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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1804.10493
|
Jicun Wang-Michelitsch
|
Jicun Wang-Michelitsch, Thomas M Michelitsch
|
Three pathways of cell transformation of lymphoid cell: a slow, a rapid,
and an accelerated
|
24 pages, 4 figures
| null | null | null |
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Lymphoid leukemia (LL) and lymphoma are neoplasms developed from lymphoid
cells (LCs). To understand why different forms of LL/lymphoma occur at
different ages, we analyzed the effects of different types of DNA changes on a
LC and the cellular characteristics of LCs. Point DNA mutations (PDMs) and
chromosome changes (CCs) are the two major types of DNA changes. CCs have three
subtypes by their effects on a LC: great-effect CCs (GECCs), mild-effect CCs
(MECCs), and intermediate-effect CCs (IECCs). PDMs and MECCs are mostly mild
thus can accumulate in cells. Some of the PDMs/MECCs contribute to cell
transformation. A GECC affects one or more genes and can alone drive cell
transformation. An IECC affects one or more genes and participates in cell
transformation. Due to cellular characteristics, a LC may have higher
survivability from DNA changes and require obtaining fewer cancerous properties
for transformation than a tissue cell. Hence, a LC can be more rapidly
transformed by a CC. On this basis, we hypothesize that a LC may have three
pathways on transformation: a slow, a rapid, and an accelerated. Slow pathway
is driven by accumulation of PDMs/MECCs. Rapid pathway is driven by a GECC in
"one step". Accelerated pathway is driven by accumulation of
PDMs/MECCs/IECC(s). Cell transformations of a LC via different pathways occur
at different ages. A transformation via slow pathway occurs mainly in adults. A
transformation via rapid pathway occurs at any age and has no increasing
incidence with age. A transformation via accelerated pathway occurs also at any
age but has increasing incidence with age. In conclusion, a LC may have three
pathways on cell transformation, and the occurring age of LL/lymphoma may be
determined by the transforming pathway of a LC.
|
[
{
"created": "Fri, 27 Apr 2018 13:28:20 GMT",
"version": "v1"
}
] |
2018-04-30
|
[
[
"Wang-Michelitsch",
"Jicun",
""
],
[
"Michelitsch",
"Thomas M",
""
]
] |
Lymphoid leukemia (LL) and lymphoma are neoplasms developed from lymphoid cells (LCs). To understand why different forms of LL/lymphoma occur at different ages, we analyzed the effects of different types of DNA changes on a LC and the cellular characteristics of LCs. Point DNA mutations (PDMs) and chromosome changes (CCs) are the two major types of DNA changes. CCs have three subtypes by their effects on a LC: great-effect CCs (GECCs), mild-effect CCs (MECCs), and intermediate-effect CCs (IECCs). PDMs and MECCs are mostly mild thus can accumulate in cells. Some of the PDMs/MECCs contribute to cell transformation. A GECC affects one or more genes and can alone drive cell transformation. An IECC affects one or more genes and participates in cell transformation. Due to cellular characteristics, a LC may have higher survivability from DNA changes and require obtaining fewer cancerous properties for transformation than a tissue cell. Hence, a LC can be more rapidly transformed by a CC. On this basis, we hypothesize that a LC may have three pathways on transformation: a slow, a rapid, and an accelerated. Slow pathway is driven by accumulation of PDMs/MECCs. Rapid pathway is driven by a GECC in "one step". Accelerated pathway is driven by accumulation of PDMs/MECCs/IECC(s). Cell transformations of a LC via different pathways occur at different ages. A transformation via slow pathway occurs mainly in adults. A transformation via rapid pathway occurs at any age and has no increasing incidence with age. A transformation via accelerated pathway occurs also at any age but has increasing incidence with age. In conclusion, a LC may have three pathways on cell transformation, and the occurring age of LL/lymphoma may be determined by the transforming pathway of a LC.
|
q-bio/0611054
|
Nicolas Jacq
|
N. Jacq (LPC-Clermont, CS-Si), J. Salzemann (LPC-Clermont), F. Jacq
(LPC-Clermont), Y. Legr\'e (LPC-Clermont), E. Medernach (LPC-Clermont), J.
Montagnat (Informatique Signaux Et Syst\`emes), A. Maass (SCAI), M.
Reichstadt (LPC-Clermont), H. Schwichtenberg (SCAI), M. Sridhar (SCAI), V.
Kasam (SCAI), M. Zimmermann (SCAI), M. Hofmann (SCAI), V. Breton
(LPC-Clermont)
|
Grid enabled virtual screening against malaria
|
34 pages, 5 figures, 3 tables, to appear in Journal of Grid Computing
|
Journal of Grid Computing 6 (2008) 29-43
| null | null |
q-bio.QM cs.DC
| null |
WISDOM is an international initiative to enable a virtual screening pipeline
on a grid infrastructure. Its first attempt was to deploy large scale in silico
docking on a public grid infrastructure. Protein-ligand docking is about
computing the binding energy of a protein target to a library of potential
drugs using a scoring algorithm. Previous deployments were either limited to
one cluster, to grids of clusters in the tightly protected environment of a
pharmaceutical laboratory or to pervasive grids. The first large scale docking
experiment ran on the EGEE grid production service from 11 July 2005 to 19
August 2005 against targets relevant to research on malaria and saw over 41
million compounds docked for the equivalent of 80 years of CPU time. Up to
1,700 computers were simultaneously used in 15 countries around the world.
Issues related to the deployment and the monitoring of the in silico docking
experiment as well as experience with grid operation and services are reported
in the paper. The main problem encountered for such a large scale deployment
was the grid infrastructure stability. Although the overall success rate was
above 80%, a lot of monitoring and supervision was still required at the
application level to resubmit the jobs that failed. But the experiment
demonstrated how grid infrastructures have a tremendous capacity to mobilize
very large CPU resources for well targeted goals during a significant period of
time. This success leads to a second computing challenge targeting Avian Flu
neuraminidase N1.
|
[
{
"created": "Fri, 17 Nov 2006 10:26:07 GMT",
"version": "v1"
}
] |
2008-12-09
|
[
[
"Jacq",
"N.",
"",
"LPC-Clermont, CS-Si"
],
[
"Salzemann",
"J.",
"",
"LPC-Clermont"
],
[
"Jacq",
"F.",
"",
"LPC-Clermont"
],
[
"Legré",
"Y.",
"",
"LPC-Clermont"
],
[
"Medernach",
"E.",
"",
"LPC-Clermont"
],
[
"Montagnat",
"J.",
"",
"Informatique Signaux Et Systèmes"
],
[
"Maass",
"A.",
"",
"SCAI"
],
[
"Reichstadt",
"M.",
"",
"LPC-Clermont"
],
[
"Schwichtenberg",
"H.",
"",
"SCAI"
],
[
"Sridhar",
"M.",
"",
"SCAI"
],
[
"Kasam",
"V.",
"",
"SCAI"
],
[
"Zimmermann",
"M.",
"",
"SCAI"
],
[
"Hofmann",
"M.",
"",
"SCAI"
],
[
"Breton",
"V.",
"",
"LPC-Clermont"
]
] |
WISDOM is an international initiative to enable a virtual screening pipeline on a grid infrastructure. Its first attempt was to deploy large scale in silico docking on a public grid infrastructure. Protein-ligand docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm. Previous deployments were either limited to one cluster, to grids of clusters in the tightly protected environment of a pharmaceutical laboratory or to pervasive grids. The first large scale docking experiment ran on the EGEE grid production service from 11 July 2005 to 19 August 2005 against targets relevant to research on malaria and saw over 41 million compounds docked for the equivalent of 80 years of CPU time. Up to 1,700 computers were simultaneously used in 15 countries around the world. Issues related to the deployment and the monitoring of the in silico docking experiment as well as experience with grid operation and services are reported in the paper. The main problem encountered for such a large scale deployment was the grid infrastructure stability. Although the overall success rate was above 80%, a lot of monitoring and supervision was still required at the application level to resubmit the jobs that failed. But the experiment demonstrated how grid infrastructures have a tremendous capacity to mobilize very large CPU resources for well targeted goals during a significant period of time. This success leads to a second computing challenge targeting Avian Flu neuraminidase N1.
|
1709.00339
|
Mirko Lukovic
|
Tatiana A. Amor, Mirko Lukovic, Hans J. Herrmann, and Jose S. Andrade
Jr
|
How images determine our visual search strategy
| null |
J. R. Soc. Interface 14, 20170406, 2017
|
10.1098/rsif.2017.0406
| null |
q-bio.NC cond-mat.other
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When searching for a target within an image our brain can adopt different
strategies, but which one does it choose? This question can be answered by
tracking the motion of the eye while it executes the task. Following many
individuals performing various search tasks we distinguish between two
competing strategies. Motivated by these findings, we introduce a model that
captures the interplay of the search strategies and allows us to create
artificial eye-tracking trajectories, which could be compared to the
experimental ones. Identifying the model parameters allows us to quantify the
strategy employed in terms of ensemble averages, characterizing each
experimental cohort. In this way we can discern with high sensitivity the
relation between the visual landscape and the average strategy, disclosing how
small variations in the image induce changes in the strategy.
|
[
{
"created": "Thu, 31 Aug 2017 11:14:59 GMT",
"version": "v1"
}
] |
2017-09-04
|
[
[
"Amor",
"Tatiana A.",
""
],
[
"Lukovic",
"Mirko",
""
],
[
"Herrmann",
"Hans J.",
""
],
[
"Andrade",
"Jose S.",
"Jr"
]
] |
When searching for a target within an image our brain can adopt different strategies, but which one does it choose? This question can be answered by tracking the motion of the eye while it executes the task. Following many individuals performing various search tasks we distinguish between two competing strategies. Motivated by these findings, we introduce a model that captures the interplay of the search strategies and allows us to create artificial eye-tracking trajectories, which could be compared to the experimental ones. Identifying the model parameters allows us to quantify the strategy employed in terms of ensemble averages, characterizing each experimental cohort. In this way we can discern with high sensitivity the relation between the visual landscape and the average strategy, disclosing how small variations in the image induce changes in the strategy.
|
1808.04262
|
Anvar Kurmukov
|
Anvar Kurmukov and Ayagoz Mussabayeva and Yulia Denisova and Daniel
Moyer and Boris Gutman
|
Connectivity-Driven Brain Parcellation via Consensus Clustering
| null | null | null | null |
q-bio.NC cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present two related methods for deriving connectivity-based brain atlases
from individual connectomes. The proposed methods exploit a previously proposed
dense connectivity representation, termed continuous connectivity, by first
performing graph-based hierarchical clustering of individual brains, and
subsequently aggregating the individual parcellations into a consensus
parcellation. The search for consensus minimizes the sum of cluster membership
distances, effectively estimating a pseudo-Karcher mean of individual
parcellations. We assess the quality of our parcellations using (1)
Kullback-Liebler and Jensen-Shannon divergence with respect to the dense
connectome representation, (2) inter-hemispheric symmetry, and (3) performance
of the simplified connectome in a biological sex classification task. We find
that the parcellation based-atlas computed using a greedy search at a
hierarchical depth 3 outperforms all other parcellation-based atlases as well
as the standard Dessikan-Killiany anatomical atlas in all three assessments.
|
[
{
"created": "Fri, 10 Aug 2018 08:54:31 GMT",
"version": "v1"
}
] |
2018-08-14
|
[
[
"Kurmukov",
"Anvar",
""
],
[
"Mussabayeva",
"Ayagoz",
""
],
[
"Denisova",
"Yulia",
""
],
[
"Moyer",
"Daniel",
""
],
[
"Gutman",
"Boris",
""
]
] |
We present two related methods for deriving connectivity-based brain atlases from individual connectomes. The proposed methods exploit a previously proposed dense connectivity representation, termed continuous connectivity, by first performing graph-based hierarchical clustering of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. We assess the quality of our parcellations using (1) Kullback-Liebler and Jensen-Shannon divergence with respect to the dense connectome representation, (2) inter-hemispheric symmetry, and (3) performance of the simplified connectome in a biological sex classification task. We find that the parcellation based-atlas computed using a greedy search at a hierarchical depth 3 outperforms all other parcellation-based atlases as well as the standard Dessikan-Killiany anatomical atlas in all three assessments.
|
0707.4321
|
Razvan Radulescu M.D.
|
Razvan Tudor Radulescu
|
The insulin superfamily of growth-promoting proteins
|
3 pages, 1 figure
| null | null | null |
q-bio.BM q-bio.SC
| null |
Recently, structural analysis of the human transferrin and growth hormone
(GH) amino acid sequences has unravelled that they harbor a motif identical to
a pattern found in viral oncoproteins known to bind the primarily nuclear tumor
suppressor retinoblastoma protein (RB). Since related signatures had previously
been identified also in insulin and the two insulin-like growth factors (IGFs),
the aim of the current study has been to investigate whether further hints
substantiating these reported homologies can be found in silico. Here,
additional similarities are presented supporting the notion of an insulin
superfamily of growth-promoting proteins with dual localization in the
extracellular environment and the intracellular space, particularly in the
nucleus, as well as characterized by a tropism for RB.
|
[
{
"created": "Sun, 29 Jul 2007 22:54:16 GMT",
"version": "v1"
}
] |
2007-07-31
|
[
[
"Radulescu",
"Razvan Tudor",
""
]
] |
Recently, structural analysis of the human transferrin and growth hormone (GH) amino acid sequences has unravelled that they harbor a motif identical to a pattern found in viral oncoproteins known to bind the primarily nuclear tumor suppressor retinoblastoma protein (RB). Since related signatures had previously been identified also in insulin and the two insulin-like growth factors (IGFs), the aim of the current study has been to investigate whether further hints substantiating these reported homologies can be found in silico. Here, additional similarities are presented supporting the notion of an insulin superfamily of growth-promoting proteins with dual localization in the extracellular environment and the intracellular space, particularly in the nucleus, as well as characterized by a tropism for RB.
|
2102.03667
|
Mohammed Alser
|
Mohammed Alser, Jeremie S. Kim, Nour Almadhoun Alserr, Stefan W. Tell,
Onur Mutlu
|
COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source
COVID-19 Outbreak Simulation Model
| null | null | null | null |
q-bio.PE cs.LG cs.SI stat.OT
|
http://creativecommons.org/licenses/by/4.0/
|
Background: Early detection and isolation of COVID-19 patients are essential
for successful implementation of mitigation strategies and eventually curbing
the disease spread. With a limited number of daily COVID-19 tests performed in
every country, simulating the COVID-19 spread along with the potential effect
of each mitigation strategy currently remains one of the most effective ways in
managing the healthcare system and guiding policy-makers. Methods: We introduce
COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that
evaluates the current mitigation measures that are applied to a region and
provides suggestions on what strength the upcoming mitigation measure should
be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a
geographical region by simulating the average number of new infections caused
by an infected person considering the effect of external factors, such as
environmental conditions (e.g., climate, temperature, humidity) and mitigation
measures. Results: Using Switzerland as a case study, COVIDHunter estimates
that if the policy-makers relax the mitigation measures by 50% for 30 days then
both the daily capacity need for hospital beds and daily number of deaths
increase exponentially by an average of 5.1x, who may occupy ICU beds and
ventilators for a period of time. Unlike existing models, the COVIDHunter model
accurately monitors and predicts the daily number of cases, hospitalizations,
and deaths due to COVID-19. Our model is flexible to configure and simple to
modify for modeling different scenarios under different environmental
conditions and mitigation measures. Availability: We release the source code of
the COVIDHunter implementation at https://github.com/CMU- SAFARI/COVIDHunter
and show how to flexibly configure our model for any scenario and easily extend
it for different measures and conditions than we account for.
|
[
{
"created": "Sat, 6 Feb 2021 21:01:56 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Jun 2022 10:41:02 GMT",
"version": "v2"
}
] |
2022-06-09
|
[
[
"Alser",
"Mohammed",
""
],
[
"Kim",
"Jeremie S.",
""
],
[
"Alserr",
"Nour Almadhoun",
""
],
[
"Tell",
"Stefan W.",
""
],
[
"Mutlu",
"Onur",
""
]
] |
Background: Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests performed in every country, simulating the COVID-19 spread along with the potential effect of each mitigation strategy currently remains one of the most effective ways in managing the healthcare system and guiding policy-makers. Methods: We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region and provides suggestions on what strength the upcoming mitigation measure should be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e.g., climate, temperature, humidity) and mitigation measures. Results: Using Switzerland as a case study, COVIDHunter estimates that if the policy-makers relax the mitigation measures by 50% for 30 days then both the daily capacity need for hospital beds and daily number of deaths increase exponentially by an average of 5.1x, who may occupy ICU beds and ventilators for a period of time. Unlike existing models, the COVIDHunter model accurately monitors and predicts the daily number of cases, hospitalizations, and deaths due to COVID-19. Our model is flexible to configure and simple to modify for modeling different scenarios under different environmental conditions and mitigation measures. Availability: We release the source code of the COVIDHunter implementation at https://github.com/CMU- SAFARI/COVIDHunter and show how to flexibly configure our model for any scenario and easily extend it for different measures and conditions than we account for.
|
2210.03488
|
Igor Melnyk
|
Igor Melnyk, Aurelie Lozano, Payel Das, Vijil Chenthamarakshan
|
AlphaFold Distillation for Protein Design
|
Preprint
| null | null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Inverse protein folding, the process of designing sequences that fold into a
specific 3D structure, is crucial in bio-engineering and drug discovery.
Traditional methods rely on experimentally resolved structures, but these cover
only a small fraction of protein sequences. Forward folding models like
AlphaFold offer a potential solution by accurately predicting structures from
sequences. However, these models are too slow for integration into the
optimization loop of inverse folding models during training. To address this,
we propose using knowledge distillation on folding model confidence metrics,
such as pTM or pLDDT scores, to create a faster and end-to-end differentiable
distilled model. This model can then be used as a structure consistency
regularizer in training the inverse folding model. Our technique is versatile
and can be applied to other design tasks, such as sequence-based protein
infilling. Experimental results show that our method outperforms
non-regularized baselines, yielding up to 3% improvement in sequence recovery
and up to 45% improvement in protein diversity while maintaining structural
consistency in generated sequences. Code is available at
https://github.com/IBM/AFDistill
|
[
{
"created": "Wed, 5 Oct 2022 19:43:06 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Nov 2023 22:52:47 GMT",
"version": "v2"
}
] |
2023-11-27
|
[
[
"Melnyk",
"Igor",
""
],
[
"Lozano",
"Aurelie",
""
],
[
"Das",
"Payel",
""
],
[
"Chenthamarakshan",
"Vijil",
""
]
] |
Inverse protein folding, the process of designing sequences that fold into a specific 3D structure, is crucial in bio-engineering and drug discovery. Traditional methods rely on experimentally resolved structures, but these cover only a small fraction of protein sequences. Forward folding models like AlphaFold offer a potential solution by accurately predicting structures from sequences. However, these models are too slow for integration into the optimization loop of inverse folding models during training. To address this, we propose using knowledge distillation on folding model confidence metrics, such as pTM or pLDDT scores, to create a faster and end-to-end differentiable distilled model. This model can then be used as a structure consistency regularizer in training the inverse folding model. Our technique is versatile and can be applied to other design tasks, such as sequence-based protein infilling. Experimental results show that our method outperforms non-regularized baselines, yielding up to 3% improvement in sequence recovery and up to 45% improvement in protein diversity while maintaining structural consistency in generated sequences. Code is available at https://github.com/IBM/AFDistill
|
2405.19936
|
Anindita Bhadra
|
Srijaya Nandi, Mousumi Chakraborty, Aesha Lahiri, Hindolii Gope,
Sujata Khan Bhaduri, Anindita Bhadra
|
Free-ranging dogs quickly learn to recognize a rewarding person
| null | null | null | null |
q-bio.OT
|
http://creativecommons.org/licenses/by/4.0/
|
Individual human recognition is important for species that live in close
proximity to humans. Numerous studies on domesticated species and urban-adapted
birds have highlighted this ability. One such species which is heavily reliant
on humans is the free-ranging dog. Very little knowledge exists on the amount
of time taken by free-ranging dogs to learn and remember individual humans. Due
to their territorial nature, they have a high probability of encountering the
same people multiple times on the streets. Being able to distinguish individual
humans might be helpful in making decisions regarding people from whom to beg
for food or social reward. We investigated if free-ranging dogs are capable of
identifying the person rewarding them and the amount of time required for them
to learn it. We conducted field trials on randomly selected adult free-ranging
dogs in West Bengal, India. On Day 1, a choice test was conducted. The
experimenter chosen did not provide reward while the other experimenter
provided a piece of boiled chicken followed by petting. The person giving
reward on Day 1 served as the correct choice on four subsequent days of
training. Day 6 was the test day when none of the experimenters had a reward.
We analyzed the choice made by the dogs, the time taken to approach during the
choice tests, and the socialization index, which was calculated based on the
intensity of affiliative behaviour shown towards the experimenters. The dogs
made correct choices at a significantly higher rate on the fifth and sixth
days, as compared to Day 2, suggesting learning. This is the first study aiming
to understand the time taken for individual human recognition in free-ranging
dogs and can serve as the scaffold for future studies to understand the
dog-human relationship in open environments, like urban ecosystems.
|
[
{
"created": "Thu, 30 May 2024 10:56:32 GMT",
"version": "v1"
}
] |
2024-05-31
|
[
[
"Nandi",
"Srijaya",
""
],
[
"Chakraborty",
"Mousumi",
""
],
[
"Lahiri",
"Aesha",
""
],
[
"Gope",
"Hindolii",
""
],
[
"Bhaduri",
"Sujata Khan",
""
],
[
"Bhadra",
"Anindita",
""
]
] |
Individual human recognition is important for species that live in close proximity to humans. Numerous studies on domesticated species and urban-adapted birds have highlighted this ability. One such species which is heavily reliant on humans is the free-ranging dog. Very little knowledge exists on the amount of time taken by free-ranging dogs to learn and remember individual humans. Due to their territorial nature, they have a high probability of encountering the same people multiple times on the streets. Being able to distinguish individual humans might be helpful in making decisions regarding people from whom to beg for food or social reward. We investigated if free-ranging dogs are capable of identifying the person rewarding them and the amount of time required for them to learn it. We conducted field trials on randomly selected adult free-ranging dogs in West Bengal, India. On Day 1, a choice test was conducted. The experimenter chosen did not provide reward while the other experimenter provided a piece of boiled chicken followed by petting. The person giving reward on Day 1 served as the correct choice on four subsequent days of training. Day 6 was the test day when none of the experimenters had a reward. We analyzed the choice made by the dogs, the time taken to approach during the choice tests, and the socialization index, which was calculated based on the intensity of affiliative behaviour shown towards the experimenters. The dogs made correct choices at a significantly higher rate on the fifth and sixth days, as compared to Day 2, suggesting learning. This is the first study aiming to understand the time taken for individual human recognition in free-ranging dogs and can serve as the scaffold for future studies to understand the dog-human relationship in open environments, like urban ecosystems.
|
1307.3857
|
Osamu Narikiyo
|
Hayato Tsuda, Osamu Narikiyo
|
Translation by adaptor-helicase cycle in oligomer world
| null | null | null | null |
q-bio.BM q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A mechanism of the translation in oligomer world is proposed. The translation
is carried out by a minimum cycle, which is sustained by adaptors and
helicases, and the first information processing in oligomer world. We expect
that such a cycle actually worked in a primitive cell and can be constructed in
vitro. By computer simulation we have shown that a proofreading is achieved by
the fluctuation in the cell. It is rather paradoxical that the proofreading is
effective for the system consisting of molecular machines with low efficiency.
|
[
{
"created": "Mon, 15 Jul 2013 09:08:42 GMT",
"version": "v1"
}
] |
2013-07-16
|
[
[
"Tsuda",
"Hayato",
""
],
[
"Narikiyo",
"Osamu",
""
]
] |
A mechanism of the translation in oligomer world is proposed. The translation is carried out by a minimum cycle, which is sustained by adaptors and helicases, and the first information processing in oligomer world. We expect that such a cycle actually worked in a primitive cell and can be constructed in vitro. By computer simulation we have shown that a proofreading is achieved by the fluctuation in the cell. It is rather paradoxical that the proofreading is effective for the system consisting of molecular machines with low efficiency.
|
1109.2239
|
Joel Zylberberg
|
Joel Zylberberg, Jason Timothy Murphy, and Michael Robert DeWeese
|
A sparse coding model with synaptically local plasticity and spiking
neurons can account for the diverse shapes of V1 simple cell receptive fields
|
33 pages, 6 figures. To appear in PLoS Computational Biology. Some of
these data were presented by author JZ at the 2011 CoSyNe meeting in Salt
Lake City
|
PLoS Computational Biology (2011) 7(10): e1002250
|
10.1371/journal.pcbi.1002250
| null |
q-bio.NC cond-mat.dis-nn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sparse coding algorithms trained on natural images can accurately predict the
features that excite visual cortical neurons, but it is not known whether such
codes can be learned using biologically realistic plasticity rules. We have
developed a biophysically motivated spiking network, relying solely on
synaptically local information, that can predict the full diversity of V1
simple cell receptive field shapes when trained on natural images. This
represents the first demonstration that sparse coding principles, operating
within the constraints imposed by cortical architecture, can successfully
reproduce these receptive fields. We further prove, mathematically, that
sparseness and decorrelation are the key ingredients that allow for
synaptically local plasticity rules to optimize a cooperative, linear
generative image model formed by the neural representation. Finally, we discuss
several interesting emergent properties of our network, with the intent of
bridging the gap between theoretical and experimental studies of visual cortex.
|
[
{
"created": "Sat, 10 Sep 2011 17:36:38 GMT",
"version": "v1"
}
] |
2011-11-01
|
[
[
"Zylberberg",
"Joel",
""
],
[
"Murphy",
"Jason Timothy",
""
],
[
"DeWeese",
"Michael Robert",
""
]
] |
Sparse coding algorithms trained on natural images can accurately predict the features that excite visual cortical neurons, but it is not known whether such codes can be learned using biologically realistic plasticity rules. We have developed a biophysically motivated spiking network, relying solely on synaptically local information, that can predict the full diversity of V1 simple cell receptive field shapes when trained on natural images. This represents the first demonstration that sparse coding principles, operating within the constraints imposed by cortical architecture, can successfully reproduce these receptive fields. We further prove, mathematically, that sparseness and decorrelation are the key ingredients that allow for synaptically local plasticity rules to optimize a cooperative, linear generative image model formed by the neural representation. Finally, we discuss several interesting emergent properties of our network, with the intent of bridging the gap between theoretical and experimental studies of visual cortex.
|
1607.00437
|
Rafael Tuma Guariento
|
Rafael Tuma Guariento, Thiago Schiavo Mosqueiro, Paulo Matias,
Vinicius Burani Cesarino, Lirio Onofre Baptista de Almeida, Jan Frans Willem
Slaets, Leonardo Paulo Maia, Reynaldo Daniel Pinto
|
Automated pulse discrimination of two freely-swimming weakly electric
fish and analysis of their electrical behavior during a dominance contest
|
15 pages, 8 figures
| null |
10.1016/j.jphysparis.2017.02.001
| null |
q-bio.QM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Electric fishes modulate their electric organ discharges with a remarkable
variability. Some patterns can be easily identified, such as pulse rate
changes, offs and chirps, which are often associated with important behavioral
contexts, including aggression, hiding and mating. However, these behaviors are
only observed when at least two fish are freely interacting. Although their
electrical pulses can be easily recorded by non-invasive techniques,
discriminating the emitter of each pulse is challenging when physically similar
fish are allowed to freely move and interact. Here we optimized a custom-made
software recently designed to identify the emitter of pulses by using automated
chirp detection, adaptive threshold for pulse detection and slightly changing
how the recorded signals are integrated. With these optimizations, we performed
a quantitative analysis of the statistical changes throughout the dominance
contest with respect to Inter Pulse Intervals, Chirps and Offs dyads of freely
moving Gymnotus carapo. In all dyads, chirps were signatures of subsequent
submission, even when they occurred early in the contest. Although offs were
observed in both dominant and submissive fish, they were substantially more
frequent in submissive individuals, in agreement with the idea from previous
studies that offs are electric cues of submission. In general, after the
dominance is established the submissive fish significantly changes its average
pulse rate, while the pulse rate of the dominant remained unchanged.
Additionally, no chirps or offs were observed when two fish were manually kept
in direct physical contact, suggesting that these electric behaviors are not
automatic responses to physical contact.
|
[
{
"created": "Sat, 2 Jul 2016 00:14:43 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Mar 2017 14:38:40 GMT",
"version": "v2"
}
] |
2017-03-23
|
[
[
"Guariento",
"Rafael Tuma",
""
],
[
"Mosqueiro",
"Thiago Schiavo",
""
],
[
"Matias",
"Paulo",
""
],
[
"Cesarino",
"Vinicius Burani",
""
],
[
"de Almeida",
"Lirio Onofre Baptista",
""
],
[
"Slaets",
"Jan Frans Willem",
""
],
[
"Maia",
"Leonardo Paulo",
""
],
[
"Pinto",
"Reynaldo Daniel",
""
]
] |
Electric fishes modulate their electric organ discharges with a remarkable variability. Some patterns can be easily identified, such as pulse rate changes, offs and chirps, which are often associated with important behavioral contexts, including aggression, hiding and mating. However, these behaviors are only observed when at least two fish are freely interacting. Although their electrical pulses can be easily recorded by non-invasive techniques, discriminating the emitter of each pulse is challenging when physically similar fish are allowed to freely move and interact. Here we optimized a custom-made software recently designed to identify the emitter of pulses by using automated chirp detection, adaptive threshold for pulse detection and slightly changing how the recorded signals are integrated. With these optimizations, we performed a quantitative analysis of the statistical changes throughout the dominance contest with respect to Inter Pulse Intervals, Chirps and Offs dyads of freely moving Gymnotus carapo. In all dyads, chirps were signatures of subsequent submission, even when they occurred early in the contest. Although offs were observed in both dominant and submissive fish, they were substantially more frequent in submissive individuals, in agreement with the idea from previous studies that offs are electric cues of submission. In general, after the dominance is established the submissive fish significantly changes its average pulse rate, while the pulse rate of the dominant remained unchanged. Additionally, no chirps or offs were observed when two fish were manually kept in direct physical contact, suggesting that these electric behaviors are not automatic responses to physical contact.
|
2301.06454
|
Jonas Ditz
|
Jonas Christian Ditz and Jacqueline Wistuba-Hamprecht and Timo Maier
and Rolf Fendel and Nico Pfeifer and Bernhard Reuter
|
PlasmoFAB: A Benchmark to Foster Machine Learning for Plasmodium
falciparum Protein Antigen Candidate Prediction
| null | null | null | null |
q-bio.QM cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Motivation: Machine learning methods can be used to support scientific
discovery in healthcare-related research fields. However, these methods can
only be reliably used if they can be trained on high-quality and curated
datasets. Currently, no such dataset for the exploration of Plasmodium
falciparum protein antigen candidates exists. The parasite Plasmodium
falciparum causes the infectious disease malaria. Thus, identifying potential
antigens is of utmost importance for the development of antimalarial drugs and
vaccines. Since exploring antigen candidates experimentally is an expensive and
time-consuming process, applying machine learning methods to support this
process has the potential to accelerate the development of drugs and vaccines,
which are needed for fighting and controlling malaria. Results: We developed
PlasmoFAB, a curated benchmark that can be used to train machine learning
methods for the exploration of Plasmodium falciparum protein antigen
candidates. We combined an extensive literature search with domain expertise to
create high-quality labels for Plasmodium falciparum specific proteins that
distinguish between antigen candidates and intracellular proteins.
Additionally, we used our benchmark to compare different well-known prediction
models and available protein localization prediction services on the task of
identifying protein antigen candidates. We show that available general-purpose
services are unable to provide sufficient performance on identifying protein
antigen candidates and are outperformed by our models that were trained on this
tailored data. Availability: PlasmoFAB is publicly available on Zenodo with DOI
10.5281/zenodo.7433087. Furthermore, all scripts that were used in the creation
of PlasmoFAB and the training and evaluation of machine learning models are
open source and publicly available on GitHub here:
https://github.com/msmdev/PlasmoFAB.
|
[
{
"created": "Mon, 16 Jan 2023 14:57:42 GMT",
"version": "v1"
},
{
"created": "Wed, 3 May 2023 11:41:56 GMT",
"version": "v2"
}
] |
2023-05-04
|
[
[
"Ditz",
"Jonas Christian",
""
],
[
"Wistuba-Hamprecht",
"Jacqueline",
""
],
[
"Maier",
"Timo",
""
],
[
"Fendel",
"Rolf",
""
],
[
"Pfeifer",
"Nico",
""
],
[
"Reuter",
"Bernhard",
""
]
] |
Motivation: Machine learning methods can be used to support scientific discovery in healthcare-related research fields. However, these methods can only be reliably used if they can be trained on high-quality and curated datasets. Currently, no such dataset for the exploration of Plasmodium falciparum protein antigen candidates exists. The parasite Plasmodium falciparum causes the infectious disease malaria. Thus, identifying potential antigens is of utmost importance for the development of antimalarial drugs and vaccines. Since exploring antigen candidates experimentally is an expensive and time-consuming process, applying machine learning methods to support this process has the potential to accelerate the development of drugs and vaccines, which are needed for fighting and controlling malaria. Results: We developed PlasmoFAB, a curated benchmark that can be used to train machine learning methods for the exploration of Plasmodium falciparum protein antigen candidates. We combined an extensive literature search with domain expertise to create high-quality labels for Plasmodium falciparum specific proteins that distinguish between antigen candidates and intracellular proteins. Additionally, we used our benchmark to compare different well-known prediction models and available protein localization prediction services on the task of identifying protein antigen candidates. We show that available general-purpose services are unable to provide sufficient performance on identifying protein antigen candidates and are outperformed by our models that were trained on this tailored data. Availability: PlasmoFAB is publicly available on Zenodo with DOI 10.5281/zenodo.7433087. Furthermore, all scripts that were used in the creation of PlasmoFAB and the training and evaluation of machine learning models are open source and publicly available on GitHub here: https://github.com/msmdev/PlasmoFAB.
|
q-bio/0702051
|
Ralf Blossey
|
C. Russo, C.V. Giuraniuc, R. Blossey and J.-F. Bodart
|
On the equilibria of the MAPK cascade: cooperativity, modularity and
bistability
| null |
Physica A 388 (2009), pp. 5070-5080
|
10.1016/j.physa.2009.08.018
| null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we present a discussion of a phenomenological model of the MAPK
cascade which was originally proposed by Angeli et al. (PNAS 101, 1822 (2004)).
The model and its solution are extended in several respects: a) an analytical
solution is given for the cascade equilibria, exploiting a parameter-based
symmetry of the rate equations; b) we discuss the cooperativity (Hill
coefficients) of the cascade and show that a feedforward loop within the
cascade increases its cooperativity. The relevance of this result for the
notion of modularity is discussed; c) the feedback model for cascade
bistability by Angeli et al. is reconsidered. We argue that care must be taken
in modeling the interactions and a biologically realistic phenomenological
model cannot be too reductionist. The inclusion of a time-dependent degradation
rate is needed to account for a switching of the cascade.
|
[
{
"created": "Sat, 24 Feb 2007 12:13:57 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Oct 2008 15:23:54 GMT",
"version": "v2"
},
{
"created": "Mon, 2 Feb 2009 10:52:44 GMT",
"version": "v3"
},
{
"created": "Wed, 26 Aug 2009 13:03:52 GMT",
"version": "v4"
}
] |
2009-09-21
|
[
[
"Russo",
"C.",
""
],
[
"Giuraniuc",
"C. V.",
""
],
[
"Blossey",
"R.",
""
],
[
"Bodart",
"J. -F.",
""
]
] |
In this paper we present a discussion of a phenomenological model of the MAPK cascade which was originally proposed by Angeli et al. (PNAS 101, 1822 (2004)). The model and its solution are extended in several respects: a) an analytical solution is given for the cascade equilibria, exploiting a parameter-based symmetry of the rate equations; b) we discuss the cooperativity (Hill coefficients) of the cascade and show that a feedforward loop within the cascade increases its cooperativity. The relevance of this result for the notion of modularity is discussed; c) the feedback model for cascade bistability by Angeli et al. is reconsidered. We argue that care must be taken in modeling the interactions and a biologically realistic phenomenological model cannot be too reductionist. The inclusion of a time-dependent degradation rate is needed to account for a switching of the cascade.
|
1301.3981
|
Armita Nourmohammad
|
Armita Nourmohammad, Stephan Schiffels, Michael Laessig
|
Evolution of molecular phenotypes under stabilizing selection
| null |
J. Stat. Mech. (2013) P01012
|
10.1088/1742-5468/2013/01/P01012
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Molecular phenotypes are important links between genomic information and
organismic functions, fitness, and evolution. Complex phenotypes, which are
also called quantitative traits, often depend on multiple genomic loci. Their
evolution builds on genome evolution in a complicated way, which involves
selection, genetic drift, mutations and recombination. Here we develop a
coarse-grained evolutionary statistics for phenotypes, which decouples from
details of the underlying genotypes. We derive approximate evolution equations
for the distribution of phenotype values within and across populations. This
dynamics covers evolutionary processes at high and low recombination rates,
that is, it applies to sexual and asexual populations. In a fitness landscape
with a single optimal phenotype value, the phenotypic diversity within
populations and the divergence between populations reach evolutionary
equilibria, which describe stabilizing selection. We compute the equilibrium
distributions of both quantities analytically and we show that the ratio of
mean divergence and diversity depends on the strength of selection in a
universal way: it is largely independent of the phenotype's genomic encoding
and of the recombination rate. This establishes a new method for the inference
of selection on molecular phenotypes beyond the genome level. We discuss the
implications of our findings for the predictability of evolutionary processes.
|
[
{
"created": "Thu, 17 Jan 2013 04:51:07 GMT",
"version": "v1"
}
] |
2015-06-12
|
[
[
"Nourmohammad",
"Armita",
""
],
[
"Schiffels",
"Stephan",
""
],
[
"Laessig",
"Michael",
""
]
] |
Molecular phenotypes are important links between genomic information and organismic functions, fitness, and evolution. Complex phenotypes, which are also called quantitative traits, often depend on multiple genomic loci. Their evolution builds on genome evolution in a complicated way, which involves selection, genetic drift, mutations and recombination. Here we develop a coarse-grained evolutionary statistics for phenotypes, which decouples from details of the underlying genotypes. We derive approximate evolution equations for the distribution of phenotype values within and across populations. This dynamics covers evolutionary processes at high and low recombination rates, that is, it applies to sexual and asexual populations. In a fitness landscape with a single optimal phenotype value, the phenotypic diversity within populations and the divergence between populations reach evolutionary equilibria, which describe stabilizing selection. We compute the equilibrium distributions of both quantities analytically and we show that the ratio of mean divergence and diversity depends on the strength of selection in a universal way: it is largely independent of the phenotype's genomic encoding and of the recombination rate. This establishes a new method for the inference of selection on molecular phenotypes beyond the genome level. We discuss the implications of our findings for the predictability of evolutionary processes.
|
1410.2990
|
R.K. Brojen Singh
|
Md. Jahoor Alam, Sanjay Kumar, Vikram Singh and R.K. Brojen Singh
|
Bifurcation in cell cycle dynamics regulated by p53
| null | null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the regulating mechanism of p53 on the properties of cell cycle
dynamics in the light of the proposed model of interacting p53 and cell cycle
networks via p53. Irradiation (IR) introduce to p53 compel p53 dynamics to
suffer different phases, namely oscillating and oscillation death (stabilized)
phases. The IR induced p53 dynamics undergo collapse of oscillation with
collapse time \Delta t which depends on IR strength. The stress p53 via IR
drive cell cycle molecular species MPF and cyclin dynamics to different states,
namely, oscillation death, oscillations of periods, chaotic and sustain
oscillation in their bifurcation diagram. We predict that there could be a
critical \Delta t_c induced by p53 via IR_c, where, if \Delta t < \Delta t_c
the cell cycle may come back to normal state, otherwise it will go to cell
cycle arrest (apoptosis).
|
[
{
"created": "Sat, 11 Oct 2014 12:04:46 GMT",
"version": "v1"
}
] |
2014-10-14
|
[
[
"Alam",
"Md. Jahoor",
""
],
[
"Kumar",
"Sanjay",
""
],
[
"Singh",
"Vikram",
""
],
[
"Singh",
"R. K. Brojen",
""
]
] |
We study the regulating mechanism of p53 on the properties of cell cycle dynamics in the light of the proposed model of interacting p53 and cell cycle networks via p53. Irradiation (IR) introduce to p53 compel p53 dynamics to suffer different phases, namely oscillating and oscillation death (stabilized) phases. The IR induced p53 dynamics undergo collapse of oscillation with collapse time \Delta t which depends on IR strength. The stress p53 via IR drive cell cycle molecular species MPF and cyclin dynamics to different states, namely, oscillation death, oscillations of periods, chaotic and sustain oscillation in their bifurcation diagram. We predict that there could be a critical \Delta t_c induced by p53 via IR_c, where, if \Delta t < \Delta t_c the cell cycle may come back to normal state, otherwise it will go to cell cycle arrest (apoptosis).
|
2006.13530
|
Thierry Mora
|
Thomas Dupic, Meriem Bensouda Koraichi, Anastasia Minervina, Mikhail
Pogorelyy, Thierry Mora, Aleksandra M. Walczak
|
Immune Fingerprinting through Repertoire Similarity
| null |
PLoS Genetics 17 (1) e1009301 (2021)
|
10.1371/journal.pgen.1009301
| null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Immune repertoires provide a unique fingerprint reflecting the immune history
of individuals, with potential applications in precision medicine. However, the
question of how personal that information is and how it can be used to identify
individuals has not been explored. Here, we show that individuals can be
uniquely identified from repertoires of just a few thousands lymphocytes. We
present "Immprint," a classifier using an information-theoretic measure of
repertoire similarity to distinguish pairs of repertoire samples coming from
the same versus different individuals. Using published T-cell receptor
repertoires and statistical modeling, we tested its ability to identify
individuals with great accuracy, including identical twins, by computing false
positive and false negative rates $< 10^{-6}$ from samples composed of 10,000
T-cells. We verified through longitudinal datasets and simulations that the
method is robust to acute infections and the passage of time. These results
emphasize the private and personal nature of repertoire data.
|
[
{
"created": "Wed, 24 Jun 2020 07:33:10 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Oct 2020 19:55:05 GMT",
"version": "v2"
}
] |
2021-02-05
|
[
[
"Dupic",
"Thomas",
""
],
[
"Koraichi",
"Meriem Bensouda",
""
],
[
"Minervina",
"Anastasia",
""
],
[
"Pogorelyy",
"Mikhail",
""
],
[
"Mora",
"Thierry",
""
],
[
"Walczak",
"Aleksandra M.",
""
]
] |
Immune repertoires provide a unique fingerprint reflecting the immune history of individuals, with potential applications in precision medicine. However, the question of how personal that information is and how it can be used to identify individuals has not been explored. Here, we show that individuals can be uniquely identified from repertoires of just a few thousands lymphocytes. We present "Immprint," a classifier using an information-theoretic measure of repertoire similarity to distinguish pairs of repertoire samples coming from the same versus different individuals. Using published T-cell receptor repertoires and statistical modeling, we tested its ability to identify individuals with great accuracy, including identical twins, by computing false positive and false negative rates $< 10^{-6}$ from samples composed of 10,000 T-cells. We verified through longitudinal datasets and simulations that the method is robust to acute infections and the passage of time. These results emphasize the private and personal nature of repertoire data.
|
2308.04478
|
Ziyu Zhu
|
Ziyu Zhu and Ximing Xu
|
EasyMergeR: an interactive Shiny application to manipulate multiple XLSX
files of multiple sheets
|
6 pages, 1 figure
| null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
The integration of sequencing data with clinical information is a widely
accepted strategy in bioinformatics and health informatics. Despite advanced
databases and sophisticated tools for processing omics data, challenges remain
in handling the raw clinical data (typically in XLSX format with multiple
sheets inside), either exported from health information system (HIS) or
manually collected by investigators. This is particularly difficult for
time-constrained medical staff with little or no programming background, and it
is typically the first bottleneck in many clinical-oriented studies. To fill
this gap, we developed EasyMergeR, a simple, user-friendly, code-free R Shiny
application that allows interactive manipulation of multiple XLSX files with
multiple sheets and provides basic data manipulation capabilities based on the
tidyverse and other handy R packages.
|
[
{
"created": "Tue, 8 Aug 2023 15:08:55 GMT",
"version": "v1"
}
] |
2023-08-10
|
[
[
"Zhu",
"Ziyu",
""
],
[
"Xu",
"Ximing",
""
]
] |
The integration of sequencing data with clinical information is a widely accepted strategy in bioinformatics and health informatics. Despite advanced databases and sophisticated tools for processing omics data, challenges remain in handling the raw clinical data (typically in XLSX format with multiple sheets inside), either exported from health information system (HIS) or manually collected by investigators. This is particularly difficult for time-constrained medical staff with little or no programming background, and it is typically the first bottleneck in many clinical-oriented studies. To fill this gap, we developed EasyMergeR, a simple, user-friendly, code-free R Shiny application that allows interactive manipulation of multiple XLSX files with multiple sheets and provides basic data manipulation capabilities based on the tidyverse and other handy R packages.
|
1602.05981
|
Amit Chattopadhyay
|
Arghya Panigrahi, Amit K Chattopadhyay, Goutam Paul and Soumya
Panigrahi
|
HIV, Cardiovascular Diseases, and Chronic Arsenic Exposure co-exist in a
Positive Synergy
|
15 pages, 4 figures; accepted in Bengal Heart Journal
| null | null | null |
q-bio.TO physics.bio-ph q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent epidemiological evidences indicate that arsenic exposure increases
risk of atherosclerosis, cardiovascular diseases and microangiopathies in
addition to the serious global health concern related to its carcinogenic
effects. In experiments on animals, acute and chronic exposure to arsenic
directly correlates cardiac tachyarrhythmia, and atherogenesis in a
concentration and duration dependent manner. Moreover, the other effects of
long-term arsenic exposure include induction of non-insulin dependent diabetes
by mechanisms yet to be understood. On the other hand, there are controversial
issues, gaps in knowledge, and future research priorities of accelerated
incidences of CVD and mortalities in patients with HIV who are under long-term
anti-retroviral therapy (ART). Although, both HIV infection itself and various
components of ART initiate significant pathological alterations in the
myocardium and the vasculature, simultaneous environmental exposure to arsenic
which is more convincingly being recognized as a facilitator of HIV viral
cycling in the infected immune cells, may contribute an additional layer of
adversity in these patients. In this mini-review which have been fortified with
our own preliminary data, we will discuss some of the key current understating
of chronic arsenic exposure, and its possible impact on the accelerated HIV/ART
induced CVD. The review will conclude with notes on recent developments in
mathematical modeling in this field that probabilistically forecast incidence
prevalence as functions of aging and life style parameters, most of which vary
with time themselves; this interdisciplinary approach provides a complementary
kernel to conventional biology.
|
[
{
"created": "Tue, 9 Feb 2016 21:36:51 GMT",
"version": "v1"
}
] |
2016-02-22
|
[
[
"Panigrahi",
"Arghya",
""
],
[
"Chattopadhyay",
"Amit K",
""
],
[
"Paul",
"Goutam",
""
],
[
"Panigrahi",
"Soumya",
""
]
] |
Recent epidemiological evidences indicate that arsenic exposure increases risk of atherosclerosis, cardiovascular diseases and microangiopathies in addition to the serious global health concern related to its carcinogenic effects. In experiments on animals, acute and chronic exposure to arsenic directly correlates cardiac tachyarrhythmia, and atherogenesis in a concentration and duration dependent manner. Moreover, the other effects of long-term arsenic exposure include induction of non-insulin dependent diabetes by mechanisms yet to be understood. On the other hand, there are controversial issues, gaps in knowledge, and future research priorities of accelerated incidences of CVD and mortalities in patients with HIV who are under long-term anti-retroviral therapy (ART). Although, both HIV infection itself and various components of ART initiate significant pathological alterations in the myocardium and the vasculature, simultaneous environmental exposure to arsenic which is more convincingly being recognized as a facilitator of HIV viral cycling in the infected immune cells, may contribute an additional layer of adversity in these patients. In this mini-review which have been fortified with our own preliminary data, we will discuss some of the key current understating of chronic arsenic exposure, and its possible impact on the accelerated HIV/ART induced CVD. The review will conclude with notes on recent developments in mathematical modeling in this field that probabilistically forecast incidence prevalence as functions of aging and life style parameters, most of which vary with time themselves; this interdisciplinary approach provides a complementary kernel to conventional biology.
|
2401.08605
|
Michael Shapiro
|
Anna Laddach, Michael Shapiro
|
Long cycles in linear thresholding systems
|
3 pages
| null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
Linear thresholding systems have been used as a model of neural activation
and more recently proposed as a model of gene regulation. Here we exhibit
linear thresholding systems whose dynamics produce surprisingly long cycles.
|
[
{
"created": "Wed, 22 Nov 2023 13:05:47 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Jan 2024 10:38:47 GMT",
"version": "v2"
}
] |
2024-01-19
|
[
[
"Laddach",
"Anna",
""
],
[
"Shapiro",
"Michael",
""
]
] |
Linear thresholding systems have been used as a model of neural activation and more recently proposed as a model of gene regulation. Here we exhibit linear thresholding systems whose dynamics produce surprisingly long cycles.
|
2204.12607
|
Charlie Burlingham
|
Charlie S. Burlingham, Mengjian Hua, Oliver Xu, Kathryn Bonnen, David
J. Heeger
|
Heading perception and the structure of the optic acceleration field
| null | null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Visual estimation of heading in the human brain is widely believed to be
based on instantaneous optic flow, the velocity of retinal image motion.
However, we previously found that humans are unable to use instantaneous optic
flow to accurately estimate heading and require time-varying optic flow
(Burlingham and Heeger, 2020). We proposed the hypothesis that heading
perception is computed from optic acceleration, the temporal derivative of
optic flow, based on the observation that heading is aligned perfectly with a
point on the retina with zero optic acceleration. However, this result was
derived for a specific scenario used in our experiments, when retinal heading
and rotational velocity are constant over time. We previously speculated that
as the change over time in heading or rotation increases, the bias of the
estimator would increase proportionally, based on the idea that our derived
case would approximate what happens in a small interval of time (when heading
and rotation are nearly constant). In this technical report, we characterize
the properties of the optic acceleration field and derive the bias of this
estimator for the more common case of a fixating observer, i.e., one that moves
while counter-rotating their eyes to stabilize an object on the fovea. For a
fixating observer tracking a point on a fronto-parallel plane, there are in
fact two singularities of optic acceleration: one that is always at fixation
(due to image stabilization) and a second whose bias scales inversely with
heading, inconsistent with human behavior. For movement parallel to a ground
plane, there is only one singularity of optic acceleration (at the fixation
point), which is uninformative about heading. We conclude that the singularity
of optic acceleration is not an accurate estimator of heading under natural
conditions.
|
[
{
"created": "Tue, 26 Apr 2022 21:44:22 GMT",
"version": "v1"
}
] |
2022-04-28
|
[
[
"Burlingham",
"Charlie S.",
""
],
[
"Hua",
"Mengjian",
""
],
[
"Xu",
"Oliver",
""
],
[
"Bonnen",
"Kathryn",
""
],
[
"Heeger",
"David J.",
""
]
] |
Visual estimation of heading in the human brain is widely believed to be based on instantaneous optic flow, the velocity of retinal image motion. However, we previously found that humans are unable to use instantaneous optic flow to accurately estimate heading and require time-varying optic flow (Burlingham and Heeger, 2020). We proposed the hypothesis that heading perception is computed from optic acceleration, the temporal derivative of optic flow, based on the observation that heading is aligned perfectly with a point on the retina with zero optic acceleration. However, this result was derived for a specific scenario used in our experiments, when retinal heading and rotational velocity are constant over time. We previously speculated that as the change over time in heading or rotation increases, the bias of the estimator would increase proportionally, based on the idea that our derived case would approximate what happens in a small interval of time (when heading and rotation are nearly constant). In this technical report, we characterize the properties of the optic acceleration field and derive the bias of this estimator for the more common case of a fixating observer, i.e., one that moves while counter-rotating their eyes to stabilize an object on the fovea. For a fixating observer tracking a point on a fronto-parallel plane, there are in fact two singularities of optic acceleration: one that is always at fixation (due to image stabilization) and a second whose bias scales inversely with heading, inconsistent with human behavior. For movement parallel to a ground plane, there is only one singularity of optic acceleration (at the fixation point), which is uninformative about heading. We conclude that the singularity of optic acceleration is not an accurate estimator of heading under natural conditions.
|
1808.03666
|
Rocio Joo
|
Rocio Joo, Marie-Pierre Etienne, Nicolas Bez, St\'ephanie Mah\'evas
|
Metrics for describing dyadic movement: a review
|
4 tables, 10 figures and 8 supplementary sections
| null |
10.1186/s40462-018-0144-2
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In movement ecology, the few works that have taken collective behaviour into
account are data-driven and rely on simplistic theoretical assumptions, relying
in metrics that may or may not be measuring what is intended. In the present
paper, we focus on pairwise joint-movement behaviour, where individuals move
together during at least a segment of their path. We investigate the adequacy
of twelve metrics introduced in previous works for assessing joint movement by
analysing their theoretical properties and confronting them with contrasting
case scenarios. Three criteria are taken into account for review of those
metrics: 1) practical use, 2) dependence on parameters and underlying
assumptions, and 3) computational cost. When analysing the similarities between
the metrics as defined, we show how some of them can be expressed using general
mathematical forms. In addition, we evaluate the ability of each metric to
assess specific aspects of joint-movement behaviour: proximity (closeness in
space-time) and coordination (synchrony) in direction and speed. We found that
some metrics are better suited to assess proximity and others are more
sensitive to coordination. To help readers choose metrics, we elaborate a
graphical representation of the metrics in the coordination and proximity space
based on our results, and give a few examples of proximity and coordination
focus in different movement studies.
|
[
{
"created": "Fri, 10 Aug 2018 18:32:28 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Aug 2018 17:37:29 GMT",
"version": "v2"
}
] |
2019-03-18
|
[
[
"Joo",
"Rocio",
""
],
[
"Etienne",
"Marie-Pierre",
""
],
[
"Bez",
"Nicolas",
""
],
[
"Mahévas",
"Stéphanie",
""
]
] |
In movement ecology, the few works that have taken collective behaviour into account are data-driven and rely on simplistic theoretical assumptions, relying in metrics that may or may not be measuring what is intended. In the present paper, we focus on pairwise joint-movement behaviour, where individuals move together during at least a segment of their path. We investigate the adequacy of twelve metrics introduced in previous works for assessing joint movement by analysing their theoretical properties and confronting them with contrasting case scenarios. Three criteria are taken into account for review of those metrics: 1) practical use, 2) dependence on parameters and underlying assumptions, and 3) computational cost. When analysing the similarities between the metrics as defined, we show how some of them can be expressed using general mathematical forms. In addition, we evaluate the ability of each metric to assess specific aspects of joint-movement behaviour: proximity (closeness in space-time) and coordination (synchrony) in direction and speed. We found that some metrics are better suited to assess proximity and others are more sensitive to coordination. To help readers choose metrics, we elaborate a graphical representation of the metrics in the coordination and proximity space based on our results, and give a few examples of proximity and coordination focus in different movement studies.
|
2008.06051
|
Tatsushi Oka
|
Tatsushi Oka and Wei Wei and Dan Zhu
|
A Spatial Stochastic SIR Model for Transmission Networks with
Application to COVID-19 Epidemic in China
|
Typos were fixed
| null | null | null |
q-bio.PE econ.GN physics.soc-ph q-fin.EC stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Governments around the world have implemented preventive measures against the
spread of the coronavirus disease (COVID-19). In this study, we consider a
multivariate discrete-time Markov model to analyze the propagation of COVID-19
across 33 provincial regions in China. This approach enables us to evaluate the
effect of mobility restriction policies on the spread of the disease. We use
data on daily human mobility across regions and apply the Bayesian framework to
estimate the proposed model. The results show that the spread of the disease in
China was predominately driven by community transmission within regions and the
lockdown policy introduced by local governments curbed the spread of the
pandemic. Further, we document that Hubei was only the epicenter of the early
epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had
already become established by late January 2020, and the disease spread out to
connected regions. The transmission from these epicenters substantially
declined following the introduction of human mobility restrictions across
regions.
|
[
{
"created": "Thu, 13 Aug 2020 14:25:40 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Aug 2020 01:54:26 GMT",
"version": "v2"
}
] |
2020-08-18
|
[
[
"Oka",
"Tatsushi",
""
],
[
"Wei",
"Wei",
""
],
[
"Zhu",
"Dan",
""
]
] |
Governments around the world have implemented preventive measures against the spread of the coronavirus disease (COVID-19). In this study, we consider a multivariate discrete-time Markov model to analyze the propagation of COVID-19 across 33 provincial regions in China. This approach enables us to evaluate the effect of mobility restriction policies on the spread of the disease. We use data on daily human mobility across regions and apply the Bayesian framework to estimate the proposed model. The results show that the spread of the disease in China was predominately driven by community transmission within regions and the lockdown policy introduced by local governments curbed the spread of the pandemic. Further, we document that Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020, and the disease spread out to connected regions. The transmission from these epicenters substantially declined following the introduction of human mobility restrictions across regions.
|
1103.5279
|
Chun-Chung Chen
|
Chun-Chung Chen and David Jasnow
|
Event-driven simulations of a plastic, spiking neural network
|
9 pages, 6 figures
|
Phys. Rev. E 84, 031908 (2011)
|
10.1103/PhysRevE.84.031908
| null |
q-bio.NC cond-mat.dis-nn physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider a fully-connected network of leaky integrate-and-fire neurons
with spike-timing-dependent plasticity. The plasticity is controlled by a
parameter representing the expected weight of a synapse between neurons that
are firing randomly with the same mean frequency. For low values of the
plasticity parameter, the activities of the system are dominated by noise,
while large values of the plasticity parameter lead to self-sustaining activity
in the network. We perform event-driven simulations on finite-size networks
with up to 128 neurons to find the stationary synaptic weight conformations for
different values of the plasticity parameter. In both the low and high activity
regimes, the synaptic weights are narrowly distributed around the plasticity
parameter value consistent with the predictions of mean-field theory. However,
the distribution broadens in the transition region between the two regimes,
representing emergent network structures. Using a pseudophysical approach for
visualization, we show that the emergent structures are of "path" or "hub"
type, observed at different values of the plasticity parameter in the
transition region.
|
[
{
"created": "Mon, 28 Mar 2011 04:22:50 GMT",
"version": "v1"
}
] |
2011-09-23
|
[
[
"Chen",
"Chun-Chung",
""
],
[
"Jasnow",
"David",
""
]
] |
We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing randomly with the same mean frequency. For low values of the plasticity parameter, the activities of the system are dominated by noise, while large values of the plasticity parameter lead to self-sustaining activity in the network. We perform event-driven simulations on finite-size networks with up to 128 neurons to find the stationary synaptic weight conformations for different values of the plasticity parameter. In both the low and high activity regimes, the synaptic weights are narrowly distributed around the plasticity parameter value consistent with the predictions of mean-field theory. However, the distribution broadens in the transition region between the two regimes, representing emergent network structures. Using a pseudophysical approach for visualization, we show that the emergent structures are of "path" or "hub" type, observed at different values of the plasticity parameter in the transition region.
|
1705.07182
|
Jayavel Arumugam
|
Jayavel Arumugam, Arun Srinivasa
|
A Novel Simplified Model for Blood Coagulation: A piecewise dynamical
model for thrombin with robust predictive capabilities
|
20 pages, 15 figues
| null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Realistic description of patient-specific mechanical properties of clotting
dynamics presents a major challenge. Available patient-specific data falls
short of robustly characterizing myriads of complex dynamic interactions that
happen during clotting. We propose a simplified switching model for a key part
of the coagulation cascade that describes dynamics of just four variables. The
model correctly predicts prolonged activity of thrombin, an important enzyme in
the clotting process, in certain plasma factor compositions. The activity
sustains beyond the time which is conventionally considered to be the end of
clotting. This observation along with the simplified model is hypothesized as a
necessary step towards effectively studying patient-specific properties of
clotting dynamics in realistic geometries.
|
[
{
"created": "Fri, 19 May 2017 20:55:28 GMT",
"version": "v1"
}
] |
2017-05-23
|
[
[
"Arumugam",
"Jayavel",
""
],
[
"Srinivasa",
"Arun",
""
]
] |
Realistic description of patient-specific mechanical properties of clotting dynamics presents a major challenge. Available patient-specific data falls short of robustly characterizing myriads of complex dynamic interactions that happen during clotting. We propose a simplified switching model for a key part of the coagulation cascade that describes dynamics of just four variables. The model correctly predicts prolonged activity of thrombin, an important enzyme in the clotting process, in certain plasma factor compositions. The activity sustains beyond the time which is conventionally considered to be the end of clotting. This observation along with the simplified model is hypothesized as a necessary step towards effectively studying patient-specific properties of clotting dynamics in realistic geometries.
|
2104.09424
|
Yu-Juan Sun
|
Yu-Juan Sun and Wei-Min Zhang
|
Modeling the Nervous System as An Open Quantum System
|
9 pages, 9 figures
| null | null | null |
q-bio.NC quant-ph
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a neural network model of multi-neuron interacting system that
simulates neurons to interact each other through the surroundings of neuronal
cell bodies. We physically model the neuronal cell surroundings, include the
dendrites, the axons and the synapses as well as the surrounding glial cells,
as a collection of all kinds of oscillating modes arisen from the electric
circuital environment of neuronal action potentials. By analyzing the dynamics
of this neural model through the master equation approach of open quantum
systems, we investigate the collective behavior of neurons. After applying
stimulations to the neural network, the neuronal collective state is activated
and shows the action potential behavior. We find that this model can generate
random neuron-neuron interactions and is proper to describe the process of
information transmission in the nervous system physically, which may pave a
potential route toward understanding the dynamics of nervous system.
|
[
{
"created": "Thu, 18 Mar 2021 10:17:09 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Jul 2021 08:56:21 GMT",
"version": "v2"
}
] |
2021-07-05
|
[
[
"Sun",
"Yu-Juan",
""
],
[
"Zhang",
"Wei-Min",
""
]
] |
We propose a neural network model of multi-neuron interacting system that simulates neurons to interact each other through the surroundings of neuronal cell bodies. We physically model the neuronal cell surroundings, include the dendrites, the axons and the synapses as well as the surrounding glial cells, as a collection of all kinds of oscillating modes arisen from the electric circuital environment of neuronal action potentials. By analyzing the dynamics of this neural model through the master equation approach of open quantum systems, we investigate the collective behavior of neurons. After applying stimulations to the neural network, the neuronal collective state is activated and shows the action potential behavior. We find that this model can generate random neuron-neuron interactions and is proper to describe the process of information transmission in the nervous system physically, which may pave a potential route toward understanding the dynamics of nervous system.
|
1312.3206
|
Joel Adamson
|
Joel James Adamson
|
Evolution of female choice and age-dependent male traits with paternal
germ-line mutation
|
10 pages, 5 figures
| null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
Several studies question the adaptive value of female preferences for older
males. Theory and evidence show that older males carry more deleterious
mutations in their sperm than younger males carry. These mutations are not
visible to females choosing mates. Germ-line mutations could oppose preferences
for "good genes." Choosy females run the risk that offspring of older males
will be no more attractive or healthy than offspring of younger males.
Germ-line mutations could pose a particular problem when females can only judge
male trait size, rather than assessing age directly. I ask whether or not
females will prefer extreme traits, despite reduced offspring survival due to
age-dependent mutation. I use a quantitative genetic model to examine the
evolution of female preferences, an age-dependent male trait, and overall
health ("condition"). My dynamical equation includes mutation bias that depends
on the generation time of the population. I focus on the case where females
form preferences for older males because male trait size depends on male age.
My findings agree with good genes theory. Females at equilibrium always select
above-average males. The trait size preferred by females directly correlates
with the direct costs of the preference. Direct costs can accentuate the
equilibrium preference at a higher rate than mutational parameters. Females can
always offset direct costs by mating with older, more ornamented males.
Age-dependent mutation in condition maintains genetic variation in condition
and thereby maintains the selective value of female preferences. Rather than
eliminating female preferences, germ-line mutations provide an essential
ingredient in sexual selection.
|
[
{
"created": "Wed, 11 Dec 2013 15:20:04 GMT",
"version": "v1"
}
] |
2013-12-12
|
[
[
"Adamson",
"Joel James",
""
]
] |
Several studies question the adaptive value of female preferences for older males. Theory and evidence show that older males carry more deleterious mutations in their sperm than younger males carry. These mutations are not visible to females choosing mates. Germ-line mutations could oppose preferences for "good genes." Choosy females run the risk that offspring of older males will be no more attractive or healthy than offspring of younger males. Germ-line mutations could pose a particular problem when females can only judge male trait size, rather than assessing age directly. I ask whether or not females will prefer extreme traits, despite reduced offspring survival due to age-dependent mutation. I use a quantitative genetic model to examine the evolution of female preferences, an age-dependent male trait, and overall health ("condition"). My dynamical equation includes mutation bias that depends on the generation time of the population. I focus on the case where females form preferences for older males because male trait size depends on male age. My findings agree with good genes theory. Females at equilibrium always select above-average males. The trait size preferred by females directly correlates with the direct costs of the preference. Direct costs can accentuate the equilibrium preference at a higher rate than mutational parameters. Females can always offset direct costs by mating with older, more ornamented males. Age-dependent mutation in condition maintains genetic variation in condition and thereby maintains the selective value of female preferences. Rather than eliminating female preferences, germ-line mutations provide an essential ingredient in sexual selection.
|
2112.13271
|
Seyedeh Sajedeh Mousavi Dr
|
S. Sajedeh Mousavi, Sara Razi
|
Cell-in-cell structures are involved in the competition between cells in
breast cancer
|
Presented as a poster in 4th International biotechnology congress of
Islamic Republic of Iran (2021)
| null | null | null |
q-bio.GN
|
http://creativecommons.org/licenses/by/4.0/
|
Breast cancer is the most common cancer in women worldwide, and discovering
the biomarkers of this disease became so vital nowadays and Cell in Cell
structure could be one of them, and it may be used as an available proxy for
tumor malignancy. (CICs) are unusual in that keep morphologically healthy cells
within another cell. They are found in various human cancers and result from
active cell-cell interaction, and it has different kinds. In this study, we
analyzed the microarray data from GEO (GSE103865) to genetically evaluate CICs'
incidence in samples obtained from breast cancer patients to understand the
relationship between the rate of CIC and the prognosis of breast cancer. The
preprocessing was performed using R software. The DAVID website was used to
analyze gene ontology (GO) and Gene and Genome (KEGG) pathways. The
protein-protein interactions (PPIs) of the obtained DEGs were assessed using
the STRING website, and hub modules in Cytoscape and cytoHubba were screened.
According to the results from analyzing the 20 hub genes, we understood that
overexpression of our Top genes is effective in focal adhesion, ECM-receptor
interaction, platelet activation and PI3K-Akt signaling pathway, which shows
that changes in these pathways could be the reason the overexpression of CICs
in breast cancer. These data and research by many others have uncovered various
genes involved in CIC formation and have started to give us an idea of why they
are formed and how they could contribute to breast cancer
|
[
{
"created": "Sat, 25 Dec 2021 19:17:59 GMT",
"version": "v1"
}
] |
2021-12-28
|
[
[
"Mousavi",
"S. Sajedeh",
""
],
[
"Razi",
"Sara",
""
]
] |
Breast cancer is the most common cancer in women worldwide, and discovering the biomarkers of this disease became so vital nowadays and Cell in Cell structure could be one of them, and it may be used as an available proxy for tumor malignancy. (CICs) are unusual in that keep morphologically healthy cells within another cell. They are found in various human cancers and result from active cell-cell interaction, and it has different kinds. In this study, we analyzed the microarray data from GEO (GSE103865) to genetically evaluate CICs' incidence in samples obtained from breast cancer patients to understand the relationship between the rate of CIC and the prognosis of breast cancer. The preprocessing was performed using R software. The DAVID website was used to analyze gene ontology (GO) and Gene and Genome (KEGG) pathways. The protein-protein interactions (PPIs) of the obtained DEGs were assessed using the STRING website, and hub modules in Cytoscape and cytoHubba were screened. According to the results from analyzing the 20 hub genes, we understood that overexpression of our Top genes is effective in focal adhesion, ECM-receptor interaction, platelet activation and PI3K-Akt signaling pathway, which shows that changes in these pathways could be the reason the overexpression of CICs in breast cancer. These data and research by many others have uncovered various genes involved in CIC formation and have started to give us an idea of why they are formed and how they could contribute to breast cancer
|
q-bio/0509009
|
Georgy Karev
|
Georgy P. Karev, Artem S. Novozhilov, and Faina S. Berezovskaya
|
Modeling the dynamics of inhomogeneous natural rotifer populations under
toxicant exposure
|
12 pages, 4 figures; submitted to Ecological Modelling
| null | null | null |
q-bio.PE q-bio.QM
| null |
Most population models assume that individuals within a given population are
identical, that is, the fundamental role of variation is ignored. Here we
develop a general approach to modeling heterogeneous populations with discrete
evolutionary time step. The theory is applied to population dynamics of natural
rotifer populations. We show that under particular conditions the behavior of
the inhomogeneous model possesses complex transition regimes, which depends
both on the mean and the variance of the initial parameter distribution and the
final state of the population depends on the least possible value from the
domain of the parameter. The question of persistence of the population is
discussed.
|
[
{
"created": "Fri, 9 Sep 2005 14:50:44 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Karev",
"Georgy P.",
""
],
[
"Novozhilov",
"Artem S.",
""
],
[
"Berezovskaya",
"Faina S.",
""
]
] |
Most population models assume that individuals within a given population are identical, that is, the fundamental role of variation is ignored. Here we develop a general approach to modeling heterogeneous populations with discrete evolutionary time step. The theory is applied to population dynamics of natural rotifer populations. We show that under particular conditions the behavior of the inhomogeneous model possesses complex transition regimes, which depends both on the mean and the variance of the initial parameter distribution and the final state of the population depends on the least possible value from the domain of the parameter. The question of persistence of the population is discussed.
|
1702.08560
|
Deborah Shutt
|
Deborah P. Shutt, Carrie A. Manore, Stephen Pankavich, Aaron T.
Porter, Sara Y. Del Valle
|
Estimating the reproductive number, total outbreak size, and reporting
rates for Zika epidemics in South and Central America
|
35 pages, 16 figures
| null | null | null |
q-bio.PE q-bio.QM stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As South and Central American countries prepare for increased birth defects
from Zika virus outbreaks and plan for mitigation strategies to minimize
ongoing and future outbreaks, understanding important characteristics of Zika
outbreaks and how they vary across regions is a challenging and important
problem. We developed a mathematical model for the 2015 Zika virus outbreak
dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly
available data provided by the Pan American Health Organization, using
Approximate Bayesian Computation to estimate parameter distributions and
provide uncertainty quantification. An important model input is the at-risk
susceptible population, which can vary with a number of factors including
climate, elevation, population density, and socio-economic status. We informed
this initial condition using the highest historically reported dengue incidence
modified by the probable dengue reporting rates in the chosen countries. The
model indicated that a country-level analysis was not appropriate for Colombia.
We then estimated the basic reproduction number, or the expected number of new
human infections arising from a single infected human, to range between 4 and 6
for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We
estimated the reporting rate to be around 16% in El Salvador and 18% in
Suriname with estimated total outbreak sizes of 73,395 and 21,647 people,
respectively. The uncertainty in parameter estimates highlights a need for
research and data collection that will better constrain parameter ranges.
|
[
{
"created": "Mon, 27 Feb 2017 22:22:03 GMT",
"version": "v1"
}
] |
2017-03-01
|
[
[
"Shutt",
"Deborah P.",
""
],
[
"Manore",
"Carrie A.",
""
],
[
"Pankavich",
"Stephen",
""
],
[
"Porter",
"Aaron T.",
""
],
[
"Del Valle",
"Sara Y.",
""
]
] |
As South and Central American countries prepare for increased birth defects from Zika virus outbreaks and plan for mitigation strategies to minimize ongoing and future outbreaks, understanding important characteristics of Zika outbreaks and how they vary across regions is a challenging and important problem. We developed a mathematical model for the 2015 Zika virus outbreak dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly available data provided by the Pan American Health Organization, using Approximate Bayesian Computation to estimate parameter distributions and provide uncertainty quantification. An important model input is the at-risk susceptible population, which can vary with a number of factors including climate, elevation, population density, and socio-economic status. We informed this initial condition using the highest historically reported dengue incidence modified by the probable dengue reporting rates in the chosen countries. The model indicated that a country-level analysis was not appropriate for Colombia. We then estimated the basic reproduction number, or the expected number of new human infections arising from a single infected human, to range between 4 and 6 for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We estimated the reporting rate to be around 16% in El Salvador and 18% in Suriname with estimated total outbreak sizes of 73,395 and 21,647 people, respectively. The uncertainty in parameter estimates highlights a need for research and data collection that will better constrain parameter ranges.
|
1806.06724
|
Enrico Gavagnin
|
Enrico Gavagnin and Christian A. Yates
|
Stochastic and deterministic modelling of cell migration
| null | null | null | null |
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mathematical models are vital interpretive and predictive tools used to
assist in the understanding of cell migration. There are typically two
approaches to modelling cell migration: either micro-scale, discrete or
macro-scale, continuum. The discrete approach, using agent-based models (ABMs),
is typically stochastic and accounts for properties at the cell-scale.
Conversely, the continuum approach, in which cell density is often modelled as
a system of deterministic partial differential equations (PDEs), provides a
global description of the migration at the population level. Deterministic
models have the advantage that they are generally more amenable to mathematical
analysis. They can lead to significant insights for situations in which the
system comprises a large number of cells, at which point simulating a
stochastic ABM becomes computationally expensive. However, finding an
appropriate continuum model to describe the collective behaviour of a system of
individual cells can be a difficult task. Deterministic models are often
specified on a phenomenological basis, which reduces their predictive power.
Stochastic ABMs have advantages over their deterministic continuum
counterparts. In particular, ABMs can represent individual-level behaviours
(such as cell proliferation and cell-cell interaction) appropriately and are
amenable to direct parameterisation using experimental data. It is essential,
therefore, to establish direct connections between stochastic micro-scale
behaviours and deterministic macro-scale dynamics. In this Chapter we describe
how, in some situations, these two distinct modelling approaches can be unified
into a discrete-continuum equivalence framework. We provide an overview of some
of the more recent advances in this field and we point out some of the relevant
questions that remain unanswered.
|
[
{
"created": "Mon, 18 Jun 2018 14:18:14 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Aug 2018 10:05:44 GMT",
"version": "v2"
}
] |
2018-08-16
|
[
[
"Gavagnin",
"Enrico",
""
],
[
"Yates",
"Christian A.",
""
]
] |
Mathematical models are vital interpretive and predictive tools used to assist in the understanding of cell migration. There are typically two approaches to modelling cell migration: either micro-scale, discrete or macro-scale, continuum. The discrete approach, using agent-based models (ABMs), is typically stochastic and accounts for properties at the cell-scale. Conversely, the continuum approach, in which cell density is often modelled as a system of deterministic partial differential equations (PDEs), provides a global description of the migration at the population level. Deterministic models have the advantage that they are generally more amenable to mathematical analysis. They can lead to significant insights for situations in which the system comprises a large number of cells, at which point simulating a stochastic ABM becomes computationally expensive. However, finding an appropriate continuum model to describe the collective behaviour of a system of individual cells can be a difficult task. Deterministic models are often specified on a phenomenological basis, which reduces their predictive power. Stochastic ABMs have advantages over their deterministic continuum counterparts. In particular, ABMs can represent individual-level behaviours (such as cell proliferation and cell-cell interaction) appropriately and are amenable to direct parameterisation using experimental data. It is essential, therefore, to establish direct connections between stochastic micro-scale behaviours and deterministic macro-scale dynamics. In this Chapter we describe how, in some situations, these two distinct modelling approaches can be unified into a discrete-continuum equivalence framework. We provide an overview of some of the more recent advances in this field and we point out some of the relevant questions that remain unanswered.
|
1505.04656
|
Fernando Vericat
|
C. Manuel Carlevaro, Ramiro M. Irastorza and Fernando Vericat
|
Quaternionic representation of the genetic code
|
19 pages, 11 figures
| null | null | null |
q-bio.OT physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A heuristic diagram of the evolution of the standard genetic code is
presented. It incorporates, in a way that resembles the energy levels of an
atom, the physical notion of broken symmetry and it is consistent with original
ideas by Crick on the origin and evolution of the code as well as with the
chronological order of appearence of the amino acids along the evolution as
inferred from work that mixtures known experimental results with theoretical
speculations. Suggested by the diagram we propose a Hamilton quaternions based
mathematical representation of the code as it stands now-a-days. The central
object in the description is a codon function that assigns to each amino acid
an integer quaternion in such a way that the observed code degeneration is
preserved. We emphasize the advantages of a quaternionic representation of
amino acids taking as an example the folding of proteins. With this aim we
propose an algorithm to go from the quaternions sequence to the protein three
dimensional structure which can be compared with the corresponding experimental
one stored at the Protein Data Bank. In our criterion the mathematical
representation of the genetic code in terms of quaternions merits to be taken
into account because it describes not only most of the known properties of the
genetic code but also opens new perspectives that are mainly derived from the
close relationship between quaternions and rotations.
|
[
{
"created": "Mon, 18 May 2015 14:23:18 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Dec 2015 19:15:25 GMT",
"version": "v2"
}
] |
2015-12-31
|
[
[
"Carlevaro",
"C. Manuel",
""
],
[
"Irastorza",
"Ramiro M.",
""
],
[
"Vericat",
"Fernando",
""
]
] |
A heuristic diagram of the evolution of the standard genetic code is presented. It incorporates, in a way that resembles the energy levels of an atom, the physical notion of broken symmetry and it is consistent with original ideas by Crick on the origin and evolution of the code as well as with the chronological order of appearence of the amino acids along the evolution as inferred from work that mixtures known experimental results with theoretical speculations. Suggested by the diagram we propose a Hamilton quaternions based mathematical representation of the code as it stands now-a-days. The central object in the description is a codon function that assigns to each amino acid an integer quaternion in such a way that the observed code degeneration is preserved. We emphasize the advantages of a quaternionic representation of amino acids taking as an example the folding of proteins. With this aim we propose an algorithm to go from the quaternions sequence to the protein three dimensional structure which can be compared with the corresponding experimental one stored at the Protein Data Bank. In our criterion the mathematical representation of the genetic code in terms of quaternions merits to be taken into account because it describes not only most of the known properties of the genetic code but also opens new perspectives that are mainly derived from the close relationship between quaternions and rotations.
|
1811.09673
|
Veronica Tozzo
|
Veronica Tozzo, Federico Tomasi, Margherita Squillario, Annalisa Barla
|
Group induced graphical lasso allows for discovery of molecular
pathways-pathways interactions
|
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
arXiv:1811.07216
| null | null | null |
q-bio.QM cs.LG q-bio.GN stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Complex systems may contain heterogeneous types of variables that interact in
a multi-level and multi-scale manner. In this context, high-level layers may
considered as groups of variables interacting in lower-level layers. This is
particularly true in biology, where, for example, genes are grouped in pathways
and two types of interactions are present: pathway-pathway interactions and
gene-gene interactions. However, from data it is only possible to measure the
expression of genes while it is impossible to directly measure the activity of
pathways. Nevertheless, the knowledge on the inter-dependence between the
groups and the variables allows for a multi-layer network inference, on both
observed variables and groups, even if no direct information on the latter is
present in the data (hence groups are considered as latent). In this paper, we
propose an extension of the latent graphical lasso method that leverages on the
knowledge of the inter-links between the hidden (groups) and observed layers.
The method exploits the knowledge of group structure that influence the
behaviour of observed variables to retrieve a two layers network. Its efficacy
was tested on synthetic data to check its ability in retrieving the network
structure compared to the ground truth. We present a case study on
Neuroblastoma, which shows how our multi-level inference is relevant in real
contexts to infer biologically meaningful connections.
|
[
{
"created": "Wed, 21 Nov 2018 10:39:59 GMT",
"version": "v1"
}
] |
2018-11-28
|
[
[
"Tozzo",
"Veronica",
""
],
[
"Tomasi",
"Federico",
""
],
[
"Squillario",
"Margherita",
""
],
[
"Barla",
"Annalisa",
""
]
] |
Complex systems may contain heterogeneous types of variables that interact in a multi-level and multi-scale manner. In this context, high-level layers may considered as groups of variables interacting in lower-level layers. This is particularly true in biology, where, for example, genes are grouped in pathways and two types of interactions are present: pathway-pathway interactions and gene-gene interactions. However, from data it is only possible to measure the expression of genes while it is impossible to directly measure the activity of pathways. Nevertheless, the knowledge on the inter-dependence between the groups and the variables allows for a multi-layer network inference, on both observed variables and groups, even if no direct information on the latter is present in the data (hence groups are considered as latent). In this paper, we propose an extension of the latent graphical lasso method that leverages on the knowledge of the inter-links between the hidden (groups) and observed layers. The method exploits the knowledge of group structure that influence the behaviour of observed variables to retrieve a two layers network. Its efficacy was tested on synthetic data to check its ability in retrieving the network structure compared to the ground truth. We present a case study on Neuroblastoma, which shows how our multi-level inference is relevant in real contexts to infer biologically meaningful connections.
|
2408.00040
|
Maximilian Schuh
|
Maximilian G. Schuh, Davide Boldini, Stephan A. Sieber
|
Barlow Twins Deep Neural Network for Advanced 1D Drug-Target Interaction
Prediction
|
9 pages, 2 figures
| null | null | null |
q-bio.BM cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate prediction of drug-target interactions is critical for advancing
drug discovery. By reducing time and cost, machine learning and deep learning
can accelerate this discovery process. Our approach utilises the powerful
Barlow Twins architecture for feature-extraction while considering the
structure of the target protein, achieving state-of-the-art predictive
performance against multiple established benchmarks. The use of gradient
boosting machine as the underlying predictor ensures fast and efficient
predictions without the need for large computational resources. In addition, we
further benchmarked new baselines against existing methods. Together, these
innovations improve the efficiency and effectiveness of drug-target interaction
predictions, providing robust tools for accelerating drug development and
deepening the understanding of molecular interactions.
|
[
{
"created": "Wed, 31 Jul 2024 14:06:18 GMT",
"version": "v1"
}
] |
2024-08-02
|
[
[
"Schuh",
"Maximilian G.",
""
],
[
"Boldini",
"Davide",
""
],
[
"Sieber",
"Stephan A.",
""
]
] |
Accurate prediction of drug-target interactions is critical for advancing drug discovery. By reducing time and cost, machine learning and deep learning can accelerate this discovery process. Our approach utilises the powerful Barlow Twins architecture for feature-extraction while considering the structure of the target protein, achieving state-of-the-art predictive performance against multiple established benchmarks. The use of gradient boosting machine as the underlying predictor ensures fast and efficient predictions without the need for large computational resources. In addition, we further benchmarked new baselines against existing methods. Together, these innovations improve the efficiency and effectiveness of drug-target interaction predictions, providing robust tools for accelerating drug development and deepening the understanding of molecular interactions.
|
1809.08045
|
Dominik Dold
|
Dominik Dold, Ilja Bytschok, Akos F. Kungl, Andreas Baumbach, Oliver
Breitwieser, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A.
Petrovici
|
Stochasticity from function -- why the Bayesian brain may need no noise
| null |
Neural Networks 119C (2019) pp. 200-213
|
10.1016/j.neunet.2019.08.002
| null |
q-bio.NC cond-mat.dis-nn cs.NE physics.bio-ph stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
An increasing body of evidence suggests that the trial-to-trial variability
of spiking activity in the brain is not mere noise, but rather the reflection
of a sampling-based encoding scheme for probabilistic computing. Since the
precise statistical properties of neural activity are important in this
context, many models assume an ad-hoc source of well-behaved, explicit noise,
either on the input or on the output side of single neuron dynamics, most often
assuming an independent Poisson process in either case. However, these
assumptions are somewhat problematic: neighboring neurons tend to share
receptive fields, rendering both their input and their output correlated; at
the same time, neurons are known to behave largely deterministically, as a
function of their membrane potential and conductance. We suggest that spiking
neural networks may, in fact, have no need for noise to perform sampling-based
Bayesian inference. We study analytically the effect of auto- and
cross-correlations in functionally Bayesian spiking networks and demonstrate
how their effect translates to synaptic interaction strengths, rendering them
controllable through synaptic plasticity. This allows even small ensembles of
interconnected deterministic spiking networks to simultaneously and
co-dependently shape their output activity through learning, enabling them to
perform complex Bayesian computation without any need for noise, which we
demonstrate in silico, both in classical simulation and in neuromorphic
emulation. These results close a gap between the abstract models and the
biology of functionally Bayesian spiking networks, effectively reducing the
architectural constraints imposed on physical neural substrates required to
perform probabilistic computing, be they biological or artificial.
|
[
{
"created": "Fri, 21 Sep 2018 11:37:14 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Aug 2019 15:53:49 GMT",
"version": "v2"
},
{
"created": "Sat, 24 Aug 2019 12:58:19 GMT",
"version": "v3"
}
] |
2019-08-27
|
[
[
"Dold",
"Dominik",
""
],
[
"Bytschok",
"Ilja",
""
],
[
"Kungl",
"Akos F.",
""
],
[
"Baumbach",
"Andreas",
""
],
[
"Breitwieser",
"Oliver",
""
],
[
"Senn",
"Walter",
""
],
[
"Schemmel",
"Johannes",
""
],
[
"Meier",
"Karlheinz",
""
],
[
"Petrovici",
"Mihai A.",
""
]
] |
An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing. Since the precise statistical properties of neural activity are important in this context, many models assume an ad-hoc source of well-behaved, explicit noise, either on the input or on the output side of single neuron dynamics, most often assuming an independent Poisson process in either case. However, these assumptions are somewhat problematic: neighboring neurons tend to share receptive fields, rendering both their input and their output correlated; at the same time, neurons are known to behave largely deterministically, as a function of their membrane potential and conductance. We suggest that spiking neural networks may, in fact, have no need for noise to perform sampling-based Bayesian inference. We study analytically the effect of auto- and cross-correlations in functionally Bayesian spiking networks and demonstrate how their effect translates to synaptic interaction strengths, rendering them controllable through synaptic plasticity. This allows even small ensembles of interconnected deterministic spiking networks to simultaneously and co-dependently shape their output activity through learning, enabling them to perform complex Bayesian computation without any need for noise, which we demonstrate in silico, both in classical simulation and in neuromorphic emulation. These results close a gap between the abstract models and the biology of functionally Bayesian spiking networks, effectively reducing the architectural constraints imposed on physical neural substrates required to perform probabilistic computing, be they biological or artificial.
|
2310.16057
|
Jonathon Mellor
|
Jonathon Mellor, Rachel Christie, James Guilder, Robert S Paton,
Suzanne Elgohari, Conall Watson, Sarah Deeny, Thomas Ward
|
Influenza Hospitalisations in England during the 2022/23 Season: do
different data sources drive divergence in modelled waves? A comparison of
surveillance and administrative data
| null | null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate and representative data is vital for precisely reporting the impact
of influenza in healthcare systems. Northern hemisphere winter 2022/23
experienced the most substantial influenza wave since the COVID-19 pandemic
began in 2020. Simultaneously, new data streams become available within health
services because of the pandemic. Comparing these data, surveillance and
administrative, supports the accurate monitoring of population level disease
trends. We analysed admissions rates per capita from four different collection
mechanisms covering National Health Service hospital Trusts in England over the
winter 2022/23 wave. We adjust for difference in reporting and extracted key
epidemic characteristics including the maximum admission rate, peak timing,
cumulative season admissions and growth rates by fitting generalised additive
models at national and regional levels. By modelling the admission rates per
capita across surveillance and administrative data systems we show that
different data measuring the epidemic produce different estimates of key
quantities. Nationally and in most regions the data correspond well for the
maximum admission rate, date of peak and growth rate, however, in subnational
analysis discrepancies in estimates arose, particularly for the cumulative
admission rate. This research shows that the choice of data used to measure
seasonal influenza epidemics can influence analysis substantially at
sub-national levels. For the admission rate per capita there is comparability
in the sentinel surveillance approach (which has other important functions),
rapid situational reports, operational databases and time lagged administrative
data giving assurance in their combined value. Utilising multiple sources of
data aids understanding of the impact of seasonal influenza epidemics in the
population.
|
[
{
"created": "Mon, 16 Oct 2023 15:22:19 GMT",
"version": "v1"
}
] |
2023-10-26
|
[
[
"Mellor",
"Jonathon",
""
],
[
"Christie",
"Rachel",
""
],
[
"Guilder",
"James",
""
],
[
"Paton",
"Robert S",
""
],
[
"Elgohari",
"Suzanne",
""
],
[
"Watson",
"Conall",
""
],
[
"Deeny",
"Sarah",
""
],
[
"Ward",
"Thomas",
""
]
] |
Accurate and representative data is vital for precisely reporting the impact of influenza in healthcare systems. Northern hemisphere winter 2022/23 experienced the most substantial influenza wave since the COVID-19 pandemic began in 2020. Simultaneously, new data streams become available within health services because of the pandemic. Comparing these data, surveillance and administrative, supports the accurate monitoring of population level disease trends. We analysed admissions rates per capita from four different collection mechanisms covering National Health Service hospital Trusts in England over the winter 2022/23 wave. We adjust for difference in reporting and extracted key epidemic characteristics including the maximum admission rate, peak timing, cumulative season admissions and growth rates by fitting generalised additive models at national and regional levels. By modelling the admission rates per capita across surveillance and administrative data systems we show that different data measuring the epidemic produce different estimates of key quantities. Nationally and in most regions the data correspond well for the maximum admission rate, date of peak and growth rate, however, in subnational analysis discrepancies in estimates arose, particularly for the cumulative admission rate. This research shows that the choice of data used to measure seasonal influenza epidemics can influence analysis substantially at sub-national levels. For the admission rate per capita there is comparability in the sentinel surveillance approach (which has other important functions), rapid situational reports, operational databases and time lagged administrative data giving assurance in their combined value. Utilising multiple sources of data aids understanding of the impact of seasonal influenza epidemics in the population.
|
1912.00672
|
Zina Ibrahim
|
Zina Ibrahim and Honghan Wu and Ahmed Hamoud and Lukas Stappen and
Richard Dobson and Andrea Agarossi
|
On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine
Learning
|
3 Figures and 2 tables. Accepted for publication at the Journal of
American Medical Informatics Association
|
Journal of the American Medical Informatics Association 27 (2020)
437-443
|
10.1093/jamia/ocz211
| null |
q-bio.QM cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current machine learning models aiming to predict sepsis from Electronic
Health Records (EHR) do not account for the heterogeneity of the condition,
despite its emerging importance in prognosis and treatment. This work
demonstrates the added value of stratifying the types of organ dysfunction
observed in patients who develop sepsis in the ICU in improving the ability to
recognise patients at risk of sepsis from their EHR data. Using an ICU dataset
of 13,728 records, we identify clinically significant sepsis subpopulations
with distinct organ dysfunction patterns. Classification experiments using
Random Forest, Gradient Boost Trees and Support Vector Machines, aiming to
distinguish patients who develop sepsis in the ICU from those who do not, show
that features selected using sepsis subpopulations as background knowledge
yield a superior performance regardless of the classification model used. Our
findings can steer machine learning efforts towards more personalised models
for complex conditions including sepsis.
|
[
{
"created": "Mon, 2 Dec 2019 10:32:40 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Dec 2019 12:42:51 GMT",
"version": "v2"
}
] |
2020-11-24
|
[
[
"Ibrahim",
"Zina",
""
],
[
"Wu",
"Honghan",
""
],
[
"Hamoud",
"Ahmed",
""
],
[
"Stappen",
"Lukas",
""
],
[
"Dobson",
"Richard",
""
],
[
"Agarossi",
"Andrea",
""
]
] |
Current machine learning models aiming to predict sepsis from Electronic Health Records (EHR) do not account for the heterogeneity of the condition, despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the ICU in improving the ability to recognise patients at risk of sepsis from their EHR data. Using an ICU dataset of 13,728 records, we identify clinically significant sepsis subpopulations with distinct organ dysfunction patterns. Classification experiments using Random Forest, Gradient Boost Trees and Support Vector Machines, aiming to distinguish patients who develop sepsis in the ICU from those who do not, show that features selected using sepsis subpopulations as background knowledge yield a superior performance regardless of the classification model used. Our findings can steer machine learning efforts towards more personalised models for complex conditions including sepsis.
|
1405.5128
|
Michael Sadovsky
|
Michael G.Sadovsky
|
Evidence for strong co-evolution of mitochondrial and somatic genomes
| null | null | null | null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We studied a relations between the triplet frequency composition of
mitochondria genomes, and the phylogeny of their bearers. First, the clusters
in 63dimensional space were developed due to $K$-means. Second, the clade
composition of those clusters has been studied. It was found that genomes are
distributed among the clusters very regularly, with strong correlation to
taxonomy. Strong co-evolution manifests through this correlation: the proximity
in frequency space was determined over the mitochondrion genomes, while the
proximity in taxonomy was determined morphologically.
|
[
{
"created": "Tue, 20 May 2014 15:35:01 GMT",
"version": "v1"
}
] |
2014-05-21
|
[
[
"Sadovsky",
"Michael G.",
""
]
] |
We studied a relations between the triplet frequency composition of mitochondria genomes, and the phylogeny of their bearers. First, the clusters in 63dimensional space were developed due to $K$-means. Second, the clade composition of those clusters has been studied. It was found that genomes are distributed among the clusters very regularly, with strong correlation to taxonomy. Strong co-evolution manifests through this correlation: the proximity in frequency space was determined over the mitochondrion genomes, while the proximity in taxonomy was determined morphologically.
|
1904.08094
|
Atsushi Kamimura
|
Atsushi Kamimura and Kunihiko Kaneko
|
Molecular Diversity and Network Complexity in Growing Protocells
|
16 pages, 7 figures, submitted for publication
| null | null | null |
q-bio.CB q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A great variety of molecular components is encapsulated in cells. Each of
these components is replicated for cell reproduction. To address an essential
role of the huge diversity of cellular components, we study a model of
protocells that convert resources into catalysts with the aid of a catalytic
reaction network. As the resources are limited, it is shown that diversity in
intracellular components is increased to allow the use of diverse resources for
cellular growth. Scaling relation is demonstrated between resource abundances
and molecular diversity. We then study how the molecule species diversify and
complex catalytic reaction networks develop through the evolutionary course. It
is shown that molecule species first appear, at some generations, as parasitic
ones that do not contribute to replication of other molecules. Later, the
species turn to be host species that support the replication of other species.
With this successive increase of host species, a complex joint network evolves.
The present study sheds new light on the origin of molecular diversity and
complex reaction network at the primitive stage of a cell.
|
[
{
"created": "Wed, 17 Apr 2019 06:02:19 GMT",
"version": "v1"
}
] |
2019-04-18
|
[
[
"Kamimura",
"Atsushi",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] |
A great variety of molecular components is encapsulated in cells. Each of these components is replicated for cell reproduction. To address an essential role of the huge diversity of cellular components, we study a model of protocells that convert resources into catalysts with the aid of a catalytic reaction network. As the resources are limited, it is shown that diversity in intracellular components is increased to allow the use of diverse resources for cellular growth. Scaling relation is demonstrated between resource abundances and molecular diversity. We then study how the molecule species diversify and complex catalytic reaction networks develop through the evolutionary course. It is shown that molecule species first appear, at some generations, as parasitic ones that do not contribute to replication of other molecules. Later, the species turn to be host species that support the replication of other species. With this successive increase of host species, a complex joint network evolves. The present study sheds new light on the origin of molecular diversity and complex reaction network at the primitive stage of a cell.
|
2310.11854
|
Dibakar Ghosh Dr.
|
Palash Kumar Pal, Md Sayeed Anwar and Dibakar Ghosh
|
Desynchrony induced by higher-order interactions in triplex
metapopulations
|
12 Pages, 10 figures. Accepted for publication in Physical Review E,
2023
| null | null | null |
q-bio.PE nlin.AO nlin.CD physics.soc-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In a predator-prey metapopulation, the two traits are adversely related:
synchronization and persistence. A decrease in synchrony apparently leads to an
increase in persistence and, therefore, necessitates the study of desynchrony
in a metapopulation. In this article, we study predator-prey patches that
communicate with one another while being interconnected through distinct
dispersal structures in the layers of a three-layer multiplex network. We
investigate the synchronization phenomenon among the patches of the outer
layers by introducing higher-order interactions (specifically three-body
interactions) in the middle layer. We observe a decrease in the synchronous
behavior or, alternatively, an increase in desynchrony due to the inclusion of
group interactions among the patches of the middle layer. The advancement of
desynchrony becomes more prominent with increasing strength and numbers of
three-way interactions in the middle layer. We analytically validated our
numerical results by performing the stability analysis of the referred
synchronous solution using the master stability function approach.
Additionally, we verify our findings by taking into account two distinct
predator-prey models and dispersal topologies, which ultimately assert that the
findings are generalizable across various models and dispersal structures.
|
[
{
"created": "Wed, 18 Oct 2023 10:09:03 GMT",
"version": "v1"
}
] |
2023-10-19
|
[
[
"Pal",
"Palash Kumar",
""
],
[
"Anwar",
"Md Sayeed",
""
],
[
"Ghosh",
"Dibakar",
""
]
] |
In a predator-prey metapopulation, the two traits are adversely related: synchronization and persistence. A decrease in synchrony apparently leads to an increase in persistence and, therefore, necessitates the study of desynchrony in a metapopulation. In this article, we study predator-prey patches that communicate with one another while being interconnected through distinct dispersal structures in the layers of a three-layer multiplex network. We investigate the synchronization phenomenon among the patches of the outer layers by introducing higher-order interactions (specifically three-body interactions) in the middle layer. We observe a decrease in the synchronous behavior or, alternatively, an increase in desynchrony due to the inclusion of group interactions among the patches of the middle layer. The advancement of desynchrony becomes more prominent with increasing strength and numbers of three-way interactions in the middle layer. We analytically validated our numerical results by performing the stability analysis of the referred synchronous solution using the master stability function approach. Additionally, we verify our findings by taking into account two distinct predator-prey models and dispersal topologies, which ultimately assert that the findings are generalizable across various models and dispersal structures.
|
1704.02693
|
Jiancheng Zhuang
|
Jiancheng Zhuang
|
Detecting neural activity and connectivity by perfusion-based fMRI
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This study proposes an approach to estimate the functional localization and
connectivity from CBF and BOLD signals simultaneously measured by ASL (arterial
spin labeling) MRI, especially using exploratory Structural Equation Modeling
analysis. In a visual task experiment, the primary visual cortices were located
by analyzing the perfusion data. In the resting state experiment, two
structural equation models were estimated at each voxel regarding to the
sensory-motor network and default-mode network. The resulting connectivity maps
indicate that supplementary motor area has significant connections to
left/right primary motor areas, and inferior parietal lobules link
significantly with posterior cingulate cortex and medial prefrontal cortex. The
model fitting results imply that BOLD signal is more directly linked to the
underlying cause of functional connectivity than CBF signal.
|
[
{
"created": "Mon, 10 Apr 2017 03:23:16 GMT",
"version": "v1"
}
] |
2017-04-11
|
[
[
"Zhuang",
"Jiancheng",
""
]
] |
This study proposes an approach to estimate the functional localization and connectivity from CBF and BOLD signals simultaneously measured by ASL (arterial spin labeling) MRI, especially using exploratory Structural Equation Modeling analysis. In a visual task experiment, the primary visual cortices were located by analyzing the perfusion data. In the resting state experiment, two structural equation models were estimated at each voxel regarding to the sensory-motor network and default-mode network. The resulting connectivity maps indicate that supplementary motor area has significant connections to left/right primary motor areas, and inferior parietal lobules link significantly with posterior cingulate cortex and medial prefrontal cortex. The model fitting results imply that BOLD signal is more directly linked to the underlying cause of functional connectivity than CBF signal.
|
2301.06692
|
Yue Wang
|
Yue Wang and Siqi He
|
Using Fano factors to determine certain types of gene autoregulation
| null | null | null | null |
q-bio.MN
|
http://creativecommons.org/licenses/by/4.0/
|
The expression of one gene might be regulated by its corresponding protein,
which is called autoregulation. Although gene regulation is a central topic in
biology, autoregulation is much less studied. In general, it is extremely
difficult to determine the existence of autoregulation with direct biochemical
approaches. Nevertheless, some papers have observed that certain types of
autoregulations are linked to noise levels in gene expression. We generalize
these results by two propositions on discrete-state continuous-time Markov
chains. These two propositions form a simple but robust method to infer the
existence of autoregulation in certain scenarios from gene expression data.
This method only depends on the Fano factor, namely the ratio of variance and
mean of the gene expression level. Compared to other methods for inferring
autoregulation, our method only requires non-interventional one-time data, and
does not need to estimate parameters. Besides, our method has few restrictions
on the model. We apply this method to four groups of experimental data and find
some genes that might have autoregulation. Some inferred autoregulations have
been verified by experiments or other theoretical works.
|
[
{
"created": "Tue, 17 Jan 2023 04:13:09 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Mar 2023 02:15:49 GMT",
"version": "v2"
}
] |
2023-03-31
|
[
[
"Wang",
"Yue",
""
],
[
"He",
"Siqi",
""
]
] |
The expression of one gene might be regulated by its corresponding protein, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation in certain scenarios from gene expression data. This method only depends on the Fano factor, namely the ratio of variance and mean of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.
|
1811.11443
|
Irina Mizeva
|
Irina A. Mizeva, Elena V. Potapova, Viktor V. Dremin, Evgeny A.
Zherebtsov, Mikhail A. Mezentsev, Valerii V. Shupletsov, Andrey V. Dunaev
|
Optical probe pressure effects on cutaneous blood flow
|
8 pages, 6 figures
| null | null | null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The variation of blood flow characteristics caused by the probe pressure
during noninvasive studies is of particular interest within the context of
fundamental and applied research. It has been shown previously that the weak
local pressure induces vasodilation, whereas the increased pressure is able to
stop the blood flow in the compressed area, as well as to significantly change
optical signals.
The blood flow oscillations measured by laser Doppler flowmetry (LDF)
characterize the functional state of the microvascular system and can be used
for noninvasive diagnostics of its abnormality. This study was intended to
identify the patterns of the relationship between the oscillating components of
blood flow registered by the LDF method under different levels of pressure
applied to an optical fiber probe.
For this purpose we have developed an original optical probe capable of
regulating the applied pressure. The developed protocol included six sequential
records of the blood perfusion at pressure within the 0 to 200 mmHg range with
unloading at the last stage.
Using wavelet analyses, we traced the variation of energy of oscillations for
these records in five frequency bands associated with different vascular tone
regulation mechanisms. Six young volunteers of the same age (three males and
three females) were included in this preliminary study and the protocol was
repeated five times in each volunteer. Totally 30 LDF records were available
for the analyses. As expected, the LDF signal increases at weak pressure (30
mmHg) and decreases at increased pressure. The statistically stable
amplification of endothelial associated blood flow oscillations under the 90
mmHg pressure allowed us to put forward a hypothesis that the endothelial
activity increases. The possible causes of this phenomenon are discussed.
|
[
{
"created": "Wed, 28 Nov 2018 08:38:59 GMT",
"version": "v1"
}
] |
2018-11-29
|
[
[
"Mizeva",
"Irina A.",
""
],
[
"Potapova",
"Elena V.",
""
],
[
"Dremin",
"Viktor V.",
""
],
[
"Zherebtsov",
"Evgeny A.",
""
],
[
"Mezentsev",
"Mikhail A.",
""
],
[
"Shupletsov",
"Valerii V.",
""
],
[
"Dunaev",
"Andrey V.",
""
]
] |
The variation of blood flow characteristics caused by the probe pressure during noninvasive studies is of particular interest within the context of fundamental and applied research. It has been shown previously that the weak local pressure induces vasodilation, whereas the increased pressure is able to stop the blood flow in the compressed area, as well as to significantly change optical signals. The blood flow oscillations measured by laser Doppler flowmetry (LDF) characterize the functional state of the microvascular system and can be used for noninvasive diagnostics of its abnormality. This study was intended to identify the patterns of the relationship between the oscillating components of blood flow registered by the LDF method under different levels of pressure applied to an optical fiber probe. For this purpose we have developed an original optical probe capable of regulating the applied pressure. The developed protocol included six sequential records of the blood perfusion at pressure within the 0 to 200 mmHg range with unloading at the last stage. Using wavelet analyses, we traced the variation of energy of oscillations for these records in five frequency bands associated with different vascular tone regulation mechanisms. Six young volunteers of the same age (three males and three females) were included in this preliminary study and the protocol was repeated five times in each volunteer. Totally 30 LDF records were available for the analyses. As expected, the LDF signal increases at weak pressure (30 mmHg) and decreases at increased pressure. The statistically stable amplification of endothelial associated blood flow oscillations under the 90 mmHg pressure allowed us to put forward a hypothesis that the endothelial activity increases. The possible causes of this phenomenon are discussed.
|
q-bio/0509036
|
Felix Naef
|
Jacques Rougemont and Felix Naef
|
Collective synchronization in populations of globally coupled phase
oscillators with drifting frequencies
|
5 pages, 5 figures, accepted in Phys. Rev. E
| null |
10.1103/PhysRevE.73.011104
| null |
q-bio.QM cond-mat.stat-mech
| null |
We generalize the Kuramoto model for coupled phase oscillators by allowing
the frequencies to drift in time according to Ornstein-Uhlenbeck dynamics. Such
drifting frequencies were recently measured in cellular populations of
circadian oscillator and inspired our work. Linear stability analysis of the
Fokker-Planck equation for an infinite population is amenable to exact solution
and we show that the incoherent state is unstable passed a critical coupling
strength $K_c(\ga, \sigf)$, where $\ga$ is the inverse characteristic drifting
time and $\sigf$ the asymptotic frequency dispersion. Expectedly $K_c$ agrees
with the noisy Kuramoto model in the large $\ga$ (Schmolukowski) limit but
increases slower as $\ga$ decreases. Asymptotic expansion of the solution for
$\ga\to 0$ shows that the noiseless Kuramoto model with Gaussian frequency
distribution is recovered in that limit. Thus varying a single parameter allows
to interpolate smoothly between two regimes: one dominated by the frequency
dispersion and the other by phase diffusion.
|
[
{
"created": "Tue, 27 Sep 2005 13:14:12 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Nov 2005 14:13:27 GMT",
"version": "v2"
}
] |
2009-11-11
|
[
[
"Rougemont",
"Jacques",
""
],
[
"Naef",
"Felix",
""
]
] |
We generalize the Kuramoto model for coupled phase oscillators by allowing the frequencies to drift in time according to Ornstein-Uhlenbeck dynamics. Such drifting frequencies were recently measured in cellular populations of circadian oscillator and inspired our work. Linear stability analysis of the Fokker-Planck equation for an infinite population is amenable to exact solution and we show that the incoherent state is unstable passed a critical coupling strength $K_c(\ga, \sigf)$, where $\ga$ is the inverse characteristic drifting time and $\sigf$ the asymptotic frequency dispersion. Expectedly $K_c$ agrees with the noisy Kuramoto model in the large $\ga$ (Schmolukowski) limit but increases slower as $\ga$ decreases. Asymptotic expansion of the solution for $\ga\to 0$ shows that the noiseless Kuramoto model with Gaussian frequency distribution is recovered in that limit. Thus varying a single parameter allows to interpolate smoothly between two regimes: one dominated by the frequency dispersion and the other by phase diffusion.
|
2011.02081
|
Matthew Aguirre
|
Matthew Aguirre, Jan Sokol, Guhan Venkataraman, Alexander Ioannidis
|
A deep learning classifier for local ancestry inference
|
Accepted to Learning Meaningful Representations of Life (LMRL),
Workshop at the 34th Conference on Neural Information Processing Systems
(NeurIPS 2020), Vancouver, Canada
| null | null | null |
q-bio.GN cs.LG cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
Local ancestry inference (LAI) identifies the ancestry of each segment of an
individual's genome and is an important step in medical and population genetic
studies of diverse cohorts. Several techniques have been used for LAI,
including Hidden Markov Models and Random Forests. Here, we formulate the LAI
task as an image segmentation problem and develop a new LAI tool using a deep
convolutional neural network with an encoder-decoder architecture. We train our
model using complete genome sequences from 982 unadmixed individuals from each
of five continental ancestry groups, and we evaluate it using simulated admixed
data derived from an additional 279 individuals selected from the same
populations. We show that our model is able to learn admixture as a zero-shot
task, yielding ancestry assignments that are nearly as accurate as those from
the existing gold standard tool, RFMix.
|
[
{
"created": "Wed, 4 Nov 2020 00:42:01 GMT",
"version": "v1"
}
] |
2020-11-05
|
[
[
"Aguirre",
"Matthew",
""
],
[
"Sokol",
"Jan",
""
],
[
"Venkataraman",
"Guhan",
""
],
[
"Ioannidis",
"Alexander",
""
]
] |
Local ancestry inference (LAI) identifies the ancestry of each segment of an individual's genome and is an important step in medical and population genetic studies of diverse cohorts. Several techniques have been used for LAI, including Hidden Markov Models and Random Forests. Here, we formulate the LAI task as an image segmentation problem and develop a new LAI tool using a deep convolutional neural network with an encoder-decoder architecture. We train our model using complete genome sequences from 982 unadmixed individuals from each of five continental ancestry groups, and we evaluate it using simulated admixed data derived from an additional 279 individuals selected from the same populations. We show that our model is able to learn admixture as a zero-shot task, yielding ancestry assignments that are nearly as accurate as those from the existing gold standard tool, RFMix.
|
1408.6849
|
Liane Gabora
|
Nicole Carbert, Liane Gabora, Jasmine Schwartz, and Apara Ranjan
|
Cognitive States of Potentiality in Art-making
|
6 pages
|
Carbert, N., Gabora, L., Schwartz, J., & Ranjan, A. (2014). States
of cognitive potentiality in art-making. Proceedings of the AIEA Congress on
Empirical Aesthetics (pp. 121-126). Held August 22-24, New York
| null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Creativity is thought to involve searching and selecting amongst multiple
discrete idea candidates. Honing theory predicts that it involves actualizing
the potentiality of as few as a single ill-defined idea by viewing it from
different contexts. This paper reports on a study that tests between these
theories. Participants were invited to "Create a painting that expresses
yourself in any style that appeals to you", and asked "Were all of your ideas
for your painting distinct and separate ideas?" Naive judges were provided with
descriptions of the two theories of creativity, sample answers, and practice
responses to classify. The judges were significantly more likely to classify
the artists' responses as 'H', indicative of honing theory rather than 'S'
indicative of a search-select view of creativity.
|
[
{
"created": "Thu, 28 Aug 2014 20:05:17 GMT",
"version": "v1"
}
] |
2014-09-01
|
[
[
"Carbert",
"Nicole",
""
],
[
"Gabora",
"Liane",
""
],
[
"Schwartz",
"Jasmine",
""
],
[
"Ranjan",
"Apara",
""
]
] |
Creativity is thought to involve searching and selecting amongst multiple discrete idea candidates. Honing theory predicts that it involves actualizing the potentiality of as few as a single ill-defined idea by viewing it from different contexts. This paper reports on a study that tests between these theories. Participants were invited to "Create a painting that expresses yourself in any style that appeals to you", and asked "Were all of your ideas for your painting distinct and separate ideas?" Naive judges were provided with descriptions of the two theories of creativity, sample answers, and practice responses to classify. The judges were significantly more likely to classify the artists' responses as 'H', indicative of honing theory rather than 'S' indicative of a search-select view of creativity.
|
1907.03512
|
S Chatterjee
|
S. Chatterjee, B. S. Sanjeev
|
Role of Toll-Like Receptors in the interplay between pathogen and damage
associated molecular patterns
|
10 pages, 11 figures
| null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Various theoretical studies have been carried out to infer relevant
protein-protein interactions among pathogens and their hosts. Such studies are
generally based on preferential attachment of bacteria / virus to their human
receptor homologs. We have analyzed 17 pathogenic species mainly belonging to
bacterial and viral pathogenic classes, with the aim to identify the
interacting human proteins which are targeted by both bacteria and virus
specifically. Our study reveals that the TLRs play a crucial role between the
pathogen-associated molecular patterns (PAMPs) and the damage associated
molecular patterns (DAMPS). PAMPs include bacterial lipopolysaccharides (LPs),
endotoxins, bacterial flagellin, lipoteichoic acid, peptidoglycan in bacterial
organisms and nucleic acid variants associated with viral organisms, whereas
DAMPs are associated with host biomolecules that perpetuate non-infectious
inflammatory responses. We found out the presence of SOD1 and SOD2 (superoxide
dismutase) is crucial, as it acts as an anti-oxidant and helps in eliminating
oxidative stress by preventing damage from reactive oxygen species. Hence, such
strategies can be used as new therapies for anti-inflammatory diseases with
significant clinical outcomes.
|
[
{
"created": "Mon, 8 Jul 2019 11:13:31 GMT",
"version": "v1"
}
] |
2019-07-09
|
[
[
"Chatterjee",
"S.",
""
],
[
"Sanjeev",
"B. S.",
""
]
] |
Various theoretical studies have been carried out to infer relevant protein-protein interactions among pathogens and their hosts. Such studies are generally based on preferential attachment of bacteria / virus to their human receptor homologs. We have analyzed 17 pathogenic species mainly belonging to bacterial and viral pathogenic classes, with the aim to identify the interacting human proteins which are targeted by both bacteria and virus specifically. Our study reveals that the TLRs play a crucial role between the pathogen-associated molecular patterns (PAMPs) and the damage associated molecular patterns (DAMPS). PAMPs include bacterial lipopolysaccharides (LPs), endotoxins, bacterial flagellin, lipoteichoic acid, peptidoglycan in bacterial organisms and nucleic acid variants associated with viral organisms, whereas DAMPs are associated with host biomolecules that perpetuate non-infectious inflammatory responses. We found out the presence of SOD1 and SOD2 (superoxide dismutase) is crucial, as it acts as an anti-oxidant and helps in eliminating oxidative stress by preventing damage from reactive oxygen species. Hence, such strategies can be used as new therapies for anti-inflammatory diseases with significant clinical outcomes.
|
2405.12422
|
Wera M Schmerer
|
Wera M Schmerer
|
Extraction of Human DNA from Soil: Protocol Adaptations
|
11 pages, 3 figures
| null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
PCR-based analysis of DNA is utilized in a wide variety of fields, including
Forensic Science. Aside from the more common ample sources, material analyzed
here can refer to specimen excavated from a soil environment, or a sampling of
the soil itself to recover DNA leached into the soil from decomposing human
remains or from body fluids intermingled with the soil in an outdoor crime
scene. The common problematic of these types of sample is the presence of humic
acids, which are a component of any soil environment, and when the co-extracted
with the DNA, lead to inhibition of enzyme-based procedures including PCR.
While a variety of methods exist for the extraction of DNA from excavated
skeletal remains, protocols for extraction of DNA from the soil directly are
usually targeting soil microorganism. To address the need for methodology
suitable for extraction of human DNA from soil, a selection of three published
protocols were adapted for this purpose, to be tested and evaluated using
standardized samples. The resulting protocols are presented here.
|
[
{
"created": "Tue, 21 May 2024 00:01:06 GMT",
"version": "v1"
}
] |
2024-05-22
|
[
[
"Schmerer",
"Wera M",
""
]
] |
PCR-based analysis of DNA is utilized in a wide variety of fields, including Forensic Science. Aside from the more common ample sources, material analyzed here can refer to specimen excavated from a soil environment, or a sampling of the soil itself to recover DNA leached into the soil from decomposing human remains or from body fluids intermingled with the soil in an outdoor crime scene. The common problematic of these types of sample is the presence of humic acids, which are a component of any soil environment, and when the co-extracted with the DNA, lead to inhibition of enzyme-based procedures including PCR. While a variety of methods exist for the extraction of DNA from excavated skeletal remains, protocols for extraction of DNA from the soil directly are usually targeting soil microorganism. To address the need for methodology suitable for extraction of human DNA from soil, a selection of three published protocols were adapted for this purpose, to be tested and evaluated using standardized samples. The resulting protocols are presented here.
|
2402.18396
|
Gabriele Corso
|
Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi, Regina
Barzilay, Tommi Jaakkola
|
Deep Confident Steps to New Pockets: Strategies for Docking
Generalization
| null |
International Conference on Learning Representations 2024
| null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate blind docking has the potential to lead to new biological
breakthroughs, but for this promise to be realized, docking methods must
generalize well across the proteome. Existing benchmarks, however, fail to
rigorously assess generalizability. Therefore, we develop DockGen, a new
benchmark based on the ligand-binding domains of proteins, and we show that
existing machine learning-based docking models have very weak generalization
abilities. We carefully analyze the scaling laws of ML-based docking and show
that, by scaling data and model size, as well as integrating synthetic data
strategies, we are able to significantly increase the generalization capacity
and set new state-of-the-art performance across benchmarks. Further, we propose
Confidence Bootstrapping, a new training paradigm that solely relies on the
interaction between diffusion and confidence models and exploits the
multi-resolution generation process of diffusion models. We demonstrate that
Confidence Bootstrapping significantly improves the ability of ML-based docking
methods to dock to unseen protein classes, edging closer to accurate and
generalizable blind docking methods.
|
[
{
"created": "Wed, 28 Feb 2024 15:15:23 GMT",
"version": "v1"
}
] |
2024-02-29
|
[
[
"Corso",
"Gabriele",
""
],
[
"Deng",
"Arthur",
""
],
[
"Fry",
"Benjamin",
""
],
[
"Polizzi",
"Nicholas",
""
],
[
"Barzilay",
"Regina",
""
],
[
"Jaakkola",
"Tommi",
""
]
] |
Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Existing benchmarks, however, fail to rigorously assess generalizability. Therefore, we develop DockGen, a new benchmark based on the ligand-binding domains of proteins, and we show that existing machine learning-based docking models have very weak generalization abilities. We carefully analyze the scaling laws of ML-based docking and show that, by scaling data and model size, as well as integrating synthetic data strategies, we are able to significantly increase the generalization capacity and set new state-of-the-art performance across benchmarks. Further, we propose Confidence Bootstrapping, a new training paradigm that solely relies on the interaction between diffusion and confidence models and exploits the multi-resolution generation process of diffusion models. We demonstrate that Confidence Bootstrapping significantly improves the ability of ML-based docking methods to dock to unseen protein classes, edging closer to accurate and generalizable blind docking methods.
|
2408.01949
|
Jonathan Kadmon
|
Jonathan Kadmon
|
Efficient coding with chaotic neural networks: A journey from
neuroscience to physics and back
| null | null | null | null |
q-bio.NC nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This essay, derived from a lecture at "The Physics Modeling of Thought"
workshop in Berlin in winter 2023, explores the mutually beneficial
relationship between theoretical neuroscience and statistical physics through
the lens of efficient coding and computation in cortical circuits. It
highlights how the study of neural networks has enhanced our understanding of
complex, nonequilibrium, and disordered systems, while also demonstrating how
neuroscientific challenges have spurred novel developments in physics. The
paper traces the evolution of ideas from seminal work on chaos in random neural
networks to recent developments in efficient coding and the partial suppression
of chaotic fluctuations. It emphasizes how concepts from statistical physics,
such as phase transitions and critical phenomena, have been instrumental in
elucidating the computational capabilities of neural networks.
By examining the interplay between order and disorder in neural computation,
the essay illustrates the deep connection between theoretical neuroscience and
the statistical physics of nonequilibrium systems. This synthesis underscores
the ongoing importance of interdisciplinary approaches in advancing both
fields, offering fresh perspectives on the fundamental principles governing
information processing in biological and artificial systems. This
multidisciplinary approach not only advances our understanding of neural
computation and complex systems but also points toward future challenges at the
intersection of neuroscience and physics.
|
[
{
"created": "Sun, 4 Aug 2024 07:34:35 GMT",
"version": "v1"
}
] |
2024-08-06
|
[
[
"Kadmon",
"Jonathan",
""
]
] |
This essay, derived from a lecture at "The Physics Modeling of Thought" workshop in Berlin in winter 2023, explores the mutually beneficial relationship between theoretical neuroscience and statistical physics through the lens of efficient coding and computation in cortical circuits. It highlights how the study of neural networks has enhanced our understanding of complex, nonequilibrium, and disordered systems, while also demonstrating how neuroscientific challenges have spurred novel developments in physics. The paper traces the evolution of ideas from seminal work on chaos in random neural networks to recent developments in efficient coding and the partial suppression of chaotic fluctuations. It emphasizes how concepts from statistical physics, such as phase transitions and critical phenomena, have been instrumental in elucidating the computational capabilities of neural networks. By examining the interplay between order and disorder in neural computation, the essay illustrates the deep connection between theoretical neuroscience and the statistical physics of nonequilibrium systems. This synthesis underscores the ongoing importance of interdisciplinary approaches in advancing both fields, offering fresh perspectives on the fundamental principles governing information processing in biological and artificial systems. This multidisciplinary approach not only advances our understanding of neural computation and complex systems but also points toward future challenges at the intersection of neuroscience and physics.
|
2405.14545
|
Hongzhi Zhang
|
Hongzhi Zhang, Xiuwen Gong, Shirui Pan, Jia Wu, Bo Du, Wenbin Hu
|
A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction
| null | null | null | null |
q-bio.BM cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Drug-target interaction (DTI) prediction is a critical component of the drug
discovery process. In the drug development engineering field, predicting novel
drug-target interactions is extremely crucial.However, although existing
methods have achieved high accuracy levels in predicting known drugs and drug
targets, they fail to utilize global protein information during DTI prediction.
This leads to an inability to effectively predict interaction the interactions
between novel drugs and their targets. As a result, the cross-field information
fusion strategy is employed to acquire local and global protein information.
Thus, we propose the siamese drug-target interaction SiamDTI prediction method,
which utilizes a double channel network structure for cross-field supervised
learning.Experimental results on three benchmark datasets demonstrate that
SiamDTI achieves higher accuracy levels than other state-of-the-art (SOTA)
methods on novel drugs and targets.Additionally, SiamDTI's performance with
known drugs and targets is comparable to that of SOTA approachs. The code is
available at https://anonymous.4open.science/r/DDDTI-434D.
|
[
{
"created": "Thu, 23 May 2024 13:25:20 GMT",
"version": "v1"
}
] |
2024-05-24
|
[
[
"Zhang",
"Hongzhi",
""
],
[
"Gong",
"Xiuwen",
""
],
[
"Pan",
"Shirui",
""
],
[
"Wu",
"Jia",
""
],
[
"Du",
"Bo",
""
],
[
"Hu",
"Wenbin",
""
]
] |
Drug-target interaction (DTI) prediction is a critical component of the drug discovery process. In the drug development engineering field, predicting novel drug-target interactions is extremely crucial.However, although existing methods have achieved high accuracy levels in predicting known drugs and drug targets, they fail to utilize global protein information during DTI prediction. This leads to an inability to effectively predict interaction the interactions between novel drugs and their targets. As a result, the cross-field information fusion strategy is employed to acquire local and global protein information. Thus, we propose the siamese drug-target interaction SiamDTI prediction method, which utilizes a double channel network structure for cross-field supervised learning.Experimental results on three benchmark datasets demonstrate that SiamDTI achieves higher accuracy levels than other state-of-the-art (SOTA) methods on novel drugs and targets.Additionally, SiamDTI's performance with known drugs and targets is comparable to that of SOTA approachs. The code is available at https://anonymous.4open.science/r/DDDTI-434D.
|
2402.09703
|
Claus Kadelka
|
Claus Kadelka and David Murrugarra
|
Canalization reduces the nonlinearity of regulation in biological
networks
|
21 pages, 8 figures
| null | null | null |
q-bio.MN math.DS
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Biological networks such as gene regulatory networks possess desirable
properties. They are more robust and controllable than random networks. This
motivates the search for structural and dynamical features that evolution has
incorporated in biological networks. A recent meta-analysis of published,
expert-curated Boolean biological network models has revealed several such
features, often referred to as design principles. Among others, the biological
networks are enriched for certain recurring network motifs, the dynamic update
rules are more redundant, more biased and more canalizing than expected, and
the dynamics of biological networks are better approximable by linear and
lower-order approximations than those of comparable random networks. Since most
of these features are interrelated, it is paramount to disentangle cause and
effect, that is, to understand which features evolution actively selects for,
and thus truly constitute evolutionary design principles. Here, we show that
approximability is strongly dependent on the dynamical robustness of a network,
and that increased canalization in biological networks can almost completely
explain their recently postulated high approximability.
|
[
{
"created": "Thu, 15 Feb 2024 04:38:26 GMT",
"version": "v1"
}
] |
2024-02-16
|
[
[
"Kadelka",
"Claus",
""
],
[
"Murrugarra",
"David",
""
]
] |
Biological networks such as gene regulatory networks possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated in biological networks. A recent meta-analysis of published, expert-curated Boolean biological network models has revealed several such features, often referred to as design principles. Among others, the biological networks are enriched for certain recurring network motifs, the dynamic update rules are more redundant, more biased and more canalizing than expected, and the dynamics of biological networks are better approximable by linear and lower-order approximations than those of comparable random networks. Since most of these features are interrelated, it is paramount to disentangle cause and effect, that is, to understand which features evolution actively selects for, and thus truly constitute evolutionary design principles. Here, we show that approximability is strongly dependent on the dynamical robustness of a network, and that increased canalization in biological networks can almost completely explain their recently postulated high approximability.
|
1608.02574
|
Robert J. Gooding
|
Neil L Wesch, Laura J Burlock and Robert J Gooding
|
Critical Telomerase Activity for Uncontrolled Cell Growth
| null |
Physical Biology 13 (2016) 046005
|
10.1088/1478-3975/13/4/046005
| null |
q-bio.MN q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The lengths of the telomere regions of chromosomes in a population of cells
are modelled using a chemical master equation formalism, from which the
evolution of the average number of cells of each telomere length is extracted.
In particular, the role of the telomere-elongating enzyme telomerase on these
dynamics is investigated. We show that for biologically relevant rates of cell
birth and death, one finds a critical rate, R_crit, of telomerase activity such
that the total number of cells diverges. Further, R_crit is similar in
magnitude to the rates of mitosis and cell death. The possible relationship of
this result to replicative immortality and its associated hallmark of cancer is
discussed.
|
[
{
"created": "Mon, 8 Aug 2016 19:56:42 GMT",
"version": "v1"
}
] |
2016-08-09
|
[
[
"Wesch",
"Neil L",
""
],
[
"Burlock",
"Laura J",
""
],
[
"Gooding",
"Robert J",
""
]
] |
The lengths of the telomere regions of chromosomes in a population of cells are modelled using a chemical master equation formalism, from which the evolution of the average number of cells of each telomere length is extracted. In particular, the role of the telomere-elongating enzyme telomerase on these dynamics is investigated. We show that for biologically relevant rates of cell birth and death, one finds a critical rate, R_crit, of telomerase activity such that the total number of cells diverges. Further, R_crit is similar in magnitude to the rates of mitosis and cell death. The possible relationship of this result to replicative immortality and its associated hallmark of cancer is discussed.
|
1608.06305
|
Benjamin Pope
|
Michael W. Dee and Benjamin J. S. Pope
|
Anchoring historical sequences using a new source of astro-chronological
tie-points
|
11 pages, accepted to Royal Society Proc A
|
Proc. R. Soc. A 2016 472 20160263; Published 17 August 2016
|
10.1098/rspa.2016.0263
| null |
q-bio.OT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The discovery of past spikes in atmospheric radiocarbon activity, caused by
major solar energetic particle events, has opened up new possibilities for
high-precision chronometry. The two spikes, or Miyake Events, have now been
widely identified in tree-rings that grew in the years 775 and 994 CE.
Furthermore, all other plant material that grew in these years would also have
incorporated the anomalously high concentrations of radiocarbon. Crucially,
some plant-based artefacts, such as papyrus documents, timber beams and linen
garments, can also be allocated to specific positions within long, currently
unfixed, historical sequences. Thus, Miyake Events represent a new source of
tie-points that could provide the means for anchoring early chronologies to the
absolute timescale. Here, we explore this possibility, outlining the most
expeditious approaches, the current challenges and obstacles, and how they
might best be overcome.
|
[
{
"created": "Wed, 17 Aug 2016 12:47:02 GMT",
"version": "v1"
}
] |
2016-08-24
|
[
[
"Dee",
"Michael W.",
""
],
[
"Pope",
"Benjamin J. S.",
""
]
] |
The discovery of past spikes in atmospheric radiocarbon activity, caused by major solar energetic particle events, has opened up new possibilities for high-precision chronometry. The two spikes, or Miyake Events, have now been widely identified in tree-rings that grew in the years 775 and 994 CE. Furthermore, all other plant material that grew in these years would also have incorporated the anomalously high concentrations of radiocarbon. Crucially, some plant-based artefacts, such as papyrus documents, timber beams and linen garments, can also be allocated to specific positions within long, currently unfixed, historical sequences. Thus, Miyake Events represent a new source of tie-points that could provide the means for anchoring early chronologies to the absolute timescale. Here, we explore this possibility, outlining the most expeditious approaches, the current challenges and obstacles, and how they might best be overcome.
|
1512.04293
|
Torbj{\o}rn V Ness
|
Torbj{\o}rn V Ness, Michiel W H Remme, Gaute T Einevoll
|
Active subthreshold dendritic conductances shape the local field
potential
| null |
J Physiol 2016, 594 (13)
|
10.1113/JP272022
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The main contribution to the local field potential (LFP) is thought to stem
from synaptic input to neurons and the ensuing subthreshold dendritic
processing. The role of active dendritic conductances in shaping the LFP has
received little attention, even though such ion channels are known to affect
the subthreshold neuron dynamics. Here we used a modeling approach to
investigate the effects of subthreshold dendritic conductances on the LFP.
Using a biophysically detailed, experimentally constrained model of a cortical
pyramidal neuron, we identified conditions under which subthreshold active
conductances are a major factor in shaping the LFP. We found that particularly
the hyperpolarization-activated inward current, I$_{\rm h}$, can have a sizable
effect and cause a resonance in the LFP power spectral density. To get a
general, qualitative understanding of how any subthreshold active dendritic
conductance and its cellular distribution can affect the LFP, we next performed
a systematic study with a simplified model. We found that the effect on the LFP
is most pronounced when (1) the synaptic drive to the cell is asymmetrically
distributed (i.e., either basal or apical), (2) the active conductances are
distributed non-uniformly with the highest channel densities near the synaptic
input, and (3) when the LFP is measured at the opposite pole of the cell
relative to the synaptic input. In summary, we show that subthreshold active
conductances can be strongly reflected in LFP signals, opening up the
possibility that the LFP can be used to characterize the properties and
cellular distributions of active conductances.
|
[
{
"created": "Mon, 14 Dec 2015 13:02:56 GMT",
"version": "v1"
}
] |
2016-11-22
|
[
[
"Ness",
"Torbjørn V",
""
],
[
"Remme",
"Michiel W H",
""
],
[
"Einevoll",
"Gaute T",
""
]
] |
The main contribution to the local field potential (LFP) is thought to stem from synaptic input to neurons and the ensuing subthreshold dendritic processing. The role of active dendritic conductances in shaping the LFP has received little attention, even though such ion channels are known to affect the subthreshold neuron dynamics. Here we used a modeling approach to investigate the effects of subthreshold dendritic conductances on the LFP. Using a biophysically detailed, experimentally constrained model of a cortical pyramidal neuron, we identified conditions under which subthreshold active conductances are a major factor in shaping the LFP. We found that particularly the hyperpolarization-activated inward current, I$_{\rm h}$, can have a sizable effect and cause a resonance in the LFP power spectral density. To get a general, qualitative understanding of how any subthreshold active dendritic conductance and its cellular distribution can affect the LFP, we next performed a systematic study with a simplified model. We found that the effect on the LFP is most pronounced when (1) the synaptic drive to the cell is asymmetrically distributed (i.e., either basal or apical), (2) the active conductances are distributed non-uniformly with the highest channel densities near the synaptic input, and (3) when the LFP is measured at the opposite pole of the cell relative to the synaptic input. In summary, we show that subthreshold active conductances can be strongly reflected in LFP signals, opening up the possibility that the LFP can be used to characterize the properties and cellular distributions of active conductances.
|
2310.01454
|
Adrien Berquer
|
Adrien Berquer, Antoine Gazaix, Laura Czerniak, Valentin Dromard,
Guillaume Meire, Ga\"etan Rivi\`ere
|
Improving alkaline fen functioning and Liparis loeselii (L.) Rich., 1817
preservation: towards a better water level management
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Alkaline fens are known to be wetlands that host large quantities of carbon,
but also a huge biodiversity. However, anthropic pressures are degrading the
peatlands with drainage, and conversion to agriculture or urbanization. These
pressures induce externalities like a large release of greenhouse gas (GHG) by
peat mineralization, and a loss of biodiversity, since they host numerous
protected or endangered species. One of them is the Liparis loeselii, a small
orchid facing declines in Europe and for which conservation measures are taken.
Nevertheless, if recent studies inferred some factors shaping its population
dynamics, they are still not clearly understood, particularly in fen contexts.
This study aims at disentangle the processes shaping a continental population
of L. loeselii in the Somme valley, among factors related to the hydrology of
the site and the elevation of the individuals. We used Bayesian generalized
linear models to infer the seasonal water level effects on the presence of L.
loeselii. Moreover, we showed that the elevation of L. loeselii population is
around the mean water levels of winter, and that the occurrence can be promoted
by water level, notably in summer. Conversely, the highest water levels
reported seemed detrimental to the L. loeselii population, suggesting a
probably negative effect of flooding acting on local dispersal. Finally, this
study provides insights to take restoration measures of alkaline fens, like a
better hydrological functioning and an optimal water management.
|
[
{
"created": "Mon, 2 Oct 2023 09:25:13 GMT",
"version": "v1"
}
] |
2023-10-04
|
[
[
"Berquer",
"Adrien",
""
],
[
"Gazaix",
"Antoine",
""
],
[
"Czerniak",
"Laura",
""
],
[
"Dromard",
"Valentin",
""
],
[
"Meire",
"Guillaume",
""
],
[
"Rivière",
"Gaëtan",
""
]
] |
Alkaline fens are known to be wetlands that host large quantities of carbon, but also a huge biodiversity. However, anthropic pressures are degrading the peatlands with drainage, and conversion to agriculture or urbanization. These pressures induce externalities like a large release of greenhouse gas (GHG) by peat mineralization, and a loss of biodiversity, since they host numerous protected or endangered species. One of them is the Liparis loeselii, a small orchid facing declines in Europe and for which conservation measures are taken. Nevertheless, if recent studies inferred some factors shaping its population dynamics, they are still not clearly understood, particularly in fen contexts. This study aims at disentangle the processes shaping a continental population of L. loeselii in the Somme valley, among factors related to the hydrology of the site and the elevation of the individuals. We used Bayesian generalized linear models to infer the seasonal water level effects on the presence of L. loeselii. Moreover, we showed that the elevation of L. loeselii population is around the mean water levels of winter, and that the occurrence can be promoted by water level, notably in summer. Conversely, the highest water levels reported seemed detrimental to the L. loeselii population, suggesting a probably negative effect of flooding acting on local dispersal. Finally, this study provides insights to take restoration measures of alkaline fens, like a better hydrological functioning and an optimal water management.
|
0806.3340
|
Vladimir Ivancevic
|
Vladimir G. Ivancevic
|
New Mechanics of Spinal Injury
|
14 pages, 1 figure, Latex
| null |
10.1142/S1758825109000174
| null |
q-bio.TO q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The prediction and prevention of spinal injury is an important aspect of
preventive health science. The spine, or vertebral column, represents a chain
of 26 movable vertebral bodies, joint together by transversal viscoelastic
intervertebral discs and longitudinal elastic tendons. This paper proposes a
new locally-coupled loading-rate hypothesis}, which states that the main cause
of both soft- and hard-tissue spinal injury is a localized Euclidean jolt, or
SE(3)-jolt, an impulsive loading that strikes a localized spine in several
coupled degrees-of-freedom simultaneously. To show this, based on the
previously defined covariant force law, we formulate the coupled Newton-Euler
dynamics of the local spinal motions and derive from it the corresponding
coupled SE(3)-jolt dynamics. The SE(3)-jolt is the main cause of two basic
forms of spinal injury: (i) hard-tissue injury of local translational
dislocations; and (ii) soft-tissue injury of local rotational disclinations.
Both the spinal dislocations and disclinations, as caused by the SE(3)-jolt,
are described using the Cosserat multipolar viscoelastic continuum model.
Keywords: localized spinal injury, coupled loading-rate hypothesis, coupled
Newton-Euler dynamics, Euclidean jolt dynamics, spinal dislocations and
disclinations
|
[
{
"created": "Fri, 20 Jun 2008 08:08:10 GMT",
"version": "v1"
},
{
"created": "Fri, 4 Jul 2008 03:31:30 GMT",
"version": "v2"
},
{
"created": "Thu, 10 Jul 2008 02:38:32 GMT",
"version": "v3"
},
{
"created": "Mon, 4 Aug 2008 04:59:08 GMT",
"version": "v4"
},
{
"created": "Tue, 18 Nov 2008 02:24:45 GMT",
"version": "v5"
}
] |
2015-05-13
|
[
[
"Ivancevic",
"Vladimir G.",
""
]
] |
The prediction and prevention of spinal injury is an important aspect of preventive health science. The spine, or vertebral column, represents a chain of 26 movable vertebral bodies, joint together by transversal viscoelastic intervertebral discs and longitudinal elastic tendons. This paper proposes a new locally-coupled loading-rate hypothesis}, which states that the main cause of both soft- and hard-tissue spinal injury is a localized Euclidean jolt, or SE(3)-jolt, an impulsive loading that strikes a localized spine in several coupled degrees-of-freedom simultaneously. To show this, based on the previously defined covariant force law, we formulate the coupled Newton-Euler dynamics of the local spinal motions and derive from it the corresponding coupled SE(3)-jolt dynamics. The SE(3)-jolt is the main cause of two basic forms of spinal injury: (i) hard-tissue injury of local translational dislocations; and (ii) soft-tissue injury of local rotational disclinations. Both the spinal dislocations and disclinations, as caused by the SE(3)-jolt, are described using the Cosserat multipolar viscoelastic continuum model. Keywords: localized spinal injury, coupled loading-rate hypothesis, coupled Newton-Euler dynamics, Euclidean jolt dynamics, spinal dislocations and disclinations
|
0803.1923
|
Adam Barrett
|
Adam B. Barrett and M.C.W. van Rossum
|
Shannon Information Capacity of Discrete Synapses
|
5 pages, 2 figures
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There is evidence that biological synapses have only a fixed number of
discrete weight states. Memory storage with such synapses behaves quite
differently from synapses with unbounded, continuous weights as old memories
are automatically overwritten by new memories. We calculate the storage
capacity of discrete, bounded synapses in terms of Shannon information. For
optimal learning rules, we investigate how information storage depends on the
number of synapses, the number of synaptic states and the coding sparseness.
|
[
{
"created": "Thu, 13 Mar 2008 09:15:27 GMT",
"version": "v1"
}
] |
2008-03-14
|
[
[
"Barrett",
"Adam B.",
""
],
[
"van Rossum",
"M. C. W.",
""
]
] |
There is evidence that biological synapses have only a fixed number of discrete weight states. Memory storage with such synapses behaves quite differently from synapses with unbounded, continuous weights as old memories are automatically overwritten by new memories. We calculate the storage capacity of discrete, bounded synapses in terms of Shannon information. For optimal learning rules, we investigate how information storage depends on the number of synapses, the number of synaptic states and the coding sparseness.
|
1409.0024
|
Maria Fabiana Laguna
|
M. F. Laguna, G. Abramson, M. N. Kuperman, J. L. Lanata and J. A.
Monjeau
|
Mathematical model of livestock and wildlife: Predation and competition
under environmental disturbances
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inspired by real scenarios in Northern Patagonia, we analyze a mathematical
model of a simple trophic web with two herbivores and one predator. The studied
situations represent a common practice in the steppes of Argentine Patagonia,
where livestock are raised in a semi-wild state, either on the open range or
enclosed, coexisting with competitors and predators. In the present work, the
competing herbivores represent sheep and guanacos, while the predator is
associated with the puma. The proposed model combines the concepts of
metapopulations and patches dynamics, and includes an explicit hierarchical
competition between species, which affects their prospect to colonize an empty
patch when having to compete with other species. We perform numerical
simulations of spatially extended metapopulations assemblages of the system,
which allow us to incorporate the effects of habitat heterogeneity and
destruction. The numerical results are compared with those obtained from mean
field calculations. We find that the model provides a good theoretical
framework in several situations, including the control of the wild populations
that the ranchers exert to different extent. Furthermore, the present
formulation incorporates new terms in previously analyzed models, that help to
reveal the important effects due to the heterogeneous nature of the system.
|
[
{
"created": "Fri, 29 Aug 2014 20:12:05 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Feb 2015 16:57:18 GMT",
"version": "v2"
}
] |
2015-02-05
|
[
[
"Laguna",
"M. F.",
""
],
[
"Abramson",
"G.",
""
],
[
"Kuperman",
"M. N.",
""
],
[
"Lanata",
"J. L.",
""
],
[
"Monjeau",
"J. A.",
""
]
] |
Inspired by real scenarios in Northern Patagonia, we analyze a mathematical model of a simple trophic web with two herbivores and one predator. The studied situations represent a common practice in the steppes of Argentine Patagonia, where livestock are raised in a semi-wild state, either on the open range or enclosed, coexisting with competitors and predators. In the present work, the competing herbivores represent sheep and guanacos, while the predator is associated with the puma. The proposed model combines the concepts of metapopulations and patches dynamics, and includes an explicit hierarchical competition between species, which affects their prospect to colonize an empty patch when having to compete with other species. We perform numerical simulations of spatially extended metapopulations assemblages of the system, which allow us to incorporate the effects of habitat heterogeneity and destruction. The numerical results are compared with those obtained from mean field calculations. We find that the model provides a good theoretical framework in several situations, including the control of the wild populations that the ranchers exert to different extent. Furthermore, the present formulation incorporates new terms in previously analyzed models, that help to reveal the important effects due to the heterogeneous nature of the system.
|
1702.02876
|
Esteban Vargas Bernal
|
Esteban Vargas and Camilo Sanabria
|
The role of disease cycles in the endemicity of infectious diseases
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vector-borne diseases with reservoir cycles are complex to understand because
new infections come from contacts of the vector with humans and different
reservoirs. In this scenario, the basic reproductive number $\mathcal{R}^h_0$
of the system where the reservoirs are not included could turn out to be less
than one, yet, an endemic equilibrium be observed. Indeed, when the reservoirs
are taken back into account, the basic reproductive number $\mathcal{R}_0^r$,
of only vectors and reservoirs, explains the endemic state. Furthermore,
reservoirs cycles with a small basic reproductive number could contribute to
reach an endemic state in the human cycle. Therefore, when controlling for the
spread of a disease, it could not be enough to focus on specific reservoir
cycles or only on the vector. In this work, we created a simple epidemiological
model with a network of reservoirs where $\mathcal{R}_0^r$ is a bifurcation
parameter of the system, explaining disease endemicity in the absence of a
strong reservoir cycle. This simple model may help to explain transmission
dynamics of diseases such as Chagas, Leishmaniasis and Dengue.
|
[
{
"created": "Wed, 8 Feb 2017 03:50:33 GMT",
"version": "v1"
}
] |
2017-02-10
|
[
[
"Vargas",
"Esteban",
""
],
[
"Sanabria",
"Camilo",
""
]
] |
Vector-borne diseases with reservoir cycles are complex to understand because new infections come from contacts of the vector with humans and different reservoirs. In this scenario, the basic reproductive number $\mathcal{R}^h_0$ of the system where the reservoirs are not included could turn out to be less than one, yet, an endemic equilibrium be observed. Indeed, when the reservoirs are taken back into account, the basic reproductive number $\mathcal{R}_0^r$, of only vectors and reservoirs, explains the endemic state. Furthermore, reservoirs cycles with a small basic reproductive number could contribute to reach an endemic state in the human cycle. Therefore, when controlling for the spread of a disease, it could not be enough to focus on specific reservoir cycles or only on the vector. In this work, we created a simple epidemiological model with a network of reservoirs where $\mathcal{R}_0^r$ is a bifurcation parameter of the system, explaining disease endemicity in the absence of a strong reservoir cycle. This simple model may help to explain transmission dynamics of diseases such as Chagas, Leishmaniasis and Dengue.
|
1204.3670
|
Svetlana Poznanovik
|
Svetlana Poznanovik and Christine E. Heitsch
|
Asymptotic distribution of motifs in a stochastic context-free grammar
model of RNA folding
|
22 pages, 3 figures
| null | null | null |
q-bio.BM math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We analyze the distribution of RNA secondary structures given by the
Knudsen-Hein stochastic context-free grammar used in the prediction program
Pfold. We prove that the distribution of base pairs, helices and various types
of loops in RNA secondary structures in this probabilistic model is
asymptotically Gaussian, for a generic choice of the grammar probabilities. Our
proofs are based on singularity analysis of probability generating functions.
Finally, we use our results to discuss how this model reflects the properties
of some known ribosomal secondary structures.
|
[
{
"created": "Mon, 16 Apr 2012 23:34:39 GMT",
"version": "v1"
}
] |
2012-04-18
|
[
[
"Poznanovik",
"Svetlana",
""
],
[
"Heitsch",
"Christine E.",
""
]
] |
We analyze the distribution of RNA secondary structures given by the Knudsen-Hein stochastic context-free grammar used in the prediction program Pfold. We prove that the distribution of base pairs, helices and various types of loops in RNA secondary structures in this probabilistic model is asymptotically Gaussian, for a generic choice of the grammar probabilities. Our proofs are based on singularity analysis of probability generating functions. Finally, we use our results to discuss how this model reflects the properties of some known ribosomal secondary structures.
|
2004.08588
|
Sumit Kumar
|
Sumit Kumar, Sandeep Sharma and Nitu Kumari
|
Future of COVID-19 in Italy: A mathematical perspective
| null |
Mathematical Engineering. Springer, Singapore. (2021) 101--124
|
10.1007/978-981-33-6264-2_6
| null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We have proposed an SEIR compartmental mathematical model. The prime
objective of this study is to analyze and forecast the pandemic in Italy for
the upcoming months. The basic reproduction number has been calculated. Based
on the current situation in Italy, in this paper, we will estimate the possible
time for the end of the pandemic in the country. The impact of lockdown and
rapid isolation on the spread of the pandemic are also discussed. Further, we
have studied four of the most pandemic affected regions in Italy. Using the
proposed model, a prediction has been made about the duration of pandemic in
these regions. The variation in the basic reproduction number corresponding to
the sensitive parameters of the model is also examined.
|
[
{
"created": "Sat, 18 Apr 2020 10:44:03 GMT",
"version": "v1"
}
] |
2021-04-06
|
[
[
"Kumar",
"Sumit",
""
],
[
"Sharma",
"Sandeep",
""
],
[
"Kumari",
"Nitu",
""
]
] |
We have proposed an SEIR compartmental mathematical model. The prime objective of this study is to analyze and forecast the pandemic in Italy for the upcoming months. The basic reproduction number has been calculated. Based on the current situation in Italy, in this paper, we will estimate the possible time for the end of the pandemic in the country. The impact of lockdown and rapid isolation on the spread of the pandemic are also discussed. Further, we have studied four of the most pandemic affected regions in Italy. Using the proposed model, a prediction has been made about the duration of pandemic in these regions. The variation in the basic reproduction number corresponding to the sensitive parameters of the model is also examined.
|
2405.18639
|
Brian Yuan
|
Brian A. Yuan, Joseph G. Makin
|
Improving Speech Decoding from ECoG with Self-Supervised Pretraining
| null | null | null | null |
q-bio.NC cs.CL cs.LG cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Recent work on intracranial brain-machine interfaces has demonstrated that
spoken speech can be decoded with high accuracy, essentially by treating the
problem as an instance of supervised learning and training deep neural networks
to map from neural activity to text. However, such networks pay for their
expressiveness with very large numbers of labeled data, a requirement that is
particularly burdensome for invasive neural recordings acquired from human
patients. On the other hand, these patients typically produce speech outside of
the experimental blocks used for training decoders. Making use of such data,
and data from other patients, to improve decoding would ease the burden of data
collection -- especially onerous for dys- and anarthric patients. Here we
demonstrate that this is possible, by reengineering wav2vec -- a simple,
self-supervised, fully convolutional model that learns latent representations
of audio using a noise-contrastive loss -- for electrocorticographic (ECoG)
data. We train this model on unlabelled ECoG recordings, and subsequently use
it to transform ECoG from labeled speech sessions into wav2vec's representation
space, before finally training a supervised encoder-decoder to map these
representations to text. We experiment with various numbers of labeled blocks;
for almost all choices, the new representations yield superior decoding
performance to the original ECoG data, and in no cases do they yield worse.
Performance can also be improved in some cases by pretraining wav2vec on
another patient's data. In the best cases, wav2vec's representations decrease
word error rates over the original data by upwards of 50%.
|
[
{
"created": "Tue, 28 May 2024 22:48:53 GMT",
"version": "v1"
}
] |
2024-05-30
|
[
[
"Yuan",
"Brian A.",
""
],
[
"Makin",
"Joseph G.",
""
]
] |
Recent work on intracranial brain-machine interfaces has demonstrated that spoken speech can be decoded with high accuracy, essentially by treating the problem as an instance of supervised learning and training deep neural networks to map from neural activity to text. However, such networks pay for their expressiveness with very large numbers of labeled data, a requirement that is particularly burdensome for invasive neural recordings acquired from human patients. On the other hand, these patients typically produce speech outside of the experimental blocks used for training decoders. Making use of such data, and data from other patients, to improve decoding would ease the burden of data collection -- especially onerous for dys- and anarthric patients. Here we demonstrate that this is possible, by reengineering wav2vec -- a simple, self-supervised, fully convolutional model that learns latent representations of audio using a noise-contrastive loss -- for electrocorticographic (ECoG) data. We train this model on unlabelled ECoG recordings, and subsequently use it to transform ECoG from labeled speech sessions into wav2vec's representation space, before finally training a supervised encoder-decoder to map these representations to text. We experiment with various numbers of labeled blocks; for almost all choices, the new representations yield superior decoding performance to the original ECoG data, and in no cases do they yield worse. Performance can also be improved in some cases by pretraining wav2vec on another patient's data. In the best cases, wav2vec's representations decrease word error rates over the original data by upwards of 50%.
|
1201.1740
|
Bao-quan Ai
|
Bao-quan Ai and Shi-liang Zhu
|
Quantum scattering model of energy transfer in photosynthetic complexes
|
5 pages, 3 figures
|
Laser Physics Letters 12 (2015) 125201
|
10.1088/1612-2011/12/12/125201
| null |
q-bio.BM physics.chem-ph quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We develop a quantum scattering model to describe the exciton transport
through the Fenna-Matthews-Olson(FMO) complex. It is found that the exciton
transport involved the optimal quantum coherence is more efficient than that
involved classical behavior alone. Furthermore, we also find that the quantum
resonance condition is easier to be fulfilled in multiple pathways than that in
one pathway. We then definitely demonstrate that the optimal distribution of
the pigments, the multitude of energy delivery pathways and the quantum
effects, are combined together to contribute to the perfect energy transport in
the FMO complex.
|
[
{
"created": "Mon, 9 Jan 2012 11:38:42 GMT",
"version": "v1"
}
] |
2016-12-28
|
[
[
"Ai",
"Bao-quan",
""
],
[
"Zhu",
"Shi-liang",
""
]
] |
We develop a quantum scattering model to describe the exciton transport through the Fenna-Matthews-Olson(FMO) complex. It is found that the exciton transport involved the optimal quantum coherence is more efficient than that involved classical behavior alone. Furthermore, we also find that the quantum resonance condition is easier to be fulfilled in multiple pathways than that in one pathway. We then definitely demonstrate that the optimal distribution of the pigments, the multitude of energy delivery pathways and the quantum effects, are combined together to contribute to the perfect energy transport in the FMO complex.
|
2010.15308
|
Kohei Ichikawa
|
Kohei Ichikawa and Kunihiko Kaneko
|
Short term memory by transient oscillatory dynamics in recurrent neural
networks
| null |
Phys. Rev. Research 3, 033193 (2021)
|
10.1103/PhysRevResearch.3.033193
| null |
q-bio.NC nlin.AO
|
http://creativecommons.org/licenses/by/4.0/
|
Despite the significance of short-term memory in cognitive function, the
process of encoding and sustaining the input information in neural activity
dynamics remains elusive. Herein, we unveiled the significance of transient
neural dynamics to short-term memory. By training recurrent neural networks to
short-term memory tasks and analyzing the dynamics, the characteristics of the
short-term memory mechanism were obtained in which the input information was
encoded in the amplitude of transient oscillations, rather than the stationary
neural activities. This transient trajectory was attracted to a slow manifold,
which permitted the discarding of irrelevant information. Additionally, we
investigated the process by which the dynamics acquire robustness to noise. In
this transient oscillation, the robustness to noise was obtained by a strong
contraction of the neural states after perturbation onto the manifold. This
mechanism works for several neural network models and tasks, which implies its
relevance to neural information processing in general.
|
[
{
"created": "Thu, 29 Oct 2020 01:50:34 GMT",
"version": "v1"
},
{
"created": "Sat, 14 Aug 2021 01:30:09 GMT",
"version": "v2"
}
] |
2021-09-01
|
[
[
"Ichikawa",
"Kohei",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] |
Despite the significance of short-term memory in cognitive function, the process of encoding and sustaining the input information in neural activity dynamics remains elusive. Herein, we unveiled the significance of transient neural dynamics to short-term memory. By training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristics of the short-term memory mechanism were obtained in which the input information was encoded in the amplitude of transient oscillations, rather than the stationary neural activities. This transient trajectory was attracted to a slow manifold, which permitted the discarding of irrelevant information. Additionally, we investigated the process by which the dynamics acquire robustness to noise. In this transient oscillation, the robustness to noise was obtained by a strong contraction of the neural states after perturbation onto the manifold. This mechanism works for several neural network models and tasks, which implies its relevance to neural information processing in general.
|
q-bio/0611073
|
Eugene Shakhnovich
|
Stefan Wallin, Konstantin B Zeldovich, Eugene I Shakhnovich
|
The folding mechanics of a knotted protein
| null | null | null | null |
q-bio.BM
| null |
An increasing number of proteins are being discovered with a remarkable and
somewhat surprising feature, a knot in their native structures. How the
polypeptide chain is able to knot itself during the folding process to form
these highly intricate protein topologies is not known. Here, we perform a
computational study on the 160-amino acid homodimeric protein YibK which, like
other proteins in the SpoU family of MTases, contains a deep trefoil knot in
its C-terminal region. In this study, we use a coarse-grained C-alpha-chain
representation and Langevin dynamics to study folding kinetics. We find that
specific, attractive nonnative interactions are critical for knot formation. In
the absence of these interactions, i.e. in an energetics driven entirely by
native interactions, knot formation is exceedingly unlikely. Further, we find,
in concert with recent experimental data on YibK, two parallel folding pathways
which we attribute to an early and a late formation of the trefoil knot,
respectively. For both pathways, knot formation occurs before dimerization. A
bioinformatics analysis of the SpoU family of proteins reveals further that the
critical nonnative interactions may originate from evolutionary conserved
hydrophobic segments around the knotted region.
|
[
{
"created": "Wed, 22 Nov 2006 23:59:54 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Wallin",
"Stefan",
""
],
[
"Zeldovich",
"Konstantin B",
""
],
[
"Shakhnovich",
"Eugene I",
""
]
] |
An increasing number of proteins are being discovered with a remarkable and somewhat surprising feature, a knot in their native structures. How the polypeptide chain is able to knot itself during the folding process to form these highly intricate protein topologies is not known. Here, we perform a computational study on the 160-amino acid homodimeric protein YibK which, like other proteins in the SpoU family of MTases, contains a deep trefoil knot in its C-terminal region. In this study, we use a coarse-grained C-alpha-chain representation and Langevin dynamics to study folding kinetics. We find that specific, attractive nonnative interactions are critical for knot formation. In the absence of these interactions, i.e. in an energetics driven entirely by native interactions, knot formation is exceedingly unlikely. Further, we find, in concert with recent experimental data on YibK, two parallel folding pathways which we attribute to an early and a late formation of the trefoil knot, respectively. For both pathways, knot formation occurs before dimerization. A bioinformatics analysis of the SpoU family of proteins reveals further that the critical nonnative interactions may originate from evolutionary conserved hydrophobic segments around the knotted region.
|
1611.05474
|
Rubem Mondaini
|
R.P. Mondaini, S.C. de Albuquerque Neto (Federal University of Rio de
Janeiro, Rio de Janeiro, RJ, Brazil)
|
The Pattern Recognition of Probability Distributions of Amino acids in
protein families
|
20 pages, 13 figures, 02 tables
| null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A pattern Recognition of a probability distribution of amino acids is
obtained for selected families of proteins. The mathematical model is derived
from a theory of protein families formation which is derived from application
of a Pauli's master equation method.
|
[
{
"created": "Thu, 13 Oct 2016 23:52:41 GMT",
"version": "v1"
}
] |
2016-11-18
|
[
[
"Mondaini",
"R. P.",
"",
"Federal University of Rio de\n Janeiro, Rio de Janeiro, RJ, Brazil"
],
[
"Neto",
"S. C. de Albuquerque",
"",
"Federal University of Rio de\n Janeiro, Rio de Janeiro, RJ, Brazil"
]
] |
A pattern Recognition of a probability distribution of amino acids is obtained for selected families of proteins. The mathematical model is derived from a theory of protein families formation which is derived from application of a Pauli's master equation method.
|
2107.11381
|
Seonwoo Min
|
Seonwoo Min, Byunghan Lee, and Sungroh Yoon
|
TargetNet: Functional microRNA Target Prediction with Deep Neural
Networks
|
This article has been accepted for publication in Bioinformatics
published by Oxford University Press
| null | null | null |
q-bio.GN cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression
regulation by binding to target sites of messenger RNAs (mRNAs). While
identifying functional targets of miRNAs is of utmost importance, their
prediction remains a great challenge. Previous computational algorithms have
major limitations. They use conservative candidate target site (CTS) selection
criteria mainly focusing on canonical site types, rely on laborious and
time-consuming manual feature extraction, and do not fully capitalize on the
information underlying miRNA-CTS interactions. Results: In this paper, we
introduce TargetNet, a novel deep learning-based algorithm for functional miRNA
target prediction. To address the limitations of previous approaches, TargetNet
has three key components: (1) relaxed CTS selection criteria accommodating
irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding
scheme incorporating extended seed region alignments, and (3) a deep residual
network-based prediction model. The proposed model was trained with miRNA-CTS
pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances
the previous state-of-the-art algorithms used in functional miRNA target
classification. Furthermore, it demonstrates great potential for distinguishing
high-functional miRNA targets.
|
[
{
"created": "Fri, 23 Jul 2021 07:31:23 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Oct 2021 06:31:29 GMT",
"version": "v2"
}
] |
2021-10-14
|
[
[
"Min",
"Seonwoo",
""
],
[
"Lee",
"Byunghan",
""
],
[
"Yoon",
"Sungroh",
""
]
] |
Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (1) relaxed CTS selection criteria accommodating irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments, and (3) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-the-art algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets.
|
1902.02016
|
Thomas Schneider
|
Thomas D. Schneider (1) and Vishnu Jejjala (2) ((1) National
Institutes of Health, (2) University of the Witwatersrand)
|
Restriction enzymes use a 24 dimensional coding space to recognize 6
base long DNA sequences
|
Version 1: 31 pages, 3 figures, 1 table; Version 2: 33 pages, 3
figures, 1 table, responses to reviewers, new refs
| null |
10.1371/journal.pone.0222419
| null |
q-bio.QM cs.IT math.IT
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Restriction enzymes recognize and bind to specific sequences on invading
bacteriophage DNA. Like a key in a lock, these proteins require many contacts
to specify the correct DNA sequence. Using information theory we develop an
equation that defines the number of independent contacts, which is the
dimensionality of the binding. We show that EcoRI, which binds to the sequence
GAATTC, functions in 24 dimensions. Information theory represents messages as
spheres in high dimensional spaces. Better sphere packing leads to better
communications systems. The densest known packing of hyperspheres occurs on the
Leech lattice in 24 dimensions. We suggest that the single protein EcoRI
molecule employs a Leech lattice in its operation. Optimizing density of sphere
packing explains why 6 base restriction enzymes are so common.
|
[
{
"created": "Wed, 6 Feb 2019 03:53:22 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Oct 2019 03:10:09 GMT",
"version": "v2"
}
] |
2019-10-31
|
[
[
"Schneider",
"Thomas D.",
""
],
[
"Jejjala",
"Vishnu",
""
]
] |
Restriction enzymes recognize and bind to specific sequences on invading bacteriophage DNA. Like a key in a lock, these proteins require many contacts to specify the correct DNA sequence. Using information theory we develop an equation that defines the number of independent contacts, which is the dimensionality of the binding. We show that EcoRI, which binds to the sequence GAATTC, functions in 24 dimensions. Information theory represents messages as spheres in high dimensional spaces. Better sphere packing leads to better communications systems. The densest known packing of hyperspheres occurs on the Leech lattice in 24 dimensions. We suggest that the single protein EcoRI molecule employs a Leech lattice in its operation. Optimizing density of sphere packing explains why 6 base restriction enzymes are so common.
|
1511.00953
|
Sergio Gabriel Quesada Acuna
|
Juli\'an Monge-N\'ajera and Gabriela P\'erez-G\'omez
|
Urban vegetation change after a hundred years in a tropical city (San
Jos\'e de Costa Rica)
|
20 pages, 19 figures
|
Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 58 (4):
1367-1386, December 2010
| null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Urban vegetation is of key importance because a large proportion of the human
population lives in cities. Nevertheless, urban vegetation is understudied
outside central Europe and particularly, little is known about the flora of
tropical Asian, African and Latin American cities. We present an estimate of
how the vegetation has changed in the city of San Jos\'e, Costa Rica, after
about one century, with the repeat photography technique (based on a collection
of 19th and early 20th century photographs by Jos\'e Fidel Trist\'an and
others) and with data from the Costa Rican National Herbarium. We found little
vegetation change in the landscape of San Jos\'e during the 20th century, where
a total of 95 families and 458 species were collected in the late 19th and
early 20th century. The families with most species were Asteraceae, Fabaceae,
Poaceae, Lamiaceae, Euphorbiaceae, Solanaceae, Cyperaceae, Acanthaceae,
Malvaceae, Piperaceae and Verbenaceae. Similar results have been found in
Europe, where the number of plant species often is stable for long periods even
when the individual species vary.
|
[
{
"created": "Tue, 3 Nov 2015 15:53:07 GMT",
"version": "v1"
}
] |
2015-11-04
|
[
[
"Monge-Nájera",
"Julián",
""
],
[
"Pérez-Gómez",
"Gabriela",
""
]
] |
Urban vegetation is of key importance because a large proportion of the human population lives in cities. Nevertheless, urban vegetation is understudied outside central Europe and particularly, little is known about the flora of tropical Asian, African and Latin American cities. We present an estimate of how the vegetation has changed in the city of San Jos\'e, Costa Rica, after about one century, with the repeat photography technique (based on a collection of 19th and early 20th century photographs by Jos\'e Fidel Trist\'an and others) and with data from the Costa Rican National Herbarium. We found little vegetation change in the landscape of San Jos\'e during the 20th century, where a total of 95 families and 458 species were collected in the late 19th and early 20th century. The families with most species were Asteraceae, Fabaceae, Poaceae, Lamiaceae, Euphorbiaceae, Solanaceae, Cyperaceae, Acanthaceae, Malvaceae, Piperaceae and Verbenaceae. Similar results have been found in Europe, where the number of plant species often is stable for long periods even when the individual species vary.
|
1011.4794
|
Michel Pleimling
|
Sara O. Case, Clinton H. Durney, Michel Pleimling, and R.K.P. Zia
|
Cyclic competition of four species: mean field theory and stochastic
evolution
|
6 pages, 4 figures, to appear in EPL
|
EPL 92 (2010) 58003
|
10.1209/0295-5075/92/58003
| null |
q-bio.PE cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generalizing the cyclically competing three-species model (often referred to
as the rock-paper-scissors game), we consider a simple system of population
dynamics without spatial structures that involves four species. Unlike the
previous model, the four form alliance pairs which resemble partnership in the
game of Bridge. In a finite system with discrete stochastic dynamics, all but 4
of the absorbing states consist of coexistence of a partner-pair. From a master
equation, we derive a set of mean field equations of evolution. This approach
predicts complex time dependence of the system and that the surviving
partner-pair is the one with the larger product of their strengths (rates of
consumption). Simulations typically confirm these scenarios. Beyond that, much
richer behavior is revealed, including complicated extinction probabilities and
non-trivial distributions of the population ratio in the surviving pair. These
discoveries naturally raise a number of intriguing questions, which in turn
suggests a variety of future avenues of research, especially for more realistic
models of multispecies competition in nature.
|
[
{
"created": "Mon, 22 Nov 2010 13:06:07 GMT",
"version": "v1"
}
] |
2011-01-06
|
[
[
"Case",
"Sara O.",
""
],
[
"Durney",
"Clinton H.",
""
],
[
"Pleimling",
"Michel",
""
],
[
"Zia",
"R. K. P.",
""
]
] |
Generalizing the cyclically competing three-species model (often referred to as the rock-paper-scissors game), we consider a simple system of population dynamics without spatial structures that involves four species. Unlike the previous model, the four form alliance pairs which resemble partnership in the game of Bridge. In a finite system with discrete stochastic dynamics, all but 4 of the absorbing states consist of coexistence of a partner-pair. From a master equation, we derive a set of mean field equations of evolution. This approach predicts complex time dependence of the system and that the surviving partner-pair is the one with the larger product of their strengths (rates of consumption). Simulations typically confirm these scenarios. Beyond that, much richer behavior is revealed, including complicated extinction probabilities and non-trivial distributions of the population ratio in the surviving pair. These discoveries naturally raise a number of intriguing questions, which in turn suggests a variety of future avenues of research, especially for more realistic models of multispecies competition in nature.
|
0905.2071
|
Fernando Falo
|
Diego Prada-Gracia, Jesus Gomez-Gardenes, Pablo Echenique and Fernando
Falo
|
Exploring the Free Energy Landscape: From Dynamics to Networks and Back
|
PLoS Computational Biology (in press)
|
PLoS Comput Biol 5(6): e1000415 (2009)
|
10.1371/journal.pcbi.1000415
| null |
q-bio.BM cond-mat.soft physics.bio-ph q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The knowledge of the Free Energy Landscape topology is the essential key to
understand many biochemical processes. The determination of the conformers of a
protein and their basins of attraction takes a central role for studying
molecular isomerization reactions. In this work, we present a novel framework
to unveil the features of a Free Energy Landscape answering questions such as
how many meta-stable conformers are, how the hierarchical relationship among
them is, or what the structure and kinetics of the transition paths are.
Exploring the landscape by molecular dynamics simulations, the microscopic data
of the trajectory are encoded into a Conformational Markov Network. The
structure of this graph reveals the regions of the conformational space
corresponding to the basins of attraction. In addition, handling the
Conformational Markov Network, relevant kinetic magnitudes as dwell times or
rate constants, and the hierarchical relationship among basins, complete the
global picture of the landscape. We show the power of the analysis studying a
toy model of a funnel-like potential and computing efficiently the conformers
of a short peptide, the dialanine, paving the way to a systematic study of the
Free Energy Landscape in large peptides.
|
[
{
"created": "Wed, 13 May 2009 11:36:28 GMT",
"version": "v1"
}
] |
2009-06-26
|
[
[
"Prada-Gracia",
"Diego",
""
],
[
"Gomez-Gardenes",
"Jesus",
""
],
[
"Echenique",
"Pablo",
""
],
[
"Falo",
"Fernando",
""
]
] |
The knowledge of the Free Energy Landscape topology is the essential key to understand many biochemical processes. The determination of the conformers of a protein and their basins of attraction takes a central role for studying molecular isomerization reactions. In this work, we present a novel framework to unveil the features of a Free Energy Landscape answering questions such as how many meta-stable conformers are, how the hierarchical relationship among them is, or what the structure and kinetics of the transition paths are. Exploring the landscape by molecular dynamics simulations, the microscopic data of the trajectory are encoded into a Conformational Markov Network. The structure of this graph reveals the regions of the conformational space corresponding to the basins of attraction. In addition, handling the Conformational Markov Network, relevant kinetic magnitudes as dwell times or rate constants, and the hierarchical relationship among basins, complete the global picture of the landscape. We show the power of the analysis studying a toy model of a funnel-like potential and computing efficiently the conformers of a short peptide, the dialanine, paving the way to a systematic study of the Free Energy Landscape in large peptides.
|
0906.4683
|
Sa\'ul Ares
|
G. Kalosakas and S. Ares
|
Dependence on temperature and GC content of bubble length distributions
in DNA
|
8 pages, 6 figures. Published on The Journal of Chemical Physics
|
J. Chem. Phys. 130, 235104 (2009)
|
10.1063/1.3149859
| null |
q-bio.BM cond-mat.stat-mech nlin.PS physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present numerical results on the temperature dependence of the
distribution of bubble lengths in DNA segments of various guanine-cytosine (GC)
concentrations. Base-pair openings are described by the Peyrard-Bishop-Dauxois
model and the corresponding thermal equilibrium distributions of bubbles are
obtained through Monte Carlo calculations for bubble sizes up to the order of a
hundred base pairs. The dependence of the parameters of bubble length
distribution on temperature and the GC content is investigated. We provide
simple expressions which approximately describe these relations. The variation
of the average bubble length is also presented. We find a temperature
dependence of the exponent c that appears in the distribution of bubble
lengths. If an analogous dependence exists in the loop entropy exponent of real
DNA, it may be relevant to understand overstretching in force-extension
experiments.
|
[
{
"created": "Thu, 25 Jun 2009 12:42:55 GMT",
"version": "v1"
}
] |
2009-06-26
|
[
[
"Kalosakas",
"G.",
""
],
[
"Ares",
"S.",
""
]
] |
We present numerical results on the temperature dependence of the distribution of bubble lengths in DNA segments of various guanine-cytosine (GC) concentrations. Base-pair openings are described by the Peyrard-Bishop-Dauxois model and the corresponding thermal equilibrium distributions of bubbles are obtained through Monte Carlo calculations for bubble sizes up to the order of a hundred base pairs. The dependence of the parameters of bubble length distribution on temperature and the GC content is investigated. We provide simple expressions which approximately describe these relations. The variation of the average bubble length is also presented. We find a temperature dependence of the exponent c that appears in the distribution of bubble lengths. If an analogous dependence exists in the loop entropy exponent of real DNA, it may be relevant to understand overstretching in force-extension experiments.
|
2204.02474
|
Hsu Kiang Ooi
|
Mohammad Sajjad Ghaemi, Karl Grantham, Isaac Tamblyn, Yifeng Li, Hsu
Kiang Ooi
|
Generative Enriched Sequential Learning (ESL) Approach for Molecular
Design via Augmented Domain Knowledge
|
6 pages
| null | null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Deploying generative machine learning techniques to generate novel chemical
structures based on molecular fingerprint representation has been well
established in molecular design. Typically, sequential learning (SL) schemes
such as hidden Markov models (HMM) and, more recently, in the sequential deep
learning context, recurrent neural network (RNN) and long short-term memory
(LSTM) were used extensively as generative models to discover unprecedented
molecules. To this end, emission probability between two states of atoms plays
a central role without considering specific chemical or physical properties.
Lack of supervised domain knowledge can mislead the learning procedure to be
relatively biased to the prevalent molecules observed in the training data that
are not necessarily of interest. We alleviated this drawback by augmenting the
training data with domain knowledge, e.g. quantitative estimates of the
drug-likeness score (QEDs). As such, our experiments demonstrated that with
this subtle trick called enriched sequential learning (ESL), specific patterns
of particular interest can be learnt better, which led to generating de novo
molecules with ameliorated QEDs.
|
[
{
"created": "Tue, 5 Apr 2022 20:16:11 GMT",
"version": "v1"
}
] |
2022-04-07
|
[
[
"Ghaemi",
"Mohammad Sajjad",
""
],
[
"Grantham",
"Karl",
""
],
[
"Tamblyn",
"Isaac",
""
],
[
"Li",
"Yifeng",
""
],
[
"Ooi",
"Hsu Kiang",
""
]
] |
Deploying generative machine learning techniques to generate novel chemical structures based on molecular fingerprint representation has been well established in molecular design. Typically, sequential learning (SL) schemes such as hidden Markov models (HMM) and, more recently, in the sequential deep learning context, recurrent neural network (RNN) and long short-term memory (LSTM) were used extensively as generative models to discover unprecedented molecules. To this end, emission probability between two states of atoms plays a central role without considering specific chemical or physical properties. Lack of supervised domain knowledge can mislead the learning procedure to be relatively biased to the prevalent molecules observed in the training data that are not necessarily of interest. We alleviated this drawback by augmenting the training data with domain knowledge, e.g. quantitative estimates of the drug-likeness score (QEDs). As such, our experiments demonstrated that with this subtle trick called enriched sequential learning (ESL), specific patterns of particular interest can be learnt better, which led to generating de novo molecules with ameliorated QEDs.
|
1904.12914
|
Robert Marsland III
|
Robert Marsland III and Wenping Cui and Pankaj Mehta
|
A minimal model for microbial biodiversity can reproduce experimentally
observed ecological patterns
|
35 pages, 10 figures
|
Scientific Reports 10:3308 (2020)
|
10.1038/s41598-020-60130-2
| null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Surveys of microbial biodiversity such as the Earth Microbiome Project (EMP)
and the Human Microbiome Project (HMP) have revealed robust ecological patterns
across different environments. A major goal in ecology is to leverage these
patterns to identify the ecological processes shaping microbial ecosystems. One
promising approach is to use minimal models that can relate mechanistic
assumptions at the microbe scale to community-level patterns. Here, we
demonstrate the utility of this approach by showing that the Microbial Consumer
Resource Model (MiCRM) -- a minimal model for microbial communities with
resource competition, metabolic crossfeeding and stochastic colonization -- can
qualitatively reproduce patterns found in survey data including compositional
gradients, dissimilarity/overlap correlations, richness/harshness correlations,
and nestedness of community composition. By using the MiCRM to generate
synthetic data with different environmental and taxonomical structure, we show
that large scale patterns in the EMP can be reproduced by considering the
energetic cost of surviving in harsh environments and HMP patterns may reflect
the importance of environmental filtering in shaping competition. We also show
that recently discovered dissimilarity-overlap correlations in the HMP likely
arise from communities that share similar environments rather than reflecting
universal dynamics. We identify ecologically meaningful changes in parameters
that alter or destroy each one of these patterns, suggesting new mechanistic
hypotheses for further investigation. These findings highlight the promise of
minimal models for microbial ecology.
|
[
{
"created": "Mon, 29 Apr 2019 19:18:43 GMT",
"version": "v1"
},
{
"created": "Fri, 3 May 2019 16:12:32 GMT",
"version": "v2"
},
{
"created": "Thu, 23 Jan 2020 22:42:13 GMT",
"version": "v3"
}
] |
2020-03-05
|
[
[
"Marsland",
"Robert",
"III"
],
[
"Cui",
"Wenping",
""
],
[
"Mehta",
"Pankaj",
""
]
] |
Surveys of microbial biodiversity such as the Earth Microbiome Project (EMP) and the Human Microbiome Project (HMP) have revealed robust ecological patterns across different environments. A major goal in ecology is to leverage these patterns to identify the ecological processes shaping microbial ecosystems. One promising approach is to use minimal models that can relate mechanistic assumptions at the microbe scale to community-level patterns. Here, we demonstrate the utility of this approach by showing that the Microbial Consumer Resource Model (MiCRM) -- a minimal model for microbial communities with resource competition, metabolic crossfeeding and stochastic colonization -- can qualitatively reproduce patterns found in survey data including compositional gradients, dissimilarity/overlap correlations, richness/harshness correlations, and nestedness of community composition. By using the MiCRM to generate synthetic data with different environmental and taxonomical structure, we show that large scale patterns in the EMP can be reproduced by considering the energetic cost of surviving in harsh environments and HMP patterns may reflect the importance of environmental filtering in shaping competition. We also show that recently discovered dissimilarity-overlap correlations in the HMP likely arise from communities that share similar environments rather than reflecting universal dynamics. We identify ecologically meaningful changes in parameters that alter or destroy each one of these patterns, suggesting new mechanistic hypotheses for further investigation. These findings highlight the promise of minimal models for microbial ecology.
|
1611.02482
|
Roland Kr\"amer
|
Ulrich Warttinger, Roland Kr\"amer
|
Quantification of heparin in complex matrices (including urine) using a
mix-and-read fluorescence assay
|
Article, 16 pages, 7 figures
| null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Heparin is an important anticoagulant drug, about one billion doses are
produced annually. It is a polydisperse sulfated polysaccharide, and the
inherent heterogeneity makes the analysis of heparin difficult. The global
crisis resulting from adulterated heparin in 2008 has drawn renewed attention
to the challenges that are associated with the quality control and
characterization of this complex biological medicine from natural sources. The
present study addresses the need for simple and user-friendly analytical
methods for the fast and accurate quantification of heparin in complex
matrices. Direct quantification of heparin in the low microgram per mL range
was accomplished using a specific commercially available assay based on the
fluorescent molecular probe Heparin Red, simply by mixing the heparin
containing sample and a reagent solution in a 96-well microplate followed by
fluorescence readout. A screening of typical impurities in raw heparin
(selected other glycosaminoglycans, residual nucleic acids and proteins),
related to the extraction from animal tissues, as well as of components of the
urine matrix (inorganic salts, amino acids, trace proteins) revealed that these
compounds even in large excess have no or very little effect on the accuracy of
heparin determination. Heparin spike detection in urine, a biological
multicomponent matrix, also showed good accuracy. We envision applications of
this mix-and-read assay in the process and quality control in heparin
manufacturing, but also in pharmacokinetic studies as a convenient tool for
measuring of the urinary excretion of heparins.
|
[
{
"created": "Tue, 8 Nov 2016 11:29:04 GMT",
"version": "v1"
}
] |
2016-11-09
|
[
[
"Warttinger",
"Ulrich",
""
],
[
"Krämer",
"Roland",
""
]
] |
Heparin is an important anticoagulant drug, about one billion doses are produced annually. It is a polydisperse sulfated polysaccharide, and the inherent heterogeneity makes the analysis of heparin difficult. The global crisis resulting from adulterated heparin in 2008 has drawn renewed attention to the challenges that are associated with the quality control and characterization of this complex biological medicine from natural sources. The present study addresses the need for simple and user-friendly analytical methods for the fast and accurate quantification of heparin in complex matrices. Direct quantification of heparin in the low microgram per mL range was accomplished using a specific commercially available assay based on the fluorescent molecular probe Heparin Red, simply by mixing the heparin containing sample and a reagent solution in a 96-well microplate followed by fluorescence readout. A screening of typical impurities in raw heparin (selected other glycosaminoglycans, residual nucleic acids and proteins), related to the extraction from animal tissues, as well as of components of the urine matrix (inorganic salts, amino acids, trace proteins) revealed that these compounds even in large excess have no or very little effect on the accuracy of heparin determination. Heparin spike detection in urine, a biological multicomponent matrix, also showed good accuracy. We envision applications of this mix-and-read assay in the process and quality control in heparin manufacturing, but also in pharmacokinetic studies as a convenient tool for measuring of the urinary excretion of heparins.
|
2107.00719
|
Po-Yu Kao
|
Po-Yu Kao, Shu-Min Kao, Nan-Lan Huang, Yen-Chu Lin
|
Toward Drug-Target Interaction Prediction via Ensemble Modeling and
Transfer Learning
|
8 pages, 1 figure, 10 tables
| null |
10.1109/BIBM52615.2021.9669729
| null |
q-bio.BM cs.LG q-bio.QM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Drug-target interaction (DTI) prediction plays a crucial role in drug
discovery, and deep learning approaches have achieved state-of-the-art
performance in this field. We introduce an ensemble of deep learning models
(EnsembleDLM) for DTI prediction. EnsembleDLM only uses the sequence
information of chemical compounds and proteins, and it aggregates the
predictions from multiple deep neural networks. This approach not only achieves
state-of-the-art performance in Davis and KIBA datasets but also reaches
cutting-edge performance in the cross-domain applications across different
bio-activity types and different protein classes. We also demonstrate that
EnsembleDLM achieves a good performance (Pearson correlation coefficient and
concordance index > 0.8) in the new domain with approximately 50% transfer
learning data, i.e., the training set has twice as much data as the test set.
|
[
{
"created": "Fri, 2 Jul 2021 04:00:03 GMT",
"version": "v1"
},
{
"created": "Wed, 28 Jul 2021 02:14:55 GMT",
"version": "v2"
},
{
"created": "Fri, 19 Nov 2021 03:21:49 GMT",
"version": "v3"
}
] |
2022-01-19
|
[
[
"Kao",
"Po-Yu",
""
],
[
"Kao",
"Shu-Min",
""
],
[
"Huang",
"Nan-Lan",
""
],
[
"Lin",
"Yen-Chu",
""
]
] |
Drug-target interaction (DTI) prediction plays a crucial role in drug discovery, and deep learning approaches have achieved state-of-the-art performance in this field. We introduce an ensemble of deep learning models (EnsembleDLM) for DTI prediction. EnsembleDLM only uses the sequence information of chemical compounds and proteins, and it aggregates the predictions from multiple deep neural networks. This approach not only achieves state-of-the-art performance in Davis and KIBA datasets but also reaches cutting-edge performance in the cross-domain applications across different bio-activity types and different protein classes. We also demonstrate that EnsembleDLM achieves a good performance (Pearson correlation coefficient and concordance index > 0.8) in the new domain with approximately 50% transfer learning data, i.e., the training set has twice as much data as the test set.
|
1911.12279
|
Thierry Mora
|
Giulio Isacchini, Zachary Sethna, Yuval Elhanati, Armita Nourmohammad,
Aleksandra M. Walczak, Thierry Mora
|
On generative models of T-cell receptor sequences
| null |
Phys. Rev. E 101, 062414 (2020)
|
10.1103/PhysRevE.101.062414
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
T-cell receptors (TCR) are key proteins of the adaptive immune system,
generated randomly in each individual, whose diversity underlies our ability to
recognize infections and malignancies. Modeling the distribution of TCR
sequences is of key importance for immunology and medical applications. Here,
we compare two inference methods trained on high-throughput sequencing data: a
knowledge-guided approach, which accounts for the details of sequence
generation, supplemented by a physics-inspired model of selection; and a
knowledge-free Variational Auto-Encoder based on deep artificial neural
networks. We show that the knowledge-guided model outperforms the deep network
approach at predicting TCR probabilities, while being more interpretable, at a
lower computational cost.
|
[
{
"created": "Wed, 27 Nov 2019 16:49:52 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Mar 2020 16:49:43 GMT",
"version": "v2"
}
] |
2020-07-01
|
[
[
"Isacchini",
"Giulio",
""
],
[
"Sethna",
"Zachary",
""
],
[
"Elhanati",
"Yuval",
""
],
[
"Nourmohammad",
"Armita",
""
],
[
"Walczak",
"Aleksandra M.",
""
],
[
"Mora",
"Thierry",
""
]
] |
T-cell receptors (TCR) are key proteins of the adaptive immune system, generated randomly in each individual, whose diversity underlies our ability to recognize infections and malignancies. Modeling the distribution of TCR sequences is of key importance for immunology and medical applications. Here, we compare two inference methods trained on high-throughput sequencing data: a knowledge-guided approach, which accounts for the details of sequence generation, supplemented by a physics-inspired model of selection; and a knowledge-free Variational Auto-Encoder based on deep artificial neural networks. We show that the knowledge-guided model outperforms the deep network approach at predicting TCR probabilities, while being more interpretable, at a lower computational cost.
|
2004.13024
|
Giuseppe Gaeta
|
Mariano Cadoni, Giuseppe Gaeta
|
Size and timescale of epidemics in the SIR framework
|
16 pages in PhysRev format, 12 figures; some superposition with
arXiv:2004.11633, in particular for the application part
|
Physica D 411 (2020) 132626
|
10.1016/j.physd.2020.132626
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The most important features to assess the severity of an epidemic are its
size and its timescale. We discuss these features in a systematic way in the
context of SIR and SIR-type models. We investigate in detail how the size and
timescale of the epidemic can be changed by acting on the parameters
characterizing the model. Using these results and having as guideline the
COVID-19 epidemic in Italy, we compare the efficiency of different containment
strategies for contrasting an epidemic diffusion such as social distancing,
lockdown, tracing, early detection and isolation.
|
[
{
"created": "Mon, 27 Apr 2020 09:23:29 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Jun 2020 09:16:20 GMT",
"version": "v2"
}
] |
2020-06-29
|
[
[
"Cadoni",
"Mariano",
""
],
[
"Gaeta",
"Giuseppe",
""
]
] |
The most important features to assess the severity of an epidemic are its size and its timescale. We discuss these features in a systematic way in the context of SIR and SIR-type models. We investigate in detail how the size and timescale of the epidemic can be changed by acting on the parameters characterizing the model. Using these results and having as guideline the COVID-19 epidemic in Italy, we compare the efficiency of different containment strategies for contrasting an epidemic diffusion such as social distancing, lockdown, tracing, early detection and isolation.
|
2404.09812
|
Arkadiy Dushatskiy
|
Arkadiy Dushatskiy, Esther Julien, Leen Stougie, Leo van Iersel
|
Solving the Tree Containment Problem Using Graph Neural Networks
| null | null | null | null |
q-bio.PE cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Tree Containment is a fundamental problem in phylogenetics useful for
verifying a proposed phylogenetic network, representing the evolutionary
history of certain species. Tree Containment asks whether the given
phylogenetic tree (for instance, constructed from a DNA fragment showing
tree-like evolution) is contained in the given phylogenetic network. In the
general case, this is an NP-complete problem. We propose to solve it
approximately using Graph Neural Networks. In particular, we propose to combine
the given network and the tree and apply a Graph Neural Network to this
network-tree graph. This way, we achieve the capability of solving the tree
containment instances representing a larger number of species than the
instances contained in the training dataset (i.e., our algorithm has the
inductive learning ability). Our algorithm demonstrates an accuracy of over
$95\%$ in solving the tree containment problem on instances with up to 100
leaves.
|
[
{
"created": "Mon, 15 Apr 2024 14:10:06 GMT",
"version": "v1"
},
{
"created": "Thu, 13 Jun 2024 09:20:40 GMT",
"version": "v2"
}
] |
2024-06-14
|
[
[
"Dushatskiy",
"Arkadiy",
""
],
[
"Julien",
"Esther",
""
],
[
"Stougie",
"Leen",
""
],
[
"van Iersel",
"Leo",
""
]
] |
Tree Containment is a fundamental problem in phylogenetics useful for verifying a proposed phylogenetic network, representing the evolutionary history of certain species. Tree Containment asks whether the given phylogenetic tree (for instance, constructed from a DNA fragment showing tree-like evolution) is contained in the given phylogenetic network. In the general case, this is an NP-complete problem. We propose to solve it approximately using Graph Neural Networks. In particular, we propose to combine the given network and the tree and apply a Graph Neural Network to this network-tree graph. This way, we achieve the capability of solving the tree containment instances representing a larger number of species than the instances contained in the training dataset (i.e., our algorithm has the inductive learning ability). Our algorithm demonstrates an accuracy of over $95\%$ in solving the tree containment problem on instances with up to 100 leaves.
|
2112.01987
|
Jesse Marshall
|
Jesse D. Marshall, Tianqing Li, Joshua H. Wu, Timothy W. Dunn
|
Leaving Flatland: Advances in 3D behavioral measurement
|
11 pages, 3 figures, 1 table, 1 box
| null | null | null |
q-bio.NC q-bio.QM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Animals move in three dimensions (3D). Thus, 3D measurement is necessary to
report the true kinematics of animal movement. Existing 3D measurement
techniques draw on specialized hardware, such as motion capture or depth
cameras, as well as deep multi-view and monocular computer vision. Continued
advances at the intersection of deep learning and computer vision will
facilitate 3D tracking across more anatomical features, with less training
data, in additional species, and within more natural, occlusive environments.
3D behavioral measurement enables unique applications in phenotyping,
investigating the neural basis of behavior, and designing artificial agents
capable of imitating animal behavior.
|
[
{
"created": "Fri, 3 Dec 2021 15:59:11 GMT",
"version": "v1"
}
] |
2021-12-06
|
[
[
"Marshall",
"Jesse D.",
""
],
[
"Li",
"Tianqing",
""
],
[
"Wu",
"Joshua H.",
""
],
[
"Dunn",
"Timothy W.",
""
]
] |
Animals move in three dimensions (3D). Thus, 3D measurement is necessary to report the true kinematics of animal movement. Existing 3D measurement techniques draw on specialized hardware, such as motion capture or depth cameras, as well as deep multi-view and monocular computer vision. Continued advances at the intersection of deep learning and computer vision will facilitate 3D tracking across more anatomical features, with less training data, in additional species, and within more natural, occlusive environments. 3D behavioral measurement enables unique applications in phenotyping, investigating the neural basis of behavior, and designing artificial agents capable of imitating animal behavior.
|
0806.0449
|
Nicanor Moldovan
|
Nicanor I. Moldovan
|
Anapedesis: Implications and Applications of Bio-Structural Robustness
| null | null | null | null |
q-bio.OT q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Here we develop an approach to bio-structural robustness integrated with
structure-function relationship in a unified conceptual and methodological
framework, and envision its study using adequate computational and experimental
methods. To distinguish this structural robustness from the abstract
organizational robustness of systems, we call it anapedesis, and define it as
the scale-independent property of biological objects, from biomolecules to
organisms, to deform and recover while minimizing and/or repairing the damage
produced by stretch. We propose to study the consequences of deformation of
biological objects closer to their structural and/or functional failure than
previously considered relevant. We show that structural robustness is present
as a basic principle in many facets of biomedicine: many pathological
conditions may derive from the failure of molecules, cells and their
higher-order assemblies to maintain robustness against deformation.
Furthermore, structural robustness could have been the key selective criterion
during pre-biotic evolution and afterwards, and its universality can be
demonstrated by modeling using genetic algorithms. Thus, the specific
investigation of bio-structural robustness as anapedesis could help the solving
of fundamental problems of biology and medicine.
|
[
{
"created": "Tue, 3 Jun 2008 07:05:02 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Jun 2008 23:09:43 GMT",
"version": "v2"
}
] |
2008-06-04
|
[
[
"Moldovan",
"Nicanor I.",
""
]
] |
Here we develop an approach to bio-structural robustness integrated with structure-function relationship in a unified conceptual and methodological framework, and envision its study using adequate computational and experimental methods. To distinguish this structural robustness from the abstract organizational robustness of systems, we call it anapedesis, and define it as the scale-independent property of biological objects, from biomolecules to organisms, to deform and recover while minimizing and/or repairing the damage produced by stretch. We propose to study the consequences of deformation of biological objects closer to their structural and/or functional failure than previously considered relevant. We show that structural robustness is present as a basic principle in many facets of biomedicine: many pathological conditions may derive from the failure of molecules, cells and their higher-order assemblies to maintain robustness against deformation. Furthermore, structural robustness could have been the key selective criterion during pre-biotic evolution and afterwards, and its universality can be demonstrated by modeling using genetic algorithms. Thus, the specific investigation of bio-structural robustness as anapedesis could help the solving of fundamental problems of biology and medicine.
|
q-bio/0509017
|
Sandeep Krishna
|
Sandeep Krishna, Mogens H. Jensen, Kim Sneppen (Niels Bohr Institute,
Copenhagen, Denmark)
|
Spiky oscillations in NF-kB signalling
|
11 pages, 13 figures
|
PNAS 2006 103: 10840-10845
|
10.1073/pnas.0604085103
| null |
q-bio.MN cond-mat.other q-bio.OT
| null |
The NF-kB signalling system is involved in a variety of cellular processes
including immune response, inflammation, and apoptosis. Recent experiments have
found oscillations in the nuclear-cytoplasmic translocation of the NF-kB
transcription factor. How the cell uses the oscillations to differentiate input
conditions and send specific signals to downstream genes is an open problem. We
shed light on this issue by examining the small core network driving the
oscillations, which, we show, is designed to produce periodic spikes in nuclear
NF-kB concentration. The oscillations can be used to regulate downstream genes
in a variety of ways. In particular, we show that genes to whose operator sites
NF-kB binds and dissociates fast can respond very sensitively to changes in the
input signal, with effective Hill coefficients in excess of 20.
|
[
{
"created": "Wed, 14 Sep 2005 19:50:07 GMT",
"version": "v1"
}
] |
2009-11-11
|
[
[
"Krishna",
"Sandeep",
"",
"Niels Bohr Institute,\n Copenhagen, Denmark"
],
[
"Jensen",
"Mogens H.",
"",
"Niels Bohr Institute,\n Copenhagen, Denmark"
],
[
"Sneppen",
"Kim",
"",
"Niels Bohr Institute,\n Copenhagen, Denmark"
]
] |
The NF-kB signalling system is involved in a variety of cellular processes including immune response, inflammation, and apoptosis. Recent experiments have found oscillations in the nuclear-cytoplasmic translocation of the NF-kB transcription factor. How the cell uses the oscillations to differentiate input conditions and send specific signals to downstream genes is an open problem. We shed light on this issue by examining the small core network driving the oscillations, which, we show, is designed to produce periodic spikes in nuclear NF-kB concentration. The oscillations can be used to regulate downstream genes in a variety of ways. In particular, we show that genes to whose operator sites NF-kB binds and dissociates fast can respond very sensitively to changes in the input signal, with effective Hill coefficients in excess of 20.
|
q-bio/0702056
|
Mark White MD
|
Mark White
|
The G-Ball, a New Icon for Codon Symmetry and the Genetic Code
|
PDF format, 82 pages and 33 figures. This is a low resolution
document. For a higher resolution document please contact the author
| null | null | null |
q-bio.OT
| null |
A codon table is a useful tool for mapping codons to amino acids as they have
been assigned by nature. It has become a scientific icon because of the way it
embodies our understanding of this natural process and the way it immediately
communicates this understanding. However, advancements in molecular biology
over the past several decades must lead to a realization that our basic
understanding of genetic translation is fundamentally flawed and incomplete,
and, therefore, our icon is inadequate. A better understanding of symmetry and
an appreciation for the essential role it has played in codon formation will
improve our understanding of nature's coding processes. Incorporation of that
symmetry into our icon will facilitate that improvement.
|
[
{
"created": "Mon, 26 Feb 2007 18:25:39 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"White",
"Mark",
""
]
] |
A codon table is a useful tool for mapping codons to amino acids as they have been assigned by nature. It has become a scientific icon because of the way it embodies our understanding of this natural process and the way it immediately communicates this understanding. However, advancements in molecular biology over the past several decades must lead to a realization that our basic understanding of genetic translation is fundamentally flawed and incomplete, and, therefore, our icon is inadequate. A better understanding of symmetry and an appreciation for the essential role it has played in codon formation will improve our understanding of nature's coding processes. Incorporation of that symmetry into our icon will facilitate that improvement.
|
0812.0841
|
C.-M. Ghim
|
C.-M. Ghim, E. Almaas
|
Genetic noise control via protein oligomerization
| null |
BMC Systems Biology 2008, 2:94
|
10.1186/1752-0509-2-94
| null |
q-bio.MN q-bio.CB
|
http://creativecommons.org/licenses/publicdomain/
|
Gene expression in a cell entails random reaction events occurring over
disparate time scales. Thus, molecular noise that often results in phenotypic
and population-dynamic consequences sets a fundamental limit to biochemical
signaling. While there have been numerous studies correlating the architecture
of cellular reaction networks with noise tolerance, only a limited effort has
been made to understand the dynamic role of protein-protein interactions. Here
we have developed a fully stochastic model for the positive feedback control of
a single gene, as well as a pair of genes (toggle switch), integrating
quantitative results from previous in vivo and in vitro studies. We find that
the overall noise-level is reduced and the frequency content of the noise is
dramatically shifted to the physiologically irrelevant high-frequency regime in
the presence of protein dimerization. This is independent of the choice of
monomer or dimer as transcription factor and persists throughout the multiple
model topologies considered. For the toggle switch, we additionally find that
the presence of a protein dimer, either homodimer or heterodimer, may
significantly reduce its random switching rate. Hence, the dimer promotes the
robust function of bistable switches by preventing the uninduced (induced)
state from randomly being induced (uninduced). The specific binding between
regulatory proteins provides a buffer that may prevent the propagation of
fluctuations in genetic activity. The capacity of the buffer is a non-monotonic
function of association-dissociation rates. Since the protein oligomerization
per se does not require extra protein components to be expressed, it provides a
basis for the rapid control of intrinsic or extrinsic noise.
|
[
{
"created": "Thu, 4 Dec 2008 01:02:15 GMT",
"version": "v1"
}
] |
2009-02-19
|
[
[
"Ghim",
"C. -M.",
""
],
[
"Almaas",
"E.",
""
]
] |
Gene expression in a cell entails random reaction events occurring over disparate time scales. Thus, molecular noise that often results in phenotypic and population-dynamic consequences sets a fundamental limit to biochemical signaling. While there have been numerous studies correlating the architecture of cellular reaction networks with noise tolerance, only a limited effort has been made to understand the dynamic role of protein-protein interactions. Here we have developed a fully stochastic model for the positive feedback control of a single gene, as well as a pair of genes (toggle switch), integrating quantitative results from previous in vivo and in vitro studies. We find that the overall noise-level is reduced and the frequency content of the noise is dramatically shifted to the physiologically irrelevant high-frequency regime in the presence of protein dimerization. This is independent of the choice of monomer or dimer as transcription factor and persists throughout the multiple model topologies considered. For the toggle switch, we additionally find that the presence of a protein dimer, either homodimer or heterodimer, may significantly reduce its random switching rate. Hence, the dimer promotes the robust function of bistable switches by preventing the uninduced (induced) state from randomly being induced (uninduced). The specific binding between regulatory proteins provides a buffer that may prevent the propagation of fluctuations in genetic activity. The capacity of the buffer is a non-monotonic function of association-dissociation rates. Since the protein oligomerization per se does not require extra protein components to be expressed, it provides a basis for the rapid control of intrinsic or extrinsic noise.
|
2110.11814
|
Jingcheng Xu
|
Jingcheng Xu and C\'ecile An\'e
|
Identifiability of local and global features of phylogenetic networks
from average distances
| null | null | null | null |
q-bio.PE math.ST stat.TH
|
http://creativecommons.org/licenses/by/4.0/
|
Phylogenetic networks extend phylogenetic trees to model non-vertical
inheritance, by which a lineage inherits material from multiple parents. The
computational complexity of estimating phylogenetic networks from genome-wide
data with likelihood-based methods limits the size of networks that can be
handled. Methods based on pairwise distances could offer faster alternatives.
We study here the information that average pairwise distances contain on the
underlying phylogenetic network, by characterizing local and global features
that can or cannot be identified. For general networks, we clarify that the
root and edge lengths adjacent to reticulations are not identifiable, and then
focus on the class of zipped-up semidirected networks. We provide a criterion
to swap subgraphs locally, such as 3-cycles, resulting in indistinguishable
networks. We propose the "distance split tree", which can be constructed from
pairwise distances, and prove that it is a refinement of the network's tree of
blobs, capturing the tree-like features of the network. For level-1 networks,
this distance split tree is equal to the tree of blobs refined to separate
polytomies from blobs, and we prove that the mixed representation of the
network is identifiable. The information loss is localized around 4-cycles, for
which the placement of the reticulation is unidentifiable. The mixed
representation combines split edges for 4-cycles, regular tree and hybrid edges
from the semidirected network, and edge parameters that encode all information
identifiable from average pairwise distances.
|
[
{
"created": "Fri, 22 Oct 2021 14:36:59 GMT",
"version": "v1"
},
{
"created": "Sat, 25 Jun 2022 20:46:40 GMT",
"version": "v2"
}
] |
2022-06-28
|
[
[
"Xu",
"Jingcheng",
""
],
[
"Ané",
"Cécile",
""
]
] |
Phylogenetic networks extend phylogenetic trees to model non-vertical inheritance, by which a lineage inherits material from multiple parents. The computational complexity of estimating phylogenetic networks from genome-wide data with likelihood-based methods limits the size of networks that can be handled. Methods based on pairwise distances could offer faster alternatives. We study here the information that average pairwise distances contain on the underlying phylogenetic network, by characterizing local and global features that can or cannot be identified. For general networks, we clarify that the root and edge lengths adjacent to reticulations are not identifiable, and then focus on the class of zipped-up semidirected networks. We provide a criterion to swap subgraphs locally, such as 3-cycles, resulting in indistinguishable networks. We propose the "distance split tree", which can be constructed from pairwise distances, and prove that it is a refinement of the network's tree of blobs, capturing the tree-like features of the network. For level-1 networks, this distance split tree is equal to the tree of blobs refined to separate polytomies from blobs, and we prove that the mixed representation of the network is identifiable. The information loss is localized around 4-cycles, for which the placement of the reticulation is unidentifiable. The mixed representation combines split edges for 4-cycles, regular tree and hybrid edges from the semidirected network, and edge parameters that encode all information identifiable from average pairwise distances.
|
1905.07256
|
Luciano Stucchi
|
Luciano Stucchi, Javier Galeano, Desiderio A. Vasquez
|
Pattern formation induced by intraspecific interactions in a
predator-prey system
|
13 pages, 11 figures
|
Phys. Rev. E 100, 062414 (2019)
|
10.1103/PhysRevE.100.062414
| null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Differential diffusion is a source of instability in population dynamics
systems when species diffuse with different rates. Predator-prey systems show
this instability only under certain specific conditions, usually requiring
Holling-type functionals involved. Here we study the effects of intraspecific
cooperation and competition on diffusion-driven instability in a predator-prey
system with a different structure. We conduct the analysis on a generalized
population dynamics that bounds intraspecific and interspecific interactions
with Verhulst-type saturation terms instead of Holling-type functionals. We
find that instability occurs due to the intraspecific saturation or
intraspecific interactions, both cooperative and competitive. We present
numerical simulations and show spatial patterns due to diffusion.
|
[
{
"created": "Fri, 17 May 2019 13:35:20 GMT",
"version": "v1"
}
] |
2020-01-01
|
[
[
"Stucchi",
"Luciano",
""
],
[
"Galeano",
"Javier",
""
],
[
"Vasquez",
"Desiderio A.",
""
]
] |
Differential diffusion is a source of instability in population dynamics systems when species diffuse with different rates. Predator-prey systems show this instability only under certain specific conditions, usually requiring Holling-type functionals involved. Here we study the effects of intraspecific cooperation and competition on diffusion-driven instability in a predator-prey system with a different structure. We conduct the analysis on a generalized population dynamics that bounds intraspecific and interspecific interactions with Verhulst-type saturation terms instead of Holling-type functionals. We find that instability occurs due to the intraspecific saturation or intraspecific interactions, both cooperative and competitive. We present numerical simulations and show spatial patterns due to diffusion.
|
0805.1859
|
Leo van Iersel
|
Leo van Iersel and Steven Kelk
|
Constructing the Simplest Possible Phylogenetic Network from Triplets
|
The proof of Lemma 4 has been extended
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A phylogenetic network is a directed acyclic graph that visualises an
evolutionary history containing so-called reticulations such as recombinations,
hybridisations or lateral gene transfers. Here we consider the construction of
a simplest possible phylogenetic network consistent with an input set T, where
T contains at least one phylogenetic tree on three leaves (a triplet) for each
combination of three taxa. To quantify the complexity of a network we consider
both the total number of reticulations and the number of reticulations per
biconnected component, called the level of the network. We give polynomial-time
algorithms for constructing a level-1 respectively a level-2 network that
contains a minimum number of reticulations and is consistent with T (if such a
network exists). In addition, we show that if T is precisely equal to the set
of triplets consistent with some network, then we can construct such a network
with smallest possible level in time O(|T|^(k+1)), if k is a fixed upper bound
on the level of the network.
|
[
{
"created": "Tue, 13 May 2008 14:02:30 GMT",
"version": "v1"
},
{
"created": "Fri, 16 May 2008 15:23:54 GMT",
"version": "v2"
}
] |
2008-05-16
|
[
[
"van Iersel",
"Leo",
""
],
[
"Kelk",
"Steven",
""
]
] |
A phylogenetic network is a directed acyclic graph that visualises an evolutionary history containing so-called reticulations such as recombinations, hybridisations or lateral gene transfers. Here we consider the construction of a simplest possible phylogenetic network consistent with an input set T, where T contains at least one phylogenetic tree on three leaves (a triplet) for each combination of three taxa. To quantify the complexity of a network we consider both the total number of reticulations and the number of reticulations per biconnected component, called the level of the network. We give polynomial-time algorithms for constructing a level-1 respectively a level-2 network that contains a minimum number of reticulations and is consistent with T (if such a network exists). In addition, we show that if T is precisely equal to the set of triplets consistent with some network, then we can construct such a network with smallest possible level in time O(|T|^(k+1)), if k is a fixed upper bound on the level of the network.
|
q-bio/0403041
|
Anna Ochab-Marcinek
|
Anna Ochab-Marcinek, Ewa Gudowska-Nowak
|
Population growth and control in stochastic models of cancer development
|
Submitted to Physica A Corrected spelling errors. Title changed to
more appropriate for the content. Extended Conclusions section. Added a short
explanation about the meaning of parameters t_t and t_r. References collected
in proper order
|
Physica A 343 (2004) 557-572
|
10.1016/j.physa.2004.06.071
| null |
q-bio.PE cond-mat.stat-mech
| null |
We study the joint effect of thermal bath fluctuations and an external noise
tuning activity of cytotoxic cells on the triggered immune response in a
growing cancerous tissue. The immune response is assumed to be primarily
mediated by effector cells that develop a cytotoxic activity against the
abnormal tissue. The kinetics of such a reaction is represented by an
enzymatic-like Michaelis-Menten two step process. Effective free-energy surface
for the process is further parametrized by the fluctuating energy barrier
between the states of high and low concentration of cancerous cells. By
analyzing the far from equilibrium escape problem across the fluctuating
potential barrier, we determine condtions of the most efficient decay kinetics
of the cancer cell-population in the presence of dichotomously fluctuating
concentration of cytotoxic cells.
|
[
{
"created": "Mon, 29 Mar 2004 09:32:49 GMT",
"version": "v1"
},
{
"created": "Thu, 13 May 2004 13:46:24 GMT",
"version": "v2"
}
] |
2015-06-26
|
[
[
"Ochab-Marcinek",
"Anna",
""
],
[
"Gudowska-Nowak",
"Ewa",
""
]
] |
We study the joint effect of thermal bath fluctuations and an external noise tuning activity of cytotoxic cells on the triggered immune response in a growing cancerous tissue. The immune response is assumed to be primarily mediated by effector cells that develop a cytotoxic activity against the abnormal tissue. The kinetics of such a reaction is represented by an enzymatic-like Michaelis-Menten two step process. Effective free-energy surface for the process is further parametrized by the fluctuating energy barrier between the states of high and low concentration of cancerous cells. By analyzing the far from equilibrium escape problem across the fluctuating potential barrier, we determine condtions of the most efficient decay kinetics of the cancer cell-population in the presence of dichotomously fluctuating concentration of cytotoxic cells.
|
1109.3173
|
Horacio Ceva
|
R.P.J. Perazzo, Laura Hern\'andez, Horacio Ceva, Enrique Burgos,
Jos\'e Ignacio Alvarez-Hamelin
|
Does Phylogenetic Proximity Explain Nestedness in Mutualistic
Ecosystems?
|
9 pages, 4 figures. Keywords: Nested networks; Mutualistic
communities; Phylogenetic proximity; Ultrametricity. arXiv admin note: text
overlap with arXiv:1007.5519
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate how the pattern of contacts between species in mutualistic
ecosystems is affected by the phylogenetic proximity between the species of
each guild. We develop a dynamical model geared to establish the role of such
proximity in the emergence of a nested pattern of contacts. We also define a
parameter that provides a direct measure of the influence of phylogenetic
proximity in a given pattern of contacts. We conclude that although
phylogenetic proximity is compatible with nestedness it can not be claimed to
be a cause of it. We find that nestedness can instead be attributed to a
general rule by which species tend to hold contacts with counterparts that
already have a large number of contacts. If the phylogenetic structure of both
guilds is brought into the analysis, this rule is equivalent to maximize the
phylogenetic diversity of the mutualistic counterparts of species of either
guild.
|
[
{
"created": "Wed, 14 Sep 2011 19:27:28 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Oct 2011 11:35:22 GMT",
"version": "v2"
},
{
"created": "Mon, 17 Oct 2011 18:03:24 GMT",
"version": "v3"
}
] |
2011-10-18
|
[
[
"Perazzo",
"R. P. J.",
""
],
[
"Hernández",
"Laura",
""
],
[
"Ceva",
"Horacio",
""
],
[
"Burgos",
"Enrique",
""
],
[
"Alvarez-Hamelin",
"José Ignacio",
""
]
] |
We investigate how the pattern of contacts between species in mutualistic ecosystems is affected by the phylogenetic proximity between the species of each guild. We develop a dynamical model geared to establish the role of such proximity in the emergence of a nested pattern of contacts. We also define a parameter that provides a direct measure of the influence of phylogenetic proximity in a given pattern of contacts. We conclude that although phylogenetic proximity is compatible with nestedness it can not be claimed to be a cause of it. We find that nestedness can instead be attributed to a general rule by which species tend to hold contacts with counterparts that already have a large number of contacts. If the phylogenetic structure of both guilds is brought into the analysis, this rule is equivalent to maximize the phylogenetic diversity of the mutualistic counterparts of species of either guild.
|
2003.02062
|
Giuseppe Gaeta
|
Giuseppe Gaeta
|
Data analysis for the COVID-19 early dynamics in Northern Italy
|
15 pages, 8 Figures. Analyzed data go until March 2 version 2: typos
corrected, discussion of SIR corrected and enlarged; data analyzed remain the
same
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The COVID-19 epidemics, started in China in January 2020, was recognized to
have reached Italy around February 20; recent estimates show that most probably
the virus circulated in the country already in January, but was not recognized.
Data for the early dynamics of COVID-19 in Northern Italy are analyzed.
|
[
{
"created": "Wed, 4 Mar 2020 13:14:23 GMT",
"version": "v1"
},
{
"created": "Thu, 5 Mar 2020 18:31:36 GMT",
"version": "v2"
}
] |
2020-03-09
|
[
[
"Gaeta",
"Giuseppe",
""
]
] |
The COVID-19 epidemics, started in China in January 2020, was recognized to have reached Italy around February 20; recent estimates show that most probably the virus circulated in the country already in January, but was not recognized. Data for the early dynamics of COVID-19 in Northern Italy are analyzed.
|
2001.03656
|
Sungwoo Ahn
|
Sungwoo Ahn, Leonid L Rubchinsky
|
Temporal patterns of dispersal-induced synchronization in population
dynamics
| null |
J Theor Biol 490:110159, 2020
|
10.1016/j.jtbi.2020.110159
| null |
q-bio.PE nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The mechanisms and properties of synchronization of oscillating ecological
populations attract attention because it is a fairly common phenomenon and
because spatial synchrony may elevate a risk of extinction and may lead to
other environmental impacts. Conditions for stable synchronization in a system
of linearly coupled predator-prey oscillators have been considered in the past.
However, the spatial dispersion coupling may be relatively weak and may not
necessarily lead to a stable, complete synchrony. If the coupling between
oscillators is too weak to induce a stable synchrony, oscillators may be
engaged into intermittent synchrony, when episodes of synchronized dynamics are
interspersed with the episodes of desynchronized dynamics. In the present study
we consider the temporal patterning of this kind of intermittent synchronized
dynamics in a system of two dispersal-coupled Rosenzweig-MacArthur
predator-prey oscillators. We consider the properties of the distributions of
durations of desynchronized intervals and their dependence on the model
parameters. We show that the temporal patterning of synchronous dynamics (an
ecological network phenomenon) may depend on the properties of individual
predator-prey patch (individual oscillator) and may vary independently of the
strength of dispersal. We also show that if the dynamics of predator is slow
relative to the dynamics of the prey (a situation that may promote brief but
large outbreaks), dispersal-coupled predator-prey oscillating populations
exhibit numerous short desynchronizations (as opposed to few long
desynchronizations) and may require weaker dispersal in order to reach strong
synchrony.
|
[
{
"created": "Fri, 10 Jan 2020 20:28:05 GMT",
"version": "v1"
}
] |
2021-04-26
|
[
[
"Ahn",
"Sungwoo",
""
],
[
"Rubchinsky",
"Leonid L",
""
]
] |
The mechanisms and properties of synchronization of oscillating ecological populations attract attention because it is a fairly common phenomenon and because spatial synchrony may elevate a risk of extinction and may lead to other environmental impacts. Conditions for stable synchronization in a system of linearly coupled predator-prey oscillators have been considered in the past. However, the spatial dispersion coupling may be relatively weak and may not necessarily lead to a stable, complete synchrony. If the coupling between oscillators is too weak to induce a stable synchrony, oscillators may be engaged into intermittent synchrony, when episodes of synchronized dynamics are interspersed with the episodes of desynchronized dynamics. In the present study we consider the temporal patterning of this kind of intermittent synchronized dynamics in a system of two dispersal-coupled Rosenzweig-MacArthur predator-prey oscillators. We consider the properties of the distributions of durations of desynchronized intervals and their dependence on the model parameters. We show that the temporal patterning of synchronous dynamics (an ecological network phenomenon) may depend on the properties of individual predator-prey patch (individual oscillator) and may vary independently of the strength of dispersal. We also show that if the dynamics of predator is slow relative to the dynamics of the prey (a situation that may promote brief but large outbreaks), dispersal-coupled predator-prey oscillating populations exhibit numerous short desynchronizations (as opposed to few long desynchronizations) and may require weaker dispersal in order to reach strong synchrony.
|
1507.00327
|
Vince Grolmusz
|
Csaba Kerepesi and Bal\'azs Szalkai and B\'alint Varga and Vince
Grolmusz
|
Comparative Connectomics: Mapping the Inter-Individual Variability of
Connections within the Regions of the Human Brain
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The human braingraph, or connectome is a description of the connections of
the brain: the nodes of the graph correspond to small areas of the gray matter,
and two nodes are connected by an edge if a diffusion MRI-based workflow finds
fibers between those brain areas. We have constructed 1015-vertex graphs from
the diffusion MRI brain images of 395 human subjects and compared the
individual graphs with respect to several different areas of the brain. The
inter-individual variability of the graphs within different brain regions was
discovered and described. We have found that the frontal and the limbic lobes
are more conservative, while the edges in the temporal and occipital lobes are
more diverse. Interestingly, a "hybrid" conservative and diverse distribution
was found in the paracentral lobule and the fusiform gyrus. Smaller cortical
areas were also evaluated: precentral gyri were found to be more conservative,
and the postcentral and the superior temporal gyri to be very diverse.
|
[
{
"created": "Wed, 1 Jul 2015 19:39:51 GMT",
"version": "v1"
}
] |
2015-07-02
|
[
[
"Kerepesi",
"Csaba",
""
],
[
"Szalkai",
"Balázs",
""
],
[
"Varga",
"Bálint",
""
],
[
"Grolmusz",
"Vince",
""
]
] |
The human braingraph, or connectome is a description of the connections of the brain: the nodes of the graph correspond to small areas of the gray matter, and two nodes are connected by an edge if a diffusion MRI-based workflow finds fibers between those brain areas. We have constructed 1015-vertex graphs from the diffusion MRI brain images of 395 human subjects and compared the individual graphs with respect to several different areas of the brain. The inter-individual variability of the graphs within different brain regions was discovered and described. We have found that the frontal and the limbic lobes are more conservative, while the edges in the temporal and occipital lobes are more diverse. Interestingly, a "hybrid" conservative and diverse distribution was found in the paracentral lobule and the fusiform gyrus. Smaller cortical areas were also evaluated: precentral gyri were found to be more conservative, and the postcentral and the superior temporal gyri to be very diverse.
|
0912.2288
|
Hugo Gabriel Eyherabide
|
Hugo Gabriel Eyherabide, Ines Samengo
|
The information transmitted by spike patterns in single neurons
|
To be published in Journal of Physiology Paris 2009
| null |
10.1016/j.jphysparis.2009.11.018
| null |
q-bio.QM q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Spike patterns have been reported to encode sensory information in several
brain areas. Here we assess the role of specific patterns in the neural code,
by comparing the amount of information transmitted with different choices of
the readout neural alphabet. This allows us to rank several alternative
alphabets depending on the amount of information that can be extracted from
them. One can thereby identify the specific patterns that constitute the most
prominent ingredients of the code. We finally discuss the interplay of
categorical and temporal information in the amount of synergy or redundancy in
the neural code.
|
[
{
"created": "Fri, 11 Dec 2009 17:29:02 GMT",
"version": "v1"
}
] |
2009-12-14
|
[
[
"Eyherabide",
"Hugo Gabriel",
""
],
[
"Samengo",
"Ines",
""
]
] |
Spike patterns have been reported to encode sensory information in several brain areas. Here we assess the role of specific patterns in the neural code, by comparing the amount of information transmitted with different choices of the readout neural alphabet. This allows us to rank several alternative alphabets depending on the amount of information that can be extracted from them. One can thereby identify the specific patterns that constitute the most prominent ingredients of the code. We finally discuss the interplay of categorical and temporal information in the amount of synergy or redundancy in the neural code.
|
1809.09757
|
Joaquin Goni
|
Diana O. Svaldi, Joaqu\'in Go\~ni, Apoorva Bharthur Sanjay, Enrico
Amico, Shannon L. Risacher, John D. West, Mario Dzemidzic, Andrew Saykin,
Liana Apostolova
|
Towards Subject and Diagnostic Identifiability in the Alzheimer's
Disease Spectrum based on Functional Connectomes
|
8 pages, 3 tables, 3 figures
|
In: Stoyanov D. et al. (eds) Graphs in Biomedical Image Analysis
and Integrating Medical Imaging and Non-Imaging Modalities. GRAIL 2018,
Beyond MIC 2018. Lecture Notes in Computer Science, vol 11044. Springer, Cham
|
10.1007/978-3-030-00689-1_8
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Alzheimer's disease (AD) is the only major cause of mortality in the world
without an effective disease modifying treatment. Evidence supporting the so
called disconnection hypothesis suggests that functional connectivity
biomarkers may have clinical potential for early detection of AD. However,
known issues with low test-retest reliability and signal to noise in functional
connectivity may prevent accuracy and subsequent predictive capacity. We
validate the utility of a novel principal component based diagnostic
identifiability framework to increase separation in functional connectivity
across the Alzheimer's spectrum by identifying and reconstructing FC using only
AD sensitive components or connectivity modes. We show that this framework (1)
increases test-retest correspondence and (2) allows for better separation, in
functional connectivity, of diagnostic groups both at the whole brain and
individual resting state network level. Finally, we evaluate a posteriori the
association between connectivity mode weights with longitudinal neurocognitive
outcomes.
|
[
{
"created": "Tue, 25 Sep 2018 23:28:32 GMT",
"version": "v1"
}
] |
2018-09-27
|
[
[
"Svaldi",
"Diana O.",
""
],
[
"Goñi",
"Joaquín",
""
],
[
"Sanjay",
"Apoorva Bharthur",
""
],
[
"Amico",
"Enrico",
""
],
[
"Risacher",
"Shannon L.",
""
],
[
"West",
"John D.",
""
],
[
"Dzemidzic",
"Mario",
""
],
[
"Saykin",
"Andrew",
""
],
[
"Apostolova",
"Liana",
""
]
] |
Alzheimer's disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called disconnection hypothesis suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. We validate the utility of a novel principal component based diagnostic identifiability framework to increase separation in functional connectivity across the Alzheimer's spectrum by identifying and reconstructing FC using only AD sensitive components or connectivity modes. We show that this framework (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes.
|
q-bio/0311002
|
Debashish Chowdhury
|
Debashish Chowdhury and Dietrich Stauffer
|
Computer simulations of history of life: speciation, emergence of
complex species from simpler organisms, and extinctions
|
7 pages, including 4 EPS figures, REVTEX
| null |
10.1016/j.physa.2004.05.023
| null |
q-bio.PE
| null |
We propose a generic model of eco-systems, with a {\it hierarchical} food web
structure. In our computer simulations we let the eco-system evolve
continuously for so long that that we can monitor extinctions as well as
speciations over geological time scales. {\it Speciation} leads not only to
horizontal diversification of species at any given trophic level but also to
vertical bio-diversity that accounts for the emergence of complex species from
simpler forms of life. We find that five or six trophic levels appear as the
eco-system evolves for sufficiently long time, starting initially from just one
single level. Moreover, the time intervals between the successive collections
of ecological data is so short that we could also study ``micro''-evolution of
the eco-system, i.e., the birth, ageing and death of individual organisms.
|
[
{
"created": "Tue, 4 Nov 2003 22:13:28 GMT",
"version": "v1"
}
] |
2009-11-10
|
[
[
"Chowdhury",
"Debashish",
""
],
[
"Stauffer",
"Dietrich",
""
]
] |
We propose a generic model of eco-systems, with a {\it hierarchical} food web structure. In our computer simulations we let the eco-system evolve continuously for so long that that we can monitor extinctions as well as speciations over geological time scales. {\it Speciation} leads not only to horizontal diversification of species at any given trophic level but also to vertical bio-diversity that accounts for the emergence of complex species from simpler forms of life. We find that five or six trophic levels appear as the eco-system evolves for sufficiently long time, starting initially from just one single level. Moreover, the time intervals between the successive collections of ecological data is so short that we could also study ``micro''-evolution of the eco-system, i.e., the birth, ageing and death of individual organisms.
|
q-bio/0601004
|
Paul Smolen
|
Paul Smolen, Douglas A. Baxter, and John H. Byrne
|
A Model of the Roles of Essential Kinases in the Induction and
Expression of Late Long-Term Potentiation
|
Accepted to Biophysical Journal. Single PDF, 7 figs included
| null |
10.1529/biophysj.105.072470
| null |
q-bio.NC q-bio.MN
| null |
The induction of late long-term potentiation (L-LTP) involves complex
interactions among second messenger cascades. To gain insights into these
interactions, a mathematical model was developed for L-LTP induction in the CA1
region of the hippocampus. The differential equation-based model represents
actions of protein kinase A (PKA), MAP kinase (MAPK), and CaM kinase II
(CAMKII) in the vicinity of the synapse, and activation of transcription by CaM
kinase IV (CAMKIV) and MAPK. L-LTP is represented by increases in a synaptic
weight. Simulations suggest that steep, supralinear stimulus-response
relationships between stimuli (elevations in [Ca2+]) and kinase activation are
essential for translating brief stimuli into long-lasting gene activation and
synaptic weight increases. Convergence of multiple kinase activities to induce
L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply
with the number of tetanic electrical stimuli. The model simulates tetanic,
theta-burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to
synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of
MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to
delineate mechanisms underlying L-LTP induction and expression. For example,
the cAMP antagonist RpcAMPs, which inhibits L-LTP induction, is predicted to
inhibit ERK activation. The model also appears useful to clarify similarities
and differences between hippocampal L-LTP and long-term synaptic strengthening
in other systems.
|
[
{
"created": "Tue, 3 Jan 2006 23:51:32 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Smolen",
"Paul",
""
],
[
"Baxter",
"Douglas A.",
""
],
[
"Byrne",
"John H.",
""
]
] |
The induction of late long-term potentiation (L-LTP) involves complex interactions among second messenger cascades. To gain insights into these interactions, a mathematical model was developed for L-LTP induction in the CA1 region of the hippocampus. The differential equation-based model represents actions of protein kinase A (PKA), MAP kinase (MAPK), and CaM kinase II (CAMKII) in the vicinity of the synapse, and activation of transcription by CaM kinase IV (CAMKIV) and MAPK. L-LTP is represented by increases in a synaptic weight. Simulations suggest that steep, supralinear stimulus-response relationships between stimuli (elevations in [Ca2+]) and kinase activation are essential for translating brief stimuli into long-lasting gene activation and synaptic weight increases. Convergence of multiple kinase activities to induce L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply with the number of tetanic electrical stimuli. The model simulates tetanic, theta-burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to delineate mechanisms underlying L-LTP induction and expression. For example, the cAMP antagonist RpcAMPs, which inhibits L-LTP induction, is predicted to inhibit ERK activation. The model also appears useful to clarify similarities and differences between hippocampal L-LTP and long-term synaptic strengthening in other systems.
|
1309.4661
|
John Helliwell R
|
Simon W.M Tanley, Kay Diederichs, Loes M.J Kroon-Batenburg, Antoine
M.M Schreurs and John R Helliwell
|
Carboplatin binding to a model protein in non-NaCl conditions to
eliminate partial conversion to cisplatin, and the use of different criteria
to choose the resolution limit
|
14 pages; submitted to Acta Cryst D Biological Crystallography
reference number tz5044
| null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Hen egg white lysozyme (HEWL) co-crystallisation conditions of carboplatin
without sodium chloride (NaCl) have been utilised to eliminate partial
conversion of carboplatin to cisplatin observed previously. Tetragonal HEWL
crystals were successfully obtained in 65% MPD with 0.1M citric acid buffer at
pH 4.0 including DMSO. The X-ray diffraction data resolution to be used for the
model refinement was reviewed using several topical criteria together. The
CC1/2 criterion implemented in XDS led to data being significant to 2.0{\AA},
compared to the data only being able to be processed to 3.0{\AA} using the
Bruker software package (SAINT). Then using paired protein model refinements
and DPI values based on the FreeR value, the resolution limit was fine tuned to
be 2.3{\AA}. Interestingly this was compared with results from the EVAL
software package which gave a resolution limit of 2.2{\AA} solely using
<I/sigI> crossing 2, but 2.8{\AA} based on the Rmerge values (60%). The
structural results showed that carboplatin bound to only the N{\delta} binding
site of His-15 one week after crystal growth, whereas five weeks after crystal
growth, two molecules of carboplatin are bound to the His-15 residue. In
summary several new results have emerged: - firstly non-NaCl conditions showed
a carboplatin molecule bound to His-15 of HEWL; secondly binding of one
molecule of carboplatin was seen after one week of crystal growth and two
molecules were bound after five weeks of crystal growth; and thirdly the use of
several criteria to determine the diffraction resolution limit led to the
successful use of data to higher resolution.
|
[
{
"created": "Wed, 18 Sep 2013 14:40:56 GMT",
"version": "v1"
}
] |
2013-09-19
|
[
[
"Tanley",
"Simon W. M",
""
],
[
"Diederichs",
"Kay",
""
],
[
"Kroon-Batenburg",
"Loes M. J",
""
],
[
"Schreurs",
"Antoine M. M",
""
],
[
"Helliwell",
"John R",
""
]
] |
Hen egg white lysozyme (HEWL) co-crystallisation conditions of carboplatin without sodium chloride (NaCl) have been utilised to eliminate partial conversion of carboplatin to cisplatin observed previously. Tetragonal HEWL crystals were successfully obtained in 65% MPD with 0.1M citric acid buffer at pH 4.0 including DMSO. The X-ray diffraction data resolution to be used for the model refinement was reviewed using several topical criteria together. The CC1/2 criterion implemented in XDS led to data being significant to 2.0{\AA}, compared to the data only being able to be processed to 3.0{\AA} using the Bruker software package (SAINT). Then using paired protein model refinements and DPI values based on the FreeR value, the resolution limit was fine tuned to be 2.3{\AA}. Interestingly this was compared with results from the EVAL software package which gave a resolution limit of 2.2{\AA} solely using <I/sigI> crossing 2, but 2.8{\AA} based on the Rmerge values (60%). The structural results showed that carboplatin bound to only the N{\delta} binding site of His-15 one week after crystal growth, whereas five weeks after crystal growth, two molecules of carboplatin are bound to the His-15 residue. In summary several new results have emerged: - firstly non-NaCl conditions showed a carboplatin molecule bound to His-15 of HEWL; secondly binding of one molecule of carboplatin was seen after one week of crystal growth and two molecules were bound after five weeks of crystal growth; and thirdly the use of several criteria to determine the diffraction resolution limit led to the successful use of data to higher resolution.
|
1906.11924
|
Vedant Sachdeva
|
Vedant Sachdeva, Kabir Husain, Jiming Sheng, Shenshen Wang, Arvind
Murugan
|
Tuning environmental timescales to evolve and maintain generalists
| null | null |
10.1073/pnas.1914586117
| null |
q-bio.PE cond-mat.stat-mech physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Natural environments can present diverse challenges, but some genotypes
remain fit across many environments. Such `generalists' can be hard to evolve,
out-competed by specialists fitter in any particular environment. Here,
inspired by the search for broadly-neutralising antibodies during B-cell
affinity maturation, we demonstrate that environmental changes on an
intermediate timescale can reliably evolve generalists, even when faster or
slower environmental changes are unable to do so. We find that changing
environments on timescales comparable to evolutionary transients in a
population enhances the rate of evolving generalists from specialists, without
enhancing the reverse process. The yield of generalists is further increased in
more complex dynamic environments, such as a `chirp' of increasing frequency.
Our work offers design principles for how non-equilibrium fitness `seascapes'
can dynamically funnel populations to genotypes unobtainable in static
environments.
|
[
{
"created": "Thu, 27 Jun 2019 19:22:53 GMT",
"version": "v1"
}
] |
2020-07-15
|
[
[
"Sachdeva",
"Vedant",
""
],
[
"Husain",
"Kabir",
""
],
[
"Sheng",
"Jiming",
""
],
[
"Wang",
"Shenshen",
""
],
[
"Murugan",
"Arvind",
""
]
] |
Natural environments can present diverse challenges, but some genotypes remain fit across many environments. Such `generalists' can be hard to evolve, out-competed by specialists fitter in any particular environment. Here, inspired by the search for broadly-neutralising antibodies during B-cell affinity maturation, we demonstrate that environmental changes on an intermediate timescale can reliably evolve generalists, even when faster or slower environmental changes are unable to do so. We find that changing environments on timescales comparable to evolutionary transients in a population enhances the rate of evolving generalists from specialists, without enhancing the reverse process. The yield of generalists is further increased in more complex dynamic environments, such as a `chirp' of increasing frequency. Our work offers design principles for how non-equilibrium fitness `seascapes' can dynamically funnel populations to genotypes unobtainable in static environments.
|
1506.04230
|
Almaz Mustafin
|
A. Mustafin
|
Coupling-induced oscillations in two intrinsically quiescent populations
|
24 pages, 4 figures, 1 table, 38 references. Postprint of the
published article. arXiv admin note: substantial text overlap with
arXiv:1409.4404
|
Communications in Nonlinear Science and Numerical Simulation,
2015, vol. 29, no. 1-3, pp. 391-399
|
10.1016/j.cnsns.2015.05.019
| null |
q-bio.PE nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A model of two consumer-resource systems linked by interspecific interference
competition of consumers is considered. The basic assumption of the model is
that the dynamics of the resource is much slower than that of the consumer. In
the absence of interaction each consumer-resource pair has a unique stable
steady state which is completely nonoscillatory. When weakly coupled, the
consumer-resource pairs are shown to exhibit sustained low-frequency
synchronous antiphase relaxation oscillations.
|
[
{
"created": "Sat, 13 Jun 2015 06:23:44 GMT",
"version": "v1"
}
] |
2015-06-16
|
[
[
"Mustafin",
"A.",
""
]
] |
A model of two consumer-resource systems linked by interspecific interference competition of consumers is considered. The basic assumption of the model is that the dynamics of the resource is much slower than that of the consumer. In the absence of interaction each consumer-resource pair has a unique stable steady state which is completely nonoscillatory. When weakly coupled, the consumer-resource pairs are shown to exhibit sustained low-frequency synchronous antiphase relaxation oscillations.
|
1607.01959
|
Shi Gu
|
Shi Gu, Matthew Cieslak, Benjamin Baird, Sarah F. Muldoon, Scott T.
Grafton, Fabio Pasqualetti, Danielle S. Bassett
|
The Energy Landscape of Neurophysiological Activity Implicit in Brain
Network Structure
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A critical mystery in neuroscience lies in determining how anatomical
structure impacts the complex functional dynamics of human thought. How does
large-scale brain circuitry constrain states of neuronal activity and
transitions between those states? We address these questions using a maximum
entropy model of brain dynamics informed by white matter tractography. We
demonstrate that the most probable brain states -- characterized by minimal
energy -- display common activation profiles across brain areas: local
spatially-contiguous sets of brain regions reminiscent of cognitive systems are
co-activated frequently. The predicted activation rate of these systems is
highly correlated with the observed activation rate measured in a separate
resting state fMRI data set, validating the utility of the maximum entropy
model in describing neurophysiologial dynamics. This approach also offers a
formal notion of the energy of activity within a system, and the energy of
activity shared between systems. We observe that within- and between-system
energies cleanly separate cognitive systems into distinct categories, optimized
for differential contributions to integrated v.s. segregated function. These
results support the notion that energetic and structural constraints
circumscribe brain dynamics, offering novel insights into the roles that
cognitive systems play in driving whole-brain activation patterns.
|
[
{
"created": "Thu, 7 Jul 2016 10:54:53 GMT",
"version": "v1"
}
] |
2016-07-08
|
[
[
"Gu",
"Shi",
""
],
[
"Cieslak",
"Matthew",
""
],
[
"Baird",
"Benjamin",
""
],
[
"Muldoon",
"Sarah F.",
""
],
[
"Grafton",
"Scott T.",
""
],
[
"Pasqualetti",
"Fabio",
""
],
[
"Bassett",
"Danielle S.",
""
]
] |
A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of human thought. How does large-scale brain circuitry constrain states of neuronal activity and transitions between those states? We address these questions using a maximum entropy model of brain dynamics informed by white matter tractography. We demonstrate that the most probable brain states -- characterized by minimal energy -- display common activation profiles across brain areas: local spatially-contiguous sets of brain regions reminiscent of cognitive systems are co-activated frequently. The predicted activation rate of these systems is highly correlated with the observed activation rate measured in a separate resting state fMRI data set, validating the utility of the maximum entropy model in describing neurophysiologial dynamics. This approach also offers a formal notion of the energy of activity within a system, and the energy of activity shared between systems. We observe that within- and between-system energies cleanly separate cognitive systems into distinct categories, optimized for differential contributions to integrated v.s. segregated function. These results support the notion that energetic and structural constraints circumscribe brain dynamics, offering novel insights into the roles that cognitive systems play in driving whole-brain activation patterns.
|
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