a signalling method can be, while still achieving self-assembly. In 2D a coupled cellular automaton/differential equation approach models organisms and chemotaxic chemicals, producing spiralling aggregation. In 3D Lennard-Jones-like particles are used to represent single cells, and their evolution in response to signalling is followed by molecular dynamics. It is found that if a single cell is able to emit a signal which induces others to move towards it, then a colony of single-cell organisms can assemble into shapes as complex as a tower, a ball atop a stalk, or a fast-moving slug. The similarity with the behaviour of modern Dictyostelium slime molds signalling with cyclic adenosine monophosphate (cAMP) is striking. |
1607.01746 | Tim Kiemel | Tim Kiemel, David Logan, John J. Jeka | Using Perturbations to Probe the Neural Control of Rhythmic Movements | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Small continuous sensory and mechanical perturbations have often been used to
identify properties of the closed-loop neural control of posture and other
systems that are approximately linear time invariant. Here we extend this
approach to study the neural control of rhythmic behaviors such as walking. Our
method is based on the theory of linear time periodic systems, with
modifications to account for ability of perturbations to reset the phase of a
rhythmic behavior. We characterize responses to perturbations in the frequency
domain using harmonic transfer functions and then convert to the time domain to
obtain phase-dependent impulse response functions (IRFs) that describe the
response to a small brief perturbation at any phase of the rhythmic behavior.
IRFs describing responses of kinematic variables and muscle activations
(measured by EMG) to sensory and mechanical perturbations can be used to infer
properties of the plant, the mapping from muscle activation to movement, and of
neural feedback, the mapping from movement to muscle activation. We illustrate
our method by applying it to simulated data from a model and experimental data
of subjects walking on a treadmill perturbed by movement of the visual scene.
| [
{
"created": "Wed, 6 Jul 2016 19:08:50 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Oct 2016 20:32:37 GMT",
"version": "v2"
}
] | 2016-10-31 | [
[
"Kiemel",
"Tim",
""
],
[
"Logan",
"David",
""
],
[
"Jeka",
"John J.",
""
]
] | Small continuous sensory and mechanical perturbations have often been used to identify properties of the closed-loop neural control of posture and other systems that are approximately linear time invariant. Here we extend this approach to study the neural control of rhythmic behaviors such as walking. Our method is based on the theory of linear time periodic systems, with modifications to account for ability of perturbations to reset the phase of a rhythmic behavior. We characterize responses to perturbations in the frequency domain using harmonic transfer functions and then convert to the time domain to obtain phase-dependent impulse response functions (IRFs) that describe the response to a small brief perturbation at any phase of the rhythmic behavior. IRFs describing responses of kinematic variables and muscle activations (measured by EMG) to sensory and mechanical perturbations can be used to infer properties of the plant, the mapping from muscle activation to movement, and of neural feedback, the mapping from movement to muscle activation. We illustrate our method by applying it to simulated data from a model and experimental data of subjects walking on a treadmill perturbed by movement of the visual scene. |
1903.03565 | Georg Prokert | Thomas de Jong, Josephus Hulshof, Georg Prokert | Modeling fungal hypha tip growth via viscous sheet approximation | null | null | null | null | q-bio.CB math.DS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a new model for single-celled, non-branching hypha
tip growth. The growth mechanism of hypha cells consists of transport of cell
wall building material to the cell wall and subsequent incorporation of this
material in the wall as it arrives. To model the transport of cell wall
building material to the cell wall we follow Bartnicki-Garcia et al in assuming
that the cell wall building material is transported in straight lines by an
isotropic point source. To model the dynamics of the cell wall, including its
growth by new material, we use the approach of Campas and Mahadevan, which
assumes that the cell wall is a thin viscous sheet sustained by a pressure
difference. Furthermore, we include a novel equation which models the hardening
of the cell wall as it ages. We present numerical results which give evidence
that our model can describe tip growth, and briefly discuss validation aspects.
| [
{
"created": "Fri, 8 Mar 2019 17:19:30 GMT",
"version": "v1"
}
] | 2019-03-11 | [
[
"de Jong",
"Thomas",
""
],
[
"Hulshof",
"Josephus",
""
],
[
"Prokert",
"Georg",
""
]
] | In this paper we present a new model for single-celled, non-branching hypha tip growth. The growth mechanism of hypha cells consists of transport of cell wall building material to the cell wall and subsequent incorporation of this material in the wall as it arrives. To model the transport of cell wall building material to the cell wall we follow Bartnicki-Garcia et al in assuming that the cell wall building material is transported in straight lines by an isotropic point source. To model the dynamics of the cell wall, including its growth by new material, we use the approach of Campas and Mahadevan, which assumes that the cell wall is a thin viscous sheet sustained by a pressure difference. Furthermore, we include a novel equation which models the hardening of the cell wall as it ages. We present numerical results which give evidence that our model can describe tip growth, and briefly discuss validation aspects. |
2012.09624 | Jingbo Zhou | Jingbo Zhou, Shuangli Li, Liang Huang, Haoyi Xiong, Fan Wang, Tong Xu,
Hui Xiong, Dejing Dou | Distance-aware Molecule Graph Attention Network for Drug-Target Binding
Affinity Prediction | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Accurately predicting the binding affinity between drugs and proteins is an
essential step for computational drug discovery. Since graph neural networks
(GNNs) have demonstrated remarkable success in various graph-related tasks,
GNNs have been considered as a promising tool to improve the binding affinity
prediction in recent years. However, most of the existing GNN architectures can
only encode the topological graph structure of drugs and proteins without
considering the relative spatial information among their atoms. Whereas,
different from other graph datasets such as social networks and commonsense
knowledge graphs, the relative spatial position and chemical bonds among atoms
have significant impacts on the binding affinity. To this end, in this paper,
we propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored
to drug-target binding affinity prediction. As a dedicated solution, we first
propose a position encoding mechanism to integrate the topological structure
and spatial position information into the constructed pocket-ligand graph.
Moreover, we propose a novel edge-node hierarchical attentive aggregation
structure which has edge-level aggregation and node-level aggregation. The
hierarchical attentive aggregation can capture spatial dependencies among
atoms, as well as fuse the position-enhanced information with the capability of
discriminating multiple spatial relations among atoms. Finally, we conduct
extensive experiments on two standard datasets to demonstrate the effectiveness
of S-MAN.
| [
{
"created": "Thu, 17 Dec 2020 17:44:01 GMT",
"version": "v1"
}
] | 2020-12-18 | [
[
"Zhou",
"Jingbo",
""
],
[
"Li",
"Shuangli",
""
],
[
"Huang",
"Liang",
""
],
[
"Xiong",
"Haoyi",
""
],
[
"Wang",
"Fan",
""
],
[
"Xu",
"Tong",
""
],
[
"Xiong",
"Hui",
""
],
[
"Dou",
"Dejing",
""
]
] | Accurately predicting the binding affinity between drugs and proteins is an essential step for computational drug discovery. Since graph neural networks (GNNs) have demonstrated remarkable success in various graph-related tasks, GNNs have been considered as a promising tool to improve the binding affinity prediction in recent years. However, most of the existing GNN architectures can only encode the topological graph structure of drugs and proteins without considering the relative spatial information among their atoms. Whereas, different from other graph datasets such as social networks and commonsense knowledge graphs, the relative spatial position and chemical bonds among atoms have significant impacts on the binding affinity. To this end, in this paper, we propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction. As a dedicated solution, we first propose a position encoding mechanism to integrate the topological structure and spatial position information into the constructed pocket-ligand graph. Moreover, we propose a novel edge-node hierarchical attentive aggregation structure which has edge-level aggregation and node-level aggregation. The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms. Finally, we conduct extensive experiments on two standard datasets to demonstrate the effectiveness of S-MAN. |
2305.11199 | Munib Mesinovic | Munib Mesinovic, Xin Ci Wong, Giri Shan Rajahram, Barbara Wanjiru
Citarella, Kalaiarasu M. Peariasamy, Frank van Someren Greve, Piero Olliaro,
Laura Merson, Lei Clifton, Christiana Kartsonaki, ISARIC Characterisation
Group | At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19
Patients Using Statistical and Machine Learning Methods: An International
Cohort Study | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | By September, 2022, more than 600 million cases of SARS-CoV-2 infection have
been reported globally, resulting in over 6.5 million deaths. COVID-19
mortality risk estimators are often, however, developed with small
unrepresentative samples and with methodological limitations. It is highly
important to develop predictive tools for pulmonary embolism (PE) in COVID-19
patients as one of the most severe preventable complications of COVID-19. Using
a dataset of more than 800,000 COVID-19 patients from an international cohort,
we propose a cost-sensitive gradient-boosted machine learning model that
predicts occurrence of PE and death at admission. Logistic regression, Cox
proportional hazards models, and Shapley values were used to identify key
predictors for PE and death. Our prediction model had a test AUROC of 75.9% and
74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality
respectively on a highly diverse and held-out test set. The PE prediction model
was also evaluated on patients in UK and Spain separately with test results of
74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex,
region of admission, comorbidities (chronic cardiac and pulmonary disease,
dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any,
confusion, chest pain, fatigue, headache, fever, muscle or joint pain,
shortness of breath) were the most important clinical predictors at admission.
Our machine learning model developed from an international cohort can serve to
better regulate hospital risk prioritisation of at-risk patients.
| [
{
"created": "Thu, 18 May 2023 14:55:27 GMT",
"version": "v1"
}
] | 2023-05-22 | [
[
"Mesinovic",
"Munib",
""
],
[
"Wong",
"Xin Ci",
""
],
[
"Rajahram",
"Giri Shan",
""
],
[
"Citarella",
"Barbara Wanjiru",
""
],
[
"Peariasamy",
"Kalaiarasu M.",
""
],
[
"Greve",
"Frank van Someren",
""
],
[
"Olliaro",
"Piero",
""
],
[
"Merson",
"Laura",
""
],
[
"Clifton",
"Lei",
""
],
[
"Kartsonaki",
"Christiana",
""
],
[
"Group",
"ISARIC Characterisation",
""
]
] | By September, 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. |
1211.3858 | Benoit Sarels | Benoit Sarels | Generalized traveling wave in heterogeneous media: Allee effect can
inhibit invasion | 15 pages, 3 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study a simple one-dimensional model of reaction-diffusion
with bistable non-linearity and in heterogeneous media. The bistable term
accounts for the so-called Allee effect, and the heterogeneity in the media is
localized. We recall the definition of a transition wave used in similar
situations by well-known authors, and propose an alternative definition for
discussion. We call it generalized traveling wave. As a consequence, we give
new results of pinning in such media, due to Allee effect.
| [
{
"created": "Fri, 16 Nov 2012 11:22:52 GMT",
"version": "v1"
}
] | 2012-11-19 | [
[
"Sarels",
"Benoit",
""
]
] | In this paper, we study a simple one-dimensional model of reaction-diffusion with bistable non-linearity and in heterogeneous media. The bistable term accounts for the so-called Allee effect, and the heterogeneity in the media is localized. We recall the definition of a transition wave used in similar situations by well-known authors, and propose an alternative definition for discussion. We call it generalized traveling wave. As a consequence, we give new results of pinning in such media, due to Allee effect. |
0906.3936 | Gilles Chiocchia | L\'ea Tourneur (IC), Alain Schmitt (IC), Gilles Chiocchia (IC) | In Vivo Localization of Fas-Associated Death Domain Protein in the
Nucleus and Cytoplasm of Normal Thyroid and Liver Cells | null | open autoimmunty journal 1 (2009) 27-32 | null | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | FADD (Fas-associated death domain) is the main death receptor adaptor
molecule that transmits apoptotic signal. Recently, FADD protein was shown to
be expressed both in the cytoplasm and nucleus of in vitro cell lines. In
contrast to the cytoplasmic FADD, the nuclear FADD was shown to protect cells
from apoptosis. However, in vivo subcellular localization of FADD was still
unknown. Here, we demonstrated that FADD protein was expressed in both
cytoplasmic and nuclear compartment in ex vivo thyroid cells demonstrating that
nuclear sublocalization of FADD protein was a relevant phenomenon occurring in
vivo. Moreover, we showed that in the nucleus of untransformed thyroid cells
FADD localized mainly on euchromatin. We confirmed the nuclear localization of
FADD in ex vivo liver and showed that in this organ FADD and MBD4 interact
together. These results demonstrate that FADD is physiologically expressed in
the nucleus of cells in at least two mouse organs. This particular localization
opens new possible role of FADD in vivo either asan inhibitor of cell death, or
as a transcription factor, or as a molecular link between apoptosis and genome
surveillance.
| [
{
"created": "Mon, 22 Jun 2009 07:21:54 GMT",
"version": "v1"
}
] | 2009-06-23 | [
[
"Tourneur",
"Léa",
"",
"IC"
],
[
"Schmitt",
"Alain",
"",
"IC"
],
[
"Chiocchia",
"Gilles",
"",
"IC"
]
] | FADD (Fas-associated death domain) is the main death receptor adaptor molecule that transmits apoptotic signal. Recently, FADD protein was shown to be expressed both in the cytoplasm and nucleus of in vitro cell lines. In contrast to the cytoplasmic FADD, the nuclear FADD was shown to protect cells from apoptosis. However, in vivo subcellular localization of FADD was still unknown. Here, we demonstrated that FADD protein was expressed in both cytoplasmic and nuclear compartment in ex vivo thyroid cells demonstrating that nuclear sublocalization of FADD protein was a relevant phenomenon occurring in vivo. Moreover, we showed that in the nucleus of untransformed thyroid cells FADD localized mainly on euchromatin. We confirmed the nuclear localization of FADD in ex vivo liver and showed that in this organ FADD and MBD4 interact together. These results demonstrate that FADD is physiologically expressed in the nucleus of cells in at least two mouse organs. This particular localization opens new possible role of FADD in vivo either asan inhibitor of cell death, or as a transcription factor, or as a molecular link between apoptosis and genome surveillance. |
1411.1903 | Zhan Li | Zhan Li, Chunmei Wang, Longlong Tian, Jing Bai, Yang Zhao, Xin Zhang,
Shiwei Cao, Wei Qi, Hongdeng Qiu, Suomin Wang, Keliang Shi, Youwen Xu, Zhang
Mingliang, Bo Liu, Huijun Yao, Jie Liu, Wangsuo Wu, Xiaoli Wang | An embryo of protocell membrane: The capsule of graphene oxide | 1411.1903 | null | null | null | q-bio.PE cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many signs indicate that the graphene could widely occur on the early Earth.
Here, we report a new theory that graphene might be an embryo of protocell
membrane, and found several evidences. Firstly, the graphene oxide and
phospholipid-graphene oxide composite would curl into capsules in strongly
acidic saturated solution of Pb(NO3)2 at low temperature, providing a
protective space for biochemical reactions. Secondly, L-animi acids exhibit
higher reactivity than D-animi acids for graphene oxides in favor of the
formation of left-handed proteins. Thirdly, monolayer graphene with nanopores
prepared by unfocused 84Kr25+ has high selectivity for permeation of the
monovalent metal ions (Rb+ > K+ > Cs+ > Na+ > Li+), but does not allow Cl-
through, which could be attributed to the ion exchange of oxygen-containing
groups on the rim of nanopores. It is similar to K+ channels, which would cause
efflux of some ions from capsule of graphene oxides with the decrease of pH in
the primitive ocean, creating a suitable inner condition for the origin of
life. Consequently, the strongly acidic, high salinity and strong radiation as
well as temperature changes in the early Earth, regarded as negative factors,
would be indispensable for the origin of protocell. In short, graphene bred
life, but digested gradually by the evolution.
| [
{
"created": "Fri, 7 Nov 2014 13:24:03 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Nov 2014 23:39:58 GMT",
"version": "v2"
}
] | 2014-11-14 | [
[
"Li",
"Zhan",
""
],
[
"Wang",
"Chunmei",
""
],
[
"Tian",
"Longlong",
""
],
[
"Bai",
"Jing",
""
],
[
"Zhao",
"Yang",
""
],
[
"Zhang",
"Xin",
""
],
[
"Cao",
"Shiwei",
""
],
[
"Qi",
"Wei",
""
],
[
"Qiu",
"Hongdeng",
""
],
[
"Wang",
"Suomin",
""
],
[
"Shi",
"Keliang",
""
],
[
"Xu",
"Youwen",
""
],
[
"Mingliang",
"Zhang",
""
],
[
"Liu",
"Bo",
""
],
[
"Yao",
"Huijun",
""
],
[
"Liu",
"Jie",
""
],
[
"Wu",
"Wangsuo",
""
],
[
"Wang",
"Xiaoli",
""
]
] | Many signs indicate that the graphene could widely occur on the early Earth. Here, we report a new theory that graphene might be an embryo of protocell membrane, and found several evidences. Firstly, the graphene oxide and phospholipid-graphene oxide composite would curl into capsules in strongly acidic saturated solution of Pb(NO3)2 at low temperature, providing a protective space for biochemical reactions. Secondly, L-animi acids exhibit higher reactivity than D-animi acids for graphene oxides in favor of the formation of left-handed proteins. Thirdly, monolayer graphene with nanopores prepared by unfocused 84Kr25+ has high selectivity for permeation of the monovalent metal ions (Rb+ > K+ > Cs+ > Na+ > Li+), but does not allow Cl- through, which could be attributed to the ion exchange of oxygen-containing groups on the rim of nanopores. It is similar to K+ channels, which would cause efflux of some ions from capsule of graphene oxides with the decrease of pH in the primitive ocean, creating a suitable inner condition for the origin of life. Consequently, the strongly acidic, high salinity and strong radiation as well as temperature changes in the early Earth, regarded as negative factors, would be indispensable for the origin of protocell. In short, graphene bred life, but digested gradually by the evolution. |
2301.12422 | Jiayu Shang | Jiayu Shang and Cheng Peng and Xubo Tang and Yanni Sun | PhaVIP: Phage VIrion Protein classification based on chaos game
representation and Vision Transformer | 15 pages, 13 figures | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Motivation: As viruses that mainly infect bacteria, phages are key players
across a wide range of ecosystems. Analyzing phage proteins is indispensable
for understanding phages' functions and roles in microbiomes. High-throughput
sequencing enables us to obtain phages in different microbiomes with low cost.
However, compared to the fast accumulation of newly identified phages, phage
protein classification remains difficult. In particular, a fundamental need is
to annotate virion proteins, the structural proteins such as major tail,
baseplate etc. Although there are experimental methods for virion protein
identification, they are too expensive or time-consuming, leaving a large
number of proteins unclassified. Thus, there is a great demand to develop a
computational method for fast and accurate phage virion protein classification.
Results: In this work, we adapted the state-of-the-art image classification
model, Vision Transformer, to conduct virion protein classification. By
encoding protein sequences into unique images using chaos gaming
representation, we can leverage Vision Transformer to learn both local and
global features from sequence ``images''. Our method, PhaVIP, has two main
functions: classifying PVP and non-PVP sequences and annotating the types of
PVP, such as capsid and tail. We tested PhaVIP on several datasets with
increasing difficulty and benchmarked it against alternative tools. The
experimental results show that PhaVIP has superior performance. After
validating the performance of PhaVIP, we investigated two applications that can
use the output of PhaVIP: phage taxonomy classification and phage host
prediction. The results show the benefit of using classified proteins rather
than all proteins.
| [
{
"created": "Sun, 29 Jan 2023 11:17:52 GMT",
"version": "v1"
},
{
"created": "Tue, 31 Jan 2023 02:13:57 GMT",
"version": "v2"
}
] | 2023-02-01 | [
[
"Shang",
"Jiayu",
""
],
[
"Peng",
"Cheng",
""
],
[
"Tang",
"Xubo",
""
],
[
"Sun",
"Yanni",
""
]
] | Motivation: As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages' functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins such as major tail, baseplate etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein classification. Results: In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos gaming representation, we can leverage Vision Transformer to learn both local and global features from sequence ``images''. Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results show the benefit of using classified proteins rather than all proteins. |
0807.3521 | Otto Pulkkinen | Otto Pulkkinen and Johannes Berg | Dynamics of gene expression under feedback | null | null | null | null | q-bio.CB cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene expression is a stochastic process governed by the presence of specific
transcription factors. Here we study the dynamics of gene expression in the
presence of feedback, where a gene regulates its own expression. The nonlinear
coupling between input and output of gene expression can generate a dynamics
different from simple scenarios such as the Poisson process. This is
exemplified by our findings for the time intervals over which genes are
transcriptionally active and inactive. We apply our results to the lac system
in E. coli, where parametric inference on experimental data results in a broad
distribution of gene activity intervals.
| [
{
"created": "Tue, 22 Jul 2008 17:10:20 GMT",
"version": "v1"
}
] | 2008-07-23 | [
[
"Pulkkinen",
"Otto",
""
],
[
"Berg",
"Johannes",
""
]
] | Gene expression is a stochastic process governed by the presence of specific transcription factors. Here we study the dynamics of gene expression in the presence of feedback, where a gene regulates its own expression. The nonlinear coupling between input and output of gene expression can generate a dynamics different from simple scenarios such as the Poisson process. This is exemplified by our findings for the time intervals over which genes are transcriptionally active and inactive. We apply our results to the lac system in E. coli, where parametric inference on experimental data results in a broad distribution of gene activity intervals. |
2107.12536 | Caetano Mazzoni Ranieri | Caetano M. Ranieri, Jhielson M. Pimentel, Marcelo R. Romano, Leonardo
A. Elias, Roseli A. F. Romero, Michael A. Lones, Mariana F. P. Araujo,
Patricia A. Vargas, Renan C. Moioli | A Data-Driven Biophysical Computational Model of Parkinson's Disease
based on Marmoset Monkeys | null | IEEE Access, 2021 | 10.1109/ACCESS.2021.3108682 | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this work we propose a new biophysical computational model of brain
regions relevant to Parkinson's Disease based on local field potential data
collected from the brain of marmoset monkeys. Parkinson's disease is a
neurodegenerative disorder, linked to the death of dopaminergic neurons at the
substantia nigra pars compacta, which affects the normal dynamics of the basal
ganglia-thalamus-cortex neuronal circuit of the brain. Although there are
multiple mechanisms underlying the disease, a complete description of those
mechanisms and molecular pathogenesis are still missing, and there is still no
cure. To address this gap, computational models that resemble neurobiological
aspects found in animal models have been proposed. In our model, we performed a
data-driven approach in which a set of biologically constrained parameters is
optimised using differential evolution. Evolved models successfully resembled
single-neuron mean firing rates and spectral signatures of local field
potentials from healthy and parkinsonian marmoset brain data. As far as we are
concerned, this is the first computational model of Parkinson's Disease based
on simultaneous electrophysiological recordings from seven brain regions of
Marmoset monkeys. Results show that the proposed model could facilitate the
investigation of the mechanisms of PD and support the development of techniques
that can indicate new therapies. It could also be applied to other
computational neuroscience problems in which biological data could be used to
fit multi-scale models of brain circuits.
| [
{
"created": "Tue, 27 Jul 2021 01:09:11 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Sep 2021 17:48:01 GMT",
"version": "v2"
}
] | 2021-09-02 | [
[
"Ranieri",
"Caetano M.",
""
],
[
"Pimentel",
"Jhielson M.",
""
],
[
"Romano",
"Marcelo R.",
""
],
[
"Elias",
"Leonardo A.",
""
],
[
"Romero",
"Roseli A. F.",
""
],
[
"Lones",
"Michael A.",
""
],
[
"Araujo",
"Mariana F. P.",
""
],
[
"Vargas",
"Patricia A.",
""
],
[
"Moioli",
"Renan C.",
""
]
] | In this work we propose a new biophysical computational model of brain regions relevant to Parkinson's Disease based on local field potential data collected from the brain of marmoset monkeys. Parkinson's disease is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled single-neuron mean firing rates and spectral signatures of local field potentials from healthy and parkinsonian marmoset brain data. As far as we are concerned, this is the first computational model of Parkinson's Disease based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results show that the proposed model could facilitate the investigation of the mechanisms of PD and support the development of techniques that can indicate new therapies. It could also be applied to other computational neuroscience problems in which biological data could be used to fit multi-scale models of brain circuits. |
2306.00706 | Alexander Zubarev | A.Kh. Bikulov and A.P. Zubarev | Oscillations in p-adic diffusion processes and simulation of the
conformational dynamics of protein | 23 pages, 5 figures | null | null | null | q-bio.BM cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logarithmic oscillations superimposed on a power-law trend appear in the
behavior of various complex hierarchical systems. In this paper, we study the
logarithmic oscillations of relaxation curves in p-adic diffusion models that
are used to describe the conformational dynamics of protein. We consider the
case of a purely p-adic diffusion, as well as the case of p-adic diffusion with
a reaction sink. We show that, relaxation curves for large times in these two
cases are described by a power law on which logarithmic oscillations are
superimposed whose period and amplitude are determined by the parameters of the
model. We also provide a physical explanation of the emergence of oscillations
in relaxation curves and discuss the relation of the results to the experiments
on relaxation dynamics of protein.
| [
{
"created": "Thu, 27 Apr 2023 06:08:17 GMT",
"version": "v1"
}
] | 2023-06-02 | [
[
"Bikulov",
"A. Kh.",
""
],
[
"Zubarev",
"A. P.",
""
]
] | Logarithmic oscillations superimposed on a power-law trend appear in the behavior of various complex hierarchical systems. In this paper, we study the logarithmic oscillations of relaxation curves in p-adic diffusion models that are used to describe the conformational dynamics of protein. We consider the case of a purely p-adic diffusion, as well as the case of p-adic diffusion with a reaction sink. We show that, relaxation curves for large times in these two cases are described by a power law on which logarithmic oscillations are superimposed whose period and amplitude are determined by the parameters of the model. We also provide a physical explanation of the emergence of oscillations in relaxation curves and discuss the relation of the results to the experiments on relaxation dynamics of protein. |
1308.6367 | Ralph Brinks | Ralph Brinks | Partial differential equation about the prevalence of a chronic disease
in the presence of duration dependency | 10 pages, 3 figures | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The illness-death model of a chronic disease consists of the states 'Normal',
'Disease' and 'Death'. In general, the transition rates between the states
depend on three time scales: calendar time, age and duration of the chronic
disease. Previous works have shown that the age-specific prevalence of the
chronic disease can be described by differential equations if the duration is
negligible. This article derives a partial differential equation (PDE) in the
presence of duration dependency. As an important application, the PDE allows
the calculation of the age-specific incidence from cross-sectional surveys.
| [
{
"created": "Thu, 29 Aug 2013 05:18:44 GMT",
"version": "v1"
}
] | 2013-08-30 | [
[
"Brinks",
"Ralph",
""
]
] | The illness-death model of a chronic disease consists of the states 'Normal', 'Disease' and 'Death'. In general, the transition rates between the states depend on three time scales: calendar time, age and duration of the chronic disease. Previous works have shown that the age-specific prevalence of the chronic disease can be described by differential equations if the duration is negligible. This article derives a partial differential equation (PDE) in the presence of duration dependency. As an important application, the PDE allows the calculation of the age-specific incidence from cross-sectional surveys. |
2011.05514 | Tatjana Skrbic | Tatjana \v{S}krbi\'c, Amos Maritan, Achille Giacometti, George D.
Rose, Jayanth R. Banavar | Building blocks of protein structures -- Physics meets Biology | 32 pages, 6 figures, 2 tables; Added key sentence about the important
role of amino acid side chains in providing steric constraints | Phys. Rev. E 104, 014402 (2021) | 10.1103/PhysRevE.104.014402 | null | q-bio.BM cond-mat.soft cond-mat.stat-mech physics.bio-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The native state structures of globular proteins are stable and well-packed
indicating that self-interactions are favored over protein-solvent interactions
under folding conditions. We use this as a guiding principle to derive the
geometry of the building blocks of protein structures, alpha-helices and
strands assembled into beta-sheets, with no adjustable parameters, no amino
acid sequence information, and no chemistry. There is an almost perfect fit
between the dictates of mathematics and physics and the rules of quantum
chemistry. Our theory establishes an energy landscape that channels protein
evolution by providing sequence-independent platforms for elaborating
sequence-dependent functional diversity. Our work highlights the vital role of
discreteness in life and has implications for the creation of artificial life
and on the nature of life elsewhere in the cosmos.
| [
{
"created": "Wed, 11 Nov 2020 02:18:02 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Jan 2021 16:14:20 GMT",
"version": "v2"
}
] | 2021-07-14 | [
[
"Škrbić",
"Tatjana",
""
],
[
"Maritan",
"Amos",
""
],
[
"Giacometti",
"Achille",
""
],
[
"Rose",
"George D.",
""
],
[
"Banavar",
"Jayanth R.",
""
]
] | The native state structures of globular proteins are stable and well-packed indicating that self-interactions are favored over protein-solvent interactions under folding conditions. We use this as a guiding principle to derive the geometry of the building blocks of protein structures, alpha-helices and strands assembled into beta-sheets, with no adjustable parameters, no amino acid sequence information, and no chemistry. There is an almost perfect fit between the dictates of mathematics and physics and the rules of quantum chemistry. Our theory establishes an energy landscape that channels protein evolution by providing sequence-independent platforms for elaborating sequence-dependent functional diversity. Our work highlights the vital role of discreteness in life and has implications for the creation of artificial life and on the nature of life elsewhere in the cosmos. |
1603.08127 | Duncan Ralph | Duncan K. Ralph and Frederick A. Matsen IV | Likelihood-based inference of B-cell clonal families | null | null | 10.1371/journal.pcbi.1005086 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human immune system depends on a highly diverse collection of
antibody-making B cells. B cell receptor sequence diversity is generated by a
random recombination process called "rearrangement" forming progenitor B cells,
then a Darwinian process of lineage diversification and selection called
"affinity maturation." The resulting receptors can be sequenced in high
throughput for research and diagnostics. Such a collection of sequences
contains a mixture of various lineages, each of which may be quite numerous, or
may consist of only a single member. As a step to understanding the process and
result of this diversification, one may wish to reconstruct lineage membership,
i.e. to cluster sampled sequences according to which came from the same
rearrangement events. We call this clustering problem "clonal family
inference." In this paper we describe and validate a likelihood-based framework
for clonal family inference based on a multi-hidden Markov Model (multi-HMM)
framework for B cell receptor sequences. We describe an agglomerative algorithm
to find a maximum likelihood clustering, two approximate algorithms with
various trade-offs of speed versus accuracy, and a third, fast algorithm for
finding specific lineages. We show that under simulation these algorithms
greatly improve upon existing clonal family inference methods, and that they
also give significantly different clusters than previous methods when applied
to two real data sets.
| [
{
"created": "Sat, 26 Mar 2016 17:52:13 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Jun 2016 23:17:51 GMT",
"version": "v2"
}
] | 2017-02-08 | [
[
"Ralph",
"Duncan K.",
""
],
[
"Matsen",
"Frederick A.",
"IV"
]
] | The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called "rearrangement" forming progenitor B cells, then a Darwinian process of lineage diversification and selection called "affinity maturation." The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem "clonal family inference." In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets. |
1912.04680 | Zihao Wang | Zihao Wang, Zhenquan Zhang and Tianshou Zhou | Exact distributions for stochastic models of gene expression with
arbitrary regulation | This papar has been accepted for publication in SCIENCE CHINA
Mathematics | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochasticity in gene expression can result in fluctuations in gene product
levels. Recent experiments indicated that feedback regulation plays an
important role in controlling the noise in gene expression. A quantitative
understanding of the feedback effect on gene expression requires analysis of
the corresponding stochastic model. However, for stochastic models of gene
expression with general regulation functions, exact analytical results for gene
product distributions have not been given so far. Here, we propose a technique
to solve a generalized ON-OFF model of stochastic gene expression with
arbitrary (positive or negative, linear or nonlinear) feedbacks including
posttranscriptional or posttranslational regulation. The obtained results,
which generalize results obtained previously, provide new insights into the
role of feedback in regulating gene expression. The proposed analytical
framework can easily be extended to analysis of more complex models of
stochastic gene expression.
| [
{
"created": "Tue, 10 Dec 2019 13:35:50 GMT",
"version": "v1"
}
] | 2019-12-11 | [
[
"Wang",
"Zihao",
""
],
[
"Zhang",
"Zhenquan",
""
],
[
"Zhou",
"Tianshou",
""
]
] | Stochasticity in gene expression can result in fluctuations in gene product levels. Recent experiments indicated that feedback regulation plays an important role in controlling the noise in gene expression. A quantitative understanding of the feedback effect on gene expression requires analysis of the corresponding stochastic model. However, for stochastic models of gene expression with general regulation functions, exact analytical results for gene product distributions have not been given so far. Here, we propose a technique to solve a generalized ON-OFF model of stochastic gene expression with arbitrary (positive or negative, linear or nonlinear) feedbacks including posttranscriptional or posttranslational regulation. The obtained results, which generalize results obtained previously, provide new insights into the role of feedback in regulating gene expression. The proposed analytical framework can easily be extended to analysis of more complex models of stochastic gene expression. |
1208.3434 | Brian Williams Dr | Brian G. Williams, Renier van Rooyen and Martin Nieuwoudt | Anti-retroviral therapy for HIV: who should we test and who should we
treat? | The lines in Figure 2 were labelled from left to right but should
have been labelled right to left. This has been corrected in the caption.
Equation 4 is an approximation and a footnote to this effect has been added.
Ia(t) has now been defined immediately after Equation 4 | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In most countries CD4+ cell counts are still used for deciding when to start
HIV-positive people on anti-retroviral therapy. However, various CD4+
thresholds, 200, 350 or 500/\muL, are chosen arbitrarily and for historical
reasons. Here we consider the optimal CD4+ threshold at which asymptomatic
HIV-positive people living in Botswana, South Africa and Zimbabwe should start
treatment depending on their prognosis given their CD4+ cell counts or viral
load. We also examine the optimal interval at which people should be retested
if they are HIV-negative. This analysis shows that while the use of CD4+ cell
counts or viral load tests could have been useful in deciding how to triage
patients for treatment at the start of the epidemic this is no longer the case
except possibly for those aged about 15 to 25 years. In order not to do harm to
individual patients everyone should be started on ART as soon as they are found
to be HIV-positive.
| [
{
"created": "Wed, 15 Aug 2012 11:19:22 GMT",
"version": "v1"
},
{
"created": "Wed, 3 Oct 2012 02:54:40 GMT",
"version": "v2"
},
{
"created": "Tue, 16 Oct 2012 11:10:41 GMT",
"version": "v3"
}
] | 2012-10-17 | [
[
"Williams",
"Brian G.",
""
],
[
"van Rooyen",
"Renier",
""
],
[
"Nieuwoudt",
"Martin",
""
]
] | In most countries CD4+ cell counts are still used for deciding when to start HIV-positive people on anti-retroviral therapy. However, various CD4+ thresholds, 200, 350 or 500/\muL, are chosen arbitrarily and for historical reasons. Here we consider the optimal CD4+ threshold at which asymptomatic HIV-positive people living in Botswana, South Africa and Zimbabwe should start treatment depending on their prognosis given their CD4+ cell counts or viral load. We also examine the optimal interval at which people should be retested if they are HIV-negative. This analysis shows that while the use of CD4+ cell counts or viral load tests could have been useful in deciding how to triage patients for treatment at the start of the epidemic this is no longer the case except possibly for those aged about 15 to 25 years. In order not to do harm to individual patients everyone should be started on ART as soon as they are found to be HIV-positive. |
1601.04419 | Gunter Scharf | Gunter Scharf, Lam Dang | Dipole density instead of potentials in electrocardiology | 11 pages, 2 figures | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss the forward and inverse problems between the potential V(x)
measured in a heart chamber and its sources represented by a dipole density
d(y) located on the heart wall. We show that the mapping from d(y) to V(x) is a
compact integral operator. Its inverse is unbounded which makes the inverse
problem ill-posed in the mathematical sense. We investigate methods to solve
the inverse problem approximately in view of the mapping of complicated cardiac
arrhythmias. We point out an analogy between phase mapping and 2-dimensional
hydrodynamics.
| [
{
"created": "Mon, 18 Jan 2016 07:33:15 GMT",
"version": "v1"
}
] | 2016-01-19 | [
[
"Scharf",
"Gunter",
""
],
[
"Dang",
"Lam",
""
]
] | We discuss the forward and inverse problems between the potential V(x) measured in a heart chamber and its sources represented by a dipole density d(y) located on the heart wall. We show that the mapping from d(y) to V(x) is a compact integral operator. Its inverse is unbounded which makes the inverse problem ill-posed in the mathematical sense. We investigate methods to solve the inverse problem approximately in view of the mapping of complicated cardiac arrhythmias. We point out an analogy between phase mapping and 2-dimensional hydrodynamics. |
2005.14062 | Anthony Gitter | David Merrell, Anthony Gitter | Inferring Signaling Pathways with Probabilistic Programming | 15 pages, 10 figures; added Appendices C and D | Bioinformatics 36:Supplement_2 (2020) i822-i830 | 10.1093/bioinformatics/btaa861 | null | q-bio.MN cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Cells regulate themselves via dizzyingly complex biochemical processes called
signaling pathways. These are usually depicted as a network, where nodes
represent proteins and edges indicate their influence on each other. In order
to understand diseases and therapies at the cellular level, it is crucial to
have an accurate understanding of the signaling pathways at work. Since
signaling pathways can be modified by disease, the ability to infer signaling
pathways from condition- or patient-specific data is highly valuable. A variety
of techniques exist for inferring signaling pathways. We build on past works
that formulate signaling pathway inference as a Dynamic Bayesian Network
structure estimation problem on phosphoproteomic time course data. We take a
Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior
distribution over possible Dynamic Bayesian Network structures. Our primary
contributions are (i) a novel proposal distribution that efficiently samples
sparse graphs and (ii) the relaxation of common restrictive modeling
assumptions. We implement our method, named Sparse Signaling Pathway Sampling,
in Julia using the Gen probabilistic programming language. Probabilistic
programming is a powerful methodology for building statistical models. The
resulting code is modular, extensible, and legible. The Gen language, in
particular, allows us to customize our inference procedure for biological
graphs and ensure efficient sampling. We evaluate our algorithm on simulated
data and the HPN-DREAM pathway reconstruction challenge, comparing our
performance against a variety of baseline methods. Our results demonstrate the
vast potential for probabilistic programming, and Gen specifically, for
biological network inference. Find the full codebase at
https://github.com/gitter-lab/ssps
| [
{
"created": "Thu, 28 May 2020 14:55:11 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Jul 2020 22:15:36 GMT",
"version": "v2"
}
] | 2021-01-13 | [
[
"Merrell",
"David",
""
],
[
"Gitter",
"Anthony",
""
]
] | Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions. We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible, and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference. Find the full codebase at https://github.com/gitter-lab/ssps |
1008.3246 | Dietrich Stauffer | D. Stauffer, S. Cebrat, T.J.P. Penna and A.O. Sousa | Love kills: Simulations in Penna Ageing Model | 14 pages, including numerous figures | null | 10.1142/S0129183111016245 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The standard Penna ageing model with sexual reproduction is enlarged by
adding additional bit-strings for love: Marriage happens only if the male love
strings are sufficiently different from the female ones. We simulate at what
level of required difference the population dies out.
| [
{
"created": "Thu, 19 Aug 2010 09:08:21 GMT",
"version": "v1"
}
] | 2015-05-19 | [
[
"Stauffer",
"D.",
""
],
[
"Cebrat",
"S.",
""
],
[
"Penna",
"T. J. P.",
""
],
[
"Sousa",
"A. O.",
""
]
] | The standard Penna ageing model with sexual reproduction is enlarged by adding additional bit-strings for love: Marriage happens only if the male love strings are sufficiently different from the female ones. We simulate at what level of required difference the population dies out. |
2003.11860 | Mariano de Souza Prof. Dr. | Isys F. Mello, Lucas Squillante, Gabriel O. Gomes, Antonio C.
Seridonio, M. de Souza | Epidemics, the Ising-model and percolation theory: a comprehensive
review focussed on Covid-19 | 33 pages, 10 figures, 2 tables (new sections and new figs. were
added, review paper with new aspects of epidemics) | null | 10.1016/j.physa.2021.125963 | null | q-bio.PE cond-mat.stat-mech physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We revisit well-established concepts of epidemiology, the Ising-model, and
percolation theory. Also, we employ a spin $S$ = 1/2 Ising-like model and a
(logistic) Fermi-Dirac-like function to describe the spread of Covid-19. Our
analysis reinforces well-established literature results, namely: \emph{i}) that
the epidemic curves can be described by a Gaussian-type function; \emph{ii})
that the temporal evolution of the accumulative number of infections and
fatalities follow a logistic function, which has some resemblance with a
distorted Fermi-Dirac-like function; \emph{iii}) the key role played by the
quarantine to block the spread of Covid-19 in terms of an \emph{interacting}
parameter, which emulates the contact between infected and non-infected people.
Furthermore, in the frame of elementary percolation theory, we show that:
\emph{i}) the percolation probability can be associated with the probability of
a person being infected with Covid-19; \emph{ii}) the concepts of blocked and
non-blocked connections can be associated, respectively, with a person
respecting or not the social distancing, impacting thus in the probability of
an infected person to infect other people. Increasing the number of infected
people leads to an increase in the number of net connections, giving rise thus
to a higher probability of new infections (percolation). We demonstrate the
importance of social distancing in preventing the spread of Covid-19 in a
pedagogical way. Given the impossibility of making a precise forecast of the
disease spread, we highlight the importance of taking into account additional
factors, such as climate changes and urbanization, in the mathematical
description of epidemics. Yet, we make a connection between the standard
mathematical models employed in epidemics and well-established concepts in
condensed matter Physics, such as the Fermi gas and the Landau Fermi-liquid
picture.
| [
{
"created": "Thu, 26 Mar 2020 12:19:50 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Jun 2020 18:23:43 GMT",
"version": "v2"
}
] | 2021-04-28 | [
[
"Mello",
"Isys F.",
""
],
[
"Squillante",
"Lucas",
""
],
[
"Gomes",
"Gabriel O.",
""
],
[
"Seridonio",
"Antonio C.",
""
],
[
"de Souza",
"M.",
""
]
] | We revisit well-established concepts of epidemiology, the Ising-model, and percolation theory. Also, we employ a spin $S$ = 1/2 Ising-like model and a (logistic) Fermi-Dirac-like function to describe the spread of Covid-19. Our analysis reinforces well-established literature results, namely: \emph{i}) that the epidemic curves can be described by a Gaussian-type function; \emph{ii}) that the temporal evolution of the accumulative number of infections and fatalities follow a logistic function, which has some resemblance with a distorted Fermi-Dirac-like function; \emph{iii}) the key role played by the quarantine to block the spread of Covid-19 in terms of an \emph{interacting} parameter, which emulates the contact between infected and non-infected people. Furthermore, in the frame of elementary percolation theory, we show that: \emph{i}) the percolation probability can be associated with the probability of a person being infected with Covid-19; \emph{ii}) the concepts of blocked and non-blocked connections can be associated, respectively, with a person respecting or not the social distancing, impacting thus in the probability of an infected person to infect other people. Increasing the number of infected people leads to an increase in the number of net connections, giving rise thus to a higher probability of new infections (percolation). We demonstrate the importance of social distancing in preventing the spread of Covid-19 in a pedagogical way. Given the impossibility of making a precise forecast of the disease spread, we highlight the importance of taking into account additional factors, such as climate changes and urbanization, in the mathematical description of epidemics. Yet, we make a connection between the standard mathematical models employed in epidemics and well-established concepts in condensed matter Physics, such as the Fermi gas and the Landau Fermi-liquid picture. |
0806.1955 | Fran Adar | Fran Adar | Observation of Cholesterol Dissolved in Microscopic Deposits of Free
Fatty Acids in the Lumen of an Aorta of a Mouse Model for Human
Atherosclerosis | 5 pages, 3 figures | SPIE 7 5321A-11 (2004) | null | null | q-bio.TO q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Raman spectrum of microscopic droplets of lipid material on the lumen of
the aorta of an apolipoprotein E knock-out mouse were reported in the
proceedings of an SPIE conference. Based on the absence of the carbonyl band in
the spectrum, at that time it was determined that the spectrum represents free
fatty acids rather than triglycerides. More recent examination of the spectrum
indicates that these droplets contain dissolved cholesterol, and thus can be
used as an early indicator of the atherosclerosis process in animal models
during drug development.
| [
{
"created": "Wed, 11 Jun 2008 20:06:12 GMT",
"version": "v1"
}
] | 2008-06-13 | [
[
"Adar",
"Fran",
""
]
] | The Raman spectrum of microscopic droplets of lipid material on the lumen of the aorta of an apolipoprotein E knock-out mouse were reported in the proceedings of an SPIE conference. Based on the absence of the carbonyl band in the spectrum, at that time it was determined that the spectrum represents free fatty acids rather than triglycerides. More recent examination of the spectrum indicates that these droplets contain dissolved cholesterol, and thus can be used as an early indicator of the atherosclerosis process in animal models during drug development. |
1210.3155 | Teruaki Okushima | Teruaki Okushima and Hiroshi Kuratsuji | DNA as a one-dimensional chiral material. II. Dynamics of the structural
transition between B form and Z form | 9 pages, 4 figures | Physical Review E 86, 041905 (2012) | 10.1103/PhysRevE.86.041905 | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyze the dynamics of structural transitions between normal right-handed
B form and unusual left-handed Z form for a linear DNA molecule. The dynamics
under the external torque in physiological buffer is modeled by a Langevin
equation, with the potential term given by the authors previously [Phys. Rev. E
84, 021926 (2011)]. With this model, we first simulate the relaxation processes
around B-form structure after sudden changes of the external torques, where
slow relaxation $\sim t^{-1/2}$ as a function of the elapsed time $t$ is
observed. Then, the dynamics of structural transition from Z form to B form is
computed under various external torque strength. For small external torques,
the transition proceeds via nucleation and the growth, while for higher
torques, Z-form structure becomes unstable, and the transition mechanism is
switched to a spinodal-like process. These numerical results are qualitatively
understood by simple phenomenological arguments.
| [
{
"created": "Thu, 11 Oct 2012 09:02:38 GMT",
"version": "v1"
}
] | 2015-06-11 | [
[
"Okushima",
"Teruaki",
""
],
[
"Kuratsuji",
"Hiroshi",
""
]
] | We analyze the dynamics of structural transitions between normal right-handed B form and unusual left-handed Z form for a linear DNA molecule. The dynamics under the external torque in physiological buffer is modeled by a Langevin equation, with the potential term given by the authors previously [Phys. Rev. E 84, 021926 (2011)]. With this model, we first simulate the relaxation processes around B-form structure after sudden changes of the external torques, where slow relaxation $\sim t^{-1/2}$ as a function of the elapsed time $t$ is observed. Then, the dynamics of structural transition from Z form to B form is computed under various external torque strength. For small external torques, the transition proceeds via nucleation and the growth, while for higher torques, Z-form structure becomes unstable, and the transition mechanism is switched to a spinodal-like process. These numerical results are qualitatively understood by simple phenomenological arguments. |
2012.12854 | Youdong Mao | Zhaolong Wu, Shuwen Zhang, Wei Li Wang, Yinping Ma, Yuanchen Dong and
Youdong Mao | Deep manifold learning reveals hidden dynamics of proteasome
autoregulation | 81 pages, 16 figures, 2 tables | null | null | null | q-bio.QM cond-mat.stat-mech cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The 2.5-MDa 26S proteasome maintains proteostasis and regulates myriad
cellular processes. How polyubiquitylated substrate interactions regulate
proteasome activity is not understood. Here we introduce a deep manifold
learning framework, named AlphaCryo4D, which enables atomic-level cryogenic
electron microscopy (cryo-EM) reconstructions of nonequilibrium conformational
continuum and reconstitutes hidden dynamics of proteasome autoregulation in the
act of substrate degradation. AlphaCryo4D integrates 3D deep residual learning
with manifold embedding of free-energy landscapes, which directs 3D clustering
via an energy-based particle-voting algorithm. In blind assessments using
simulated heterogeneous cryo-EM datasets, AlphaCryo4D achieved 3D
classification accuracy three times that of conventional method and
reconstructed continuous conformational changes of a 130-kDa protein at
sub-3-angstrom resolution. By using AlphaCryo4D to analyze a single
experimental cryo-EM dataset, we identified 64 conformers of the
substrate-bound human 26S proteasome, revealing conformational entanglement of
two regulatory particles in the doubly capped holoenzymes and their energetic
differences with singly capped ones. Novel ubiquitin-binding sites are
discovered on the RPN2, RPN10 and Alpha5 subunits to remodel polyubiquitin
chains for deubiquitylation and recycle. Importantly, AlphaCryo4D choreographs
single-nucleotide-exchange dynamics of proteasomal AAA-ATPase motor during
translocation initiation, which upregulates proteolytic activity by
allosterically promoting nucleophilic attack. Our systemic analysis illuminates
a grand hierarchical allostery for proteasome autoregulation.
| [
{
"created": "Wed, 23 Dec 2020 18:23:53 GMT",
"version": "v1"
},
{
"created": "Sun, 13 Jun 2021 06:34:31 GMT",
"version": "v2"
}
] | 2021-06-15 | [
[
"Wu",
"Zhaolong",
""
],
[
"Zhang",
"Shuwen",
""
],
[
"Wang",
"Wei Li",
""
],
[
"Ma",
"Yinping",
""
],
[
"Dong",
"Yuanchen",
""
],
[
"Mao",
"Youdong",
""
]
] | The 2.5-MDa 26S proteasome maintains proteostasis and regulates myriad cellular processes. How polyubiquitylated substrate interactions regulate proteasome activity is not understood. Here we introduce a deep manifold learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy (cryo-EM) reconstructions of nonequilibrium conformational continuum and reconstitutes hidden dynamics of proteasome autoregulation in the act of substrate degradation. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of free-energy landscapes, which directs 3D clustering via an energy-based particle-voting algorithm. In blind assessments using simulated heterogeneous cryo-EM datasets, AlphaCryo4D achieved 3D classification accuracy three times that of conventional method and reconstructed continuous conformational changes of a 130-kDa protein at sub-3-angstrom resolution. By using AlphaCryo4D to analyze a single experimental cryo-EM dataset, we identified 64 conformers of the substrate-bound human 26S proteasome, revealing conformational entanglement of two regulatory particles in the doubly capped holoenzymes and their energetic differences with singly capped ones. Novel ubiquitin-binding sites are discovered on the RPN2, RPN10 and Alpha5 subunits to remodel polyubiquitin chains for deubiquitylation and recycle. Importantly, AlphaCryo4D choreographs single-nucleotide-exchange dynamics of proteasomal AAA-ATPase motor during translocation initiation, which upregulates proteolytic activity by allosterically promoting nucleophilic attack. Our systemic analysis illuminates a grand hierarchical allostery for proteasome autoregulation. |
1811.03178 | Arni S.R. Srinivasa Rao | Arni S.R. Srinivasa Rao and James R. Carey | Computation of life expectancy from incomplete data | An alternate way to compute life expectancy when life tables are not
available | Handbook of Statistics, Elsevier Volume 43, 2020, Pages 379-389 | 10.1016/bs.host.2020.02.001 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Estimating the human longevity and computing of life expectancy are central
to the population dynamics. These aspects were studied seriously by scientists
since fifteenth century, including renowned astronomer Edmund Halley. From
basic principles of population dynamics, we propose a method to compute life
expectancy from incomplete data.
| [
{
"created": "Wed, 7 Nov 2018 23:04:53 GMT",
"version": "v1"
}
] | 2021-06-15 | [
[
"Rao",
"Arni S. R. Srinivasa",
""
],
[
"Carey",
"James R.",
""
]
] | Estimating the human longevity and computing of life expectancy are central to the population dynamics. These aspects were studied seriously by scientists since fifteenth century, including renowned astronomer Edmund Halley. From basic principles of population dynamics, we propose a method to compute life expectancy from incomplete data. |
1811.06417 | Max Allen | Maximilian L. Allen, Nathan M. Roberts, and Timothy R. Van Deelen | Age-at-harvest models as monitoring and harvest management tools for
Wisconsin carnivores | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantifying and estimating wildlife population sizes is a foundation of
wildlife management. However, many carnivore species are cryptic, leading to
innate difficulties in estimating their populations. We evaluated the potential
for using more rigorous statistical models to estimate the populations of black
bears (Ursus americanus) in Wisconisin, and their applicability to other
furbearers such as bobcats (Lynx rufus). To estimate black bear populations, we
developed an AAH state-space model in a Bayesian framework based on Norton
(2015) that can account for variation in harvest and population demographics
over time. Our state-space model created an accurate estimate of the black bear
population in Wisconsin based on age-at-harvest data and improves on previous
models by using little demographic data, no auxiliary data, and not being
sensitive to initial population size. The increased accuracy of the AAH
state-space models should allow for better management by being able to set
accurate quotas to ensure a sustainable harvest for the black bear population
in each zone. We also evaluated the demography and annual harvest data of
bobcats in Wisconsin to determine trends in harvest, method, and hunter
participation in Wisconsin. Each trait of harvested bobcats that we tested
(mass, male:female sex ratio, and age) changed over time, and because these
were interrelated, and we inferred that harvest selection for larger size
biased harvests in favor of a) larger, b) older, and c) male bobcats by hound
hunters. We also found an increase in the proportion of bobcats that were
harvested by hound hunting compared to trapping, and that hound hunters were
more likely to create taxidermy mounts than trappers. We also found that
decreasing available bobcat tags and increasing success have occurred over
time, and correlate with substantially increasing both hunter populations and
hunter interest.
| [
{
"created": "Thu, 15 Nov 2018 15:07:31 GMT",
"version": "v1"
}
] | 2018-11-16 | [
[
"Allen",
"Maximilian L.",
""
],
[
"Roberts",
"Nathan M.",
""
],
[
"Van Deelen",
"Timothy R.",
""
]
] | Quantifying and estimating wildlife population sizes is a foundation of wildlife management. However, many carnivore species are cryptic, leading to innate difficulties in estimating their populations. We evaluated the potential for using more rigorous statistical models to estimate the populations of black bears (Ursus americanus) in Wisconisin, and their applicability to other furbearers such as bobcats (Lynx rufus). To estimate black bear populations, we developed an AAH state-space model in a Bayesian framework based on Norton (2015) that can account for variation in harvest and population demographics over time. Our state-space model created an accurate estimate of the black bear population in Wisconsin based on age-at-harvest data and improves on previous models by using little demographic data, no auxiliary data, and not being sensitive to initial population size. The increased accuracy of the AAH state-space models should allow for better management by being able to set accurate quotas to ensure a sustainable harvest for the black bear population in each zone. We also evaluated the demography and annual harvest data of bobcats in Wisconsin to determine trends in harvest, method, and hunter participation in Wisconsin. Each trait of harvested bobcats that we tested (mass, male:female sex ratio, and age) changed over time, and because these were interrelated, and we inferred that harvest selection for larger size biased harvests in favor of a) larger, b) older, and c) male bobcats by hound hunters. We also found an increase in the proportion of bobcats that were harvested by hound hunting compared to trapping, and that hound hunters were more likely to create taxidermy mounts than trappers. We also found that decreasing available bobcat tags and increasing success have occurred over time, and correlate with substantially increasing both hunter populations and hunter interest. |
1607.04152 | Renzhi Cao | Renzhi Cao, Jianlin Cheng | Protein single-model quality assessment by feature-based probability
density functions | 18 pages, 3 figures, 2 tables | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Protein quality assessment (QA) has played an important role in protein
structure prediction. We developed a novel single-model quality assessment
method - Qprob. Qprob calculates the absolute error for each protein feature
value against the true quality scores (i.e. GDT-TS scores) of protein
structural models, and uses them to estimate its probability density
distribution for quality assessment. Qprob has been blindly tested on the 11th
Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as
MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one
of the top single-model QA methods. In addition, Qprob makes contributions to
our protein tertiary structure predictor MULTICOM, which is officially ranked
3rd out of 143 predictors. The good performance shows that Qprob is good at
assessing the quality of models of hard targets. These results demonstrate that
this new probability density distribution based method is effective for protein
single-model quality assessment and is useful for protein structure prediction.
The webserver and software packages of Qprob are available at:
http://calla.rnet.missouri.edu/qprob/.
| [
{
"created": "Tue, 22 Mar 2016 01:03:49 GMT",
"version": "v1"
}
] | 2016-07-15 | [
[
"Cao",
"Renzhi",
""
],
[
"Cheng",
"Jianlin",
""
]
] | Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method - Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver and software packages of Qprob are available at: http://calla.rnet.missouri.edu/qprob/. |
2007.05859 | Hamidreza Chitsaz | Ali Ebrahimpour Boroojeny and Hamidreza Chitsaz | SARS-CoV-2 orthologs of pathogenesis-involved small viral RNAs of
SARS-CoV | null | null | null | null | q-bio.GN q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: The COVID-19 pandemic clock is ticking and the survival of many
of mankind's modern institutions and or survival of many individuals is at
stake. There is a need for treatments to significantly reduce the morbidity and
mortality of COVID-19. Hence, we delved deep into the SARS-CoV-2 genome, which
is the virus that has caused COVID-19. SARS-CoV-2 is from the same family as
SARS-CoV in which three small viral RNAs (svRNA) were recently identified;
those svRNAs play a significant role in the virus pathogenesis in mice.
Contribution: In this paper, we report potential orthologs of those three
svRNAs in the SARS-CoV-2 genome. Instead of off-the-shelf search and alignment
algorithms, which failed to discover the orthologs, we used a special alignment
scoring that does not penalize C/T and A/G mismatches as much as the other
mutations. RNA bases C and U both can bind to G; similarly, A and G both can
bind to U, hence, our scoring. We also validate this hypothesis using a novel,
independent computational experiment. To validate our results, we confirmed the
discovered orthologs are fully conserved in all the tested publicly available
genomes of various strains of SARS-CoV-2; the loci at which the SARS-CoV-2
orthologs occur are close to the loci at which SARS-CoV svRNAs occur. We also
report potential targets for these svRNAs. We hypothesize that the discovered
orthologs play a role in pathogenesis of SARS-CoV-2, and therefore,
antagomir-mediated inhibition of these SARS-CoV-2 svRNAs inhibits COVID-19.
| [
{
"created": "Sat, 11 Jul 2020 21:27:48 GMT",
"version": "v1"
},
{
"created": "Sat, 5 Dec 2020 16:54:27 GMT",
"version": "v2"
}
] | 2020-12-08 | [
[
"Boroojeny",
"Ali Ebrahimpour",
""
],
[
"Chitsaz",
"Hamidreza",
""
]
] | Background: The COVID-19 pandemic clock is ticking and the survival of many of mankind's modern institutions and or survival of many individuals is at stake. There is a need for treatments to significantly reduce the morbidity and mortality of COVID-19. Hence, we delved deep into the SARS-CoV-2 genome, which is the virus that has caused COVID-19. SARS-CoV-2 is from the same family as SARS-CoV in which three small viral RNAs (svRNA) were recently identified; those svRNAs play a significant role in the virus pathogenesis in mice. Contribution: In this paper, we report potential orthologs of those three svRNAs in the SARS-CoV-2 genome. Instead of off-the-shelf search and alignment algorithms, which failed to discover the orthologs, we used a special alignment scoring that does not penalize C/T and A/G mismatches as much as the other mutations. RNA bases C and U both can bind to G; similarly, A and G both can bind to U, hence, our scoring. We also validate this hypothesis using a novel, independent computational experiment. To validate our results, we confirmed the discovered orthologs are fully conserved in all the tested publicly available genomes of various strains of SARS-CoV-2; the loci at which the SARS-CoV-2 orthologs occur are close to the loci at which SARS-CoV svRNAs occur. We also report potential targets for these svRNAs. We hypothesize that the discovered orthologs play a role in pathogenesis of SARS-CoV-2, and therefore, antagomir-mediated inhibition of these SARS-CoV-2 svRNAs inhibits COVID-19. |
1405.1269 | Andrei Khrennikov Yu | Andrei Khrennikov and Irina Basieva | Quantum model for psychological measurements: from the projection
postulate to interference of mental observables represented as positive
operator valued measures | null | NeuroQuantology. 12. 324-336, 2014 | null | null | q-bio.NC math.PR quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently foundational issues of applicability of the formalism of quantum
mechanics (QM) to cognitive psychology, decision making, and psychophysics
attracted a lot of interest. In particular, in \cite{DKBB} the possibility to
use of the projection postulate and representation of "mental observables" by
Hermitian operators was discussed in very detail. The main conclusion of the
recent discussions on the foundations of "quantum(-like) cognitive psychology"
is that one has to be careful in determination of conditions of applicability
of the projection postulate as a mathematical tool for description of
measurements of observables represented by Hermitian operators. To represent
some statistical experimental data (both physical and mental) in the
quantum(-like) way, one has to use generalized quantum observables given by
positive operator-valued measures (POVMs). This paper contains a brief review
on POVMs which can be useful for newcomers to the field of quantum(-like)
studies. Especially interesting for cognitive psychology is a variant of the
formula of total probability (FTP) with the interference term derived for
incompatible observables given by POVMs. We present an interpretation of the
interference term from the psychological viewpoint. As was shown before, the
appearance of such a term (perturbing classical FTP) plays the important role
in cognitive psychology, e.g., recognition of ambiguous figures and the
disjunction effect. The interference term for observables given by POVMs has
much more complicated structure than the corresponding term for observables
given by Hermitian operators. We elaborate cognitive interpretations of
different components of the POVMs-interference term and apply our analysis to a
quantum(-like) model of decision making.
| [
{
"created": "Tue, 6 May 2014 13:52:15 GMT",
"version": "v1"
}
] | 2014-12-30 | [
[
"Khrennikov",
"Andrei",
""
],
[
"Basieva",
"Irina",
""
]
] | Recently foundational issues of applicability of the formalism of quantum mechanics (QM) to cognitive psychology, decision making, and psychophysics attracted a lot of interest. In particular, in \cite{DKBB} the possibility to use of the projection postulate and representation of "mental observables" by Hermitian operators was discussed in very detail. The main conclusion of the recent discussions on the foundations of "quantum(-like) cognitive psychology" is that one has to be careful in determination of conditions of applicability of the projection postulate as a mathematical tool for description of measurements of observables represented by Hermitian operators. To represent some statistical experimental data (both physical and mental) in the quantum(-like) way, one has to use generalized quantum observables given by positive operator-valued measures (POVMs). This paper contains a brief review on POVMs which can be useful for newcomers to the field of quantum(-like) studies. Especially interesting for cognitive psychology is a variant of the formula of total probability (FTP) with the interference term derived for incompatible observables given by POVMs. We present an interpretation of the interference term from the psychological viewpoint. As was shown before, the appearance of such a term (perturbing classical FTP) plays the important role in cognitive psychology, e.g., recognition of ambiguous figures and the disjunction effect. The interference term for observables given by POVMs has much more complicated structure than the corresponding term for observables given by Hermitian operators. We elaborate cognitive interpretations of different components of the POVMs-interference term and apply our analysis to a quantum(-like) model of decision making. |
0904.3233 | Thomas R. Weikl | Thomas R. Weikl | Transition states in protein folding | Review, 9 pages, 9 figures | null | 10.4208/cicp.2009.08.202 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The folding dynamics of small single-domain proteins is a current focus of
simulations and experiments. Many of these proteins are 'two-state folders',
i.e. proteins that fold rather directly from the denatured state to the native
state, without populating metastable intermediate states. A central question is
how to characterize the instable, partially folded conformations of two-state
proteins, in particular the rate-limiting transition-state conformations
between the denatured and the native state. These partially folded
conformations are short-lived and cannot be observed directly in experiments.
However, experimental data from detailed mutational analyses of the folding
dynamics provide indirect access to transition states. The interpretation of
these data, in particular the reconstruction of transition-state conformations,
requires simulation and modeling. The traditional interpretation of the
mutational data aims to reconstruct the degree of structure formation of
individual residues in the transition state, while a novel interpretation aims
at degrees of structure formation of cooperative substructures such as
alpha-helices and beta-hairpins. By splitting up mutation-induced free energy
changes into secondary and tertiary structural components, the novel
interpretation resolves some of the inconsistencies of the traditional
interpretation.
| [
{
"created": "Tue, 21 Apr 2009 12:23:38 GMT",
"version": "v1"
}
] | 2020-01-08 | [
[
"Weikl",
"Thomas R.",
""
]
] | The folding dynamics of small single-domain proteins is a current focus of simulations and experiments. Many of these proteins are 'two-state folders', i.e. proteins that fold rather directly from the denatured state to the native state, without populating metastable intermediate states. A central question is how to characterize the instable, partially folded conformations of two-state proteins, in particular the rate-limiting transition-state conformations between the denatured and the native state. These partially folded conformations are short-lived and cannot be observed directly in experiments. However, experimental data from detailed mutational analyses of the folding dynamics provide indirect access to transition states. The interpretation of these data, in particular the reconstruction of transition-state conformations, requires simulation and modeling. The traditional interpretation of the mutational data aims to reconstruct the degree of structure formation of individual residues in the transition state, while a novel interpretation aims at degrees of structure formation of cooperative substructures such as alpha-helices and beta-hairpins. By splitting up mutation-induced free energy changes into secondary and tertiary structural components, the novel interpretation resolves some of the inconsistencies of the traditional interpretation. |
1910.14460 | Javier Cabrera | J. Cabrera, D. Amaratunga, W. Kostis and J Kostis | Precision disease networks (PDN) | null | null | null | null | q-bio.QM cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | This paper presents a method for building patient-based networks that we call
Precision disease networks, and its uses for predicting medical outcomes. Our
methodology consists of building networks, one for each patient or case, that
describes the dis-ease evolution of the patient (PDN) and store the networks as
a set of features in a data set of PDN's, one per observation. We cluster the
PDN data and study the within and between cluster variability. In addition, we
develop data visualization technics in order to display, compare and summarize
the network data. Finally, we analyze a dataset of heart diseases patients from
a New Jersey statewide data-base MIDAS (Myocardial Infarction Data Acquisition
System, in order to show that the network data improve on the prediction of
important patient outcomes such as death or cardiovascular death, when compared
with the standard statistical analysis.
| [
{
"created": "Wed, 30 Oct 2019 15:22:04 GMT",
"version": "v1"
}
] | 2019-11-01 | [
[
"Cabrera",
"J.",
""
],
[
"Amaratunga",
"D.",
""
],
[
"Kostis",
"W.",
""
],
[
"Kostis",
"J",
""
]
] | This paper presents a method for building patient-based networks that we call Precision disease networks, and its uses for predicting medical outcomes. Our methodology consists of building networks, one for each patient or case, that describes the dis-ease evolution of the patient (PDN) and store the networks as a set of features in a data set of PDN's, one per observation. We cluster the PDN data and study the within and between cluster variability. In addition, we develop data visualization technics in order to display, compare and summarize the network data. Finally, we analyze a dataset of heart diseases patients from a New Jersey statewide data-base MIDAS (Myocardial Infarction Data Acquisition System, in order to show that the network data improve on the prediction of important patient outcomes such as death or cardiovascular death, when compared with the standard statistical analysis. |
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