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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2107.02905
|
Meabh MacMahon
|
M\'eabh MacMahon, Woochang Hwang, Soorin Yim, Eoghan MacMahon,
Alexandre Abraham, Justin Barton, Mukunthan Tharmakulasingam, Paul Bilokon,
Vasanthi Priyadarshini Gaddi, Namshik Han
|
An in silico drug repurposing pipeline to identify drugs with the
potential to inhibit SARS-CoV-2 replication
|
23 pages, 4 figures
|
Informatics in Medicine Unlocked (2023): 101387
|
10.1016/j.imu.2023.101387
| null |
q-bio.QM cs.AI q-bio.MN
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Drug repurposing provides an opportunity to redeploy drugs, which ideally are
already approved for use in humans, for the treatment of other diseases. For
example, the repurposing of dexamethasone and baricitinib has played a crucial
role in saving patient lives during the ongoing SARS-CoV-2 pandemic. There
remains a need to expand therapeutic approaches to prevent life-threatening
complications in patients with COVID-19. Using an in silico approach based on
structural similarity to drugs already in clinical trials for COVID-19,
potential drugs were predicted for repurposing. For a subset of identified
drugs with different targets to their corresponding COVID-19 clinical trial
drug, a mechanism of action analysis was applied to establish whether they
might have a role in inhibiting the replication of SARS-CoV-2. Of sixty drugs
predicted in this study, two with the potential to inhibit SARS-CoV-2
replication were identified using mechanism of action analysis. Triamcinolone
is a corticosteroid that is structurally similar to dexamethasone; gallopamil
is a calcium channel blocker that is structurally similar to verapamil. In
silico approaches indicate possible mechanisms of action for both drugs in
inhibiting SARS-CoV-2 replication. The identification of these drugs as
potentially useful for patients with COVID-19 who are at a higher risk of
developing severe disease supports the use of in silico approaches to
facilitate quick and cost-effective drug repurposing. Such drugs could expand
the number of treatments available to patients who are not protected by
vaccination.
|
[
{
"created": "Mon, 5 Jul 2021 13:08:24 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Nov 2022 15:16:19 GMT",
"version": "v2"
}
] |
2023-11-27
|
[
[
"MacMahon",
"Méabh",
""
],
[
"Hwang",
"Woochang",
""
],
[
"Yim",
"Soorin",
""
],
[
"MacMahon",
"Eoghan",
""
],
[
"Abraham",
"Alexandre",
""
],
[
"Barton",
"Justin",
""
],
[
"Tharmakulasingam",
"Mukunthan",
""
],
[
"Bilokon",
"Paul",
""
],
[
"Gaddi",
"Vasanthi Priyadarshini",
""
],
[
"Han",
"Namshik",
""
]
] |
Drug repurposing provides an opportunity to redeploy drugs, which ideally are already approved for use in humans, for the treatment of other diseases. For example, the repurposing of dexamethasone and baricitinib has played a crucial role in saving patient lives during the ongoing SARS-CoV-2 pandemic. There remains a need to expand therapeutic approaches to prevent life-threatening complications in patients with COVID-19. Using an in silico approach based on structural similarity to drugs already in clinical trials for COVID-19, potential drugs were predicted for repurposing. For a subset of identified drugs with different targets to their corresponding COVID-19 clinical trial drug, a mechanism of action analysis was applied to establish whether they might have a role in inhibiting the replication of SARS-CoV-2. Of sixty drugs predicted in this study, two with the potential to inhibit SARS-CoV-2 replication were identified using mechanism of action analysis. Triamcinolone is a corticosteroid that is structurally similar to dexamethasone; gallopamil is a calcium channel blocker that is structurally similar to verapamil. In silico approaches indicate possible mechanisms of action for both drugs in inhibiting SARS-CoV-2 replication. The identification of these drugs as potentially useful for patients with COVID-19 who are at a higher risk of developing severe disease supports the use of in silico approaches to facilitate quick and cost-effective drug repurposing. Such drugs could expand the number of treatments available to patients who are not protected by vaccination.
|
1407.2732
|
Thierry Rabilloud
|
Sarah Triboulet (LCBM - UMR 5249), Catherine Aude-Garcia (LCBM - UMR
5249), Lucie Armand (INAC), Ad\`ele Gerdil (LFP - URA 2453), H\'el\`ene
Diemer (LSMBO-DSA-IPHC), Fabienne Proamer, V\'eronique Collin-Faure (LCBM -
UMR 5249), Aur\'elie Habert (LFP - URA 2453), Jean-Marc Strub
(LSMBO-DSA-IPHC), Daniel Hanau, Nathalie Herlin (LFP - URA 2453), Marie
Carri\`ere (INAC), Alain Van Dorsselaer (LSMBO-DSA-IPHC), Thierry Rabilloud
(LCBM - UMR 5249)
|
Analysis of cellular responses of macrophages to zinc ions and zinc
oxide nanoparticles: a combined targeted and proteomic approach
| null |
Nanoscale 6, 11 (2014) 6102-14
|
10.1039/c4nr00319e
| null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Two different zinc oxide nanoparticles, as well as zinc ions, are used to
study the cellular responses of the RAW 264 macrophage cell line. A proteomic
screen is used to provide a wide view of the molecular effects of zinc, and the
most prominent results are cross-validated by targeted studies. Furthermore,
the alteration of important macrophage functions (e.g. phagocytosis) by zinc is
also investigated. The intracellular dissolution/uptake of zinc is also studied
to further characterize zinc toxicity. Zinc oxide nanoparticles dissolve
readily in the cells, leading to high intracellular zinc concentrations, mostly
as protein-bound zinc. The proteomic screen reveals a rather weak response in
the oxidative stress response pathway, but a strong response both in the
central metabolism and in the proteasomal protein degradation pathway. Targeted
experiments confirm that carbohydrate catabolism and proteasome are critical
determinants of sensitivity to zinc, which also induces DNA damage. Conversely,
glutathione levels and phagocytosis appear unaffected at moderately toxic zinc
concentrations.
|
[
{
"created": "Thu, 10 Jul 2014 09:14:09 GMT",
"version": "v1"
}
] |
2014-07-11
|
[
[
"Triboulet",
"Sarah",
"",
"LCBM - UMR 5249"
],
[
"Aude-Garcia",
"Catherine",
"",
"LCBM - UMR\n 5249"
],
[
"Armand",
"Lucie",
"",
"INAC"
],
[
"Gerdil",
"Adèle",
"",
"LFP - URA 2453"
],
[
"Diemer",
"Hélène",
"",
"LSMBO-DSA-IPHC"
],
[
"Proamer",
"Fabienne",
"",
"LCBM -\n UMR 5249"
],
[
"Collin-Faure",
"Véronique",
"",
"LCBM -\n UMR 5249"
],
[
"Habert",
"Aurélie",
"",
"LFP - URA 2453"
],
[
"Strub",
"Jean-Marc",
"",
"LSMBO-DSA-IPHC"
],
[
"Hanau",
"Daniel",
"",
"LFP - URA 2453"
],
[
"Herlin",
"Nathalie",
"",
"LFP - URA 2453"
],
[
"Carrière",
"Marie",
"",
"INAC"
],
[
"Van Dorsselaer",
"Alain",
"",
"LSMBO-DSA-IPHC"
],
[
"Rabilloud",
"Thierry",
"",
"LCBM - UMR 5249"
]
] |
Two different zinc oxide nanoparticles, as well as zinc ions, are used to study the cellular responses of the RAW 264 macrophage cell line. A proteomic screen is used to provide a wide view of the molecular effects of zinc, and the most prominent results are cross-validated by targeted studies. Furthermore, the alteration of important macrophage functions (e.g. phagocytosis) by zinc is also investigated. The intracellular dissolution/uptake of zinc is also studied to further characterize zinc toxicity. Zinc oxide nanoparticles dissolve readily in the cells, leading to high intracellular zinc concentrations, mostly as protein-bound zinc. The proteomic screen reveals a rather weak response in the oxidative stress response pathway, but a strong response both in the central metabolism and in the proteasomal protein degradation pathway. Targeted experiments confirm that carbohydrate catabolism and proteasome are critical determinants of sensitivity to zinc, which also induces DNA damage. Conversely, glutathione levels and phagocytosis appear unaffected at moderately toxic zinc concentrations.
|
2202.09618
|
Fahimeh Palizban
|
Farshad Noravesh and Fahimeh Palizban
|
Identifying OCRs in cfDNA WGS Data by Correlation Clustering
|
19 pages
| null | null | null |
q-bio.GN q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the recent decade, the emergence of liquid biopsy has significantly
improved cancer monitoring and detection. Dying cells, including those
originating from tumors, shed their DNA into the bloodstream and contribute to
a pool of circulating fragments called cell-free DNA (cfDNA). Identifying the
tissue origin of these DNA fragments from their epigenetic features has
implications in various clinical contexts. Open chromatin regions (OCRs) are
important epigenetic features of DNA that reflect cell types of origin.
Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides
insights into tissue-specific and disease-specific regulatory mechanisms.
Integration of genomic and epigenomic features for cancer detection by liquid
biopsy has previously been reported. However, many multimodal analyses require
large amounts of cfDNA input and/or multiple types of experiments to cover the
genomic and epigenomic aspects of a single sample which is cost and time
prohibitive. Thus, methods that capture genomic and epigenomic profiles in a
single experiment type with low input requirements are of importance.
Predicting OCRs from whole genome sequencing (WGS) data is one such approach.
Here, we applied a correlation clustering algorithm to predict OCRs. We used
local sequencing depth as input to our algorithm. Multiple processing steps
were then applied as follows: count normalization, discrete Fourier transform
conversion, graph construction, graph cut optimization by linear programming,
and clustering. To validate the proposed method, we compared the output of our
predictions (OCR vs. non-OCR) with previously validated open chromatin regions
related to human blood samples of the ATAC-db. The percentage of overlap
between them is greater than 67%.
|
[
{
"created": "Sat, 19 Feb 2022 14:54:18 GMT",
"version": "v1"
},
{
"created": "Sat, 24 Dec 2022 21:17:48 GMT",
"version": "v2"
}
] |
2022-12-27
|
[
[
"Noravesh",
"Farshad",
""
],
[
"Palizban",
"Fahimeh",
""
]
] |
In the recent decade, the emergence of liquid biopsy has significantly improved cancer monitoring and detection. Dying cells, including those originating from tumors, shed their DNA into the bloodstream and contribute to a pool of circulating fragments called cell-free DNA (cfDNA). Identifying the tissue origin of these DNA fragments from their epigenetic features has implications in various clinical contexts. Open chromatin regions (OCRs) are important epigenetic features of DNA that reflect cell types of origin. Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides insights into tissue-specific and disease-specific regulatory mechanisms. Integration of genomic and epigenomic features for cancer detection by liquid biopsy has previously been reported. However, many multimodal analyses require large amounts of cfDNA input and/or multiple types of experiments to cover the genomic and epigenomic aspects of a single sample which is cost and time prohibitive. Thus, methods that capture genomic and epigenomic profiles in a single experiment type with low input requirements are of importance. Predicting OCRs from whole genome sequencing (WGS) data is one such approach. Here, we applied a correlation clustering algorithm to predict OCRs. We used local sequencing depth as input to our algorithm. Multiple processing steps were then applied as follows: count normalization, discrete Fourier transform conversion, graph construction, graph cut optimization by linear programming, and clustering. To validate the proposed method, we compared the output of our predictions (OCR vs. non-OCR) with previously validated open chromatin regions related to human blood samples of the ATAC-db. The percentage of overlap between them is greater than 67%.
|
2405.00128
|
Fanhao Wang
|
Fanhao Wang, Yuzhe Wang, Laiyi Feng, Changsheng Zhang, Luhua Lai
|
Target-Specific De Novo Peptide Binder Design with DiffPepBuilder
| null | null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite the exciting progress in target-specific de novo protein binder
design, peptide binder design remains challenging due to the flexibility of
peptide structures and the scarcity of protein-peptide complex structure data.
In this study, we curated a large synthetic dataset, referred to as PepPC-F,
from the abundant protein-protein interface data and developed DiffPepBuilder,
a de novo target-specific peptide binder generation method that utilizes an
SE(3)-equivariant diffusion model trained on PepPC-F to co-design peptide
sequences and structures. DiffPepBuilder also introduces disulfide bonds to
stabilize the generated peptide structures. We tested DiffPepBuilder on 30
experimentally verified strong peptide binders with available protein-peptide
complex structures. DiffPepBuilder was able to effectively recall the native
structures and sequences of the peptide ligands and to generate novel peptide
binders with improved binding free energy. We subsequently conducted de novo
generation case studies on three targets. In both the regeneration test and
case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with
ProteinMPNN, in terms of sequence and structure recall, interface quality, and
structural diversity. Molecular dynamics simulations confirmed that the
introduction of disulfide bonds enhanced the structural rigidity and binding
performance of the generated peptides. As a general peptide binder de novo
design tool, DiffPepBuilder can be used to design peptide binders for given
protein targets with three dimensional and binding site information.
|
[
{
"created": "Tue, 30 Apr 2024 18:24:56 GMT",
"version": "v1"
}
] |
2024-05-02
|
[
[
"Wang",
"Fanhao",
""
],
[
"Wang",
"Yuzhe",
""
],
[
"Feng",
"Laiyi",
""
],
[
"Zhang",
"Changsheng",
""
],
[
"Lai",
"Luhua",
""
]
] |
Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic dataset, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to co-design peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein-peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three dimensional and binding site information.
|
0903.0127
|
Alain Destexhe
|
Olivier Marre, Sami El Boustani, Yves Fregnac and Alain Destexhe
|
Prediction of spatio-temporal patterns of neural activity from pairwise
correlations
|
Physical Preview Letters (in press, 2009)
|
Physical Review Letters 102: 138101, 2009.
|
10.1103/PhysRevLett.102.138101
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We designed a model-based analysis to predict the occurrence of population
patterns in distributed spiking activity. Using a maximum entropy principle
with a Markovian assumption, we obtain a model that accounts for both spatial
and temporal pairwise correlations among neurons. This model is tested on data
generated with a Glauber spin-glass system and is shown to correctly predict
the occurrence probabilities of spatio-temporal patterns significantly better
than Ising models taking into account only pairwise correlations. This increase
of predictability was also observed on experimental data recorded in parietal
cortex during slow-wave sleep. This approach can also be used to generate
surrogates that reproduce the spatial and temporal correlations of a given data
set.
|
[
{
"created": "Sun, 1 Mar 2009 07:59:09 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Marre",
"Olivier",
""
],
[
"Boustani",
"Sami El",
""
],
[
"Fregnac",
"Yves",
""
],
[
"Destexhe",
"Alain",
""
]
] |
We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatio-temporal patterns significantly better than Ising models taking into account only pairwise correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.
|
1602.03093
|
Nadav M. Shnerb
|
Matan Danino, Nadav M. Shnerb, Sandro Azaele, William E. Kunin and
David A. Kessler
|
The effect of environmental stochasticity on species richness in neutral
communities
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Environmental stochasticity is known to be a destabilizing factor, increasing
abundance fluctuations and extinction rates of populations. However, the
stability of a community may benefit from the differential response of species
to environmental variations due to the storage effect. This paper provides a
systematic and comprehensive discussion of these two contradicting tendencies,
using the metacommunity version of the recently proposed time-average neutral
model of biodiversity which incorporates environmental stochasticity and
demographic noise and allows for extinction and speciation. We show that the
incorporation of demographic noise into the model is essential to its
applicability, yielding realistic behavior of the system when fitness
variations are relatively weak. The dependence of species richness on the
strength of environmental stochasticity changes sign when the correlation time
of the environmental variations increases. This transition marks the point at
which the storage effect no longer succeeds in stabilizing the community.
|
[
{
"created": "Tue, 9 Feb 2016 17:48:43 GMT",
"version": "v1"
}
] |
2016-02-10
|
[
[
"Danino",
"Matan",
""
],
[
"Shnerb",
"Nadav M.",
""
],
[
"Azaele",
"Sandro",
""
],
[
"Kunin",
"William E.",
""
],
[
"Kessler",
"David A.",
""
]
] |
Environmental stochasticity is known to be a destabilizing factor, increasing abundance fluctuations and extinction rates of populations. However, the stability of a community may benefit from the differential response of species to environmental variations due to the storage effect. This paper provides a systematic and comprehensive discussion of these two contradicting tendencies, using the metacommunity version of the recently proposed time-average neutral model of biodiversity which incorporates environmental stochasticity and demographic noise and allows for extinction and speciation. We show that the incorporation of demographic noise into the model is essential to its applicability, yielding realistic behavior of the system when fitness variations are relatively weak. The dependence of species richness on the strength of environmental stochasticity changes sign when the correlation time of the environmental variations increases. This transition marks the point at which the storage effect no longer succeeds in stabilizing the community.
|
1610.07510
|
Nicholas Battista
|
Nicholas A. Battista, Andrea N. Lane, Jiandong Liu, Laura A. Miller
|
Fluid Dynamics in Heart Development: Effects of Hematocrit and
Trabeculation
|
30 pages, 14 figures. arXiv admin note: substantial text overlap with
arXiv:1601.07917
| null |
10.1093/imammb/dqx018
| null |
q-bio.TO physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent \emph{in vivo} experiments have illustrated the importance of
understanding the hemodynamics of heart morphogenesis. In particular,
ventricular trabeculation is governed by a delicate interaction between
hemodynamic forces, myocardial activity, and morphogen gradients, all of which
are coupled to genetic regulatory networks. The underlying hemodynamics at the
stage of development in which the trabeculae form is particularly complex,
given the balance between inertial and viscous forces. Small perturbations in
the geometry, scale, and steadiness of the flow can lead to changes in the
overall flow structures and chemical morphogen gradients, including the local
direction of flow, the transport of morphogens, and the formation of vortices.
The immersed boundary method was used to solve the fluid-structure interaction
problem of fluid flow moving through a two chambered heart of a zebrafish
(\emph{Danio rerio}), with a trabeculated ventricle, at $96\ hpf$ (hours post
fertilization). Trabeculae heights and hematocrit were varied, and simulations
were conducted for two orders of magnitude of Womersley number, extending
beyond the biologically relevant range ($0.2$ -- $12.0$). Both intracardial and
intertrabecular vortices formed in the ventricle for biologically relevant
parameter values. The bifurcation from smooth streaming flow to vortical flow
depends upon the trabeculae geometry, hematocrit, and $Wo$. This work shows the
importance of hematocrit and geometry in determining the bulk flow patterns in
the heart at this stage of development.
|
[
{
"created": "Mon, 24 Oct 2016 17:55:33 GMT",
"version": "v1"
}
] |
2018-09-19
|
[
[
"Battista",
"Nicholas A.",
""
],
[
"Lane",
"Andrea N.",
""
],
[
"Liu",
"Jiandong",
""
],
[
"Miller",
"Laura A.",
""
]
] |
Recent \emph{in vivo} experiments have illustrated the importance of understanding the hemodynamics of heart morphogenesis. In particular, ventricular trabeculation is governed by a delicate interaction between hemodynamic forces, myocardial activity, and morphogen gradients, all of which are coupled to genetic regulatory networks. The underlying hemodynamics at the stage of development in which the trabeculae form is particularly complex, given the balance between inertial and viscous forces. Small perturbations in the geometry, scale, and steadiness of the flow can lead to changes in the overall flow structures and chemical morphogen gradients, including the local direction of flow, the transport of morphogens, and the formation of vortices. The immersed boundary method was used to solve the fluid-structure interaction problem of fluid flow moving through a two chambered heart of a zebrafish (\emph{Danio rerio}), with a trabeculated ventricle, at $96\ hpf$ (hours post fertilization). Trabeculae heights and hematocrit were varied, and simulations were conducted for two orders of magnitude of Womersley number, extending beyond the biologically relevant range ($0.2$ -- $12.0$). Both intracardial and intertrabecular vortices formed in the ventricle for biologically relevant parameter values. The bifurcation from smooth streaming flow to vortical flow depends upon the trabeculae geometry, hematocrit, and $Wo$. This work shows the importance of hematocrit and geometry in determining the bulk flow patterns in the heart at this stage of development.
|
1804.11083
|
Jicun Wang-Michelitsch
|
Jicun Wang-Michelitsch, Thomas M Michelitsch
|
Development of pediatric myeloid leukemia may be related to the
repeatedbone-remodeling during bone-growth
|
30 pages, 5 figures
| null | null | null |
q-bio.CB q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) are two major
formsof leukemia developed from myeloid cells (MCs). To understand why AML and
CML occurin children, we analyzed the causes and the mechanism of cell
transformation of a MC. I. Forthe MCs in marrow cavity, repeated
bone-remodeling during bone-growth may be a source ofcell injuries. II. As a
type of blood cell, a MC may have higher survivability from DNAchanges and
require obtaining fewer cancerous properties for cell transformation than a
tissuecell. III. Point DNA mutations (PDMs) and chromosome changes (CCs) are
the two majortypes of DNA changes. CCs have three subtypes by effects on a
cell: great effect CCs(GECCs), mild-effect CCs (MECCs), and intermediate-effect
CCs (IECCs). A GECC affectsone or more genes and can alone trigger cell
transformation. PDMs/MECCs are mostly mildand can accumulate in cells. Some of
the PDMs/MECCs contribute to cell transformation. AnIECC affects one or more
genes and participates in cell transformation. IV. Based on II andIII, we
hypothesize that a MC may have two pathways on transformation: a slow and
anaccelerated. Slow pathway is driven by accumulation of PDMs/MECCs.
Accelerated pathwayis driven by accumulation of PDMs/MECCs/IECC(s). A
transformation via slow pathwayoccurs at old age; whereas that via accelerated
pathway occurs at any age. Thus, CML andpediatric AML may develop via
accelerated pathway, and adult AML may develop via bothpathways. In conclusion,
pediatric AML and CML may develop as a result of transformationof a MC via
accelerated pathway; and repeated bone-remodeling for bone-growth may be
atrigger for the transformation of a MC in a child.
|
[
{
"created": "Mon, 30 Apr 2018 08:44:32 GMT",
"version": "v1"
}
] |
2018-05-01
|
[
[
"Wang-Michelitsch",
"Jicun",
""
],
[
"Michelitsch",
"Thomas M",
""
]
] |
Acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) are two major formsof leukemia developed from myeloid cells (MCs). To understand why AML and CML occurin children, we analyzed the causes and the mechanism of cell transformation of a MC. I. Forthe MCs in marrow cavity, repeated bone-remodeling during bone-growth may be a source ofcell injuries. II. As a type of blood cell, a MC may have higher survivability from DNAchanges and require obtaining fewer cancerous properties for cell transformation than a tissuecell. III. Point DNA mutations (PDMs) and chromosome changes (CCs) are the two majortypes of DNA changes. CCs have three subtypes by effects on a cell: great effect CCs(GECCs), mild-effect CCs (MECCs), and intermediate-effect CCs (IECCs). A GECC affectsone or more genes and can alone trigger cell transformation. PDMs/MECCs are mostly mildand can accumulate in cells. Some of the PDMs/MECCs contribute to cell transformation. AnIECC affects one or more genes and participates in cell transformation. IV. Based on II andIII, we hypothesize that a MC may have two pathways on transformation: a slow and anaccelerated. Slow pathway is driven by accumulation of PDMs/MECCs. Accelerated pathwayis driven by accumulation of PDMs/MECCs/IECC(s). A transformation via slow pathwayoccurs at old age; whereas that via accelerated pathway occurs at any age. Thus, CML andpediatric AML may develop via accelerated pathway, and adult AML may develop via bothpathways. In conclusion, pediatric AML and CML may develop as a result of transformationof a MC via accelerated pathway; and repeated bone-remodeling for bone-growth may be atrigger for the transformation of a MC in a child.
|
1107.4572
|
Dante Chialvo
|
Enzo Tagliazucchi, Pablo Balenzuela, Daniel Fraiman, Dante R. Chialvo
|
Point process analysis of large-scale brain fMRI dynamics
| null | null | null | null |
q-bio.NC cond-mat.dis-nn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Functional magnetic resonance imaging (fMRI) techniques have contributed
significantly to our understanding of brain function. Current methods are based
on the analysis of \emph{gradual and continuous} changes in the brain blood
oxygenated level dependent (BOLD) signal. Departing from that approach, recent
work has shown that equivalent results can be obtained by inspecting only the
relatively large amplitude BOLD signal peaks, suggesting that relevant
information can be condensed in \emph{discrete} events. This idea is further
explored here to demonstrate how brain dynamics at resting state can be
captured just by the timing and location of such events, i.e., in terms of a
spatiotemporal point process. As a proof of principle, we show that the resting
state networks (RSN) maps can be extracted from such point processes.
Furthermore, the analysis uncovers avalanches of activity which are ruled by
the same dynamical and statistical properties described previously for neuronal
events at smaller scales. Given the demonstrated functional relevance of the
resting state brain dynamics, its representation as a discrete process might
facilitate large scale analysis of brain function both in health and disease.
|
[
{
"created": "Fri, 22 Jul 2011 16:52:47 GMT",
"version": "v1"
}
] |
2011-07-25
|
[
[
"Tagliazucchi",
"Enzo",
""
],
[
"Balenzuela",
"Pablo",
""
],
[
"Fraiman",
"Daniel",
""
],
[
"Chialvo",
"Dante R.",
""
]
] |
Functional magnetic resonance imaging (fMRI) techniques have contributed significantly to our understanding of brain function. Current methods are based on the analysis of \emph{gradual and continuous} changes in the brain blood oxygenated level dependent (BOLD) signal. Departing from that approach, recent work has shown that equivalent results can be obtained by inspecting only the relatively large amplitude BOLD signal peaks, suggesting that relevant information can be condensed in \emph{discrete} events. This idea is further explored here to demonstrate how brain dynamics at resting state can be captured just by the timing and location of such events, i.e., in terms of a spatiotemporal point process. As a proof of principle, we show that the resting state networks (RSN) maps can be extracted from such point processes. Furthermore, the analysis uncovers avalanches of activity which are ruled by the same dynamical and statistical properties described previously for neuronal events at smaller scales. Given the demonstrated functional relevance of the resting state brain dynamics, its representation as a discrete process might facilitate large scale analysis of brain function both in health and disease.
|
2211.11214
|
Haitao Lin
|
Haitao Lin, Yufei Huang, Odin Zhang, Siqi Ma, Meng Liu, Xuanjing Li,
Lirong Wu, Jishui Wang, Tingjun Hou, Stan Z. Li
|
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
|
13 pages
| null | null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Generating molecules that bind to specific proteins is an important but
challenging task in drug discovery. Previous works usually generate atoms in an
auto-regressive way, where element types and 3D coordinates of atoms are
generated one by one. However, in real-world molecular systems, the
interactions among atoms in an entire molecule are global, leading to the
energy function pair-coupled among atoms. With such energy-based consideration,
the modeling of probability should be based on joint distributions, rather than
sequentially conditional ones. Thus, the unnatural sequentially auto-regressive
modeling of molecule generation is likely to violate the physical rules, thus
resulting in poor properties of the generated molecules. In this work, a
generative diffusion model for molecular 3D structures based on target proteins
as contextual constraints is established, at a full-atom level in a
non-autoregressive way. Given a designated 3D protein binding site, our model
learns the generative process that denoises both element types and 3D
coordinates of an entire molecule, with an equivariant network. Experimentally,
the proposed method shows competitive performance compared with prevailing
works in terms of high affinity with proteins and appropriate molecule sizes as
well as other drug properties such as drug-likeness of the generated molecules.
|
[
{
"created": "Mon, 21 Nov 2022 07:02:15 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Dec 2022 06:18:09 GMT",
"version": "v2"
},
{
"created": "Sat, 17 Dec 2022 08:29:11 GMT",
"version": "v3"
},
{
"created": "Sun, 14 Jul 2024 06:41:36 GMT",
"version": "v4"
}
] |
2024-07-16
|
[
[
"Lin",
"Haitao",
""
],
[
"Huang",
"Yufei",
""
],
[
"Zhang",
"Odin",
""
],
[
"Ma",
"Siqi",
""
],
[
"Liu",
"Meng",
""
],
[
"Li",
"Xuanjing",
""
],
[
"Wu",
"Lirong",
""
],
[
"Wang",
"Jishui",
""
],
[
"Hou",
"Tingjun",
""
],
[
"Li",
"Stan Z.",
""
]
] |
Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one. However, in real-world molecular systems, the interactions among atoms in an entire molecule are global, leading to the energy function pair-coupled among atoms. With such energy-based consideration, the modeling of probability should be based on joint distributions, rather than sequentially conditional ones. Thus, the unnatural sequentially auto-regressive modeling of molecule generation is likely to violate the physical rules, thus resulting in poor properties of the generated molecules. In this work, a generative diffusion model for molecular 3D structures based on target proteins as contextual constraints is established, at a full-atom level in a non-autoregressive way. Given a designated 3D protein binding site, our model learns the generative process that denoises both element types and 3D coordinates of an entire molecule, with an equivariant network. Experimentally, the proposed method shows competitive performance compared with prevailing works in terms of high affinity with proteins and appropriate molecule sizes as well as other drug properties such as drug-likeness of the generated molecules.
|
0902.4417
|
Grzegorz A Rempala
|
Gheorghe Craciun, Casian Pantea, and Grzegorz A. Rempala
|
A Dimension Reduction Method for Inferring Biochemical Networks
|
12 pages and 4 figures
| null | null |
MCG BBCB Tech Report No 1-09
|
q-bio.MN q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present herein an extension of an algebraic statistical method for
inferring biochemical reaction networks from experimental data, proposed
recently in [3]. This extension allows us to analyze reaction networks that are
not necessarily full-dimensional, i.e., the dimension of their stoichiometric
space is smaller than the number of species. Specifically, we propose to
augment the original algebraic-statistical algorithm for network inference with
a preprocessing step that identifies the subspace spanned by the correct
reaction vectors, within the space spanned by the species. This dimension
reduction step is based on principal component analysis of the input data and
its relationship with various subspaces generated by sets of candidate reaction
vectors. Simulated examples are provided to illustrate the main ideas involved
in implementing this method, and to asses its performance.
|
[
{
"created": "Wed, 25 Feb 2009 16:37:32 GMT",
"version": "v1"
}
] |
2009-02-26
|
[
[
"Craciun",
"Gheorghe",
""
],
[
"Pantea",
"Casian",
""
],
[
"Rempala",
"Grzegorz A.",
""
]
] |
We present herein an extension of an algebraic statistical method for inferring biochemical reaction networks from experimental data, proposed recently in [3]. This extension allows us to analyze reaction networks that are not necessarily full-dimensional, i.e., the dimension of their stoichiometric space is smaller than the number of species. Specifically, we propose to augment the original algebraic-statistical algorithm for network inference with a preprocessing step that identifies the subspace spanned by the correct reaction vectors, within the space spanned by the species. This dimension reduction step is based on principal component analysis of the input data and its relationship with various subspaces generated by sets of candidate reaction vectors. Simulated examples are provided to illustrate the main ideas involved in implementing this method, and to asses its performance.
|
2305.18804
|
Valentina Giunchiglia
|
Valentina Giunchiglia, Sharon Curtis, Stephen Smith, Naomi Allen, Adam
Hampshire
|
Neural correlates of cognitive ability and visuo-motor speed: validation
of IDoCT on UK Biobank Data
| null | null | null | null |
q-bio.NC q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Automated online and App-based cognitive assessment tasks are becoming
increasingly popular in large-scale cohorts and biobanks due to advantages in
affordability, scalability and repeatability. However, the summary scores that
such tasks generate typically conflate the cognitive processes that are the
intended focus of assessment with basic visuomotor speeds, testing device
latencies and speed-accuracy tradeoffs. This lack of precision presents a
fundamental limitation when studying brain-behaviour associations. Previously,
we developed a novel modelling approach that leverages continuous performance
recordings from large-cohort studies to achieve an iterative decomposition of
cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive
abilities, and device and visuomotor latencies, whilst recalibrating
trial-difficulty scales. Here, we further validate the IDoCT approach with UK
BioBank imaging data. First, we examine whether IDoCT can improve ability
distributions and trial-difficulty scales from an adaptive picture-vocabulary
task (PVT). Then, we confirm that the resultant visuomotor and cognitive
estimates associate more robustly with age and education than the original PVT
scores. Finally, we conduct a multimodal brain-wide association study with
free-text analysis to test whether the brain regions that predict the IDoCT
estimates have the expected differential relationships with visuomotor vs.
language and memory labels within the broader imaging literature. Our results
support the view that the rich performance timecourses recorded during
computerised cognitive assessments can be leveraged with modelling frameworks
like IDoCT to provide estimates of human cognitive abilities that have superior
distributions, re-test reliabilities and brain-wide associations.
|
[
{
"created": "Tue, 30 May 2023 07:41:13 GMT",
"version": "v1"
}
] |
2023-05-31
|
[
[
"Giunchiglia",
"Valentina",
""
],
[
"Curtis",
"Sharon",
""
],
[
"Smith",
"Stephen",
""
],
[
"Allen",
"Naomi",
""
],
[
"Hampshire",
"Adam",
""
]
] |
Automated online and App-based cognitive assessment tasks are becoming increasingly popular in large-scale cohorts and biobanks due to advantages in affordability, scalability and repeatability. However, the summary scores that such tasks generate typically conflate the cognitive processes that are the intended focus of assessment with basic visuomotor speeds, testing device latencies and speed-accuracy tradeoffs. This lack of precision presents a fundamental limitation when studying brain-behaviour associations. Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuomotor latencies, whilst recalibrating trial-difficulty scales. Here, we further validate the IDoCT approach with UK BioBank imaging data. First, we examine whether IDoCT can improve ability distributions and trial-difficulty scales from an adaptive picture-vocabulary task (PVT). Then, we confirm that the resultant visuomotor and cognitive estimates associate more robustly with age and education than the original PVT scores. Finally, we conduct a multimodal brain-wide association study with free-text analysis to test whether the brain regions that predict the IDoCT estimates have the expected differential relationships with visuomotor vs. language and memory labels within the broader imaging literature. Our results support the view that the rich performance timecourses recorded during computerised cognitive assessments can be leveraged with modelling frameworks like IDoCT to provide estimates of human cognitive abilities that have superior distributions, re-test reliabilities and brain-wide associations.
|
2007.14437
|
Javier Alfonso Pinedo Onofre Md Facs
|
Javier Alfonso Pinedo-Onofre, Euridice Robles-Perez, Erika Sagrario
Pe\~na-Mirabal, Jose Amado Hernandez-Carrillo, and Jose Luis Tellez-Becerra
|
Giant solitary fibrous tumor of the pleura
| null |
Pinedo-Onofre JA, Robles-P\'erez E, Pe\~na-Mirabal ES,
Hern\'andez-Carrillo JA, T\'ellez-Becerra JL. Tumor fibroso solitario gigante
de la pleura. Cir Cir 2010;78(1):31-43
| null | null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Background: Solitary fibrous tumor is the second primary malignancy of the
pleura and can reach up to 39cm in diameter; to be referred to as giant it must
occupy at least 40% of the affected hemithorax. Although this tumor usually
shows a benign behavior malignancy criteria have been described. The aim of the
study was to assess the initial evaluation diagnostic procedures surgical
management treatment outcome and prognosis. Methods: We performed a descriptive
observational longitudinal and retrospective study from 2002 to 2006 on
patients who underwent surgery with a diagnosis of giant solitary fibrous tumor
of the pleura. Results: Six patients were included; 83.3% were females. Mean
age was 48 years. All patients were symptomatic mainly dyspnea cough and chest
pain; 66.7% were left-sided. Preoperative angiography and embolization were
performed in 83.3% cases with successful surgical resection. The predominant
blood supply was derived from the internal mammalian artery. Intraoperative
complication rate was 17%. A vascular pedicle was found in 66.7%. The largest
lesion was 40cm in diameter and weighed 4500g. Only one case showed high
mitotic activity. Mean follow-up to date is 14 months. Conclusions:
Symptomatology found was consistent with previous reports but in higher
percentages. Accurate diagnosis is critical because surgical resection involves
a potential cure; long-term follow-up is mandatory. Preoperative embolization
is recommended due to tumor size.
|
[
{
"created": "Tue, 28 Jul 2020 19:11:45 GMT",
"version": "v1"
}
] |
2020-07-30
|
[
[
"Pinedo-Onofre",
"Javier Alfonso",
""
],
[
"Robles-Perez",
"Euridice",
""
],
[
"Peña-Mirabal",
"Erika Sagrario",
""
],
[
"Hernandez-Carrillo",
"Jose Amado",
""
],
[
"Tellez-Becerra",
"Jose Luis",
""
]
] |
Background: Solitary fibrous tumor is the second primary malignancy of the pleura and can reach up to 39cm in diameter; to be referred to as giant it must occupy at least 40% of the affected hemithorax. Although this tumor usually shows a benign behavior malignancy criteria have been described. The aim of the study was to assess the initial evaluation diagnostic procedures surgical management treatment outcome and prognosis. Methods: We performed a descriptive observational longitudinal and retrospective study from 2002 to 2006 on patients who underwent surgery with a diagnosis of giant solitary fibrous tumor of the pleura. Results: Six patients were included; 83.3% were females. Mean age was 48 years. All patients were symptomatic mainly dyspnea cough and chest pain; 66.7% were left-sided. Preoperative angiography and embolization were performed in 83.3% cases with successful surgical resection. The predominant blood supply was derived from the internal mammalian artery. Intraoperative complication rate was 17%. A vascular pedicle was found in 66.7%. The largest lesion was 40cm in diameter and weighed 4500g. Only one case showed high mitotic activity. Mean follow-up to date is 14 months. Conclusions: Symptomatology found was consistent with previous reports but in higher percentages. Accurate diagnosis is critical because surgical resection involves a potential cure; long-term follow-up is mandatory. Preoperative embolization is recommended due to tumor size.
|
2407.17286
|
Azadeh Hassanpour
|
Azadeh Hassanpour, Johannes Geibel, Henner Simianer, Antje Rohde,
Torsten Pook
|
Optimization of breeding program design through stochastic simulation
with evolutionary algorithms
| null | null | null | null |
q-bio.QM cs.NE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The effective planning and allocation of resources in modern breeding
programs is a complex task. Breeding program design and operational management
have a major impact on the success of a breeding program and changing
parameters such as the number of selected/phenotyped/genotyped individuals will
impact genetic gain, genetic diversity, and costs. As a result, careful
assessment and balancing of design parameters is crucial, considering the
trade-offs between different breeding goals and associated costs. In a previous
study, we optimized the resource allocation strategy in a dairy cattle breeding
scheme via the combination of stochastic simulations and kernel regression,
aiming to maximize a target function containing genetic gain and the inbreeding
rate under a given budget. However, the high number of simulations required
when using the proposed kernel regression method to optimize a breeding program
with many parameters weakens the effectiveness of such a method. In this work,
we are proposing an optimization framework that builds on the concepts of
kernel regression but additionally makes use of an evolutionary algorithm to
allow for a more effective and general optimization. The key idea is to
consider a set of potential parameterizations of the breeding program, evaluate
their performance based on stochastic simulations, and use these outputs to
derive new parametrization to test in an iterative procedure. The evolutionary
algorithm was implemented in a Snakemake pipeline to allow for efficient
scaling on large distributed computing platforms. The algorithm achieved
convergence to the same optimum with a massively reduced number of simulations.
Thereby, the incorporation of class variables and accounting for a higher
number of parameters in the optimization pipeline leads to substantially
reduced computing time and better scaling for the desired optimization of a
breeding program.
|
[
{
"created": "Mon, 22 Jul 2024 21:10:20 GMT",
"version": "v1"
}
] |
2024-07-25
|
[
[
"Hassanpour",
"Azadeh",
""
],
[
"Geibel",
"Johannes",
""
],
[
"Simianer",
"Henner",
""
],
[
"Rohde",
"Antje",
""
],
[
"Pook",
"Torsten",
""
]
] |
The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, considering the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameterizations of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parametrization to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake pipeline to allow for efficient scaling on large distributed computing platforms. The algorithm achieved convergence to the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization pipeline leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.
|
2407.00754
|
Yue Wang
|
Yue Wang, Peng Zheng, Yu-Chen Cheng, Zikun Wang, Aleksandr Aravkin
|
Gene Regulatory Network Inference with Covariance Dynamics
| null | null | null | null |
q-bio.MN
|
http://creativecommons.org/licenses/by/4.0/
|
Determining gene regulatory network (GRN) structure is a central problem in
biology, with a variety of inference methods available for different types of
data. For a widely prevalent and challenging use case, namely single-cell gene
expression data measured after intervention at multiple time points with
unknown joint distributions, there is only one known specifically developed
method, which does not fully utilize the rich information contained in this
data type. We develop an inference method for the GRN in this case, netWork
infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to
model the dynamics of the covariance matrix, and solve this dynamics as an
optimization problem to determine the regulatory relationships. To evaluate its
effectiveness, we compare WENDY with other inference methods using synthetic
data and experimental data. Our results demonstrate that WENDY performs well
across different data sets.
|
[
{
"created": "Mon, 17 Jun 2024 18:48:35 GMT",
"version": "v1"
}
] |
2024-07-02
|
[
[
"Wang",
"Yue",
""
],
[
"Zheng",
"Peng",
""
],
[
"Cheng",
"Yu-Chen",
""
],
[
"Wang",
"Zikun",
""
],
[
"Aravkin",
"Aleksandr",
""
]
] |
Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.
|
1106.5386
|
Dong-Ping Yang Dong-Ping Yang
|
Dong-Ping Yang, Hai Lin, Chen-Xu Wu and Jianwei Shuai
|
Topological conditions of scale-free networks for cooperation to evolve
|
4 pages, 4 figures
| null | null | null |
q-bio.PE cond-mat.stat-mech nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Evolutionary game theory is employed to study topological conditions of
scale-free networks for the evolution of cooperation. We show that Apollonian
Networks (ANs) are perfect scale-free networks, on which cooperation can spread
to all individuals, even though there are initially only 3 or 4 hubs occupied
by cooperators and all the others by defectors. Local topological features such
as degree, clustering coefficient, gradient as well as topology potential are
adopted to analyze the advantages of ANs in cooperation enhancement.
Furthermore, a degree-skeleton underlying ANs is uncovered for understanding
the cooperation diffusion. Constructing this kind degree-skeleton for random
scale-free networks promotes cooperation level close to that of
Barab\'asi-Albert networks, which gives deeper insights into the origin of the
latter on organization and further promotion of cooperation.
|
[
{
"created": "Mon, 27 Jun 2011 13:19:02 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Jul 2011 08:44:52 GMT",
"version": "v2"
}
] |
2015-03-19
|
[
[
"Yang",
"Dong-Ping",
""
],
[
"Lin",
"Hai",
""
],
[
"Wu",
"Chen-Xu",
""
],
[
"Shuai",
"Jianwei",
""
]
] |
Evolutionary game theory is employed to study topological conditions of scale-free networks for the evolution of cooperation. We show that Apollonian Networks (ANs) are perfect scale-free networks, on which cooperation can spread to all individuals, even though there are initially only 3 or 4 hubs occupied by cooperators and all the others by defectors. Local topological features such as degree, clustering coefficient, gradient as well as topology potential are adopted to analyze the advantages of ANs in cooperation enhancement. Furthermore, a degree-skeleton underlying ANs is uncovered for understanding the cooperation diffusion. Constructing this kind degree-skeleton for random scale-free networks promotes cooperation level close to that of Barab\'asi-Albert networks, which gives deeper insights into the origin of the latter on organization and further promotion of cooperation.
|
2107.03231
|
Arthur Prat-Carrabin
|
Arthur Prat-Carrabin, Florent Meyniel, Misha Tsodyks and Rava Azeredo
da Silveira
|
Biases and Variability from Costly Bayesian Inference
|
17 pages, 4 figures
|
Entropy 2021, 23(5), 603
|
10.3390/e23050603
| null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
When humans infer underlying probabilities from stochastic observations, they
exhibit biases and variability that cannot be explained on the basis of sound,
Bayesian manipulations of probability. This is especially salient when beliefs
are updated as a function of sequential observations. We introduce a
theoretical framework in which biases and variability emerge from a trade-off
between Bayesian inference and a cognitive cost of carrying out probabilistic
computations. We consider two forms of the cost: a precision cost and an
unpredictability cost; these penalize beliefs that are less entropic and less
deterministic, respectively. We apply our framework to the case of a Bernoulli
variable: the bias of a coin is inferred from a sequence of coin flips.
Theoretical predictions are qualitatively different depending on the form of
the cost. A precision cost induces overestimation of small probabilities on
average and a limited memory of past observations, and, consequently, a
fluctuating bias. An unpredictability cost induces underestimation of small
probabilities and a fixed bias that remains appreciable even for nearly
unbiased observations. The case of a fair (equiprobable) coin, however, is
singular, with non-trivial and slow fluctuations of the inferred bias. The
proposed framework of costly Bayesian inference illustrates the richness of a
'resource-rational' (or 'bounded-rational') picture of seemingly irrational
human cognition.
|
[
{
"created": "Wed, 7 Jul 2021 14:02:38 GMT",
"version": "v1"
}
] |
2021-07-08
|
[
[
"Prat-Carrabin",
"Arthur",
""
],
[
"Meyniel",
"Florent",
""
],
[
"Tsodyks",
"Misha",
""
],
[
"da Silveira",
"Rava Azeredo",
""
]
] |
When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and a cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities on average and a limited memory of past observations, and, consequently, a fluctuating bias. An unpredictability cost induces underestimation of small probabilities and a fixed bias that remains appreciable even for nearly unbiased observations. The case of a fair (equiprobable) coin, however, is singular, with non-trivial and slow fluctuations of the inferred bias. The proposed framework of costly Bayesian inference illustrates the richness of a 'resource-rational' (or 'bounded-rational') picture of seemingly irrational human cognition.
|
2004.00979
|
G\"unter Klambauer
|
Markus Hofmarcher, Andreas Mayr, Elisabeth Rumetshofer, Peter Ruch,
Philipp Renz, Johannes Schimunek, Philipp Seidl, Andreu Vall, Michael
Widrich, Sepp Hochreiter, G\"unter Klambauer
|
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors
using deep neural networks
|
Additional results added. Various corrections to formulations and
typos
| null | null | null |
q-bio.BM cs.LG q-bio.QM stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Due to the current severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
We conducted a large-scale virtual screening for small molecules that are
potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural
network trained on more than 220M data points across 3.6M molecules from three
public drug-discovery databases. With ChemAI, we screened and ranked one
billion molecules from the ZINC database for favourable effects against CoV-2.
We then reduced the result to the 30,000 top-ranked compounds, which are
readily accessible and purchasable via the ZINC database. Additionally, we
screened the DrugBank using ChemAI to allow for drug repurposing, which would
be a fast way towards a therapy. We provide these top-ranked compounds of ZINC
and DrugBank as a library for further screening with bioassays at
https://github.com/ml-jku/sars-cov-inhibitors-chemai.
|
[
{
"created": "Wed, 25 Mar 2020 15:24:09 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Apr 2020 09:10:24 GMT",
"version": "v2"
},
{
"created": "Mon, 17 Aug 2020 15:58:09 GMT",
"version": "v3"
}
] |
2020-08-18
|
[
[
"Hofmarcher",
"Markus",
""
],
[
"Mayr",
"Andreas",
""
],
[
"Rumetshofer",
"Elisabeth",
""
],
[
"Ruch",
"Peter",
""
],
[
"Renz",
"Philipp",
""
],
[
"Schimunek",
"Johannes",
""
],
[
"Seidl",
"Philipp",
""
],
[
"Vall",
"Andreu",
""
],
[
"Widrich",
"Michael",
""
],
[
"Hochreiter",
"Sepp",
""
],
[
"Klambauer",
"Günter",
""
]
] |
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai.
|
1601.05249
|
KaYin Leung
|
Ka Yin Leung and Odo Diekmann
|
Dangerous connections: on binding site models of infectious disease
dynamics
| null |
J Math Biol, 74: 619-671 (2017)
|
10.1007/s00285-016-1037-x
| null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We formulate models for the spread of infection on networks that are amenable
to analysis in the large population limit. We distinguish three different
levels: (1) binding sites, (2) individuals, and (3) the population. In the
tradition of Physiologically Structured Population Models, the formulation
starts on the individual level. Influences from the `outside world' on an
individual are captured by environmental variables. These environmental
variables are population level quantities. A key characteristic of the network
models is that individuals can be decomposed into a number of conditionally
independent components: each individual has a fixed number of `binding sites'
for partners. The Markov chain dynamics of binding sites are described by only
a few equations. In particular, individual-level probabilities are obtained
from binding-site-level probabilities by combinatorics while population-level
quantities are obtained by averaging over individuals in the population. Thus
we are able to characterize population-level epidemiological quantities, such
as $R_0$, $r$, the final size, and the endemic equilibrium, in terms of the
corresponding variables.
|
[
{
"created": "Wed, 20 Jan 2016 12:18:32 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Jan 2016 00:15:51 GMT",
"version": "v2"
}
] |
2017-03-03
|
[
[
"Leung",
"Ka Yin",
""
],
[
"Diekmann",
"Odo",
""
]
] |
We formulate models for the spread of infection on networks that are amenable to analysis in the large population limit. We distinguish three different levels: (1) binding sites, (2) individuals, and (3) the population. In the tradition of Physiologically Structured Population Models, the formulation starts on the individual level. Influences from the `outside world' on an individual are captured by environmental variables. These environmental variables are population level quantities. A key characteristic of the network models is that individuals can be decomposed into a number of conditionally independent components: each individual has a fixed number of `binding sites' for partners. The Markov chain dynamics of binding sites are described by only a few equations. In particular, individual-level probabilities are obtained from binding-site-level probabilities by combinatorics while population-level quantities are obtained by averaging over individuals in the population. Thus we are able to characterize population-level epidemiological quantities, such as $R_0$, $r$, the final size, and the endemic equilibrium, in terms of the corresponding variables.
|
2407.09089
|
Chun Ka Wong
|
Chun-Ka Wong, Ali Choo, Eugene C. C. Cheng, Wing-Chun San, Kelvin
Chak-Kong Cheng, Yee-Man Lau, Minqing Lin, Fei Li, Wei-Hao Liang, Song-Yan
Liao, Kwong-Man Ng, Ivan Fan-Ngai Hung, Hung-Fat Tse, Jason Wing-Hon Wong
|
Lomics: Generation of Pathways and Gene Sets using Large Language Models
for Transcriptomic Analysis
| null | null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Interrogation of biological pathways is an integral part of omics data
analysis. Large language models (LLMs) enable the generation of custom pathways
and gene sets tailored to specific scientific questions. These targeted sets
are significantly smaller than traditional pathway enrichment analysis
libraries, reducing multiple hypothesis testing and potentially enhancing
statistical power. Lomics (Large Language Models for Omics Studies) v1.0 is a
python-based bioinformatics toolkit that streamlines the generation of pathways
and gene sets for transcriptomic analysis. It operates in three steps: 1)
deriving relevant pathways based on the researcher's scientific question, 2)
generating valid gene sets for each pathway, and 3) outputting the results as
.GMX files. Lomics also provides explanations for pathway selections.
Consistency and accuracy are ensured through iterative processes, JSON format
validation, and HUGO Gene Nomenclature Committee (HGNC) gene symbol
verification. Lomics serves as a foundation for integrating LLMs into omics
research, potentially improving the specificity and efficiency of pathway
analysis.
|
[
{
"created": "Fri, 12 Jul 2024 08:34:45 GMT",
"version": "v1"
}
] |
2024-07-15
|
[
[
"Wong",
"Chun-Ka",
""
],
[
"Choo",
"Ali",
""
],
[
"Cheng",
"Eugene C. C.",
""
],
[
"San",
"Wing-Chun",
""
],
[
"Cheng",
"Kelvin Chak-Kong",
""
],
[
"Lau",
"Yee-Man",
""
],
[
"Lin",
"Minqing",
""
],
[
"Li",
"Fei",
""
],
[
"Liang",
"Wei-Hao",
""
],
[
"Liao",
"Song-Yan",
""
],
[
"Ng",
"Kwong-Man",
""
],
[
"Hung",
"Ivan Fan-Ngai",
""
],
[
"Tse",
"Hung-Fat",
""
],
[
"Wong",
"Jason Wing-Hon",
""
]
] |
Interrogation of biological pathways is an integral part of omics data analysis. Large language models (LLMs) enable the generation of custom pathways and gene sets tailored to specific scientific questions. These targeted sets are significantly smaller than traditional pathway enrichment analysis libraries, reducing multiple hypothesis testing and potentially enhancing statistical power. Lomics (Large Language Models for Omics Studies) v1.0 is a python-based bioinformatics toolkit that streamlines the generation of pathways and gene sets for transcriptomic analysis. It operates in three steps: 1) deriving relevant pathways based on the researcher's scientific question, 2) generating valid gene sets for each pathway, and 3) outputting the results as .GMX files. Lomics also provides explanations for pathway selections. Consistency and accuracy are ensured through iterative processes, JSON format validation, and HUGO Gene Nomenclature Committee (HGNC) gene symbol verification. Lomics serves as a foundation for integrating LLMs into omics research, potentially improving the specificity and efficiency of pathway analysis.
|
1610.01949
|
Jing Xu
|
Amanda J. Tan, Dail E. Chapman, Linda S. Hirst, and Jing Xu
|
Understanding the role of transport velocity in biomotor-powered
microtubule spool assembly
| null |
Royal Society of Chemistry Advances, 6, 79143-79146 (2016)
|
10.1039/C6RA19094D
| null |
q-bio.BM physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We examined the sensitivity of microtubule spools to transport velocity.
Perhaps surprisingly, we determined that the steady-state number and size of
spools remained constant over a seven-fold range of velocities. Our data on the
kinetics of spool assembly further suggest that the main mechanisms underlying
spool growth vary during assembly.
|
[
{
"created": "Thu, 6 Oct 2016 17:03:56 GMT",
"version": "v1"
}
] |
2016-10-07
|
[
[
"Tan",
"Amanda J.",
""
],
[
"Chapman",
"Dail E.",
""
],
[
"Hirst",
"Linda S.",
""
],
[
"Xu",
"Jing",
""
]
] |
We examined the sensitivity of microtubule spools to transport velocity. Perhaps surprisingly, we determined that the steady-state number and size of spools remained constant over a seven-fold range of velocities. Our data on the kinetics of spool assembly further suggest that the main mechanisms underlying spool growth vary during assembly.
|
2004.13809
|
Aneta Polewko-Klim
|
Aneta Polewko-Klim, Witold R. Rudnicki
|
Analysis of ensemble feature selection for correlated high-dimensional
RNA-Seq cancer data
|
14 pages, 1 table, 29 figure, submitted to International Conference
on Computational Science, Amsterdam 2020
| null | null | null |
q-bio.GN cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Discovery of diagnostic and prognostic molecular markers is important and
actively pursued the research field in cancer research. For complex diseases,
this process is often performed using Machine Learning. The current study
compares two approaches for the discovery of relevant variables: by application
of a single feature selection algorithm, versus by an ensemble of diverse
algorithms. These approaches are used to identify variables that are relevant
discerning of four cancer types using RNA-seq profiles from the Cancer Genome
Atlas. The comparison is carried out in two directions: evaluating the
predictive performance of models and monitoring the stability of selected
variables. The most informative features are identified using a four feature
selection algorithms, namely U-test, ReliefF, and two variants of the MDFS
algorithm. Discerning normal and tumor tissues is performed using the Random
Forest algorithm. The highest stability of the feature set was obtained when
U-test was used. Unfortunately, models built on feature sets obtained from the
ensemble of feature selection algorithms were no better than for models
developed on feature sets obtained from individual algorithms. On the other
hand, the feature selectors leading to the best classification results varied
between data sets.
|
[
{
"created": "Tue, 28 Apr 2020 20:38:53 GMT",
"version": "v1"
}
] |
2020-04-30
|
[
[
"Polewko-Klim",
"Aneta",
""
],
[
"Rudnicki",
"Witold R.",
""
]
] |
Discovery of diagnostic and prognostic molecular markers is important and actively pursued the research field in cancer research. For complex diseases, this process is often performed using Machine Learning. The current study compares two approaches for the discovery of relevant variables: by application of a single feature selection algorithm, versus by an ensemble of diverse algorithms. These approaches are used to identify variables that are relevant discerning of four cancer types using RNA-seq profiles from the Cancer Genome Atlas. The comparison is carried out in two directions: evaluating the predictive performance of models and monitoring the stability of selected variables. The most informative features are identified using a four feature selection algorithms, namely U-test, ReliefF, and two variants of the MDFS algorithm. Discerning normal and tumor tissues is performed using the Random Forest algorithm. The highest stability of the feature set was obtained when U-test was used. Unfortunately, models built on feature sets obtained from the ensemble of feature selection algorithms were no better than for models developed on feature sets obtained from individual algorithms. On the other hand, the feature selectors leading to the best classification results varied between data sets.
|
2002.06514
|
Shubhankar Patankar
|
Shubhankar P. Patankar, Jason Z. Kim, Fabio Pasqualetti, and Danielle
S. Bassett
|
Path-dependent connectivity, not modularity, consistently predicts
controllability of structural brain networks
|
32 pages, 7 figures in main text, and 22 pages, 11 figures in
supplement
| null |
10.1162/netn_a_00157
| null |
q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
The human brain displays rich communication dynamics that are thought to be
particularly well-reflected in its marked community structure. Yet, the precise
relationship between community structure in structural brain networks and the
communication dynamics that can emerge therefrom is not well-understood. In
addition to offering insight into the structure-function relationship of
networked systems, such an understanding is a critical step towards the ability
to manipulate the brain's large-scale dynamical activity in a targeted manner.
We investigate the role of community structure in the controllability of
structural brain networks. At the region level, we find that certain network
measures of community structure are sometimes statistically correlated with
measures of linear controllability. However, we then demonstrate that this
relationship depends on the distribution of network edge weights. We highlight
the complexity of the relationship between community structure and
controllability by performing numerical simulations using canonical graph
models with varying mesoscale architectures and edge weight distributions.
Finally, we demonstrate that weighted subgraph centrality, a measure rooted in
the graph spectrum, and which captures higher-order graph architecture, is a
stronger and more consistent predictor of controllability. Our study
contributes to an understanding of how the brain's diverse mesoscale structure
supports transient communication dynamics.
|
[
{
"created": "Sun, 16 Feb 2020 06:07:17 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Jul 2020 00:55:45 GMT",
"version": "v2"
},
{
"created": "Fri, 18 Dec 2020 08:49:17 GMT",
"version": "v3"
}
] |
2020-12-23
|
[
[
"Patankar",
"Shubhankar P.",
""
],
[
"Kim",
"Jason Z.",
""
],
[
"Pasqualetti",
"Fabio",
""
],
[
"Bassett",
"Danielle S.",
""
]
] |
The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well-understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step towards the ability to manipulate the brain's large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that weighted subgraph centrality, a measure rooted in the graph spectrum, and which captures higher-order graph architecture, is a stronger and more consistent predictor of controllability. Our study contributes to an understanding of how the brain's diverse mesoscale structure supports transient communication dynamics.
|
2008.02475
|
Martin Rypdal
|
Kristoffer Rypdal and Martin Rypdal
|
A parsimonious description and cross-country analysis of COVID-19
epidemic curve
|
27 pages, 14 figures
| null | null | null |
q-bio.PE physics.soc-ph q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a given country, the cumulative death toll of the first wave of the
COVID-19 epidemic follows a sigmoid curve as a function of time. In most cases,
the curve is well described by the Gompertz function, which is characterized by
two essential parameters, the initial growth rate and the decay rate as the
first epidemic wave subsides. These parameters are determined by socioeconomic
factors and the countermeasures to halt the epidemic. The Gompertz model
implies that the total death toll depends exponentially, and hence very
sensitively, on the ratio between these rates. The remarkably different
epidemic curves for the first epidemic wave in Sweden and Norway and many other
countries are classified and discussed in this framework, and their usefulness
for the planning of mitigation strategies is discussed.
|
[
{
"created": "Thu, 6 Aug 2020 06:44:18 GMT",
"version": "v1"
}
] |
2020-08-07
|
[
[
"Rypdal",
"Kristoffer",
""
],
[
"Rypdal",
"Martin",
""
]
] |
In a given country, the cumulative death toll of the first wave of the COVID-19 epidemic follows a sigmoid curve as a function of time. In most cases, the curve is well described by the Gompertz function, which is characterized by two essential parameters, the initial growth rate and the decay rate as the first epidemic wave subsides. These parameters are determined by socioeconomic factors and the countermeasures to halt the epidemic. The Gompertz model implies that the total death toll depends exponentially, and hence very sensitively, on the ratio between these rates. The remarkably different epidemic curves for the first epidemic wave in Sweden and Norway and many other countries are classified and discussed in this framework, and their usefulness for the planning of mitigation strategies is discussed.
|
1308.5124
|
Luke Tweedy
|
Luke Tweedy, B\"orn Meier, J\"urgen Stephan, Doris Heinrich, Robert G.
Endres
|
Distinct cell shapes determine accurate chemotaxis
| null | null | null | null |
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The behaviour of an organism often reflects a strategy for coping with its
environment. Such behaviour in higher organisms can often be reduced to a few
stereotyped modes of movement due to physiological limitations, but finding
such modes in amoeboid cells is more difficult as they lack these constraints.
Here, we examine cell shape and movement in starved Dictyostelium amoebae
during migration toward a chemoattractant in a microfluidic chamber. We show
that the incredible variety in amoeboid shape across a population can be
reduced to a few modes of variation. Interestingly, cells use distinct modes
depending on the applied chemical gradient, with specific cell shapes
associated with shallow, difficult-to-sense gradients. Modelling and drug
treatment reveals that these behaviours are intrinsically linked with accurate
sensing at the physical limit. Since similar behaviours are observed in a
diverse range of cell types, we propose that cell shape and behaviour are
conserved traits.
|
[
{
"created": "Fri, 23 Aug 2013 13:32:28 GMT",
"version": "v1"
}
] |
2013-08-26
|
[
[
"Tweedy",
"Luke",
""
],
[
"Meier",
"Börn",
""
],
[
"Stephan",
"Jürgen",
""
],
[
"Heinrich",
"Doris",
""
],
[
"Endres",
"Robert G.",
""
]
] |
The behaviour of an organism often reflects a strategy for coping with its environment. Such behaviour in higher organisms can often be reduced to a few stereotyped modes of movement due to physiological limitations, but finding such modes in amoeboid cells is more difficult as they lack these constraints. Here, we examine cell shape and movement in starved Dictyostelium amoebae during migration toward a chemoattractant in a microfluidic chamber. We show that the incredible variety in amoeboid shape across a population can be reduced to a few modes of variation. Interestingly, cells use distinct modes depending on the applied chemical gradient, with specific cell shapes associated with shallow, difficult-to-sense gradients. Modelling and drug treatment reveals that these behaviours are intrinsically linked with accurate sensing at the physical limit. Since similar behaviours are observed in a diverse range of cell types, we propose that cell shape and behaviour are conserved traits.
|
q-bio/0608030
|
Carla Carvalho
|
Ekaterini Vourvouhaki, Carla Carvalho, Paulo Aguiar
|
Model for Osteosarcoma-9 as a Potent Factor in Cell Survivor and
Resistance to Apoptosis
|
24 pages, 16 pictures, version accepted for publication in PRE
|
Phys. Rev. E 76, 011926 (2007)
|
10.1103/PhysRevE.76.011926
|
DF/IST-5.2007
|
q-bio.SC
| null |
In this paper we use a simple toy model to explore the function of the gene
Osteosarcoma-9. We are in particular interested in understanding the role of
this gene as a potent anti-apoptotic factor. The theoretical description is
constrained by experimental data from induction of apoptosis in cells where
OS-9 is overexpressed. The data available suggest that OS-9 promotes cell
viability and confers resistance to apoptosis, potentially implicating OS-9 in
the survival of cancer cells. Three different apoptosis inducing mechanisms
were tested and are here modelled. More complex and realistic models are also
discussed.
|
[
{
"created": "Tue, 15 Aug 2006 19:54:52 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Jan 2007 19:36:18 GMT",
"version": "v2"
},
{
"created": "Wed, 12 Sep 2007 08:48:20 GMT",
"version": "v3"
}
] |
2007-09-12
|
[
[
"Vourvouhaki",
"Ekaterini",
""
],
[
"Carvalho",
"Carla",
""
],
[
"Aguiar",
"Paulo",
""
]
] |
In this paper we use a simple toy model to explore the function of the gene Osteosarcoma-9. We are in particular interested in understanding the role of this gene as a potent anti-apoptotic factor. The theoretical description is constrained by experimental data from induction of apoptosis in cells where OS-9 is overexpressed. The data available suggest that OS-9 promotes cell viability and confers resistance to apoptosis, potentially implicating OS-9 in the survival of cancer cells. Three different apoptosis inducing mechanisms were tested and are here modelled. More complex and realistic models are also discussed.
|
1701.06871
|
Colby Long
|
Colby Long and Laura Kubatko
|
Identifiability and Reconstructibility of Species Phylogenies Under a
Modified Coalescent
|
18 pages, 5 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Coalescent models of evolution account for incomplete lineage sorting by
specifying a species tree parameter which determines a distribution on gene
trees. It has been shown that the unrooted topology of the species tree
parameter of the multispecies coalescent is generically identifiable. Moreover,
a statistically consistent reconstruction method called SVDQuartets has been
developed to recover this parameter. In this paper, we describe a modified
multispecies coalescent model that allows for varying effective population size
and violations of the molecular clock. We show that the unrooted topology of
the species tree for these models is generically identifiable and that
SVDQuartets is still a statistically consistent method for inferring this
parameter.
|
[
{
"created": "Tue, 24 Jan 2017 13:50:30 GMT",
"version": "v1"
}
] |
2017-01-25
|
[
[
"Long",
"Colby",
""
],
[
"Kubatko",
"Laura",
""
]
] |
Coalescent models of evolution account for incomplete lineage sorting by specifying a species tree parameter which determines a distribution on gene trees. It has been shown that the unrooted topology of the species tree parameter of the multispecies coalescent is generically identifiable. Moreover, a statistically consistent reconstruction method called SVDQuartets has been developed to recover this parameter. In this paper, we describe a modified multispecies coalescent model that allows for varying effective population size and violations of the molecular clock. We show that the unrooted topology of the species tree for these models is generically identifiable and that SVDQuartets is still a statistically consistent method for inferring this parameter.
|
2209.11199
|
Yuuki Matsushita
|
Yuuki Matsushita, Kunihiko Kaneko
|
Generic Adaptation by Fast Chaotic Exploration and Slow Feedback
Fixation
|
5pages, 6 figures
| null | null | null |
q-bio.QM nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Living systems adapt to various environmental conditions by changing their
internal states. Inspired by gene expression and epigenetic modification
dynamics, we herein propose a generic mechanism for adaptation by combining
fast oscillatory dynamics and a slower feedback fixation process. Through
extensive model simulations, we reveal that fast chaotic dynamics serve as
global searching for adapted states fixed by slower dynamics. The mechanism
improves as the number of elements is increased. Relevance to cellular
adaptation and optimization in artificial neural networks is also discussed
herein.
|
[
{
"created": "Wed, 21 Sep 2022 15:56:30 GMT",
"version": "v1"
}
] |
2022-09-23
|
[
[
"Matsushita",
"Yuuki",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] |
Living systems adapt to various environmental conditions by changing their internal states. Inspired by gene expression and epigenetic modification dynamics, we herein propose a generic mechanism for adaptation by combining fast oscillatory dynamics and a slower feedback fixation process. Through extensive model simulations, we reveal that fast chaotic dynamics serve as global searching for adapted states fixed by slower dynamics. The mechanism improves as the number of elements is increased. Relevance to cellular adaptation and optimization in artificial neural networks is also discussed herein.
|
2004.10060
|
Tommaso Alberti
|
Tommaso Alberti and Davide Faranda
|
On the uncertainty of real-time predictions of epidemic growths: a
COVID-19 case study for China and Italy
|
24 pages, 6 figures, Preprint submitted to Communications in
Nonlinear Science and Numerical Simulation
| null |
10.1016/j.cnsns.2020.105372
| null |
q-bio.PE physics.soc-ph
|
http://creativecommons.org/licenses/by/4.0/
|
While COVID-19 is rapidly propagating around the globe, the need for
providing real-time forecasts of the epidemics pushes fits of dynamical and
statistical models to available data beyond their capabilities. Here we focus
on statistical predictions of COVID-19 infections performed by fitting
asymptotic distributions to actual data. By taking as a case-study the epidemic
evolution of total COVID-19 infections in Chinese provinces and Italian
regions, we find that predictions are characterized by large uncertainties at
the early stages of the epidemic growth. Those uncertainties significantly
reduce after the epidemics peak is reached. Differences in the uncertainty of
the forecasts at a regional level can be used to highlight the delay in the
spread of the virus. Our results warn that long term extrapolation of epidemics
counts must be handled with extreme care as they crucially depend not only on
the quality of data, but also on the stage of the epidemics, due to the
intrinsically non-linear nature of the underlying dynamics. These results
suggest that real-time epidemiological projections should include wide
uncertainty ranges and urge for the needs of compiling high-quality datasets of
infections counts, including asymptomatic patients.
|
[
{
"created": "Mon, 20 Apr 2020 16:18:57 GMT",
"version": "v1"
}
] |
2020-06-08
|
[
[
"Alberti",
"Tommaso",
""
],
[
"Faranda",
"Davide",
""
]
] |
While COVID-19 is rapidly propagating around the globe, the need for providing real-time forecasts of the epidemics pushes fits of dynamical and statistical models to available data beyond their capabilities. Here we focus on statistical predictions of COVID-19 infections performed by fitting asymptotic distributions to actual data. By taking as a case-study the epidemic evolution of total COVID-19 infections in Chinese provinces and Italian regions, we find that predictions are characterized by large uncertainties at the early stages of the epidemic growth. Those uncertainties significantly reduce after the epidemics peak is reached. Differences in the uncertainty of the forecasts at a regional level can be used to highlight the delay in the spread of the virus. Our results warn that long term extrapolation of epidemics counts must be handled with extreme care as they crucially depend not only on the quality of data, but also on the stage of the epidemics, due to the intrinsically non-linear nature of the underlying dynamics. These results suggest that real-time epidemiological projections should include wide uncertainty ranges and urge for the needs of compiling high-quality datasets of infections counts, including asymptomatic patients.
|
2401.09490
|
Shiyu Wang
|
Jiayu Chang, Shiyu Wang, Chen Ling, Zhaohui Qin, Liang Zhao
|
Gene-associated Disease Discovery Powered by Large Language Models
|
This is the official paper accepted by AAAI 2024 Workshop on Large
Language Models for Biological Discoveries
| null | null | null |
q-bio.QM cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
The intricate relationship between genetic variation and human diseases has
been a focal point of medical research, evidenced by the identification of risk
genes regarding specific diseases. The advent of advanced genome sequencing
techniques has significantly improved the efficiency and cost-effectiveness of
detecting these genetic markers, playing a crucial role in disease diagnosis
and forming the basis for clinical decision-making and early risk assessment.
To overcome the limitations of existing databases that record disease-gene
associations from existing literature, which often lack real-time updates, we
propose a novel framework employing Large Language Models (LLMs) for the
discovery of diseases associated with specific genes. This framework aims to
automate the labor-intensive process of sifting through medical literature for
evidence linking genetic variations to diseases, thereby enhancing the
efficiency of disease identification. Our approach involves using LLMs to
conduct literature searches, summarize relevant findings, and pinpoint diseases
related to specific genes. This paper details the development and application
of our LLM-powered framework, demonstrating its potential in streamlining the
complex process of literature retrieval and summarization to identify diseases
associated with specific genetic variations.
|
[
{
"created": "Tue, 16 Jan 2024 21:03:10 GMT",
"version": "v1"
}
] |
2024-01-19
|
[
[
"Chang",
"Jiayu",
""
],
[
"Wang",
"Shiyu",
""
],
[
"Ling",
"Chen",
""
],
[
"Qin",
"Zhaohui",
""
],
[
"Zhao",
"Liang",
""
]
] |
The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases. The advent of advanced genome sequencing techniques has significantly improved the efficiency and cost-effectiveness of detecting these genetic markers, playing a crucial role in disease diagnosis and forming the basis for clinical decision-making and early risk assessment. To overcome the limitations of existing databases that record disease-gene associations from existing literature, which often lack real-time updates, we propose a novel framework employing Large Language Models (LLMs) for the discovery of diseases associated with specific genes. This framework aims to automate the labor-intensive process of sifting through medical literature for evidence linking genetic variations to diseases, thereby enhancing the efficiency of disease identification. Our approach involves using LLMs to conduct literature searches, summarize relevant findings, and pinpoint diseases related to specific genes. This paper details the development and application of our LLM-powered framework, demonstrating its potential in streamlining the complex process of literature retrieval and summarization to identify diseases associated with specific genetic variations.
|
1604.01329
|
Gao-De Li Dr
|
Gao-De Li
|
DNA to DNA transcription might exist in eukaryotic cells
|
4 pages
| null | null | null |
q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Till now, in biological sciences, the term, transcription, mainly refers to
DNA to RNA transcription. But our recently published experimental findings
obtained from Plasmodium falciparum strongly suggest the existence of DNA to
DNA transcription in the genome of eukaryotic cells, which could shed some
light on the functions of certain noncoding DNA in the human and other
eukaryotic genomes.
|
[
{
"created": "Tue, 5 Apr 2016 16:56:51 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Apr 2016 19:43:38 GMT",
"version": "v2"
},
{
"created": "Mon, 11 Apr 2016 18:18:15 GMT",
"version": "v3"
},
{
"created": "Wed, 11 May 2016 19:19:45 GMT",
"version": "v4"
},
{
"created": "Tue, 17 May 2016 10:51:10 GMT",
"version": "v5"
},
{
"created": "Mon, 29 Jan 2018 09:01:57 GMT",
"version": "v6"
}
] |
2018-01-30
|
[
[
"Li",
"Gao-De",
""
]
] |
Till now, in biological sciences, the term, transcription, mainly refers to DNA to RNA transcription. But our recently published experimental findings obtained from Plasmodium falciparum strongly suggest the existence of DNA to DNA transcription in the genome of eukaryotic cells, which could shed some light on the functions of certain noncoding DNA in the human and other eukaryotic genomes.
|
2310.02152
|
Dominik Klepl
|
Dominik Klepl, Min Wu, Fei He
|
Graph Neural Network-based EEG Classification: A Survey
|
14 pages, 3 figures
| null | null | null |
q-bio.NC cs.LG q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Graph neural networks (GNN) are increasingly used to classify EEG for tasks
such as emotion recognition, motor imagery and neurological diseases and
disorders. A wide range of methods have been proposed to design GNN-based
classifiers. Therefore, there is a need for a systematic review and
categorisation of these approaches. We exhaustively search the published
literature on this topic and derive several categories for comparison. These
categories highlight the similarities and differences among the methods. The
results suggest a prevalence of spectral graph convolutional layers over
spatial. Additionally, we identify standard forms of node features, with the
most popular being the raw EEG signal and differential entropy. Our results
summarise the emerging trends in GNN-based approaches for EEG classification.
Finally, we discuss several promising research directions, such as exploring
the potential of transfer learning methods and appropriate modelling of
cross-frequency interactions.
|
[
{
"created": "Tue, 3 Oct 2023 15:40:03 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Dec 2023 14:30:36 GMT",
"version": "v2"
}
] |
2023-12-21
|
[
[
"Klepl",
"Dominik",
""
],
[
"Wu",
"Min",
""
],
[
"He",
"Fei",
""
]
] |
Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.
|
2209.06740
|
Benjamin Hayden
|
Benjamin Yost Hayden
|
The pernicious danger of cortical brain maps
| null | null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
The parcellation of the primate cerebral cortex into numbered regions, based
on cytoarchitecture, began with the pioneering research of neuroanatomist
Kobrinian Brodmann. While the borders between regions have changed somewhat,
and in some cases continue to be disputed, the idea of dividing the cortex into
distinct numbered areas has become central to the goal of understanding brain
function. And indeed, areal maps do provide a good starting point for
functional parcellation. It is obvious, for example, that visual area V1 has a
different function than primary motor cortex. However, as with anything good,
one can take things too far. Indeed, cortical areas, while useful, have several
pernicious side effects for neuroscientists interested in function, especially
in prefrontal cortex.
|
[
{
"created": "Wed, 14 Sep 2022 16:05:00 GMT",
"version": "v1"
}
] |
2022-09-15
|
[
[
"Hayden",
"Benjamin Yost",
""
]
] |
The parcellation of the primate cerebral cortex into numbered regions, based on cytoarchitecture, began with the pioneering research of neuroanatomist Kobrinian Brodmann. While the borders between regions have changed somewhat, and in some cases continue to be disputed, the idea of dividing the cortex into distinct numbered areas has become central to the goal of understanding brain function. And indeed, areal maps do provide a good starting point for functional parcellation. It is obvious, for example, that visual area V1 has a different function than primary motor cortex. However, as with anything good, one can take things too far. Indeed, cortical areas, while useful, have several pernicious side effects for neuroscientists interested in function, especially in prefrontal cortex.
|
2406.14432
|
Oliver Keatinge Clay
|
Oliver Keatinge Clay
|
Inverse population genetic problems with noise: inferring extent and
structure of haplotype blocks from point allele frequencies
|
7 pages, 2 figures
| null | null | null |
q-bio.PE q-bio.GN
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A haplotype block, or simply a block, is a chromosomal segment, DNA base
sequence or string that occurs in only a few variants or types in the genomes
of a population of interest, and that has an encapsulated or 'private'
frequency distribution of the string types that is not shared by neighbouring
blocks or regions on the same chromosome. We consider two inverse problems of
genetic interest: from just the frequencies of the symbol types (4 base types,
possible single-base alleles) at each position (point, base/nucleotide) along
the string, infer the location of the left and right boundaries of the block
(block extent), and the number and relative frequencies of the string types
occurring in the block (block structure). The large majority of variable
positions in human and also other (e.g., fungal) genomes appear to be
biallelic, i.e., the position allows only a choice between two possible
symbols. The symbols can then be encoded as 0 (major) and 1 (minor), or as
$\uparrow$ and $\downarrow$ as in Ising models, so the scenario reduces to
problems on Boolean strings/bitstrings and Boolean matrices. The specifying of
major allele frequencies (MAF) as used in genetics fits naturally into this
framework. A simple example from human chromosome 9 is presented.
|
[
{
"created": "Thu, 20 Jun 2024 15:57:16 GMT",
"version": "v1"
}
] |
2024-06-21
|
[
[
"Clay",
"Oliver Keatinge",
""
]
] |
A haplotype block, or simply a block, is a chromosomal segment, DNA base sequence or string that occurs in only a few variants or types in the genomes of a population of interest, and that has an encapsulated or 'private' frequency distribution of the string types that is not shared by neighbouring blocks or regions on the same chromosome. We consider two inverse problems of genetic interest: from just the frequencies of the symbol types (4 base types, possible single-base alleles) at each position (point, base/nucleotide) along the string, infer the location of the left and right boundaries of the block (block extent), and the number and relative frequencies of the string types occurring in the block (block structure). The large majority of variable positions in human and also other (e.g., fungal) genomes appear to be biallelic, i.e., the position allows only a choice between two possible symbols. The symbols can then be encoded as 0 (major) and 1 (minor), or as $\uparrow$ and $\downarrow$ as in Ising models, so the scenario reduces to problems on Boolean strings/bitstrings and Boolean matrices. The specifying of major allele frequencies (MAF) as used in genetics fits naturally into this framework. A simple example from human chromosome 9 is presented.
|
2104.02603
|
Kayode Olumoyin
|
K.D. Olumoyin, A.Q.M. Khaliq, K.M. Furati
|
Data-driven deep learning algorithms for time-varying infection rates of
COVID-19 and mitigation measures
|
Made changes to authors name, F.M. Furati to K.M. Furati
| null |
10.3390/epidemiologia2040033
| null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
Epidemiological models with constant parameters may not capture satisfactory
infection patterns in the presence of pharmaceutical and non-pharmaceutical
mitigation measures during a pandemic, since infectiousness is a function of
time. In this paper, an Epidemiology-Informed Neural Network algorithm is
introduced to learn the time-varying transmission rate for the COVID-19
pandemic in the presence of various mitigation scenarios. There are
asymptomatic infectives, mostly unreported, and the proposed algorithm learns
the proportion of the total infective individuals that are asymptomatic
infectives. Using cumulative and daily reported cases of the symptomatic
infectives, we simulate the impact of non-pharmaceutical mitigation measures
such as early detection of infectives, contact tracing, and social distancing
on the basic reproduction number. We demonstrate the effectiveness of
vaccination on the transmission of COVID-19. The accuracy of the proposed
algorithm is demonstrated using error metrics in the data-driven simulation for
COVID-19 data of Italy, South Korea, the United Kingdom, and the United States.
|
[
{
"created": "Mon, 5 Apr 2021 16:05:48 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Apr 2021 12:45:55 GMT",
"version": "v2"
},
{
"created": "Fri, 13 May 2022 16:43:08 GMT",
"version": "v3"
}
] |
2022-05-16
|
[
[
"Olumoyin",
"K. D.",
""
],
[
"Khaliq",
"A. Q. M.",
""
],
[
"Furati",
"K. M.",
""
]
] |
Epidemiological models with constant parameters may not capture satisfactory infection patterns in the presence of pharmaceutical and non-pharmaceutical mitigation measures during a pandemic, since infectiousness is a function of time. In this paper, an Epidemiology-Informed Neural Network algorithm is introduced to learn the time-varying transmission rate for the COVID-19 pandemic in the presence of various mitigation scenarios. There are asymptomatic infectives, mostly unreported, and the proposed algorithm learns the proportion of the total infective individuals that are asymptomatic infectives. Using cumulative and daily reported cases of the symptomatic infectives, we simulate the impact of non-pharmaceutical mitigation measures such as early detection of infectives, contact tracing, and social distancing on the basic reproduction number. We demonstrate the effectiveness of vaccination on the transmission of COVID-19. The accuracy of the proposed algorithm is demonstrated using error metrics in the data-driven simulation for COVID-19 data of Italy, South Korea, the United Kingdom, and the United States.
|
1901.09673
|
Robert Marsland III
|
Robert Marsland III and Wenping Cui and Pankaj Mehta
|
The Minimum Environmental Perturbation Principle: A New Perspective on
Niche Theory
|
56 pages, 6 figures
| null | null | null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fifty years ago, Robert MacArthur showed that stable equilibria optimize
quadratic functions of the population sizes in several important ecological
models. Here, we generalize this finding to a broader class of systems within
the framework of contemporary niche theory, and precisely state the conditions
under which an optimization principle (not necessarily quadratic) can be
obtained. We show that conducting the optimization in the space of
environmental states instead of population sizes leads to a universal and
transparent physical interpretation of the objective function. Specifically,
the equilibrium state minimizes the perturbation of the environment induced by
the presence of the competing species, subject to the constraint that no
species has a positive net growth rate. We use this "minimum environmental
perturbation principle" to make new predictions for eco-evolution and community
assembly, and describe a simple experimental setting where its conditions of
validity have been empirically tested.
|
[
{
"created": "Mon, 28 Jan 2019 14:22:48 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Jan 2019 03:56:06 GMT",
"version": "v2"
},
{
"created": "Sat, 20 Apr 2019 00:03:10 GMT",
"version": "v3"
},
{
"created": "Wed, 18 Dec 2019 17:00:29 GMT",
"version": "v4"
}
] |
2019-12-19
|
[
[
"Marsland",
"Robert",
"III"
],
[
"Cui",
"Wenping",
""
],
[
"Mehta",
"Pankaj",
""
]
] |
Fifty years ago, Robert MacArthur showed that stable equilibria optimize quadratic functions of the population sizes in several important ecological models. Here, we generalize this finding to a broader class of systems within the framework of contemporary niche theory, and precisely state the conditions under which an optimization principle (not necessarily quadratic) can be obtained. We show that conducting the optimization in the space of environmental states instead of population sizes leads to a universal and transparent physical interpretation of the objective function. Specifically, the equilibrium state minimizes the perturbation of the environment induced by the presence of the competing species, subject to the constraint that no species has a positive net growth rate. We use this "minimum environmental perturbation principle" to make new predictions for eco-evolution and community assembly, and describe a simple experimental setting where its conditions of validity have been empirically tested.
|
2205.15020
|
Yasamin Kowsari
|
Yasamin Kowsari, Sanaz Nakhodchi, Davoud Gholamiangonabadi
|
Gene selection from microarray expression data: A Multi-objective PSO
with adaptive K-nearest neighborhood
| null | null | null | null |
q-bio.QM cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Cancer detection is one of the key research topics in the medical field.
Accurate detection of different cancer types is valuable in providing better
treatment facilities and risk minimization for patients. This paper deals with
the classification problem of human cancer diseases by using gene expression
data. It is presented a new methodology to analyze microarray datasets and
efficiently classify cancer diseases. The new method first employs Signal to
Noise Ratio (SNR) to find a list of a small subset of non-redundant genes.
Then, after normalization, it is used Multi-Objective Particle Swarm
Optimization (MOPSO) for feature selection and employed Adaptive K-Nearest
Neighborhood (KNN) for cancer disease classification. This method improves the
classification accuracy of cancer classification by reducing the number of
features. The proposed methodology is evaluated by classifying cancer diseases
in five cancer datasets. The results are compared with the most recent
approaches, which increases the classification accuracy in each dataset.
|
[
{
"created": "Fri, 27 May 2022 04:22:10 GMT",
"version": "v1"
}
] |
2022-05-31
|
[
[
"Kowsari",
"Yasamin",
""
],
[
"Nakhodchi",
"Sanaz",
""
],
[
"Gholamiangonabadi",
"Davoud",
""
]
] |
Cancer detection is one of the key research topics in the medical field. Accurate detection of different cancer types is valuable in providing better treatment facilities and risk minimization for patients. This paper deals with the classification problem of human cancer diseases by using gene expression data. It is presented a new methodology to analyze microarray datasets and efficiently classify cancer diseases. The new method first employs Signal to Noise Ratio (SNR) to find a list of a small subset of non-redundant genes. Then, after normalization, it is used Multi-Objective Particle Swarm Optimization (MOPSO) for feature selection and employed Adaptive K-Nearest Neighborhood (KNN) for cancer disease classification. This method improves the classification accuracy of cancer classification by reducing the number of features. The proposed methodology is evaluated by classifying cancer diseases in five cancer datasets. The results are compared with the most recent approaches, which increases the classification accuracy in each dataset.
|
1309.2686
|
Juan Carlos del Alamo
|
Juan C. Lasheras, BaLdomeRo Alonso-Latorre, Ruedi Meili, Effie
Bastounis, Juan C. del Alamo and Richard A. Firtel
|
Distribution of Traction Forces and Intracellular Markers Associated
with Shape Changes During Amoeboid Cell Migration
|
Conference Paper for the Sixth Interdisciplinary Transport Phenomena
Conference: Fluid, Thermal, Biological, Materials and Space Sciences.
Volterra. Italy. October 4-9, (2009)
|
Int J. Trans. Phenomena, Vol 12, 1-2, pp 3-12, 2011
| null | null |
q-bio.CB q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
During migration, amoeboid cells perform a cycle of quasi-periodic repetitive
events (motility cycle). the cell length and the strain energy exchanged with
the substrate oscillate in time with an average frequency, f, on top of which
are imposed smaller random fluctuations. the fact that a considerable portion
of the changes in cell shape are due to periodic repetitive events enables the
use of conditional statistics methods to analyze the network of biochemical
processes involved in cell motility. taking advan- tage of this cyclic nature,
we apply Principal Component analysis (PCa) and phase- average statistics to
analyze the dominant modes of shape change and their association to the
activity and localization of molecular motors. We analyze time-lapse measure-
ments of cell shape, traction forces and fluorescence from green fluorescent
protein (GfP) reporters for f-actin in Dictyostelium cells undergoing guided
chemotactic migration. using wild-type cells (wt) as reference, we investigated
the contractile and actin crosslinking functions of myosin II by studying
myosin II heavy chain null mutant cells (mhcA-) and myosin II essential light
chain null cells (mlcE-).
|
[
{
"created": "Tue, 10 Sep 2013 23:02:19 GMT",
"version": "v1"
}
] |
2013-09-12
|
[
[
"Lasheras",
"Juan C.",
""
],
[
"Alonso-Latorre",
"BaLdomeRo",
""
],
[
"Meili",
"Ruedi",
""
],
[
"Bastounis",
"Effie",
""
],
[
"del Alamo",
"Juan C.",
""
],
[
"Firtel",
"Richard A.",
""
]
] |
During migration, amoeboid cells perform a cycle of quasi-periodic repetitive events (motility cycle). the cell length and the strain energy exchanged with the substrate oscillate in time with an average frequency, f, on top of which are imposed smaller random fluctuations. the fact that a considerable portion of the changes in cell shape are due to periodic repetitive events enables the use of conditional statistics methods to analyze the network of biochemical processes involved in cell motility. taking advan- tage of this cyclic nature, we apply Principal Component analysis (PCa) and phase- average statistics to analyze the dominant modes of shape change and their association to the activity and localization of molecular motors. We analyze time-lapse measure- ments of cell shape, traction forces and fluorescence from green fluorescent protein (GfP) reporters for f-actin in Dictyostelium cells undergoing guided chemotactic migration. using wild-type cells (wt) as reference, we investigated the contractile and actin crosslinking functions of myosin II by studying myosin II heavy chain null mutant cells (mhcA-) and myosin II essential light chain null cells (mlcE-).
|
2106.01455
|
Tobias Wenzel
|
Anna Pryszlak, Tobias Wenzel, Kiley West Seitz, Falk Hildebrand, Ece
Kartal, Marco Raffaele Cosenza, Vladimir Benes, Peer Bork, Christoph Merten
|
Enrichment of gut microbiome strains for cultivation-free genome
sequencing using droplet microfluidics
|
18 pages incl. references, 4 main figures and one supplementary
figure
|
Cell Reports Methods 12/2021
|
10.1016/j.crmeth.2021.100137
| null |
q-bio.GN physics.bio-ph q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
We report a droplet microfluidic method to target and sort individual cells
directly from complex microbiome samples, and to prepare these cells for bulk
whole genome sequencing without cultivation. We characterize this approach by
recovering bacteria spiked into human stool samples at a ratio as low as 1:250
and by successfully enriching endogenous Bacteroides vulgatus to the level
required for de-novo assembly of high-quality genomes. While microbiome strains
are increasingly demanded for biomedical applications, the vast majority of
species and strains are uncultivated and without reference genomes. We address
this shortcoming by encapsulating complex microbiome samples directly into
microfluidic droplets and amplify a target-specific genomic fragment using a
custom molecular TaqMan probe. We separate those positive droplets by droplet
sorting, selectively enriching single target strain cells. Finally, we present
a protocol to purify the genomic DNA while specifically removing amplicons and
cell debris for high-quality genome sequencing.
|
[
{
"created": "Wed, 2 Jun 2021 20:24:10 GMT",
"version": "v1"
}
] |
2022-02-01
|
[
[
"Pryszlak",
"Anna",
""
],
[
"Wenzel",
"Tobias",
""
],
[
"Seitz",
"Kiley West",
""
],
[
"Hildebrand",
"Falk",
""
],
[
"Kartal",
"Ece",
""
],
[
"Cosenza",
"Marco Raffaele",
""
],
[
"Benes",
"Vladimir",
""
],
[
"Bork",
"Peer",
""
],
[
"Merten",
"Christoph",
""
]
] |
We report a droplet microfluidic method to target and sort individual cells directly from complex microbiome samples, and to prepare these cells for bulk whole genome sequencing without cultivation. We characterize this approach by recovering bacteria spiked into human stool samples at a ratio as low as 1:250 and by successfully enriching endogenous Bacteroides vulgatus to the level required for de-novo assembly of high-quality genomes. While microbiome strains are increasingly demanded for biomedical applications, the vast majority of species and strains are uncultivated and without reference genomes. We address this shortcoming by encapsulating complex microbiome samples directly into microfluidic droplets and amplify a target-specific genomic fragment using a custom molecular TaqMan probe. We separate those positive droplets by droplet sorting, selectively enriching single target strain cells. Finally, we present a protocol to purify the genomic DNA while specifically removing amplicons and cell debris for high-quality genome sequencing.
|
2005.08842
|
Ji-Seon Bang
|
Ji-Seon Bang, Ji-Hoon Jeong, and Dong-Ok Won
|
Classification of Visual Perception and Imagery based EEG Signals Using
Convolutional Neural Networks
|
Submitted to IEEE 9th International Winter Conference on
Brain-Computer Interface (BCI 2021)
| null | null | null |
q-bio.NC eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, visual perception (VP) and visual imagery (VI) paradigms are
investigated in several brain-computer interface (BCI) studies. VP and VI are
defined as a changing of brain signals when perceiving and memorizing visual
information, respectively. These paradigms could be alternatives to the
previous visual-based paradigms which have limitations such as fatigue and low
information transfer rates (ITR). In this study, we analyzed VP and VI to
investigate the possibility to control BCI. First, we conducted a
time-frequency analysis with event-related spectral perturbation. In addition,
two types of decoding accuracies were obtained with convolutional neural
network to verify whether the brain signals can be distinguished from each
class in the VP and whether they can be differentiated with VP and VI
paradigms. As a result, the 6-class classification performance in VP was 32.56%
and the binary classification performance which classifies two paradigms was
90.16%.
|
[
{
"created": "Fri, 15 May 2020 05:29:21 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Dec 2020 08:16:40 GMT",
"version": "v2"
},
{
"created": "Tue, 8 Dec 2020 02:17:39 GMT",
"version": "v3"
},
{
"created": "Fri, 5 Feb 2021 07:00:59 GMT",
"version": "v4"
}
] |
2021-02-08
|
[
[
"Bang",
"Ji-Seon",
""
],
[
"Jeong",
"Ji-Hoon",
""
],
[
"Won",
"Dong-Ok",
""
]
] |
Recently, visual perception (VP) and visual imagery (VI) paradigms are investigated in several brain-computer interface (BCI) studies. VP and VI are defined as a changing of brain signals when perceiving and memorizing visual information, respectively. These paradigms could be alternatives to the previous visual-based paradigms which have limitations such as fatigue and low information transfer rates (ITR). In this study, we analyzed VP and VI to investigate the possibility to control BCI. First, we conducted a time-frequency analysis with event-related spectral perturbation. In addition, two types of decoding accuracies were obtained with convolutional neural network to verify whether the brain signals can be distinguished from each class in the VP and whether they can be differentiated with VP and VI paradigms. As a result, the 6-class classification performance in VP was 32.56% and the binary classification performance which classifies two paradigms was 90.16%.
|
2208.00649
|
Akira Kinjo
|
Eden Tian Hwa Ng and Akira R. Kinjo
|
Computational Modelling of Plasticity-Led Evolution
|
20 pages, 2 tables, 1 box
|
Biophysical Reviews, volume 14, pages 1359-1367 (2022)
|
10.1007/s12551-022-01018-5
| null |
q-bio.PE q-bio.MN
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Plasticity-led evolution is a form of evolution where a change in the
environment induces novel traits via phenotypic plasticity, after which the
novel traits are genetically accommodated over generations under the novel
environment. This mode of evolution is expected to resolve the problem of
gradualism (i.e., evolution by the slow accumulation of mutations that induce
phenotypic variation) implied by the Modern Evolutionary Synthesis, in the face
of a large environmental change. While experimental works are essential for
validating that plasticity-led evolution indeed happened, we need computational
models to gain insight into its underlying mechanisms and make qualitative
predictions. Such computational models should include the developmental process
and gene-environment interactions in addition to genetics and natural
selection. We point out that gene regulatory network models can incorporate all
the above notions. In this review, we highlight results from computational
modelling of gene regulatory networks that consolidate the criteria of
plasticity-led evolution. Since gene regulatory networks are mathematically
equivalent to artificial recurrent neural networks, we also discuss their
analogies and discrepancies, which may help further understand the mechanisms
underlying plasticity-led evolution.
|
[
{
"created": "Mon, 1 Aug 2022 07:14:58 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Sep 2022 05:56:12 GMT",
"version": "v2"
},
{
"created": "Fri, 4 Nov 2022 07:01:44 GMT",
"version": "v3"
},
{
"created": "Mon, 19 Dec 2022 02:51:18 GMT",
"version": "v4"
}
] |
2023-01-18
|
[
[
"Ng",
"Eden Tian Hwa",
""
],
[
"Kinjo",
"Akira R.",
""
]
] |
Plasticity-led evolution is a form of evolution where a change in the environment induces novel traits via phenotypic plasticity, after which the novel traits are genetically accommodated over generations under the novel environment. This mode of evolution is expected to resolve the problem of gradualism (i.e., evolution by the slow accumulation of mutations that induce phenotypic variation) implied by the Modern Evolutionary Synthesis, in the face of a large environmental change. While experimental works are essential for validating that plasticity-led evolution indeed happened, we need computational models to gain insight into its underlying mechanisms and make qualitative predictions. Such computational models should include the developmental process and gene-environment interactions in addition to genetics and natural selection. We point out that gene regulatory network models can incorporate all the above notions. In this review, we highlight results from computational modelling of gene regulatory networks that consolidate the criteria of plasticity-led evolution. Since gene regulatory networks are mathematically equivalent to artificial recurrent neural networks, we also discuss their analogies and discrepancies, which may help further understand the mechanisms underlying plasticity-led evolution.
|
2101.05221
|
Arie Horowitz
|
Arie Horowitz
|
Membrane Trafficking of Integral Cell Junction Proteins and its
Functional Consequences
|
34 pages; 6 figures
| null | null | null |
q-bio.SC
|
http://creativecommons.org/licenses/by/4.0/
|
Though membrane trafficking of cell junction proteins has been studied
extensively for more than two decades, the accumulated knowledge remains
fragmentary. The goal of this review is to synthesize published studies on the
membrane trafficking of the five major junction transmembrane proteins:
claudins, occludin, and junction adhesion molecules (JAMs) in tight junctions;
cadherins and nectins in adherens junctions; to identify underlying common
mechanisms; to highlight their functional consequences on barrier function; and
to identify knowledge gaps. Clathrin-mediated endocytosis appears to be the
main, but not exclusive, mode of internalization. Caveolin-mediated endocytosis
and macropinocytosis are employed less frequently. PDZ-domain binding is the
predominant mode of interaction between junction protein cytoplasmic tails and
scaffold proteins. It is shared by claudins, the largest family of junction
integral proteins, by junction adhesion molecules A, B, and C, and by the three
nectins. All eight proteins are destined to either recycling via Rab4/Rab11
GTPases or to degradation. The sorting mechanisms that underlie the specificity
of their endocytic pathways and determine their fates are not fully known. New
data is presented to introduce an emerging role of junction-associated scaffold
proteins in claudin membrane trafficking.
|
[
{
"created": "Wed, 13 Jan 2021 17:34:56 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Jan 2021 15:50:26 GMT",
"version": "v2"
},
{
"created": "Mon, 25 Jan 2021 16:19:55 GMT",
"version": "v3"
},
{
"created": "Thu, 11 Feb 2021 01:51:47 GMT",
"version": "v4"
},
{
"created": "Mon, 22 Feb 2021 00:11:23 GMT",
"version": "v5"
},
{
"created": "Mon, 12 Apr 2021 00:12:33 GMT",
"version": "v6"
}
] |
2021-04-13
|
[
[
"Horowitz",
"Arie",
""
]
] |
Though membrane trafficking of cell junction proteins has been studied extensively for more than two decades, the accumulated knowledge remains fragmentary. The goal of this review is to synthesize published studies on the membrane trafficking of the five major junction transmembrane proteins: claudins, occludin, and junction adhesion molecules (JAMs) in tight junctions; cadherins and nectins in adherens junctions; to identify underlying common mechanisms; to highlight their functional consequences on barrier function; and to identify knowledge gaps. Clathrin-mediated endocytosis appears to be the main, but not exclusive, mode of internalization. Caveolin-mediated endocytosis and macropinocytosis are employed less frequently. PDZ-domain binding is the predominant mode of interaction between junction protein cytoplasmic tails and scaffold proteins. It is shared by claudins, the largest family of junction integral proteins, by junction adhesion molecules A, B, and C, and by the three nectins. All eight proteins are destined to either recycling via Rab4/Rab11 GTPases or to degradation. The sorting mechanisms that underlie the specificity of their endocytic pathways and determine their fates are not fully known. New data is presented to introduce an emerging role of junction-associated scaffold proteins in claudin membrane trafficking.
|
1402.5984
|
Melanie JI Muller PhD
|
Melanie JI Muller, Beverly I Neugeboren, David R Nelson, Andrew W
Murray
|
Genetic drift opposes mutualism during spatial population expansion
|
12 pages main text, 20 pages SI
|
Proc. Natl. Acad. Sci. USA 111, 1037-1042 (2014)
|
10.1073/pnas.1313285111
| null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mutualistic interactions benefit both partners, promoting coexistence and
genetic diversity. Spatial structure can promote cooperation, but spatial
expansions may also make it hard for mutualistic partners to stay together,
since genetic drift at the expansion front creates regions of low genetic and
species diversity. To explore the antagonism between mutualism and genetic
drift, we grew cross-feeding strains of the budding yeast S. cerevisiae on agar
surfaces as a model for mutualists undergoing spatial expansions. By supplying
varying amounts of the exchanged nutrients, we tuned strength and symmetry of
the mutualistic interaction. Strong mutualism suppresses genetic demixing
during spatial expansions and thereby maintains diversity, but weak or
asymmetric mutualism is overwhelmed by genetic drift even when mutualism is
still beneficial, slowing growth and reducing diversity. Theoretical modeling
using experimentally measured parameters predicts the size of demixed regions
and how strong mutualism must be to survive a spatial expansion.
|
[
{
"created": "Mon, 24 Feb 2014 21:15:27 GMT",
"version": "v1"
}
] |
2014-02-26
|
[
[
"Muller",
"Melanie JI",
""
],
[
"Neugeboren",
"Beverly I",
""
],
[
"Nelson",
"David R",
""
],
[
"Murray",
"Andrew W",
""
]
] |
Mutualistic interactions benefit both partners, promoting coexistence and genetic diversity. Spatial structure can promote cooperation, but spatial expansions may also make it hard for mutualistic partners to stay together, since genetic drift at the expansion front creates regions of low genetic and species diversity. To explore the antagonism between mutualism and genetic drift, we grew cross-feeding strains of the budding yeast S. cerevisiae on agar surfaces as a model for mutualists undergoing spatial expansions. By supplying varying amounts of the exchanged nutrients, we tuned strength and symmetry of the mutualistic interaction. Strong mutualism suppresses genetic demixing during spatial expansions and thereby maintains diversity, but weak or asymmetric mutualism is overwhelmed by genetic drift even when mutualism is still beneficial, slowing growth and reducing diversity. Theoretical modeling using experimentally measured parameters predicts the size of demixed regions and how strong mutualism must be to survive a spatial expansion.
|
1002.1558
|
Andrea Veglio
|
A. Veglio, A. Gamba, M. Nicodemi, F. Bussolino, G. Serini
|
A symmetry breaking mechanism for epithelial cell polarization
|
7 pages, 6 figures
|
Physical Review E 80 (3), 031919 (2009)
|
10.1103/PhysRevE.80.031919
| null |
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In multicellular organisms, epithelial cells form layers separating
compartments responsible for different physiological functions. At the early
stage of epithelial layer formation, each cell of an aggregate defines an inner
and an outer side by breaking the symmetry of its initial state, in a process
known as epithelial polarization. By integrating recent biochemical and
biophysical data with stochastic simulations of the relevant reaction-diffusion
system we provide evidence that epithelial cell polarization is a chemical
phase separation process induced by a local bistability in the signaling
network at the level of the cell membrane. The early symmetry breaking event
triggering phase separation is induced by adhesion-dependent mechanical forces
localized in the point of convergence of cell surfaces when a threshold number
of confluent cells is reached. The generality of the emerging phase separation
scenario is likely common to many processes of cell polarity formation.
|
[
{
"created": "Mon, 8 Feb 2010 09:53:55 GMT",
"version": "v1"
}
] |
2010-02-09
|
[
[
"Veglio",
"A.",
""
],
[
"Gamba",
"A.",
""
],
[
"Nicodemi",
"M.",
""
],
[
"Bussolino",
"F.",
""
],
[
"Serini",
"G.",
""
]
] |
In multicellular organisms, epithelial cells form layers separating compartments responsible for different physiological functions. At the early stage of epithelial layer formation, each cell of an aggregate defines an inner and an outer side by breaking the symmetry of its initial state, in a process known as epithelial polarization. By integrating recent biochemical and biophysical data with stochastic simulations of the relevant reaction-diffusion system we provide evidence that epithelial cell polarization is a chemical phase separation process induced by a local bistability in the signaling network at the level of the cell membrane. The early symmetry breaking event triggering phase separation is induced by adhesion-dependent mechanical forces localized in the point of convergence of cell surfaces when a threshold number of confluent cells is reached. The generality of the emerging phase separation scenario is likely common to many processes of cell polarity formation.
|
0812.0025
|
Stefan Legewie
|
Stefan Legewie, Dennis Dienst, Annegret Wilde, Hanspeter Herzel and
Ilka M. Axmann
|
Small RNAs Establish Delays and Temporal Thresholds in Gene Expression
| null |
Biophys J. 2008 Oct;95(7):3232-8
|
10.1529/biophysj.108.133819
| null |
q-bio.MN q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Non-coding RNAs are crucial regulators of gene expression in prokaryotes and
eukaryotes, but it remains poorly understood how they affect the dynamics of
transcriptional networks. We analyzed the temporal characteristics of the
cyanobacterial iron stress response by mathematical modeling and quantitative
experimental analyses, and focused on the role of a recently discovered small
non-coding RNA, IsrR. We found that IsrR is responsible for a pronounced delay
in the accumulation of isiA mRNA encoding the late-phase stress protein, IsiA,
and that it ensures a rapid decline in isiA levels once external stress
triggers are removed. These kinetic properties allow the system to selectively
respond to sustained (as opposed to transient) stimuli, and thus establish a
temporal threshold, which prevents energetically costly IsiA accumulation under
short-term stress conditions. Biological information is frequently encoded in
the quantitative aspects of intracellular signals (e.g., amplitude and
duration). Our simulations reveal that competitive inhibition and regulated
degradation allow intracellular regulatory networks to efficiently discriminate
between transient and sustained inputs.
|
[
{
"created": "Fri, 28 Nov 2008 22:30:47 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Legewie",
"Stefan",
""
],
[
"Dienst",
"Dennis",
""
],
[
"Wilde",
"Annegret",
""
],
[
"Herzel",
"Hanspeter",
""
],
[
"Axmann",
"Ilka M.",
""
]
] |
Non-coding RNAs are crucial regulators of gene expression in prokaryotes and eukaryotes, but it remains poorly understood how they affect the dynamics of transcriptional networks. We analyzed the temporal characteristics of the cyanobacterial iron stress response by mathematical modeling and quantitative experimental analyses, and focused on the role of a recently discovered small non-coding RNA, IsrR. We found that IsrR is responsible for a pronounced delay in the accumulation of isiA mRNA encoding the late-phase stress protein, IsiA, and that it ensures a rapid decline in isiA levels once external stress triggers are removed. These kinetic properties allow the system to selectively respond to sustained (as opposed to transient) stimuli, and thus establish a temporal threshold, which prevents energetically costly IsiA accumulation under short-term stress conditions. Biological information is frequently encoded in the quantitative aspects of intracellular signals (e.g., amplitude and duration). Our simulations reveal that competitive inhibition and regulated degradation allow intracellular regulatory networks to efficiently discriminate between transient and sustained inputs.
|
2405.18760
|
Irina Mizeva
|
Irina A. Mizeva, Natalia P. Podolyan, Oleg V. Mamontov, Anastasiia V.
Sakovskaia, and Alexei A. Kamshilin
|
Local nature of 0.1 Hz oscillations in microcirculation is confirmed by
imaging photoplethysmography
|
25 pages, 5 figures
| null | null | null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Low-frequency oscillations in the human circulatory system is important for
basic physiology and practical applications in clinical medicine. Our objective
was to study which mechanism (central or local) is responsible for changes in
blood flow fluctuations at around 0.1 Hz. We used the method of imaging
photoplethysmography synchronized with electrocardiography to measure
blood-flow response to local forearm heating of 18 healthy male volunteers. The
dynamics of peripheral perfusion was revealed by a correlation processing of
photoplethysmography data, and the central hemodynamics was assessed from the
electrocardiogram. Wavelet analysis was used to estimate the dynamics of
spectral components. Our results show that skin heating leads to multiple
increase in local perfusion accompanied by drop in blood flow oscillations at
0.1 Hz, whereas no changes in heart rate variability was observed. After
switching off the heating, perfusion remains at the high level, regardless
decrease in skin temperature. The 0.1 Hz oscillations are smoothly recovered to
the base level. In conclusion, we confirm the local nature of fluctuations in
peripheral blood flow in the frequency band of about 0.1 Hz. A significant, but
time-delayed, recovery of fluctuation energy in this frequency range after
cessation of the skin warming was discovered. This study reveals a novel factor
involved in the regulation microcirculatory vascular tone. A comprehensive
study of hemodynamics using the new technique of imaging photoplethysmography
synchronized with electrocardiography is a prerequisite for development of a
valuable diagnostic tool.
|
[
{
"created": "Wed, 29 May 2024 04:58:05 GMT",
"version": "v1"
}
] |
2024-05-30
|
[
[
"Mizeva",
"Irina A.",
""
],
[
"Podolyan",
"Natalia P.",
""
],
[
"Mamontov",
"Oleg V.",
""
],
[
"Sakovskaia",
"Anastasiia V.",
""
],
[
"Kamshilin",
"Alexei A.",
""
]
] |
Low-frequency oscillations in the human circulatory system is important for basic physiology and practical applications in clinical medicine. Our objective was to study which mechanism (central or local) is responsible for changes in blood flow fluctuations at around 0.1 Hz. We used the method of imaging photoplethysmography synchronized with electrocardiography to measure blood-flow response to local forearm heating of 18 healthy male volunteers. The dynamics of peripheral perfusion was revealed by a correlation processing of photoplethysmography data, and the central hemodynamics was assessed from the electrocardiogram. Wavelet analysis was used to estimate the dynamics of spectral components. Our results show that skin heating leads to multiple increase in local perfusion accompanied by drop in blood flow oscillations at 0.1 Hz, whereas no changes in heart rate variability was observed. After switching off the heating, perfusion remains at the high level, regardless decrease in skin temperature. The 0.1 Hz oscillations are smoothly recovered to the base level. In conclusion, we confirm the local nature of fluctuations in peripheral blood flow in the frequency band of about 0.1 Hz. A significant, but time-delayed, recovery of fluctuation energy in this frequency range after cessation of the skin warming was discovered. This study reveals a novel factor involved in the regulation microcirculatory vascular tone. A comprehensive study of hemodynamics using the new technique of imaging photoplethysmography synchronized with electrocardiography is a prerequisite for development of a valuable diagnostic tool.
|
2312.00691
|
Christophe Magnani
|
Christophe Magnani and Lee E. Moore
|
Power spectral analysis of voltage-gated channels in neurons
| null | null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
This article develops a fundamental insight into the behavior of neuronal
membranes, focusing on their responses to stimuli measured with power spectra
in the frequency domain. It explores the use of linear and nonlinear (quadratic
sinusoidal analysis) approaches to characterize neuronal function. It further
delves into the random theory of internal noise of biological neurons and the
use of stochastic Markov models to investigate these fluctuations. The text
also discusses the origin of conductance noise and compares different power
spectra for interpreting this noise. Importantly, it introduces a novel
sequential chemical state model, named p2, which is more general than the
Hodgkin-Huxley formulation, so that the probability for an ion channel to be
open does not imply exponentiation. In particular, it is demonstrated that the
p2 (without exponentiation) and n4 (with exponentiation) models can produce
similar neuronal responses. A striking relationship is also shown between
fluctuation and quadratic power spectra, suggesting that voltage-dependent
random mechanisms can have a significant impact on deterministic nonlinear
responses, themselves known to have a crucial role in the generation of action
potentials in biological neural networks.
|
[
{
"created": "Fri, 1 Dec 2023 16:17:17 GMT",
"version": "v1"
}
] |
2023-12-04
|
[
[
"Magnani",
"Christophe",
""
],
[
"Moore",
"Lee E.",
""
]
] |
This article develops a fundamental insight into the behavior of neuronal membranes, focusing on their responses to stimuli measured with power spectra in the frequency domain. It explores the use of linear and nonlinear (quadratic sinusoidal analysis) approaches to characterize neuronal function. It further delves into the random theory of internal noise of biological neurons and the use of stochastic Markov models to investigate these fluctuations. The text also discusses the origin of conductance noise and compares different power spectra for interpreting this noise. Importantly, it introduces a novel sequential chemical state model, named p2, which is more general than the Hodgkin-Huxley formulation, so that the probability for an ion channel to be open does not imply exponentiation. In particular, it is demonstrated that the p2 (without exponentiation) and n4 (with exponentiation) models can produce similar neuronal responses. A striking relationship is also shown between fluctuation and quadratic power spectra, suggesting that voltage-dependent random mechanisms can have a significant impact on deterministic nonlinear responses, themselves known to have a crucial role in the generation of action potentials in biological neural networks.
|
1111.3062
|
Didier Sornette
|
Ivan Osorio, Alexey Lyubushin, Didier Sornette
|
Automated Seizure Detection: Unrecognized Challenges, Unexpected
Insights
|
27 pages with 8 figures and 2 tables
|
Epilepsy & Behavior 22, S7-S17 (2011)
|
10.1016/j.yebeh.2011.09.011
| null |
q-bio.NC physics.med-ph q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of epileptology's fundamental aims is the formulation of a universal,
internally consistent seizure definition. To assess this aim's feasibility,
three signal analysis methods were applied to a seizure time series and
performance comparisons were undertaken among them and with respect to a
validated algorithm. One of the methods uses a Fisher's matrix weighted measure
of the rate of parameters change of a 2n order auto-regressive model, another
is based on the Wavelet Transform Maximum Modulus for quantification of changes
in the logarithm of the standard deviation of ECoG power and yet another
employs the ratio of short-to-long term averages computed from cortical
signals. The central finding, fluctuating concordance among all methods' output
as a function of seizure duration, uncovers unexpected hurdles in the path to a
universal definition, while furnishing relevant knowledge in the dynamical
(spectral non-stationarity and varying ictal signal complexity) and clinical
(probable attainability of consensus) domains.
|
[
{
"created": "Sun, 13 Nov 2011 21:10:40 GMT",
"version": "v1"
}
] |
2011-11-15
|
[
[
"Osorio",
"Ivan",
""
],
[
"Lyubushin",
"Alexey",
""
],
[
"Sornette",
"Didier",
""
]
] |
One of epileptology's fundamental aims is the formulation of a universal, internally consistent seizure definition. To assess this aim's feasibility, three signal analysis methods were applied to a seizure time series and performance comparisons were undertaken among them and with respect to a validated algorithm. One of the methods uses a Fisher's matrix weighted measure of the rate of parameters change of a 2n order auto-regressive model, another is based on the Wavelet Transform Maximum Modulus for quantification of changes in the logarithm of the standard deviation of ECoG power and yet another employs the ratio of short-to-long term averages computed from cortical signals. The central finding, fluctuating concordance among all methods' output as a function of seizure duration, uncovers unexpected hurdles in the path to a universal definition, while furnishing relevant knowledge in the dynamical (spectral non-stationarity and varying ictal signal complexity) and clinical (probable attainability of consensus) domains.
|
2004.01714
|
George Mohler
|
George Mohler, Frederic Schoenberg, Martin B. Short, Daniel Sledge
|
Analyzing the World-Wide Impact of Public Health Interventions on the
Transmission Dynamics of COVID-19
| null | null |
10.13140/RG.2.2.32817.12642
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We analyze changes in the reproduction number, R, of COVID-19 in response to
public health interventions. Our results indicate that public health measures
undertaken in China reduced R from 1.5 in January to 0.4 in mid-March 2020.
They also suggest, however, the limitations of isolation, quarantine, and
large-scale attempts to limit travel. While the world-wide reproduction number
briefly dropped below 1 as China implemented extensive public health measures,
the introduction of the virus to other nations swiftly led to an increasing
world-wide average value of R. In Italy, the nation hardest-hit following
China, social distancing measures brought the local value of R down from 3.71
to 2.51. Nonetheless, the value of R in Italy persisted at levels well above 1,
allowing for ongoing transmission. By mid-March 2020, as COVID-19 spread in
areas without extensive public health interventions in place, the world-wide
value of R increased to a level similar to that of late January.
|
[
{
"created": "Fri, 3 Apr 2020 16:05:20 GMT",
"version": "v1"
}
] |
2020-04-07
|
[
[
"Mohler",
"George",
""
],
[
"Schoenberg",
"Frederic",
""
],
[
"Short",
"Martin B.",
""
],
[
"Sledge",
"Daniel",
""
]
] |
We analyze changes in the reproduction number, R, of COVID-19 in response to public health interventions. Our results indicate that public health measures undertaken in China reduced R from 1.5 in January to 0.4 in mid-March 2020. They also suggest, however, the limitations of isolation, quarantine, and large-scale attempts to limit travel. While the world-wide reproduction number briefly dropped below 1 as China implemented extensive public health measures, the introduction of the virus to other nations swiftly led to an increasing world-wide average value of R. In Italy, the nation hardest-hit following China, social distancing measures brought the local value of R down from 3.71 to 2.51. Nonetheless, the value of R in Italy persisted at levels well above 1, allowing for ongoing transmission. By mid-March 2020, as COVID-19 spread in areas without extensive public health interventions in place, the world-wide value of R increased to a level similar to that of late January.
|
1309.5078
|
Jacob Scott
|
Jacob G. Scott, Alexander G. Fletcher, Philip K. Maini, Alexander R.
A. Anderson and Philip Gerlee
|
A filter-flow perspective of hematogenous metastasis offers a
non-genetic paradigm for personalized cancer therapy
|
pre-publication draft, 2 figures, 2 tables
| null | null | null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Research into mechanisms of hematogenous metastasis has largely become
genetic in focus, attempting to understand the molecular basis of `seed-soil'
relationships. Preceeding this biological mechanism is the physical process of
dissemination of circulating tumour cells (CTCs). We utilize a `filter-flow'
paradigm to show that assumptions about CTC dynamics strongly affect metastatic
efficiency: without data on CTC dynamics, any attempt to predict metastatic
spread in individual patients is impossible.
|
[
{
"created": "Thu, 19 Sep 2013 19:35:21 GMT",
"version": "v1"
}
] |
2013-09-20
|
[
[
"Scott",
"Jacob G.",
""
],
[
"Fletcher",
"Alexander G.",
""
],
[
"Maini",
"Philip K.",
""
],
[
"Anderson",
"Alexander R. A.",
""
],
[
"Gerlee",
"Philip",
""
]
] |
Research into mechanisms of hematogenous metastasis has largely become genetic in focus, attempting to understand the molecular basis of `seed-soil' relationships. Preceeding this biological mechanism is the physical process of dissemination of circulating tumour cells (CTCs). We utilize a `filter-flow' paradigm to show that assumptions about CTC dynamics strongly affect metastatic efficiency: without data on CTC dynamics, any attempt to predict metastatic spread in individual patients is impossible.
|
1211.0349
|
Anyou Wang
|
Anyou Wang
|
Systematically Dissecting the Global Mechanism of miRNA Functions in
Pluripotent Stem Cells
| null | null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
MicroRNAs (miRNAs) critically modulate stem cell properties like
pluripotency, but the fundamental mechanism remains largely unknown. This study
systematically analyzes multiple-omics data and builds a systems physical
network including genome-wide interactions between miRNAs and their targets to
reveal the systems mechanism of miRNA functions in mouse pluripotent stem
cells. Globally, miRNAs directly repress the pluripotent core factors during
differentiation state. Surprisingly, during pluripotent state, the top
important miRNAs do not directly regulate the pluripotent core factors as
thought, but they only directly target the pluripotent signal pathways and
directly repress developmental processes. Furthermore, at pluripotent state
miRNAs predominately repress DNA methyltransferases, the core enzymes for DNA
methylation. The decreasing methylation repressed by miRNAs in turn activates
the top miRNAs and pluripotent core factors, creating an active circuit system
to modulate pluripotency. MiRNAs vary their functions with different stem cell
states. While miRNAs directly repress pluripotent core factors to facilitate
the differentiation during cell differentiation, they also help stem cells to
maintain pluripotency by activating pluripotent cores through directly
repressing DNA methylation systems and primarily inhibiting development.
|
[
{
"created": "Fri, 2 Nov 2012 02:17:01 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Dec 2012 03:09:57 GMT",
"version": "v2"
},
{
"created": "Sun, 10 Feb 2013 23:54:59 GMT",
"version": "v3"
},
{
"created": "Fri, 19 Apr 2013 18:41:37 GMT",
"version": "v4"
},
{
"created": "Wed, 26 Jun 2013 12:21:21 GMT",
"version": "v5"
},
{
"created": "Mon, 4 Nov 2013 13:57:47 GMT",
"version": "v6"
},
{
"created": "Wed, 13 Aug 2014 19:15:30 GMT",
"version": "v7"
}
] |
2014-08-14
|
[
[
"Wang",
"Anyou",
""
]
] |
MicroRNAs (miRNAs) critically modulate stem cell properties like pluripotency, but the fundamental mechanism remains largely unknown. This study systematically analyzes multiple-omics data and builds a systems physical network including genome-wide interactions between miRNAs and their targets to reveal the systems mechanism of miRNA functions in mouse pluripotent stem cells. Globally, miRNAs directly repress the pluripotent core factors during differentiation state. Surprisingly, during pluripotent state, the top important miRNAs do not directly regulate the pluripotent core factors as thought, but they only directly target the pluripotent signal pathways and directly repress developmental processes. Furthermore, at pluripotent state miRNAs predominately repress DNA methyltransferases, the core enzymes for DNA methylation. The decreasing methylation repressed by miRNAs in turn activates the top miRNAs and pluripotent core factors, creating an active circuit system to modulate pluripotency. MiRNAs vary their functions with different stem cell states. While miRNAs directly repress pluripotent core factors to facilitate the differentiation during cell differentiation, they also help stem cells to maintain pluripotency by activating pluripotent cores through directly repressing DNA methylation systems and primarily inhibiting development.
|
2104.11364
|
Elana Fertig
|
Anne E Carpenter, Casey S Greene, Piero Carnici, Benilton S Carvalho,
Michiel de Hoon, Stacey Finley, Kim-Anh Le Cao, Jerry SH Lee, Luigi
Marchionni, Suzanne Sindi, Fabian J Theis, Gregory P Way, Jean YH Yang, Elana
J Fertig
|
A field guide to cultivating computational biology
| null | null | null | null |
q-bio.OT cs.CY
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Biomedical research centers can empower basic discovery and novel therapeutic
strategies by leveraging their large-scale datasets from experiments and
patients. This data, together with new technologies to create and analyze it,
has ushered in an era of data-driven discovery which requires moving beyond the
traditional individual, single-discipline investigator research model. This
interdisciplinary niche is where computational biology thrives. It has matured
over the past three decades and made major contributions to scientific
knowledge and human health, yet researchers in the field often languish in
career advancement, publication, and grant review. We propose solutions for
individual scientists, institutions, journal publishers, funding agencies, and
educators.
|
[
{
"created": "Fri, 23 Apr 2021 01:24:21 GMT",
"version": "v1"
}
] |
2021-04-26
|
[
[
"Carpenter",
"Anne E",
""
],
[
"Greene",
"Casey S",
""
],
[
"Carnici",
"Piero",
""
],
[
"Carvalho",
"Benilton S",
""
],
[
"de Hoon",
"Michiel",
""
],
[
"Finley",
"Stacey",
""
],
[
"Cao",
"Kim-Anh Le",
""
],
[
"Lee",
"Jerry SH",
""
],
[
"Marchionni",
"Luigi",
""
],
[
"Sindi",
"Suzanne",
""
],
[
"Theis",
"Fabian J",
""
],
[
"Way",
"Gregory P",
""
],
[
"Yang",
"Jean YH",
""
],
[
"Fertig",
"Elana J",
""
]
] |
Biomedical research centers can empower basic discovery and novel therapeutic strategies by leveraging their large-scale datasets from experiments and patients. This data, together with new technologies to create and analyze it, has ushered in an era of data-driven discovery which requires moving beyond the traditional individual, single-discipline investigator research model. This interdisciplinary niche is where computational biology thrives. It has matured over the past three decades and made major contributions to scientific knowledge and human health, yet researchers in the field often languish in career advancement, publication, and grant review. We propose solutions for individual scientists, institutions, journal publishers, funding agencies, and educators.
|
1705.09374
|
Michael Margaliot
|
Yoram Zarai and Alexander Ovseevich and Michael Margaliot
|
Optimal Translation Along a Circular mRNA
| null | null | null | null |
q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ribosome flow model on a ring (RFMR) is a deterministic model for
translation of a circularized mRNA. We derive a new spectral representation for
the optimal steady-state production rate and the corresponding optimal
steady-state ribosomal density in the RFMR. This representation has several
important advantages. First, it provides a simple and numerically stable
algorithm for determining the optimal values even in very long rings. Second,
it enables efficient computation of the sensitivity of the optimal production
rate to small changes in the transition rates along the mRNA. Third, it implies
that the optimal steady-state production rate is a strictly concave function of
the transition rates. Thus maximizing the optimal steady-state production rate
with respect to the rates, under an affine constraint on the rates becomes a
convex optimization problem that admits a unique solution, which can be
determined numerically using highly efficient algorithms. This optimization
problem is important, for example, when re-engineering heterologous genes in a
host organism. We describe the implications of our results to this and other
aspects of translation.
|
[
{
"created": "Thu, 25 May 2017 21:43:50 GMT",
"version": "v1"
}
] |
2017-05-29
|
[
[
"Zarai",
"Yoram",
""
],
[
"Ovseevich",
"Alexander",
""
],
[
"Margaliot",
"Michael",
""
]
] |
The ribosome flow model on a ring (RFMR) is a deterministic model for translation of a circularized mRNA. We derive a new spectral representation for the optimal steady-state production rate and the corresponding optimal steady-state ribosomal density in the RFMR. This representation has several important advantages. First, it provides a simple and numerically stable algorithm for determining the optimal values even in very long rings. Second, it enables efficient computation of the sensitivity of the optimal production rate to small changes in the transition rates along the mRNA. Third, it implies that the optimal steady-state production rate is a strictly concave function of the transition rates. Thus maximizing the optimal steady-state production rate with respect to the rates, under an affine constraint on the rates becomes a convex optimization problem that admits a unique solution, which can be determined numerically using highly efficient algorithms. This optimization problem is important, for example, when re-engineering heterologous genes in a host organism. We describe the implications of our results to this and other aspects of translation.
|
0712.0613
|
Michael Plank
|
A. James and M. J. Plank
|
On fitting power laws to ecological data
| null | null | null | null |
q-bio.QM
| null |
Heavy-tailed or power-law distributions are becoming increasingly common in
biological literature. A wide range of biological data has been fitted to
distributions with heavy tails. Many of these studies use simple fitting
methods to find the parameters in the distribution, which can give highly
misleading results. The potential pitfalls that can occur when using these
methods are pointed out, and a step-by-step guide to fitting power-law
distributions and assessing their goodness-of-fit is offered.
|
[
{
"created": "Tue, 4 Dec 2007 21:09:59 GMT",
"version": "v1"
}
] |
2007-12-06
|
[
[
"James",
"A.",
""
],
[
"Plank",
"M. J.",
""
]
] |
Heavy-tailed or power-law distributions are becoming increasingly common in biological literature. A wide range of biological data has been fitted to distributions with heavy tails. Many of these studies use simple fitting methods to find the parameters in the distribution, which can give highly misleading results. The potential pitfalls that can occur when using these methods are pointed out, and a step-by-step guide to fitting power-law distributions and assessing their goodness-of-fit is offered.
|
1209.0176
|
Anna-Sophie Fiston-Lavier
|
Anna-Sophie Fiston-Lavier, Charles E. Vejnar and Hadi Quesneville
|
Transposable element sequence evolution is influenced by gene context
|
36 pages, 4 figures, 2 supplementary figures)
| null | null | null |
q-bio.GN q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Background: Transposable elements (TEs) in eukaryote genomes are
quantitatively the main components affecting genome size, structure and
expression. The dynamics of their insertion and deletion depend on diverse
factors varying in strength and nature along the genome. We address here how TE
sequence evolution is affected by neighboring genes and the chromatin status
(euchromatin or heterochromatin) at their insertion site. Results: We estimated
ages of TE sequences in Arabidopsis thaliana, and found that they depend on the
distance to the nearest genes: TEs located close to genes are older than those
that are more distant. Consequently, TE sequences in heterochromatic regions,
which are gene-poor regions, are surprisingly younger and longer than that
elsewhere. Conclusions: We provide evidence for biased TE age distribution
close or near to genes. Interestingly, TE sequences in euchromatin and those in
heterochromatin evolve at different rates, and as a result, could explain that
TE sequences in heterochromatin tend to be younger and longer. Then, we revisit
models of TE sequence dynamics and point out differences for TE-rich genomes,
such as maize and wheat, compared to TE-poor genomes such as fly and A.
thaliana.
|
[
{
"created": "Sun, 2 Sep 2012 13:38:37 GMT",
"version": "v1"
},
{
"created": "Sun, 24 Nov 2013 16:30:17 GMT",
"version": "v2"
}
] |
2013-11-26
|
[
[
"Fiston-Lavier",
"Anna-Sophie",
""
],
[
"Vejnar",
"Charles E.",
""
],
[
"Quesneville",
"Hadi",
""
]
] |
Background: Transposable elements (TEs) in eukaryote genomes are quantitatively the main components affecting genome size, structure and expression. The dynamics of their insertion and deletion depend on diverse factors varying in strength and nature along the genome. We address here how TE sequence evolution is affected by neighboring genes and the chromatin status (euchromatin or heterochromatin) at their insertion site. Results: We estimated ages of TE sequences in Arabidopsis thaliana, and found that they depend on the distance to the nearest genes: TEs located close to genes are older than those that are more distant. Consequently, TE sequences in heterochromatic regions, which are gene-poor regions, are surprisingly younger and longer than that elsewhere. Conclusions: We provide evidence for biased TE age distribution close or near to genes. Interestingly, TE sequences in euchromatin and those in heterochromatin evolve at different rates, and as a result, could explain that TE sequences in heterochromatin tend to be younger and longer. Then, we revisit models of TE sequence dynamics and point out differences for TE-rich genomes, such as maize and wheat, compared to TE-poor genomes such as fly and A. thaliana.
|
1911.07788
|
Haitham Qaralleh
|
Mohammad Jaafreh, Haitham Qaralleh, Muhamad O. Al-limoun
|
Biological Value of Centaurea damascena: Minireview
|
8 pages. arXiv admin note: substantial text overlap with
arXiv:1911.02243
|
Journal of basic and applied Research in Biomedicine, 2019, 5(2):
99-106
| null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The family Asteraceae include large number of Centaurea species which have
been applied in folk medicine. One of the family Asteraceae members is the
Centaurea damascena which authentically been tested for its antibacterial
activity. The aim of the study was to discuss antibacterial activities of
essential oil composition and methanolic extract of the same plant aerial part
leaves. Thirty-seven components were characterized with 86 of oxygenated
terpenes. The composition in percentage was dominated by 11.45 Fokienol, 8.8
thymol, 8.21 Alpha Terpineol, 7.24 Chrysanthemumic acid, 7.13 Terpinen4-ol and
6.59 Borneol with a high degree of polymorphism in the occurrence of these
compounds as compared with the different species of centaurea.. Free radical
scavenging capacity of the C. damascna methanol extract was calculated by DPPH
and FRAP test. DPPH radicals were scavenged with an IC50 value of 17.08
microgram per ml. Antioxidant capacities obtained by the FRAP was 51.9 and
expressed in mg Trolox gram per Liter dry weight. The total phenolic compounds
of the methanol extracts of aerial parts, as estimated by Folin Ciocalteu
reagent method, was about 460 milligram GAE per gram. The phenolic contents in
the extracts highly correlate with their antioxidant activity, confirming that
the antioxidant activity of this plant extracts is considerably phenolic
contents dependent.
|
[
{
"created": "Thu, 14 Nov 2019 11:34:34 GMT",
"version": "v1"
}
] |
2019-11-19
|
[
[
"Jaafreh",
"Mohammad",
""
],
[
"Qaralleh",
"Haitham",
""
],
[
"Al-limoun",
"Muhamad O.",
""
]
] |
The family Asteraceae include large number of Centaurea species which have been applied in folk medicine. One of the family Asteraceae members is the Centaurea damascena which authentically been tested for its antibacterial activity. The aim of the study was to discuss antibacterial activities of essential oil composition and methanolic extract of the same plant aerial part leaves. Thirty-seven components were characterized with 86 of oxygenated terpenes. The composition in percentage was dominated by 11.45 Fokienol, 8.8 thymol, 8.21 Alpha Terpineol, 7.24 Chrysanthemumic acid, 7.13 Terpinen4-ol and 6.59 Borneol with a high degree of polymorphism in the occurrence of these compounds as compared with the different species of centaurea.. Free radical scavenging capacity of the C. damascna methanol extract was calculated by DPPH and FRAP test. DPPH radicals were scavenged with an IC50 value of 17.08 microgram per ml. Antioxidant capacities obtained by the FRAP was 51.9 and expressed in mg Trolox gram per Liter dry weight. The total phenolic compounds of the methanol extracts of aerial parts, as estimated by Folin Ciocalteu reagent method, was about 460 milligram GAE per gram. The phenolic contents in the extracts highly correlate with their antioxidant activity, confirming that the antioxidant activity of this plant extracts is considerably phenolic contents dependent.
|
2101.03125
|
Olivier Besson M.W.
|
Olivier Besson, Herv\'e Mugnier, Aur\'elien Neveux, Ga\"elle Rey,
Sully Vitry
|
Bee Cluster 3D: A system to monitor the temperature in a hive over time
| null | null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A new system, Bee Cluster 3D, allowing the study of the time evolution of the
3D temperature distribution in a bee hive is presented. This system can be used
to evaluate the cluster size and the location of the queen during winter. In
summer, the device can be used to quantify the size of the brood nest and the
breeding activity of the queen. The system does not disturb the activity of the
colony and can be used on any hive. This electronic system was developed to be
non-intrusive, miniaturized, and energy autonomous.
|
[
{
"created": "Fri, 8 Jan 2021 17:39:00 GMT",
"version": "v1"
}
] |
2021-01-11
|
[
[
"Besson",
"Olivier",
""
],
[
"Mugnier",
"Hervé",
""
],
[
"Neveux",
"Aurélien",
""
],
[
"Rey",
"Gaëlle",
""
],
[
"Vitry",
"Sully",
""
]
] |
A new system, Bee Cluster 3D, allowing the study of the time evolution of the 3D temperature distribution in a bee hive is presented. This system can be used to evaluate the cluster size and the location of the queen during winter. In summer, the device can be used to quantify the size of the brood nest and the breeding activity of the queen. The system does not disturb the activity of the colony and can be used on any hive. This electronic system was developed to be non-intrusive, miniaturized, and energy autonomous.
|
1701.04967
|
Shiwei Yan
|
Shuxia Liu, Yizhao Geng, and Shiwei Yan
|
Structural Effects and Competition Mechanisms Targeting the Interactions
between p53 and Mdm2 for Cancer Therapy
|
9 pages, 8 figures
| null | null | null |
q-bio.MN physics.bio-ph q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
About half of human cancers show normal TP53 gene and aberrant overexpression
of Mdm2 and/or MdmX. This fact promotes a promising cancer therapeutic strategy
which targeting the interactions between p53 and Mdm2/MdmX. For developing the
inhibitors to disrupt the p53-Mdm2/MdmX interactions, we systematically
investigate structural and interaction characteristics of p53 and inhibitors
with Mdm2 and MdmX from atomistic level by exploiting stochastic molecular
dynamics simulations. We find that some specific $\alpha$ helices in Mdm2 and
MdmX structure play key role in their bindings with inhibitors and the hydrogen
bond formed by residue Trp23 of p53 with its counterpart in Mdm2/MdmX
determines dynamical competition processes of the disruption of Mdm2-p53
interaction and replacement of p53 from Mdm2-p53 complex {\it in vivo}. We hope
that the results reported in this paper provide basic information for designing
functional inhibitors and realizing cancer gene therapy.
|
[
{
"created": "Wed, 18 Jan 2017 06:48:31 GMT",
"version": "v1"
}
] |
2017-01-25
|
[
[
"Liu",
"Shuxia",
""
],
[
"Geng",
"Yizhao",
""
],
[
"Yan",
"Shiwei",
""
]
] |
About half of human cancers show normal TP53 gene and aberrant overexpression of Mdm2 and/or MdmX. This fact promotes a promising cancer therapeutic strategy which targeting the interactions between p53 and Mdm2/MdmX. For developing the inhibitors to disrupt the p53-Mdm2/MdmX interactions, we systematically investigate structural and interaction characteristics of p53 and inhibitors with Mdm2 and MdmX from atomistic level by exploiting stochastic molecular dynamics simulations. We find that some specific $\alpha$ helices in Mdm2 and MdmX structure play key role in their bindings with inhibitors and the hydrogen bond formed by residue Trp23 of p53 with its counterpart in Mdm2/MdmX determines dynamical competition processes of the disruption of Mdm2-p53 interaction and replacement of p53 from Mdm2-p53 complex {\it in vivo}. We hope that the results reported in this paper provide basic information for designing functional inhibitors and realizing cancer gene therapy.
|
0705.1019
|
Shenbing Kuang
|
Shenbing Kuang, Jiafu Wang, Ting Zeng, Aiyin Cao
|
Theoretical Analysis of Subthreshold Oscillatory Behaviors in Nonlinear
Autonomous Systems
|
4 pages, 2 figures
| null | null | null |
q-bio.QM
| null |
We have developed a linearization method to investigate the subthreshold
oscillatory behaviors in nonlinear autonomous systems. By considering firstly
the neuronal system as an example, we show that this theoretical approach can
predict quantitatively the subthreshold oscillatory activities, including the
damping coefficients and the oscillatory frequencies which are in good
agreement with those observed in experiments. Then we generalize the
linearization method to an arbitrary autonomous nonlinear system. The detailed
extension of this theoretical approach is also presented and further discussed.
|
[
{
"created": "Tue, 8 May 2007 00:48:55 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Kuang",
"Shenbing",
""
],
[
"Wang",
"Jiafu",
""
],
[
"Zeng",
"Ting",
""
],
[
"Cao",
"Aiyin",
""
]
] |
We have developed a linearization method to investigate the subthreshold oscillatory behaviors in nonlinear autonomous systems. By considering firstly the neuronal system as an example, we show that this theoretical approach can predict quantitatively the subthreshold oscillatory activities, including the damping coefficients and the oscillatory frequencies which are in good agreement with those observed in experiments. Then we generalize the linearization method to an arbitrary autonomous nonlinear system. The detailed extension of this theoretical approach is also presented and further discussed.
|
2311.16317
|
Niket Thakkar
|
Niket Thakkar and Mike Famulare
|
A generating function perspective on the transmission forest
| null | null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
In a previous paper, we showed that a compartmental stochastic process model
of SARS-CoV-2 transmission could be fit to time series data and then
reinterpreted as a collection of interacting branching processes drawn from a
dynamic degree distribution. We called this reinterpretation a transmission
forest. This paper builds on that idea. Specifically, leveraging generating
function methods from analytic combinatorics, we develop a theory describing
the transmission forest's properties, allowing us to show for example that
transmission tree interactions fade with increasing disease prevalence. We then
validate the theory by computing forest statistics, like the tree survival
function, which we compare to estimates based on the sampling method developed
previously. The accuracy and flexibility of the analytic approach is clear, and
it allows us to comment on multi-scale features of more general transmission
processes.
|
[
{
"created": "Mon, 27 Nov 2023 21:06:07 GMT",
"version": "v1"
}
] |
2023-11-29
|
[
[
"Thakkar",
"Niket",
""
],
[
"Famulare",
"Mike",
""
]
] |
In a previous paper, we showed that a compartmental stochastic process model of SARS-CoV-2 transmission could be fit to time series data and then reinterpreted as a collection of interacting branching processes drawn from a dynamic degree distribution. We called this reinterpretation a transmission forest. This paper builds on that idea. Specifically, leveraging generating function methods from analytic combinatorics, we develop a theory describing the transmission forest's properties, allowing us to show for example that transmission tree interactions fade with increasing disease prevalence. We then validate the theory by computing forest statistics, like the tree survival function, which we compare to estimates based on the sampling method developed previously. The accuracy and flexibility of the analytic approach is clear, and it allows us to comment on multi-scale features of more general transmission processes.
|
2003.07764
|
Amirhossein Sadeghi Manesh
|
AmirHosein Sadeghimanesh and Matthew England
|
Polynomial Superlevel Set Representation of the Multistationarity Region
of Chemical Reaction Networks
|
27 pages, 9 figures
|
BMC Bioinformatics, 23 Article number 391, 2022
|
10.1186/s12859-022-04921-6
| null |
q-bio.MN math.AG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we introduce a new representation for the multistationarity
region of a reaction network, using polynomial superlevel sets. The advantages
of using this polynomial superlevel set representation over the already
existing representations (cylindrical algebraic decompositions, numeric
sampling, rectangular divisions) is discussed, and algorithms to compute this
new representation are provided. The results are given for the general
mathematical formalism of a parametric system of equations and so may be
applied to other application domains.
|
[
{
"created": "Tue, 17 Mar 2020 15:27:05 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Sep 2022 11:13:28 GMT",
"version": "v2"
}
] |
2022-09-29
|
[
[
"Sadeghimanesh",
"AmirHosein",
""
],
[
"England",
"Matthew",
""
]
] |
In this paper we introduce a new representation for the multistationarity region of a reaction network, using polynomial superlevel sets. The advantages of using this polynomial superlevel set representation over the already existing representations (cylindrical algebraic decompositions, numeric sampling, rectangular divisions) is discussed, and algorithms to compute this new representation are provided. The results are given for the general mathematical formalism of a parametric system of equations and so may be applied to other application domains.
|
1110.1803
|
Claus Metzner
|
Claus Metzner, Patrick Krauss and Ben Fabry
|
Poresizes in random line networks
|
5 pages, 3 figures, paper draft, to be extended
| null | null | null |
q-bio.QM cond-mat.soft
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many natural fibrous networks with fiber diameters much smaller than the
average poresize can be described as three-dimensional (3D) random line
networks. We consider here a `Mikado' model for such systems, consisting of
straight line segments of equal length, distributed homogeneously and
isotropically in space. First, we derive analytically the probability density
distribution $p(r_{no})$ for the `nearest obstacle distance' $r_{no}$ between a
randomly chosen test point within the network pores and its closest neighboring
point on a line segment. Second, we show that in the limit where the line
segments are much longer than the typical pore size, $p(r_{no})$ becomes a
Rayleigh distribution. The single parameter $\sigma$ of this Rayleigh
distribution represents the most probable nearest obstacle distance and can be
expressed in terms of the total line length per unit volume. Finally, we show
by numerical simulations that $\sigma$ differs only by a constant factor from
the intuitive notion of average `pore size', defined by finding the maximum
sphere that fits into each pore and then averaging over the radii of these
spheres.
|
[
{
"created": "Sun, 9 Oct 2011 07:28:26 GMT",
"version": "v1"
}
] |
2011-10-11
|
[
[
"Metzner",
"Claus",
""
],
[
"Krauss",
"Patrick",
""
],
[
"Fabry",
"Ben",
""
]
] |
Many natural fibrous networks with fiber diameters much smaller than the average poresize can be described as three-dimensional (3D) random line networks. We consider here a `Mikado' model for such systems, consisting of straight line segments of equal length, distributed homogeneously and isotropically in space. First, we derive analytically the probability density distribution $p(r_{no})$ for the `nearest obstacle distance' $r_{no}$ between a randomly chosen test point within the network pores and its closest neighboring point on a line segment. Second, we show that in the limit where the line segments are much longer than the typical pore size, $p(r_{no})$ becomes a Rayleigh distribution. The single parameter $\sigma$ of this Rayleigh distribution represents the most probable nearest obstacle distance and can be expressed in terms of the total line length per unit volume. Finally, we show by numerical simulations that $\sigma$ differs only by a constant factor from the intuitive notion of average `pore size', defined by finding the maximum sphere that fits into each pore and then averaging over the radii of these spheres.
|
2201.01830
|
Diego Martinez
|
Bingfan Zhang, Jordan Weil, Antonio Beita Guerra, Pramir Maharjan,
Katie Hilton, Nawin Suesuttajit, Diego A. Martinez, and Craig N. Coon
|
Egg shell quality and bone status as affected by environmental
temperature, Ca and non-phytate P intake and in vitro limestone solubility in
Single-Comb White Leghorn hens
| null |
International Journal of Poultry Science (2020) 19: 219-231
|
10.3923/ijps.2020.219.231
| null |
q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Environmental temperature (ET) often changes the nutrient intake/output for
layers. Changing feed formulations based on ET may need to be utilized to
obtain optimum performance, shell quality and bone status. This study was
conducted to investigate the effects of temperature, Ca intake, non-phytate P
(NPP) intake and in vitro limestone solubility (LS) on egg-shell quality and
bone status in commercial White Leghorn hens. Egg mass and shell weight per
unit surface area (SWUSA) decreased with increasing ET (p lower than 0.05),
especially when ET was 29.7 C (cycling mean ET)or a constant ET was 32.2 C.
Feeding layers a low soluble larger particle size limestone instead of a highly
soluble limestone produced beneficial effects for SWUSA at the thermoneutral ET
(21.1 C) but the beneficial effect was less or disappeared when ET was higher
than 26.6 C in EXP 1 and 2. Feeding layers 245 and 353 mg NPP/h/d supported
satisfactory bone status at 21.1 C, however layers housed at higher than 30 C
needed an additional intake of 50 mg NPP/h/d to support bone integrity. Results
of EXP 1 and 2 indicates that 48 week old layers housed in thermoneutral or
warmer ET require a minimum of 4.2 g Ca/h/d for maintaining optimum shell
quality and bone integrity. Feeding low LS (34.1% in vitro solubility) improved
egg shell quality only for hens housed in thermoneutral ET (21.1 C) and did not
improve egg shell quality at higher ET (constant or cycling). Daily NPP intake
of 245 and 353 mg/h/d supported optimum egg production and bone status at 21.1
C, respectively. A higher NPP and Ca intake may be required for bone status
compared to egg production, especially in older hens.
|
[
{
"created": "Wed, 5 Jan 2022 21:22:33 GMT",
"version": "v1"
}
] |
2022-01-07
|
[
[
"Zhang",
"Bingfan",
""
],
[
"Weil",
"Jordan",
""
],
[
"Guerra",
"Antonio Beita",
""
],
[
"Maharjan",
"Pramir",
""
],
[
"Hilton",
"Katie",
""
],
[
"Suesuttajit",
"Nawin",
""
],
[
"Martinez",
"Diego A.",
""
],
[
"Coon",
"Craig N.",
""
]
] |
Environmental temperature (ET) often changes the nutrient intake/output for layers. Changing feed formulations based on ET may need to be utilized to obtain optimum performance, shell quality and bone status. This study was conducted to investigate the effects of temperature, Ca intake, non-phytate P (NPP) intake and in vitro limestone solubility (LS) on egg-shell quality and bone status in commercial White Leghorn hens. Egg mass and shell weight per unit surface area (SWUSA) decreased with increasing ET (p lower than 0.05), especially when ET was 29.7 C (cycling mean ET)or a constant ET was 32.2 C. Feeding layers a low soluble larger particle size limestone instead of a highly soluble limestone produced beneficial effects for SWUSA at the thermoneutral ET (21.1 C) but the beneficial effect was less or disappeared when ET was higher than 26.6 C in EXP 1 and 2. Feeding layers 245 and 353 mg NPP/h/d supported satisfactory bone status at 21.1 C, however layers housed at higher than 30 C needed an additional intake of 50 mg NPP/h/d to support bone integrity. Results of EXP 1 and 2 indicates that 48 week old layers housed in thermoneutral or warmer ET require a minimum of 4.2 g Ca/h/d for maintaining optimum shell quality and bone integrity. Feeding low LS (34.1% in vitro solubility) improved egg shell quality only for hens housed in thermoneutral ET (21.1 C) and did not improve egg shell quality at higher ET (constant or cycling). Daily NPP intake of 245 and 353 mg/h/d supported optimum egg production and bone status at 21.1 C, respectively. A higher NPP and Ca intake may be required for bone status compared to egg production, especially in older hens.
|
2312.13745
|
Francisco Meseguer
|
Francisco Meseguer and Fernando Ramiro-Manzano
|
The neuron as a temporal electroacoustic medium
|
16 pages, 4 figures
| null | null | null |
q-bio.NC physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The human brain is one of the most complex and intriguing scientific topics.
The most established theory on neuronal communication is a pure electrical
model based on the propagation of intracell cationic charges along the neurons.
Here we propose a complementary model based on two properties of brain
communication: A) The Coulomb interaction associated to the Action Potential
(AP) pulse induces a deformation of the neuron membrane which travels as an
acoustic signal, i.e.: The ions play an essential role and the electric and
acoustic signals, composing the AP, are strongly correlated. B) As brain
communication is stablished through a periodic train of AP pulses it induces a
time periodic modulation of the acoustic parameters. In this framework we
propose envisaging the neuron as a temporal electro-acoustic medium. The
temporal varying media framework could help understanding brain conundrums such
as propagation routes involved in the neuronal plasticity in the consolidation
of the memory, as well as on the generation of the signals associated to the
brain field theory.
|
[
{
"created": "Thu, 21 Dec 2023 11:29:15 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Mar 2024 16:06:48 GMT",
"version": "v2"
}
] |
2024-03-26
|
[
[
"Meseguer",
"Francisco",
""
],
[
"Ramiro-Manzano",
"Fernando",
""
]
] |
The human brain is one of the most complex and intriguing scientific topics. The most established theory on neuronal communication is a pure electrical model based on the propagation of intracell cationic charges along the neurons. Here we propose a complementary model based on two properties of brain communication: A) The Coulomb interaction associated to the Action Potential (AP) pulse induces a deformation of the neuron membrane which travels as an acoustic signal, i.e.: The ions play an essential role and the electric and acoustic signals, composing the AP, are strongly correlated. B) As brain communication is stablished through a periodic train of AP pulses it induces a time periodic modulation of the acoustic parameters. In this framework we propose envisaging the neuron as a temporal electro-acoustic medium. The temporal varying media framework could help understanding brain conundrums such as propagation routes involved in the neuronal plasticity in the consolidation of the memory, as well as on the generation of the signals associated to the brain field theory.
|
2210.04479
|
Alessandro Casa
|
Maria Frizzarin, Giulio Visentin, Alessandro Ferragina, Elena Hayes,
Antonio Bevilacqua, Bhaskar Dhariyal, Katarina Domijan, Hussain Khan,
Georgiana Ifrim, Thach Le Nguyen, Joe Meagher, Laura Menchetti, Ashish Singh,
Suzy Whoriskey, Robert Williamson, Martina Zappaterra, Alessandro Casa
|
Classification of cow diet based on milk mid infrared spectra: a data
analysis competition at the "International workshop of spectroscopy and
chemometrics 2022"
|
27 pages, 9 figures
| null | null | null |
q-bio.QM stat.AP
|
http://creativecommons.org/licenses/by/4.0/
|
In April 2022, the Vistamilk SFI Research Centre organized the second edition
of the "International Workshop on Spectroscopy and Chemometrics - Applications
in Food and Agriculture". Within this event, a data challenge was organized
among participants of the workshop. Such data competition aimed at developing a
prediction model to discriminate dairy cows' diet based on milk spectral
information collected in the mid-infrared region. In fact, the development of
an accurate and reliable discriminant model for dairy cows' diet can provide
important authentication tools for dairy processors to guarantee product origin
for dairy food manufacturers from grass-fed animals. Different statistical and
machine learning modelling approaches have been employed during the workshop,
with different pre-processing steps involved and different degree of
complexity. The present paper aims to describe the statistical methods adopted
by participants to develop such classification model.
|
[
{
"created": "Mon, 10 Oct 2022 08:08:55 GMT",
"version": "v1"
}
] |
2022-10-11
|
[
[
"Frizzarin",
"Maria",
""
],
[
"Visentin",
"Giulio",
""
],
[
"Ferragina",
"Alessandro",
""
],
[
"Hayes",
"Elena",
""
],
[
"Bevilacqua",
"Antonio",
""
],
[
"Dhariyal",
"Bhaskar",
""
],
[
"Domijan",
"Katarina",
""
],
[
"Khan",
"Hussain",
""
],
[
"Ifrim",
"Georgiana",
""
],
[
"Nguyen",
"Thach Le",
""
],
[
"Meagher",
"Joe",
""
],
[
"Menchetti",
"Laura",
""
],
[
"Singh",
"Ashish",
""
],
[
"Whoriskey",
"Suzy",
""
],
[
"Williamson",
"Robert",
""
],
[
"Zappaterra",
"Martina",
""
],
[
"Casa",
"Alessandro",
""
]
] |
In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics - Applications in Food and Agriculture". Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows' diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows' diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model.
|
1902.02875
|
Christian Klos
|
Christian Klos, Yaroslav Felipe Kalle Kossio, Sven Goedeke, Aditya
Gilra, and Raoul-Martin Memmesheimer
|
Dynamical learning of dynamics
| null |
Phys. Rev. Lett. 125, 088103 (2020)
|
10.1103/PhysRevLett.125.088103
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ability of humans and animals to quickly adapt to novel tasks is
difficult to reconcile with the standard paradigm of learning by slow synaptic
weight modification. Here we show that fixed-weight neural networks can learn
to generate required dynamics by imitation. After appropriate weight
pretraining, the networks quickly and dynamically adapt to learn new tasks and
thereafter continue to achieve them without further teacher feedback. We
explain this ability and illustrate it with a variety of target dynamics,
ranging from oscillatory trajectories to driven and chaotic dynamical systems.
|
[
{
"created": "Thu, 7 Feb 2019 23:00:54 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Nov 2019 21:43:02 GMT",
"version": "v2"
},
{
"created": "Tue, 25 Aug 2020 11:35:22 GMT",
"version": "v3"
}
] |
2020-08-26
|
[
[
"Klos",
"Christian",
""
],
[
"Kossio",
"Yaroslav Felipe Kalle",
""
],
[
"Goedeke",
"Sven",
""
],
[
"Gilra",
"Aditya",
""
],
[
"Memmesheimer",
"Raoul-Martin",
""
]
] |
The ability of humans and animals to quickly adapt to novel tasks is difficult to reconcile with the standard paradigm of learning by slow synaptic weight modification. Here we show that fixed-weight neural networks can learn to generate required dynamics by imitation. After appropriate weight pretraining, the networks quickly and dynamically adapt to learn new tasks and thereafter continue to achieve them without further teacher feedback. We explain this ability and illustrate it with a variety of target dynamics, ranging from oscillatory trajectories to driven and chaotic dynamical systems.
|
q-bio/0405020
|
Laszlo Papp
|
Stan Bumble
|
The Orchestral Analog of Molecular Biology
|
17 pages, 8 figures
| null | null | null |
q-bio.MN
| null |
Signal processing (SP) techniques convert DNA and protein sequences into
information that lead to successful drug discovery. One must, however, be aware
about the difference between information and entropy1. Eight other physical
properties of DNA and protein segments are suggested for SP analysis other than
ones already used in the literature. QSAR formulations of these properties are
suggested for ranking the amino acids that maximize efficiency of the amino
acids in proteins. Multiobjective programs are suggested for constraining or
searching the components of such sequences. Geometric maps of the networks of
proteins are preferable to scale-free descriptions in most cases. The genetic
code is presented as graphlets which show interesting correspondence to each
other, leading to possible new revelations.
|
[
{
"created": "Wed, 26 May 2004 00:07:10 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Bumble",
"Stan",
""
]
] |
Signal processing (SP) techniques convert DNA and protein sequences into information that lead to successful drug discovery. One must, however, be aware about the difference between information and entropy1. Eight other physical properties of DNA and protein segments are suggested for SP analysis other than ones already used in the literature. QSAR formulations of these properties are suggested for ranking the amino acids that maximize efficiency of the amino acids in proteins. Multiobjective programs are suggested for constraining or searching the components of such sequences. Geometric maps of the networks of proteins are preferable to scale-free descriptions in most cases. The genetic code is presented as graphlets which show interesting correspondence to each other, leading to possible new revelations.
|
1701.00648
|
Wilhelm Braun
|
Wilhelm Braun and R\"udiger Thul
|
Sign-changes as a universal concept in first-passage time calculations
|
7 pages, 6 figures. Accepted for publication in Phys. Rev. E
| null |
10.1103/PhysRevE.95.012114
| null |
q-bio.NC physics.data-an
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
First-passage time problems are ubiquitous across many fields of study
including transport processes in semiconductors and biological synapses,
evolutionary game theory and percolation. Despite their prominence,
first-passage time calculations have proven to be particularly challenging.
Analytical results to date have often been obtained under strong conditions,
leaving most of the exploration of first-passage time problems to direct
numerical computations. Here we present an analytical approach that allows the
derivation of first-passage time distributions for the wide class of
non-differentiable Gaussian processes. We demonstrate that the concept of sign
changes naturally generalises the common practice of counting crossings to
determine first-passage events. Our method works across a wide range of
time-dependent boundaries and noise strengths thus alleviating common hurdles
in first-passage time calculations.
|
[
{
"created": "Tue, 3 Jan 2017 11:06:40 GMT",
"version": "v1"
}
] |
2017-02-01
|
[
[
"Braun",
"Wilhelm",
""
],
[
"Thul",
"Rüdiger",
""
]
] |
First-passage time problems are ubiquitous across many fields of study including transport processes in semiconductors and biological synapses, evolutionary game theory and percolation. Despite their prominence, first-passage time calculations have proven to be particularly challenging. Analytical results to date have often been obtained under strong conditions, leaving most of the exploration of first-passage time problems to direct numerical computations. Here we present an analytical approach that allows the derivation of first-passage time distributions for the wide class of non-differentiable Gaussian processes. We demonstrate that the concept of sign changes naturally generalises the common practice of counting crossings to determine first-passage events. Our method works across a wide range of time-dependent boundaries and noise strengths thus alleviating common hurdles in first-passage time calculations.
|
1610.01404
|
Yuri Shestopaloff
|
Yuri K. Shestopaloff
|
Why cells grow and divide? General growth mechanism and how it defines
cells' growth, reproduction and metabolic properties
|
37 pages, 13 figures, 3 tables
|
Biophysical Reviews and Letters, 2015, 10(4), 209-256
|
10.1142/S1793048015500113
| null |
q-bio.OT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider a general growth mechanism, which acts at cellular level and
above (organs, systems and whole organisms). Using its mathematical
representation, the growth equation, we study the growth and division
mechanisms of amoeba and fission yeast Schizosaccharomyces pombe. We show how
this mechanism, together with biomolecular machinery, governs growth and
reproduction of cells, and these organisms in particular. This mechanism
provides revealing answers to fundamental questions of biology, like why cells
grow and divide, why and when cells' growth stops. It also sheds light on
questions like why and how life originated and developed. Solving the growth
equation, we obtain analytical expression for the growth curve of fission yeast
as a function of geometrical characteristics and nutrient influxes for RNA and
protein synthesis, and compare the computed growth curves with 85 experiments.
Statistical evaluation shows that these growth curves correspond to
experimental data significantly better than all previous approximations. Also,
using the general growth mechanism, we show how metabolic characteristics of
cells, their size and evolutionary traits relate, considering fission yeast. In
particular, we found that fission yeast S. pombe consumes about 16-18 times
more nutrients for maintenance needs than for biomass synthesis.
|
[
{
"created": "Wed, 5 Oct 2016 13:17:37 GMT",
"version": "v1"
}
] |
2016-10-06
|
[
[
"Shestopaloff",
"Yuri K.",
""
]
] |
We consider a general growth mechanism, which acts at cellular level and above (organs, systems and whole organisms). Using its mathematical representation, the growth equation, we study the growth and division mechanisms of amoeba and fission yeast Schizosaccharomyces pombe. We show how this mechanism, together with biomolecular machinery, governs growth and reproduction of cells, and these organisms in particular. This mechanism provides revealing answers to fundamental questions of biology, like why cells grow and divide, why and when cells' growth stops. It also sheds light on questions like why and how life originated and developed. Solving the growth equation, we obtain analytical expression for the growth curve of fission yeast as a function of geometrical characteristics and nutrient influxes for RNA and protein synthesis, and compare the computed growth curves with 85 experiments. Statistical evaluation shows that these growth curves correspond to experimental data significantly better than all previous approximations. Also, using the general growth mechanism, we show how metabolic characteristics of cells, their size and evolutionary traits relate, considering fission yeast. In particular, we found that fission yeast S. pombe consumes about 16-18 times more nutrients for maintenance needs than for biomass synthesis.
|
1107.4220
|
Vladimir Chechetkin R.
|
G.I. Kravatskaya, Y.V. Kravatsky, V.R. Chechetkin, V.G. Tumanyan
|
Coexistence of different base periodicities in prokaryotic genomes as
related to DNA curvature, supercoiling, and transcription
|
23 pages, 6 figures, 2 tables
| null |
10.1016/j.ygeno.2011.06.006
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We analyzed the periodic patterns in E. coli promoters and compared the
distributions of the corresponding patterns in promoters and in the complete
genome to elucidate their function. Except the three-base periodicity,
coincident with that in the coding regions and growing stronger in the region
downstream from the transcriptions start (TS), all other salient periodicities
are peaked upstream of TS. We found that helical periodicities with the lengths
about B-helix pitch ~10.2-10.5 bp and A-helix pitch ~10.8-11.1 bp coexist in
the genomic sequences. We mapped the distributions of stretches with A-, B-,
and Z- like DNA periodicities onto E.coli genome. All three periodicities tend
to concentrate within non-coding regions when their intensity becomes stronger
and prevail in the promoter sequences. The comparison with available
experimental data indicates that promoters with the most pronounced
periodicities may be related to the supercoiling-sensitive genes.
|
[
{
"created": "Thu, 21 Jul 2011 10:03:27 GMT",
"version": "v1"
}
] |
2011-07-22
|
[
[
"Kravatskaya",
"G. I.",
""
],
[
"Kravatsky",
"Y. V.",
""
],
[
"Chechetkin",
"V. R.",
""
],
[
"Tumanyan",
"V. G.",
""
]
] |
We analyzed the periodic patterns in E. coli promoters and compared the distributions of the corresponding patterns in promoters and in the complete genome to elucidate their function. Except the three-base periodicity, coincident with that in the coding regions and growing stronger in the region downstream from the transcriptions start (TS), all other salient periodicities are peaked upstream of TS. We found that helical periodicities with the lengths about B-helix pitch ~10.2-10.5 bp and A-helix pitch ~10.8-11.1 bp coexist in the genomic sequences. We mapped the distributions of stretches with A-, B-, and Z- like DNA periodicities onto E.coli genome. All three periodicities tend to concentrate within non-coding regions when their intensity becomes stronger and prevail in the promoter sequences. The comparison with available experimental data indicates that promoters with the most pronounced periodicities may be related to the supercoiling-sensitive genes.
|
1909.00585
|
R\'obert Juh\'asz
|
R. Juh\'asz and B. Oborny
|
Percolation theory suggests some general features in range margins
across environmental gradients
|
17 pages, 5 figures
|
Ecological Complexity 42, 100814 (2020)
|
10.1016/j.ecocom.2020.100814
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The margins within the geographic range of species are often specific in
terms of ecological and evolutionary processes, and can strongly influence the
species' reaction to climate change. One of the frequently observed features at
range margins is fragmentation, caused internally by population dynamics or
externally by the limited availability of suitable habitat sites. We study both
causes, and describe the transition from a connected to a fragmented state
across space by means of a gradient metapopulation model. The main features of
our approach are the following. 1) Inhomogeneities can occur at two spatial
scales: there is a broad-scale gradient, which can be patterned by fine-scale
heterogeneities. The latter is implemented by dispersing a variable number of
small obstacles over the terrain, which can be penetrable or unpenetrable by
the spreading species. 2) We study the occupancy of this terrain in a
steady-state on two temporal scales: in snapshots and by long-term averages.
The simulations reveal some general scaling laws that are applicable in various
environments, independently of the mechanism of fragmentation. The edge of the
connected region (the hull) is a fractal with dimension 7/4. Its width and
length changes with the gradient according to universal scaling laws, that are
characteristic for percolation transitions. The results suggest that
percolation theory is a powerful tool for understanding the structure of range
margins in a broad variety of real-life scenarios, including those in which the
environmental gradient is combined with fine-scale heterogeneity. This provides
a new method for comparing the range margins of different species in various
geographic regions, and monitoring range shifts under climate change.
|
[
{
"created": "Mon, 2 Sep 2019 07:52:25 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Feb 2020 08:04:30 GMT",
"version": "v2"
}
] |
2020-02-28
|
[
[
"Juhász",
"R.",
""
],
[
"Oborny",
"B.",
""
]
] |
The margins within the geographic range of species are often specific in terms of ecological and evolutionary processes, and can strongly influence the species' reaction to climate change. One of the frequently observed features at range margins is fragmentation, caused internally by population dynamics or externally by the limited availability of suitable habitat sites. We study both causes, and describe the transition from a connected to a fragmented state across space by means of a gradient metapopulation model. The main features of our approach are the following. 1) Inhomogeneities can occur at two spatial scales: there is a broad-scale gradient, which can be patterned by fine-scale heterogeneities. The latter is implemented by dispersing a variable number of small obstacles over the terrain, which can be penetrable or unpenetrable by the spreading species. 2) We study the occupancy of this terrain in a steady-state on two temporal scales: in snapshots and by long-term averages. The simulations reveal some general scaling laws that are applicable in various environments, independently of the mechanism of fragmentation. The edge of the connected region (the hull) is a fractal with dimension 7/4. Its width and length changes with the gradient according to universal scaling laws, that are characteristic for percolation transitions. The results suggest that percolation theory is a powerful tool for understanding the structure of range margins in a broad variety of real-life scenarios, including those in which the environmental gradient is combined with fine-scale heterogeneity. This provides a new method for comparing the range margins of different species in various geographic regions, and monitoring range shifts under climate change.
|
2111.09402
|
Caden Lin
|
Caden Lin
|
A Novel Compartmental Approach to Modeling COVID-19 Disease Dynamics and
Analyzing the Effect of Common Preventative Measures
|
25 pages, 14 figures
| null | null | null |
q-bio.PE physics.soc-ph stat.AP
|
http://creativecommons.org/licenses/by/4.0/
|
As of December 2020, the COVID-19 pandemic has infected over 75 million
people, making it the deadliest pandemic in modern history. This study develops
a novel compartmental epidemiological model specific to the SARS-CoV-2 virus
and analyzes the effect of common preventative measures such as testing,
quarantine, social distancing, and vaccination. By accounting for the most
prevalent interventions that have been enacted to minimize the spread of the
virus, the model establishes a paramount foundation for future mathematical
modeling of COVID-19 and other modern pandemics. Specifically, the model
expands on the classic SIR model and introduces separate compartments for
individuals who are in the incubation period, asymptomatic, tested-positive,
quarantined, vaccinated, or deceased. It also accounts for variable infection,
testing, and death rates. I first analyze the outbreak in Santa Clara County,
California, and later generalize the findings. The results show that, although
all preventative measures reduce the spread of COVID-19, quarantine and social
distancing mandates reduce the infection rate and subsequently are the most
effective policies, followed by vaccine distribution and, finally, public
testing. Thus, governments should concentrate resources on enforcing quarantine
and social distancing policies. In addition, I find mathematical proof that the
relatively high asymptomatic rate and long incubation period are driving
factors of COVID-19's rapid spread.
|
[
{
"created": "Wed, 17 Nov 2021 21:16:19 GMT",
"version": "v1"
}
] |
2021-11-19
|
[
[
"Lin",
"Caden",
""
]
] |
As of December 2020, the COVID-19 pandemic has infected over 75 million people, making it the deadliest pandemic in modern history. This study develops a novel compartmental epidemiological model specific to the SARS-CoV-2 virus and analyzes the effect of common preventative measures such as testing, quarantine, social distancing, and vaccination. By accounting for the most prevalent interventions that have been enacted to minimize the spread of the virus, the model establishes a paramount foundation for future mathematical modeling of COVID-19 and other modern pandemics. Specifically, the model expands on the classic SIR model and introduces separate compartments for individuals who are in the incubation period, asymptomatic, tested-positive, quarantined, vaccinated, or deceased. It also accounts for variable infection, testing, and death rates. I first analyze the outbreak in Santa Clara County, California, and later generalize the findings. The results show that, although all preventative measures reduce the spread of COVID-19, quarantine and social distancing mandates reduce the infection rate and subsequently are the most effective policies, followed by vaccine distribution and, finally, public testing. Thus, governments should concentrate resources on enforcing quarantine and social distancing policies. In addition, I find mathematical proof that the relatively high asymptomatic rate and long incubation period are driving factors of COVID-19's rapid spread.
|
2012.15750
|
Samiya Alkhairy
|
Samiya A Alkhairy, Christopher A Shera
|
An analytic physically motivated model of the mammalian cochlea
|
17 pages, 12 figures. Published in JASA
|
The Journal of the Acoustical Society of America, 145(1), 45-60
(2019)
|
10.1121/1.5084042
| null |
q-bio.TO cs.SD eess.AS eess.SP q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We develop an analytic model of the mammalian cochlea. We use a mixed
physical-phenomenological approach by utilizing existing work on the physics of
classical box-representations of the cochlea, and behavior of recent
data-derived wavenumber estimates. Spatial variation is incorporated through a
single independent variable that combines space and frequency. We arrive at
closed-form expressions for the organ of Corti velocity, its impedance, the
pressure difference across the organ of Corti, and its wavenumber. We perform
model tests using real and imaginary parts of chinchilla data from multiple
locations and for multiple variables. The model also predicts impedances that
are qualitatively consistent with current literature. For implementation, the
model can leverage existing efforts for both filter bank and filter cascade
models that target improved algorithmic or analog circuit efficiencies. The
simplicity of the cochlear model, its small number of model constants, its
ability to capture the variation of tuning, its closed-form expressions for
physically-interrelated variables, and the form of these expressions that
allows for easily determining one variable from another make the model
appropriate for analytic and digital auditory filter implementations as
discussed here, as well as for extracting macromechanical insights regarding
how the cochlea works.
|
[
{
"created": "Fri, 25 Dec 2020 00:26:22 GMT",
"version": "v1"
}
] |
2021-08-20
|
[
[
"Alkhairy",
"Samiya A",
""
],
[
"Shera",
"Christopher A",
""
]
] |
We develop an analytic model of the mammalian cochlea. We use a mixed physical-phenomenological approach by utilizing existing work on the physics of classical box-representations of the cochlea, and behavior of recent data-derived wavenumber estimates. Spatial variation is incorporated through a single independent variable that combines space and frequency. We arrive at closed-form expressions for the organ of Corti velocity, its impedance, the pressure difference across the organ of Corti, and its wavenumber. We perform model tests using real and imaginary parts of chinchilla data from multiple locations and for multiple variables. The model also predicts impedances that are qualitatively consistent with current literature. For implementation, the model can leverage existing efforts for both filter bank and filter cascade models that target improved algorithmic or analog circuit efficiencies. The simplicity of the cochlear model, its small number of model constants, its ability to capture the variation of tuning, its closed-form expressions for physically-interrelated variables, and the form of these expressions that allows for easily determining one variable from another make the model appropriate for analytic and digital auditory filter implementations as discussed here, as well as for extracting macromechanical insights regarding how the cochlea works.
|
2407.16580
|
Jacob Sumner
|
Jacob Sumner, Grace Meng, Naomi Brandt, Alex T. Grigas, Andr\'es
C\'ordoba, Mark D. Shattuck, Corey S. O'Hern
|
Assessment of scoring functions for computational models of
protein-protein interfaces
|
21 pages, 7 figures
| null | null | null |
q-bio.BM
|
http://creativecommons.org/licenses/by/4.0/
|
A goal of computational studies of protein-protein interfaces (PPIs) is to
predict the binding site between two monomers that form a heterodimer. The
simplest version of this problem is to rigidly re-dock the bound forms of the
monomers, which involves generating computational models of the heterodimer and
then scoring them to determine the most native-like models. Scoring functions
have been assessed previously using rank- and classification-based metrics,
however, these methods are sensitive to the number and quality of models in the
scoring function training set. We assess the accuracy of seven PPI scoring
functions by comparing their scores to a measure of structural similarity to
the x-ray crystal structure (i.e. the DockQ score) for a non-redundant set of
heterodimers from the Protein Data Bank. For each heterodimer, we generate
re-docked models uniformly sampled over DockQ and calculate the Spearman
correlation between the PPI scores and DockQ. For some targets, the scores and
DockQ are highly correlated; however, for many targets, there are weak
correlations. Several physical features can explain the difference between
difficult- and easy-to-score targets. For example, strong correlations exist
between the score and DockQ for targets with highly intertwined monomers and
many interface contacts. We also develop a new score based on only three
physical features that matches or exceeds the performance of current PPI
scoring functions. These results emphasize that PPI prediction can be improved
by focusing on correlations between the PPI score and DockQ and incorporating
more discriminating physical features into PPI scoring functions.
|
[
{
"created": "Tue, 23 Jul 2024 15:36:47 GMT",
"version": "v1"
}
] |
2024-07-24
|
[
[
"Sumner",
"Jacob",
""
],
[
"Meng",
"Grace",
""
],
[
"Brandt",
"Naomi",
""
],
[
"Grigas",
"Alex T.",
""
],
[
"Córdoba",
"Andrés",
""
],
[
"Shattuck",
"Mark D.",
""
],
[
"O'Hern",
"Corey S.",
""
]
] |
A goal of computational studies of protein-protein interfaces (PPIs) is to predict the binding site between two monomers that form a heterodimer. The simplest version of this problem is to rigidly re-dock the bound forms of the monomers, which involves generating computational models of the heterodimer and then scoring them to determine the most native-like models. Scoring functions have been assessed previously using rank- and classification-based metrics, however, these methods are sensitive to the number and quality of models in the scoring function training set. We assess the accuracy of seven PPI scoring functions by comparing their scores to a measure of structural similarity to the x-ray crystal structure (i.e. the DockQ score) for a non-redundant set of heterodimers from the Protein Data Bank. For each heterodimer, we generate re-docked models uniformly sampled over DockQ and calculate the Spearman correlation between the PPI scores and DockQ. For some targets, the scores and DockQ are highly correlated; however, for many targets, there are weak correlations. Several physical features can explain the difference between difficult- and easy-to-score targets. For example, strong correlations exist between the score and DockQ for targets with highly intertwined monomers and many interface contacts. We also develop a new score based on only three physical features that matches or exceeds the performance of current PPI scoring functions. These results emphasize that PPI prediction can be improved by focusing on correlations between the PPI score and DockQ and incorporating more discriminating physical features into PPI scoring functions.
|
2204.10672
|
Anna Lewis
|
Madelyn Mauro, Danielle S. Allen, Bege Dauda, Santiago J. Molina,
Benjamin M. Neale, Anna C. F. Lewis
|
A systematic review of guidelines for the use of race, ethnicity, and
ancestry reveals widespread consensus but also points of ongoing disagreement
| null | null | null | null |
q-bio.OT
|
http://creativecommons.org/licenses/by/4.0/
|
The use of population descriptors like race, ethnicity, and ancestry in
science, medicine and public health has a long, complicated, and at times dark
history, particularly for genetics, given the field's perceived importance for
understanding between-group differences. The historical and potential harms
that come with irresponsible use of these categories suggests a clear need for
definitive guidance about when and how they can be used appropriately. However,
while many prior authors have provided such guidance, no established consensus
exists, and the extant literature has not been examined for implied consensus
and sources of disagreement. Here we present the results of a systematic review
of published normative recommendations regarding the use of population
categories, particularly in genetics research. Following PRISMA guidelines, we
extracted recommendations from n=121 articles matching inclusion criteria.
Articles were published consistently throughout the time period examined and in
a broad range of journals, demonstrating an ongoing and interdisciplinary
perceived need for guidance. Examined recommendations fall under one of eight
themes identified during analysis. Seven are characterized by broad agreement
across articles; one, Appropriate definitions of population categories and
contexts for use, revealed substantial fundamental disagreement among articles.
While many articles focus on the inappropriate use of race, none fundamentally
problematize ancestry. This work can be a resource to researchers looking for
normative guidance on the use of population descriptors, and can orient authors
of future guidelines to this complex field, contributing to the development of
more effective future guidelines for genetics research.
|
[
{
"created": "Thu, 21 Apr 2022 14:01:00 GMT",
"version": "v1"
}
] |
2022-04-25
|
[
[
"Mauro",
"Madelyn",
""
],
[
"Allen",
"Danielle S.",
""
],
[
"Dauda",
"Bege",
""
],
[
"Molina",
"Santiago J.",
""
],
[
"Neale",
"Benjamin M.",
""
],
[
"Lewis",
"Anna C. F.",
""
]
] |
The use of population descriptors like race, ethnicity, and ancestry in science, medicine and public health has a long, complicated, and at times dark history, particularly for genetics, given the field's perceived importance for understanding between-group differences. The historical and potential harms that come with irresponsible use of these categories suggests a clear need for definitive guidance about when and how they can be used appropriately. However, while many prior authors have provided such guidance, no established consensus exists, and the extant literature has not been examined for implied consensus and sources of disagreement. Here we present the results of a systematic review of published normative recommendations regarding the use of population categories, particularly in genetics research. Following PRISMA guidelines, we extracted recommendations from n=121 articles matching inclusion criteria. Articles were published consistently throughout the time period examined and in a broad range of journals, demonstrating an ongoing and interdisciplinary perceived need for guidance. Examined recommendations fall under one of eight themes identified during analysis. Seven are characterized by broad agreement across articles; one, Appropriate definitions of population categories and contexts for use, revealed substantial fundamental disagreement among articles. While many articles focus on the inappropriate use of race, none fundamentally problematize ancestry. This work can be a resource to researchers looking for normative guidance on the use of population descriptors, and can orient authors of future guidelines to this complex field, contributing to the development of more effective future guidelines for genetics research.
|
0805.3861
|
Vladimir Ivancevic
|
Vladimir G. Ivancevic and Eugene V. Aidman
|
Life-Space Foam: a Medium for Motivational and Cognitive Dynamics
|
25 pages, 2 figures, elsart
|
Physica A 382, 616-630, (2007)
|
10.1016/j.physa.2007.04.025
| null |
q-bio.NC q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
General stochastic dynamics, developed in a framework of Feynman path
integrals, have been applied to Lewinian field--theoretic psychodynamics,
resulting in the development of a new concept of life--space foam (LSF) as a
natural medium for motivational and cognitive psychodynamics. According to LSF
formalisms, the classic Lewinian life space can be macroscopically represented
as a smooth manifold with steady force-fields and behavioral paths, while at
the microscopic level it is more realistically represented as a collection of
wildly fluctuating force-fields, (loco)motion paths and local geometries (and
topologies with holes). A set of least-action principles is used to model the
smoothness of global, macro-level LSF paths, fields and geometry. To model the
corresponding local, micro-level LSF structures, an adaptive path integral is
used, defining a multi-phase and multi-path (multi-field and multi-geometry)
transition process from intention to goal-driven action. Application examples
of this new approach include (but are not limited to) information processing,
motivational fatigue, learning, memory and decision-making.
|
[
{
"created": "Mon, 26 May 2008 02:21:45 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Ivancevic",
"Vladimir G.",
""
],
[
"Aidman",
"Eugene V.",
""
]
] |
General stochastic dynamics, developed in a framework of Feynman path integrals, have been applied to Lewinian field--theoretic psychodynamics, resulting in the development of a new concept of life--space foam (LSF) as a natural medium for motivational and cognitive psychodynamics. According to LSF formalisms, the classic Lewinian life space can be macroscopically represented as a smooth manifold with steady force-fields and behavioral paths, while at the microscopic level it is more realistically represented as a collection of wildly fluctuating force-fields, (loco)motion paths and local geometries (and topologies with holes). A set of least-action principles is used to model the smoothness of global, macro-level LSF paths, fields and geometry. To model the corresponding local, micro-level LSF structures, an adaptive path integral is used, defining a multi-phase and multi-path (multi-field and multi-geometry) transition process from intention to goal-driven action. Application examples of this new approach include (but are not limited to) information processing, motivational fatigue, learning, memory and decision-making.
|
q-bio/0607045
|
Krzysztof Malarz
|
K.Malarz
|
The risk of extinction - the mutational meltdown or the overpopulation
|
RevTex4, 5 pages, 4 figures (8 eps files)
|
Theory Biosci. 125 (2007) 147
|
10.1016/j.thbio.2006.08.001
| null |
q-bio.PE
| null |
The phase diagrams survival-extinction for the Penna model with parameters:
(mutations rate)-(birth rate), (mutation rate)-(harmful mutations threshold),
(harmful mutation threshold)-(minimal reproduction age) are presented. The
extinction phase may be caused by either mutational meltdown or overpopulation.
When the Verhulst factor is responsible for removing only newly born babies and
does not act on adults the overpopulation is avoided and only genetic factors
may lead to species extinction.
|
[
{
"created": "Tue, 25 Jul 2006 13:38:41 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Malarz",
"K.",
""
]
] |
The phase diagrams survival-extinction for the Penna model with parameters: (mutations rate)-(birth rate), (mutation rate)-(harmful mutations threshold), (harmful mutation threshold)-(minimal reproduction age) are presented. The extinction phase may be caused by either mutational meltdown or overpopulation. When the Verhulst factor is responsible for removing only newly born babies and does not act on adults the overpopulation is avoided and only genetic factors may lead to species extinction.
|
2311.08735
|
Ha-Na Jo
|
Ha-Na Jo, Young-Seok Kweon, Gi-Hwan Shin, Heon-Gyu Kwak, Seong-Whan
Lee
|
Neurophysiological Response Based on Auditory Sense for Brain Modulation
Using Monaural Beat
|
Accepted to EMBC 2023
| null | null | null |
q-bio.NC cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Brain modulation is a modification process of brain activity through external
stimulations. However, which condition can induce the activation is still
unclear. Therefore, we aimed to identify brain activation conditions using 40
Hz monaural beat (MB). Under this stimulation, auditory sense status which is
determined by frequency and power range is the condition to consider. Hence, we
designed five sessions to compare; no stimulation, audible (AB), inaudible in
frequency, inaudible in power, and inaudible in frequency and power. Ten
healthy participants underwent each stimulation session for ten minutes with
electroencephalogram (EEG) recording. For analysis, we calculated the power
spectral density (PSD) of EEG for each session and compared them in frequency,
time, and five brain regions. As a result, we observed the prominent power peak
at 40 Hz in only AB. The induced EEG amplitude increase started at one minute
and increased until the end of the session. These results of AB had significant
differences in frontal, central, temporal, parietal, and occipital regions
compared to other stimulations. From the statistical analysis, the PSD of the
right temporal region was significantly higher than the left. We figure out the
role that the auditory sense is important to lead brain activation. These
findings help to understand the neurophysiological principle and effects of
auditory stimulation.
|
[
{
"created": "Wed, 15 Nov 2023 06:57:56 GMT",
"version": "v1"
}
] |
2023-11-16
|
[
[
"Jo",
"Ha-Na",
""
],
[
"Kweon",
"Young-Seok",
""
],
[
"Shin",
"Gi-Hwan",
""
],
[
"Kwak",
"Heon-Gyu",
""
],
[
"Lee",
"Seong-Whan",
""
]
] |
Brain modulation is a modification process of brain activity through external stimulations. However, which condition can induce the activation is still unclear. Therefore, we aimed to identify brain activation conditions using 40 Hz monaural beat (MB). Under this stimulation, auditory sense status which is determined by frequency and power range is the condition to consider. Hence, we designed five sessions to compare; no stimulation, audible (AB), inaudible in frequency, inaudible in power, and inaudible in frequency and power. Ten healthy participants underwent each stimulation session for ten minutes with electroencephalogram (EEG) recording. For analysis, we calculated the power spectral density (PSD) of EEG for each session and compared them in frequency, time, and five brain regions. As a result, we observed the prominent power peak at 40 Hz in only AB. The induced EEG amplitude increase started at one minute and increased until the end of the session. These results of AB had significant differences in frontal, central, temporal, parietal, and occipital regions compared to other stimulations. From the statistical analysis, the PSD of the right temporal region was significantly higher than the left. We figure out the role that the auditory sense is important to lead brain activation. These findings help to understand the neurophysiological principle and effects of auditory stimulation.
|
1904.07952
|
Linda Wang
|
Linda Wang
|
Response of Selective Attention in Middle Temporal Area
|
9 pages, preprint
| null | null | null |
q-bio.NC cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The primary visual cortex processes a large amount of visual information,
however, due to its large receptive fields, when multiple stimuli fall within
one receptive field, there are computational problems. To solve this problem,
the visual system uses selective attention, which allocates resources to a
specific spatial location, to attend to one of the stimuli in the receptive
field. During this process, the center and width of the attending receptive
field change. The model presented in the paper, which is extended and altered
from Bobier et al., simulates the selective attention between the primary
visual cortex, V1, and middle temporal (MT) area. The responses of the MT
columns, which encode the target stimulus, are compared to the results of an
experiment conducted by Womelsdorf et al. on the receptive field shift and
shrinkage in macaque MT area from selective attention. Based on the results,
the responses in the MT area are similar to the Gaussian shaped receptive
fields found in the experiment. As well, the responses of the MT columns are
also measured for accuracy of representing the target visual stimulus and is
found to represent the stimulus with a root mean squared error around 0.17 to
0.18. The paper also explores varying model parameters, such as the membrane
time constant and maximum firing rates, and how those affect the measurement.
This model is a start to modeling the responses of selective attention, however
there are still improvements that can be made to better compare with the
experiment, produce more accurate responses and incorporate more biologically
plausible features.
|
[
{
"created": "Tue, 16 Apr 2019 20:04:32 GMT",
"version": "v1"
}
] |
2019-04-18
|
[
[
"Wang",
"Linda",
""
]
] |
The primary visual cortex processes a large amount of visual information, however, due to its large receptive fields, when multiple stimuli fall within one receptive field, there are computational problems. To solve this problem, the visual system uses selective attention, which allocates resources to a specific spatial location, to attend to one of the stimuli in the receptive field. During this process, the center and width of the attending receptive field change. The model presented in the paper, which is extended and altered from Bobier et al., simulates the selective attention between the primary visual cortex, V1, and middle temporal (MT) area. The responses of the MT columns, which encode the target stimulus, are compared to the results of an experiment conducted by Womelsdorf et al. on the receptive field shift and shrinkage in macaque MT area from selective attention. Based on the results, the responses in the MT area are similar to the Gaussian shaped receptive fields found in the experiment. As well, the responses of the MT columns are also measured for accuracy of representing the target visual stimulus and is found to represent the stimulus with a root mean squared error around 0.17 to 0.18. The paper also explores varying model parameters, such as the membrane time constant and maximum firing rates, and how those affect the measurement. This model is a start to modeling the responses of selective attention, however there are still improvements that can be made to better compare with the experiment, produce more accurate responses and incorporate more biologically plausible features.
|
2110.06078
|
Charlotte Caucheteux
|
Charlotte Caucheteux, Alexandre Gramfort, Jean-R\'emi King
|
Model-based analysis of brain activity reveals the hierarchy of language
in 305 subjects
|
Accepted to EMNLP 2021 (Findings)
|
Findings of the Association for Computational Linguistics (EMNLP
2021)
| null | null |
q-bio.NC cs.AI cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
A popular approach to decompose the neural bases of language consists in
correlating, across individuals, the brain responses to different stimuli (e.g.
regular speech versus scrambled words, sentences, or paragraphs). Although
successful, this `model-free' approach necessitates the acquisition of a large
and costly set of neuroimaging data. Here, we show that a model-based approach
can reach equivalent results within subjects exposed to natural stimuli. We
capitalize on the recently-discovered similarities between deep language models
and the human brain to compute the mapping between i) the brain responses to
regular speech and ii) the activations of deep language models elicited by
modified stimuli (e.g. scrambled words, sentences, or paragraphs). Our
model-based approach successfully replicates the seminal study of Lerner et al.
(2011), which revealed the hierarchy of language areas by comparing the
functional-magnetic resonance imaging (fMRI) of seven subjects listening to
7min of both regular and scrambled narratives. We further extend and precise
these results to the brain signals of 305 individuals listening to 4.1 hours of
narrated stories. Overall, this study paves the way for efficient and flexible
analyses of the brain bases of language.
|
[
{
"created": "Tue, 12 Oct 2021 15:30:21 GMT",
"version": "v1"
}
] |
2023-03-21
|
[
[
"Caucheteux",
"Charlotte",
""
],
[
"Gramfort",
"Alexandre",
""
],
[
"King",
"Jean-Rémi",
""
]
] |
A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli (e.g. regular speech versus scrambled words, sentences, or paragraphs). Although successful, this `model-free' approach necessitates the acquisition of a large and costly set of neuroimaging data. Here, we show that a model-based approach can reach equivalent results within subjects exposed to natural stimuli. We capitalize on the recently-discovered similarities between deep language models and the human brain to compute the mapping between i) the brain responses to regular speech and ii) the activations of deep language models elicited by modified stimuli (e.g. scrambled words, sentences, or paragraphs). Our model-based approach successfully replicates the seminal study of Lerner et al. (2011), which revealed the hierarchy of language areas by comparing the functional-magnetic resonance imaging (fMRI) of seven subjects listening to 7min of both regular and scrambled narratives. We further extend and precise these results to the brain signals of 305 individuals listening to 4.1 hours of narrated stories. Overall, this study paves the way for efficient and flexible analyses of the brain bases of language.
|
2006.13471
|
Sergio Verduzco-Flores
|
Sergio Verduzco-Flores, William Dorrell, Erik DeSchutter
|
A differential Hebbian framework for biologically-plausible motor
control
|
35 pages, 10 figures. Appendix: 9 pages, 2 figures
|
Neural Networks (2022) 150:237-258
|
10.1016/j.neunet.2022.03.002
| null |
q-bio.NC nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we explore a neural control architecture that is both
biologically plausible, and capable of fully autonomous learning. It consists
of feedback controllers that learn to achieve a desired state by selecting the
errors that should drive them. This selection happens through a family of
differential Hebbian learning rules that, through interaction with the
environment, can learn to control systems where the error responds
monotonically to the control signal. We next show that in a more general case,
neural reinforcement learning can be coupled with a feedback controller to
reduce errors that arise non-monotonically from the control signal. The use of
feedback control can reduce the complexity of the reinforcement learning
problem, because only a desired value must be learned, with the controller
handling the details of how it is reached. This makes the function to be
learned simpler, potentially allowing learning of more complex actions. We use
simple examples to illustrate our approach, and discuss how it could be
extended to hierarchical architectures.
|
[
{
"created": "Wed, 24 Jun 2020 04:25:27 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Apr 2021 01:12:46 GMT",
"version": "v2"
},
{
"created": "Thu, 11 Nov 2021 02:24:39 GMT",
"version": "v3"
},
{
"created": "Wed, 2 Feb 2022 03:50:52 GMT",
"version": "v4"
}
] |
2022-03-23
|
[
[
"Verduzco-Flores",
"Sergio",
""
],
[
"Dorrell",
"William",
""
],
[
"DeSchutter",
"Erik",
""
]
] |
In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive them. This selection happens through a family of differential Hebbian learning rules that, through interaction with the environment, can learn to control systems where the error responds monotonically to the control signal. We next show that in a more general case, neural reinforcement learning can be coupled with a feedback controller to reduce errors that arise non-monotonically from the control signal. The use of feedback control can reduce the complexity of the reinforcement learning problem, because only a desired value must be learned, with the controller handling the details of how it is reached. This makes the function to be learned simpler, potentially allowing learning of more complex actions. We use simple examples to illustrate our approach, and discuss how it could be extended to hierarchical architectures.
|
1904.09061
|
Jingwei Liu
|
Jingwei Liu
|
Random Fragments Classification of Microbial Marker Clades with
Multi-class SVM and N-Best Algorithm
|
17 pages, 59 figurea
| null | null | null |
q-bio.QM cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Microbial clades modeling is a challenging problem in biology based on
microarray genome sequences, especially in new species gene isolates discovery
and category. Marker family genome sequences play important roles in describing
specific microbial clades within species, a framework of support vector machine
(SVM) based microbial species classification with N-best algorithm is
constructed to classify the centroid marker genome fragments randomly generated
from marker genome sequences on MetaRef. A time series feature extraction
method is proposed by segmenting the centroid gene sequences and mapping into
different dimensional spaces. Two ways of data splitting are investigated
according to random splitting fragments along genome sequence (DI) , or
separating genome sequences into two parts (DII).Two strategies of fragments
recognition tasks, dimension-by-dimension and sequence--by--sequence, are
investigated. The k-mer size selection, overlap of segmentation and effects of
random split percents are also discussed. Experiments on 12390 maker genome
sequences belonging to marker families of 17 species from MetaRef show that,
both for DI and DII in dimension-by-dimension and sequence-by-sequence
recognition, the recognition accuracy rates can achieve above 28\% in top-1
candidate, and above 91\% in top-10 candidate both on training and testing sets
overall.
|
[
{
"created": "Fri, 19 Apr 2019 03:21:48 GMT",
"version": "v1"
}
] |
2019-04-22
|
[
[
"Liu",
"Jingwei",
""
]
] |
Microbial clades modeling is a challenging problem in biology based on microarray genome sequences, especially in new species gene isolates discovery and category. Marker family genome sequences play important roles in describing specific microbial clades within species, a framework of support vector machine (SVM) based microbial species classification with N-best algorithm is constructed to classify the centroid marker genome fragments randomly generated from marker genome sequences on MetaRef. A time series feature extraction method is proposed by segmenting the centroid gene sequences and mapping into different dimensional spaces. Two ways of data splitting are investigated according to random splitting fragments along genome sequence (DI) , or separating genome sequences into two parts (DII).Two strategies of fragments recognition tasks, dimension-by-dimension and sequence--by--sequence, are investigated. The k-mer size selection, overlap of segmentation and effects of random split percents are also discussed. Experiments on 12390 maker genome sequences belonging to marker families of 17 species from MetaRef show that, both for DI and DII in dimension-by-dimension and sequence-by-sequence recognition, the recognition accuracy rates can achieve above 28\% in top-1 candidate, and above 91\% in top-10 candidate both on training and testing sets overall.
|
1812.06317
|
Nikita Pospelov
|
K.Anokhin, V.Avetisov, A.Gorsky, S.Nechaev, N.Pospelov, O.Valba
|
Spectral peculiarity and criticality of the human connectome
| null | null | null | null |
q-bio.NC cond-mat.dis-nn physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We have performed the comparative spectral analysis of structural connectomes
for various organisms using open-access data. Our analysis indicates several
new peculiar features of the human connectome. We found that the spectral
density of human connectome has the maximal deviation from the spectral density
of the randomized network compared to all other organisms. For many animals
except human structural peculiarities of connectomes are well reproduced in the
network evolution induced by the preference of 3-cycles formation. To get the
reliable fit , we discovered the crucial role of the conservation of local
clusterization in human connectome evolution. We investigated for the first
time the level spacing distribution in the spectrum of human connectome graph
Laplacian. It turns out that the spectral statistics of human connectome
corresponds exactly to the critical regime familiar in the condensed matter
physics which is hybrid of Wigner-Dyson and Poisson distributions. This
observation provides the strong support for the much debated statement of the
brain criticality.
|
[
{
"created": "Sat, 15 Dec 2018 16:28:02 GMT",
"version": "v1"
}
] |
2018-12-18
|
[
[
"Anokhin",
"K.",
""
],
[
"Avetisov",
"V.",
""
],
[
"Gorsky",
"A.",
""
],
[
"Nechaev",
"S.",
""
],
[
"Pospelov",
"N.",
""
],
[
"Valba",
"O.",
""
]
] |
We have performed the comparative spectral analysis of structural connectomes for various organisms using open-access data. Our analysis indicates several new peculiar features of the human connectome. We found that the spectral density of human connectome has the maximal deviation from the spectral density of the randomized network compared to all other organisms. For many animals except human structural peculiarities of connectomes are well reproduced in the network evolution induced by the preference of 3-cycles formation. To get the reliable fit , we discovered the crucial role of the conservation of local clusterization in human connectome evolution. We investigated for the first time the level spacing distribution in the spectrum of human connectome graph Laplacian. It turns out that the spectral statistics of human connectome corresponds exactly to the critical regime familiar in the condensed matter physics which is hybrid of Wigner-Dyson and Poisson distributions. This observation provides the strong support for the much debated statement of the brain criticality.
|
2402.10516
|
Yiheng Zhu
|
Yiheng Zhu, Zitai Kong, Jialu Wu, Weize Liu, Yuqiang Han, Mingze Yin,
Hongxia Xu, Chang-Yu Hsieh and Tingjun Hou
|
Generative AI for Controllable Protein Sequence Design: A Survey
|
9 pages
| null | null | null |
q-bio.BM cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The design of novel protein sequences with targeted functionalities underpins
a central theme in protein engineering, impacting diverse fields such as drug
discovery and enzymatic engineering. However, navigating this vast
combinatorial search space remains a severe challenge due to time and financial
constraints. This scenario is rapidly evolving as the transformative
advancements in AI, particularly in the realm of generative models and
optimization algorithms, have been propelling the protein design field towards
an unprecedented revolution. In this survey, we systematically review recent
advances in generative AI for controllable protein sequence design. To set the
stage, we first outline the foundational tasks in protein sequence design in
terms of the constraints involved and present key generative models and
optimization algorithms. We then offer in-depth reviews of each design task and
discuss the pertinent applications. Finally, we identify the unresolved
challenges and highlight research opportunities that merit deeper exploration.
|
[
{
"created": "Fri, 16 Feb 2024 09:05:02 GMT",
"version": "v1"
}
] |
2024-02-19
|
[
[
"Zhu",
"Yiheng",
""
],
[
"Kong",
"Zitai",
""
],
[
"Wu",
"Jialu",
""
],
[
"Liu",
"Weize",
""
],
[
"Han",
"Yuqiang",
""
],
[
"Yin",
"Mingze",
""
],
[
"Xu",
"Hongxia",
""
],
[
"Hsieh",
"Chang-Yu",
""
],
[
"Hou",
"Tingjun",
""
]
] |
The design of novel protein sequences with targeted functionalities underpins a central theme in protein engineering, impacting diverse fields such as drug discovery and enzymatic engineering. However, navigating this vast combinatorial search space remains a severe challenge due to time and financial constraints. This scenario is rapidly evolving as the transformative advancements in AI, particularly in the realm of generative models and optimization algorithms, have been propelling the protein design field towards an unprecedented revolution. In this survey, we systematically review recent advances in generative AI for controllable protein sequence design. To set the stage, we first outline the foundational tasks in protein sequence design in terms of the constraints involved and present key generative models and optimization algorithms. We then offer in-depth reviews of each design task and discuss the pertinent applications. Finally, we identify the unresolved challenges and highlight research opportunities that merit deeper exploration.
|
0711.2001
|
Demian Battaglia
|
Demian Battaglia, Nicolas Brunel and David Hansel
|
Temporal decorrelation of collective oscillations in neural networks
with local inhibition and long-range excitation
|
4 pages, 5 figures. accepted for publication in Physical Review
Letters
| null |
10.1103/PhysRevLett.99.238106
| null |
q-bio.NC cond-mat.dis-nn nlin.CD
| null |
We consider two neuronal networks coupled by long-range excitatory
interactions. Oscillations in the gamma frequency band are generated within
each network by local inhibition. When long-range excitation is weak, these
oscillations phase-lock with a phase-shift dependent on the strength of local
inhibition. Increasing the strength of long-range excitation induces a
transition to chaos via period-doubling or quasi-periodic scenarios. In the
chaotic regime oscillatory activity undergoes fast temporal decorrelation. The
generality of these dynamical properties is assessed in firing-rate models as
well as in large networks of conductance-based neurons.
|
[
{
"created": "Tue, 13 Nov 2007 15:01:34 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Battaglia",
"Demian",
""
],
[
"Brunel",
"Nicolas",
""
],
[
"Hansel",
"David",
""
]
] |
We consider two neuronal networks coupled by long-range excitatory interactions. Oscillations in the gamma frequency band are generated within each network by local inhibition. When long-range excitation is weak, these oscillations phase-lock with a phase-shift dependent on the strength of local inhibition. Increasing the strength of long-range excitation induces a transition to chaos via period-doubling or quasi-periodic scenarios. In the chaotic regime oscillatory activity undergoes fast temporal decorrelation. The generality of these dynamical properties is assessed in firing-rate models as well as in large networks of conductance-based neurons.
|
q-bio/0605042
|
Tobias Reichenbach
|
Tobias Reichenbach, Mauro Mobilia, and Erwin Frey
|
Coexistence versus extinction in the stochastic cyclic Lotka-Volterra
model
|
12 pages, 9 figures, minor corrections
|
Phys. Rev. E 74, 051907 (2006)
|
10.1103/PhysRevE.74.051907
|
LMU-ASC 38/06
|
q-bio.PE cond-mat.stat-mech physics.bio-ph
| null |
Cyclic dominance of species has been identified as a potential mechanism to
maintain biodiversity, see e.g. B. Kerr, M. A. Riley, M. W. Feldman and B. J.
M. Bohannan [Nature {\bf 418}, 171 (2002)] and B. Kirkup and M. A. Riley
[Nature {\bf 428}, 412 (2004)]. Through analytical methods supported by
numerical simulations, we address this issue by studying the properties of a
paradigmatic non-spatial three-species stochastic system, namely the
`rock-paper-scissors' or cyclic Lotka-Volterra model. While the deterministic
approach (rate equations) predicts the coexistence of the species resulting in
regular (yet neutrally stable) oscillations of the population densities, we
demonstrate that fluctuations arising in the system with a \emph{finite number
of agents} drastically alter this picture and are responsible for extinction:
After long enough time, two of the three species die out. As main findings we
provide analytic estimates and numerical computation of the extinction
probability at a given time. We also discuss the implications of our results
for a broad class of competing population systems.
|
[
{
"created": "Wed, 24 May 2006 17:45:31 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Jun 2006 17:55:25 GMT",
"version": "v2"
},
{
"created": "Thu, 5 Oct 2006 12:06:46 GMT",
"version": "v3"
}
] |
2007-05-23
|
[
[
"Reichenbach",
"Tobias",
""
],
[
"Mobilia",
"Mauro",
""
],
[
"Frey",
"Erwin",
""
]
] |
Cyclic dominance of species has been identified as a potential mechanism to maintain biodiversity, see e.g. B. Kerr, M. A. Riley, M. W. Feldman and B. J. M. Bohannan [Nature {\bf 418}, 171 (2002)] and B. Kirkup and M. A. Riley [Nature {\bf 428}, 412 (2004)]. Through analytical methods supported by numerical simulations, we address this issue by studying the properties of a paradigmatic non-spatial three-species stochastic system, namely the `rock-paper-scissors' or cyclic Lotka-Volterra model. While the deterministic approach (rate equations) predicts the coexistence of the species resulting in regular (yet neutrally stable) oscillations of the population densities, we demonstrate that fluctuations arising in the system with a \emph{finite number of agents} drastically alter this picture and are responsible for extinction: After long enough time, two of the three species die out. As main findings we provide analytic estimates and numerical computation of the extinction probability at a given time. We also discuss the implications of our results for a broad class of competing population systems.
|
0905.4468
|
Tina Toni
|
Tina Toni, Michael P. H. Stumpf
|
Parameter inference and model selection in signaling pathway models
|
Book chapter for Topics in Computational Biology Methods in Molecular
Biology Series, Humana Press, 2009
| null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To support and guide an extensive experimental research into systems biology
of signaling pathways, increasingly more mechanistic models are being developed
with hopes of gaining further insight into biological processes. In order to
analyse these models, computational and statistical techniques are needed to
estimate the unknown kinetic parameters. This chapter reviews methods from
frequentist and Bayesian statistics for estimation of parameters and for
choosing which model is best for modeling the underlying system. Approximate
Bayesian Computation (ABC) techniques are introduced and employed to explore
different hypothesis about the JAK-STAT signaling pathway.
|
[
{
"created": "Wed, 27 May 2009 16:35:40 GMT",
"version": "v1"
}
] |
2009-05-28
|
[
[
"Toni",
"Tina",
""
],
[
"Stumpf",
"Michael P. H.",
""
]
] |
To support and guide an extensive experimental research into systems biology of signaling pathways, increasingly more mechanistic models are being developed with hopes of gaining further insight into biological processes. In order to analyse these models, computational and statistical techniques are needed to estimate the unknown kinetic parameters. This chapter reviews methods from frequentist and Bayesian statistics for estimation of parameters and for choosing which model is best for modeling the underlying system. Approximate Bayesian Computation (ABC) techniques are introduced and employed to explore different hypothesis about the JAK-STAT signaling pathway.
|
1801.06452
|
Qinbing Fu
|
Qinbing Fu and Cheng Hu and Shigang Yue
|
Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in
Juvenile Locusts
| null | null | null | null |
q-bio.NC cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For autonomous robots in dynamic environments mixed with human, it is vital
to detect impending collision quickly and robustly. The biological visual
systems evolved over millions of years may provide us efficient solutions for
collision detection in complex environments. In the cockpit of locusts, two
Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified
which respond to looming objects rigorously with high firing rates. Compared to
LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to
dark moving objects against bright background in depth while not responding to
light objects embedded in dark background - a similar situation which ground
vehicles and robots are facing with. However, little work has been done on
modeling LGMD2, let alone its potential in robotics and other vision-based
applications. In this article, we propose a novel way of modeling LGMD2 neuron,
with biased ON and OFF pathways splitting visual streams into parallel channels
encoding brightness increments and decrements separately to fulfill its
selectivity. Moreover, we apply a biophysical mechanism of spike frequency
adaptation to shape the looming selectivity in such a collision-detecting
neuron model. The proposed visual neural network has been tested with
systematic experiments, challenged against synthetic and real physical stimuli,
as well as image streams from the sensor of a miniature robot. The results
demonstrated this framework is able to detect looming dark objects embedded in
bright backgrounds selectively, which make it ideal for ground mobile
platforms. The robotic experiments also showed its robustness in collision
detection - it performed well for near range navigation in an arena with many
obstacles. Its enhanced collision selectivity to dark approaching objects
versus receding and translating ones has also been verified via systematic
experiments.
|
[
{
"created": "Fri, 22 Dec 2017 00:34:55 GMT",
"version": "v1"
}
] |
2018-01-22
|
[
[
"Fu",
"Qinbing",
""
],
[
"Hu",
"Cheng",
""
],
[
"Yue",
"Shigang",
""
]
] |
For autonomous robots in dynamic environments mixed with human, it is vital to detect impending collision quickly and robustly. The biological visual systems evolved over millions of years may provide us efficient solutions for collision detection in complex environments. In the cockpit of locusts, two Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified which respond to looming objects rigorously with high firing rates. Compared to LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to dark moving objects against bright background in depth while not responding to light objects embedded in dark background - a similar situation which ground vehicles and robots are facing with. However, little work has been done on modeling LGMD2, let alone its potential in robotics and other vision-based applications. In this article, we propose a novel way of modeling LGMD2 neuron, with biased ON and OFF pathways splitting visual streams into parallel channels encoding brightness increments and decrements separately to fulfill its selectivity. Moreover, we apply a biophysical mechanism of spike frequency adaptation to shape the looming selectivity in such a collision-detecting neuron model. The proposed visual neural network has been tested with systematic experiments, challenged against synthetic and real physical stimuli, as well as image streams from the sensor of a miniature robot. The results demonstrated this framework is able to detect looming dark objects embedded in bright backgrounds selectively, which make it ideal for ground mobile platforms. The robotic experiments also showed its robustness in collision detection - it performed well for near range navigation in an arena with many obstacles. Its enhanced collision selectivity to dark approaching objects versus receding and translating ones has also been verified via systematic experiments.
|
2302.10599
|
Mengjie Chen
|
Mengjie Chen
|
The construction of ceRNAs network reveals the prognostic
characteristics of prostate cancer
|
in Chinese language
| null | null | null |
q-bio.MN
|
http://creativecommons.org/licenses/by/4.0/
|
The dysregulation of transcripts is characterized as one of the main
mechanisms in tumor pathogenesis. The recent discovery developed a new
hypothesis, competitive endogenous RNAs (ceRNAs), which could regulate other
RNA transcripts via competing for their shared miRNAs. The interaction of
elements in ceRNAs network was involved in a large range of biological
reactions and facilitate to cancer progression. In this study, we performed a
comprehensive investigation on the regulatory mechanisms and functional roles
of ceRNAs in prostate cancer (PCa) and constructed a ceRNAs network which could
possess potential value in patient prognosis and be evaluated as therapeutic
targets for PCa.
|
[
{
"created": "Tue, 21 Feb 2023 11:06:55 GMT",
"version": "v1"
}
] |
2023-02-22
|
[
[
"Chen",
"Mengjie",
""
]
] |
The dysregulation of transcripts is characterized as one of the main mechanisms in tumor pathogenesis. The recent discovery developed a new hypothesis, competitive endogenous RNAs (ceRNAs), which could regulate other RNA transcripts via competing for their shared miRNAs. The interaction of elements in ceRNAs network was involved in a large range of biological reactions and facilitate to cancer progression. In this study, we performed a comprehensive investigation on the regulatory mechanisms and functional roles of ceRNAs in prostate cancer (PCa) and constructed a ceRNAs network which could possess potential value in patient prognosis and be evaluated as therapeutic targets for PCa.
|
1106.4628
|
Jiapu Zhang
|
Jiapu Zhang
|
The nature of the infectious agents: PrP models of resistant species to
prion diseases (dog, rabbit and horses)
|
This paper is an Invited and Accepted Book Chapter for "Prions and
Prion Diseases: New Developments (J.M. Verdier Eds.), NOVA Publishers, 2012,
ISBN 1621000273."
|
Prions and Prion Diseases: New Developments (J.M. Verdier Eds.),
NOVA Science Publishers, 2012, ISBN 978-1-61942-768-6, Chapter 2, pages 41-48
| null | null |
q-bio.BM physics.bio-ph
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
Prion diseases are invariably fatal and highly infectious neurodegenerative
diseases affecting humans and animals. By now there have not been some
effective therapeutic approaches to treat all these prion diseases. In 2008,
canine mammals including dogs (canis familials) were the first time
academically reported to be resistant to prion diseases (Vaccine 26: 2601--2614
(2008)). Rabbits are the mammalian species known to be resistant to infection
from prion diseases from other species (Journal of Virology 77: 2003--2009
(2003)). Horses were reported to be resistant to prion diseases too
(Proceedings of the National Academy of Sciences USA 107: 19808--19813 (2010)).
By now all the NMR structures of dog, rabbit and horse prion proteins had been
released into protein data bank respectively in 2005, 2007 and 2010
(Proceedings of the National Academy of Sciences USA 102: 640--645 (2005),
Journal of Biomolecular NMR 38:181 (2007), Journal of Molecular Biology 400:
121--128 (2010)). Thus, at this moment it is very worth studying the NMR
molecular structures of horse, dog and rabbit prion proteins to obtain insights
into their immunity prion diseases.
This article reports the findings of the molecular structural dynamics of
wild-type horse, dog, and rabbit prion proteins. The dog and horse prion
proteins have stable molecular structures whether under neutral or low pH
environments. Rabbit prion protein has been found having stable molecular
structures under neutral pH environment, but without structural stability under
low pH environment. Under low pH environment, the salt bridges such as
D177--R163 were broken and caused the collapse of the stable $\alpha$-helical
molecular structures.
|
[
{
"created": "Thu, 23 Jun 2011 05:16:40 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Apr 2012 08:29:16 GMT",
"version": "v2"
}
] |
2012-10-16
|
[
[
"Zhang",
"Jiapu",
""
]
] |
Prion diseases are invariably fatal and highly infectious neurodegenerative diseases affecting humans and animals. By now there have not been some effective therapeutic approaches to treat all these prion diseases. In 2008, canine mammals including dogs (canis familials) were the first time academically reported to be resistant to prion diseases (Vaccine 26: 2601--2614 (2008)). Rabbits are the mammalian species known to be resistant to infection from prion diseases from other species (Journal of Virology 77: 2003--2009 (2003)). Horses were reported to be resistant to prion diseases too (Proceedings of the National Academy of Sciences USA 107: 19808--19813 (2010)). By now all the NMR structures of dog, rabbit and horse prion proteins had been released into protein data bank respectively in 2005, 2007 and 2010 (Proceedings of the National Academy of Sciences USA 102: 640--645 (2005), Journal of Biomolecular NMR 38:181 (2007), Journal of Molecular Biology 400: 121--128 (2010)). Thus, at this moment it is very worth studying the NMR molecular structures of horse, dog and rabbit prion proteins to obtain insights into their immunity prion diseases. This article reports the findings of the molecular structural dynamics of wild-type horse, dog, and rabbit prion proteins. The dog and horse prion proteins have stable molecular structures whether under neutral or low pH environments. Rabbit prion protein has been found having stable molecular structures under neutral pH environment, but without structural stability under low pH environment. Under low pH environment, the salt bridges such as D177--R163 were broken and caused the collapse of the stable $\alpha$-helical molecular structures.
|
1809.01722
|
Hau-tieng Wu
|
Yu-Ting Lin, Yu-Lun Lo, Chen-Yun Lin, Hau-Tieng Wu, Martin G. Frasch
|
Unexpected sawtooth artifact in beat-to-beat pulse transit time measured
from patient monitor data
| null | null |
10.1371/journal.pone.0221319
| null |
q-bio.QM cs.LG eess.SP physics.data-an stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Object: It is increasingly popular to collect as much data as possible in the
hospital setting from clinical monitors for research purposes. However, in this
setup the data calibration issue is often not discussed and, rather, implicitly
assumed, while the clinical monitors might not be designed for the data
analysis purpose. We hypothesize that this calibration issue for a secondary
analysis may become an important source of artifacts in patient monitor data.
We test an off-the-shelf integrated photoplethysmography (PPG) and
electrocardiogram (ECG) monitoring device for its ability to yield a reliable
pulse transit time (PTT) signal. Approach: This is a retrospective clinical
study using two databases: one containing 35 subjects who underwent
laparoscopic cholecystectomy, another containing 22 subjects who underwent
spontaneous breathing test in the intensive care unit. All data sets include
recordings of PPG and ECG using a commonly deployed patient monitor. We
calculated the PTT signal offline. Main Results: We report a novel constant
oscillatory pattern in the PTT signal and identify this pattern as a sawtooth
artifact. We apply an approach based on the de-shape method to visualize,
quantify and validate this sawtooth artifact. Significance: The PPG and ECG
signals not designed for the PTT evaluation may contain unwanted artifacts. The
PTT signal should be calibrated before analysis to avoid erroneous
interpretation of its physiological meaning.
|
[
{
"created": "Mon, 27 Aug 2018 17:35:09 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Aug 2019 14:41:54 GMT",
"version": "v2"
}
] |
2019-09-17
|
[
[
"Lin",
"Yu-Ting",
""
],
[
"Lo",
"Yu-Lun",
""
],
[
"Lin",
"Chen-Yun",
""
],
[
"Wu",
"Hau-Tieng",
""
],
[
"Frasch",
"Martin G.",
""
]
] |
Object: It is increasingly popular to collect as much data as possible in the hospital setting from clinical monitors for research purposes. However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis purpose. We hypothesize that this calibration issue for a secondary analysis may become an important source of artifacts in patient monitor data. We test an off-the-shelf integrated photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a reliable pulse transit time (PTT) signal. Approach: This is a retrospective clinical study using two databases: one containing 35 subjects who underwent laparoscopic cholecystectomy, another containing 22 subjects who underwent spontaneous breathing test in the intensive care unit. All data sets include recordings of PPG and ECG using a commonly deployed patient monitor. We calculated the PTT signal offline. Main Results: We report a novel constant oscillatory pattern in the PTT signal and identify this pattern as a sawtooth artifact. We apply an approach based on the de-shape method to visualize, quantify and validate this sawtooth artifact. Significance: The PPG and ECG signals not designed for the PTT evaluation may contain unwanted artifacts. The PTT signal should be calibrated before analysis to avoid erroneous interpretation of its physiological meaning.
|
2202.02849
|
Ian Wong
|
Alex M. Hruska and Haiqian Yang and Susan E. Leggett and Ming Guo and
Ian Y. Wong
|
Mechanobiology of Collective Cell Migration in 3D Microenvironments
| null | null | null | null |
q-bio.CB physics.bio-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Tumor cells invade individually or in groups, mediated by mechanical
interactions between cells and their surrounding matrix. These multicellular
dynamics are reminiscent of leader-follower coordination and
epithelial-mesenchymal transitions (EMT) in tissue development, which may occur
via dysregulation of associated molecular or physical mechanisms. However, it
remains challenging to elucidate such phenotypic heterogeneity and plasticity
without precision measurements of single cell behavior. The convergence of
technological developments in live cell imaging, biophysical measurements, and
3D biomaterials are highly promising to reveal how tumor cells cooperate in
aberrant microenvironments. Here, we highlight new results in collective
migration from the perspective of cancer biology and bioengineering. First, we
review the biology of collective cell migration. Next, we consider
physics-inspired analyses based on order parameters and phase transitions.
Further, we examine the interplay of metabolism and heterogeneity in collective
migration. We then review the extracellular matrix, and new modalities for
mechanical characterization of 3D biomaterials. We also explore
epithelial-mesenchymal plasticity and implications for tumor progression.
Finally, we speculate on future directions for integrating mechanobiology and
cancer cell biology to elucidate collective migration.
|
[
{
"created": "Sun, 6 Feb 2022 20:48:57 GMT",
"version": "v1"
},
{
"created": "Sun, 26 Jun 2022 22:12:32 GMT",
"version": "v2"
}
] |
2022-06-28
|
[
[
"Hruska",
"Alex M.",
""
],
[
"Yang",
"Haiqian",
""
],
[
"Leggett",
"Susan E.",
""
],
[
"Guo",
"Ming",
""
],
[
"Wong",
"Ian Y.",
""
]
] |
Tumor cells invade individually or in groups, mediated by mechanical interactions between cells and their surrounding matrix. These multicellular dynamics are reminiscent of leader-follower coordination and epithelial-mesenchymal transitions (EMT) in tissue development, which may occur via dysregulation of associated molecular or physical mechanisms. However, it remains challenging to elucidate such phenotypic heterogeneity and plasticity without precision measurements of single cell behavior. The convergence of technological developments in live cell imaging, biophysical measurements, and 3D biomaterials are highly promising to reveal how tumor cells cooperate in aberrant microenvironments. Here, we highlight new results in collective migration from the perspective of cancer biology and bioengineering. First, we review the biology of collective cell migration. Next, we consider physics-inspired analyses based on order parameters and phase transitions. Further, we examine the interplay of metabolism and heterogeneity in collective migration. We then review the extracellular matrix, and new modalities for mechanical characterization of 3D biomaterials. We also explore epithelial-mesenchymal plasticity and implications for tumor progression. Finally, we speculate on future directions for integrating mechanobiology and cancer cell biology to elucidate collective migration.
|
0803.0483
|
Tao Hu
|
Tao Hu, B. I. Shklovskii
|
Theory of DNA translocation through narrow ion channels and nanopores
with charged walls
|
3 pages, 1 figure
|
Phys. Rev. E 78, 032901 (2008)
|
10.1103/PhysRevE.78.032901
| null |
q-bio.SC cond-mat.soft
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Translocation of a single stranded DNA through genetically engineered
$\alpha$-hemolysin channels with positively charged walls is studied. It is
predicted that transport properties of such channels are dramatically different
from neutral wild type $\alpha$-hemolysin channel. We assume that the wall
charges compensate the fraction $x$ of the bare charge $q_{b}$ of the DNA piece
residing in the channel. Our prediction are as follows (i) At small
concentration of salt the blocked ion current decreases with $x$. (ii) The
effective charge $q$ of DNA piece, which is very small at $x = 0$ (neutral
channel) grows with $x$ and at $x=1$ reaches $q_{b}$. (iii) The rate of DNA
capture by the channel exponentially grows with $x$. Our theory is also
applicable to translocation of a double stranded DNA in narrow solid state
nanopores with positively charged walls.
|
[
{
"created": "Tue, 4 Mar 2008 16:45:26 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Jun 2008 00:08:17 GMT",
"version": "v2"
}
] |
2008-09-10
|
[
[
"Hu",
"Tao",
""
],
[
"Shklovskii",
"B. I.",
""
]
] |
Translocation of a single stranded DNA through genetically engineered $\alpha$-hemolysin channels with positively charged walls is studied. It is predicted that transport properties of such channels are dramatically different from neutral wild type $\alpha$-hemolysin channel. We assume that the wall charges compensate the fraction $x$ of the bare charge $q_{b}$ of the DNA piece residing in the channel. Our prediction are as follows (i) At small concentration of salt the blocked ion current decreases with $x$. (ii) The effective charge $q$ of DNA piece, which is very small at $x = 0$ (neutral channel) grows with $x$ and at $x=1$ reaches $q_{b}$. (iii) The rate of DNA capture by the channel exponentially grows with $x$. Our theory is also applicable to translocation of a double stranded DNA in narrow solid state nanopores with positively charged walls.
|
2004.12053
|
Daniel Mas Montserrat
|
Daniel Mas Montserrat, Arvind Kumar, Carlos Bustamante, Alexander
Ioannidis
|
Addressing Ancestry Disparities in Genomic Medicine: A Geographic-aware
Algorithm
| null | null | null | null |
q-bio.GN q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With declining sequencing costs a promising and affordable tool is emerging
in cancer diagnostics: genomics. By using association studies, genomic variants
that predispose patients to specific cancers can be identified, while by using
tumor genomics cancer types can be characterized for targeted treatment.
However, a severe disparity is rapidly emerging in this new area of precision
cancer diagnosis and treatment planning, one which separates a few genetically
well-characterized populations (predominantly European) from all other global
populations. Here we discuss the problem of population-specific genetic
associations, which is driving this disparity, and present a novel
solution--coordinate-based local ancestry--for helping to address it. We
demonstrate our boosting-based method on whole genome data from divergent
groups across Africa and in the process observe signals that may stem from the
transcontinental Bantu-expansion.
|
[
{
"created": "Sat, 25 Apr 2020 04:32:16 GMT",
"version": "v1"
}
] |
2020-04-28
|
[
[
"Montserrat",
"Daniel Mas",
""
],
[
"Kumar",
"Arvind",
""
],
[
"Bustamante",
"Carlos",
""
],
[
"Ioannidis",
"Alexander",
""
]
] |
With declining sequencing costs a promising and affordable tool is emerging in cancer diagnostics: genomics. By using association studies, genomic variants that predispose patients to specific cancers can be identified, while by using tumor genomics cancer types can be characterized for targeted treatment. However, a severe disparity is rapidly emerging in this new area of precision cancer diagnosis and treatment planning, one which separates a few genetically well-characterized populations (predominantly European) from all other global populations. Here we discuss the problem of population-specific genetic associations, which is driving this disparity, and present a novel solution--coordinate-based local ancestry--for helping to address it. We demonstrate our boosting-based method on whole genome data from divergent groups across Africa and in the process observe signals that may stem from the transcontinental Bantu-expansion.
|
1210.7083
|
Thomas Pfeil
|
Thomas Pfeil, Andreas Gr\"ubl, Sebastian Jeltsch, Eric M\"uller, Paul
M\"uller, Mihai A. Petrovici, Michael Schmuker, Daniel Br\"uderle, Johannes
Schemmel, Karlheinz Meier
|
Six networks on a universal neuromorphic computing substrate
|
21 pages, 9 figures
|
Front. Neurosci. 7:11 (2013)
|
10.3389/fnins.2013.00011
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this study, we present a highly configurable neuromorphic computing
substrate and use it for emulating several types of neural networks. At the
heart of this system lies a mixed-signal chip, with analog implementations of
neurons and synapses and digital transmission of action potentials. Major
advantages of this emulation device, which has been explicitly designed as a
universal neural network emulator, are its inherent parallelism and high
acceleration factor compared to conventional computers. Its configurability
allows the realization of almost arbitrary network topologies and the use of
widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to
analog circuitry is reduced by calibration routines. An integrated development
environment allows neuroscientists to operate the device without any prior
knowledge of neuromorphic circuit design. As a showcase for the capabilities of
the system, we describe the successful emulation of six different neural
networks which cover a broad spectrum of both structure and functionality.
|
[
{
"created": "Fri, 26 Oct 2012 09:51:03 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Dec 2012 00:13:53 GMT",
"version": "v2"
},
{
"created": "Wed, 9 Jan 2013 14:19:14 GMT",
"version": "v3"
},
{
"created": "Thu, 21 Feb 2013 16:55:08 GMT",
"version": "v4"
}
] |
2013-04-08
|
[
[
"Pfeil",
"Thomas",
""
],
[
"Grübl",
"Andreas",
""
],
[
"Jeltsch",
"Sebastian",
""
],
[
"Müller",
"Eric",
""
],
[
"Müller",
"Paul",
""
],
[
"Petrovici",
"Mihai A.",
""
],
[
"Schmuker",
"Michael",
""
],
[
"Brüderle",
"Daniel",
""
],
[
"Schemmel",
"Johannes",
""
],
[
"Meier",
"Karlheinz",
""
]
] |
In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality.
|
1112.4694
|
Jakob Bj\"ornberg
|
Jakob E. Bj\"ornberg, Tom Britton, Erik I. Broman, Eviatar Natan
|
A stochastic model for virus growth in a cell population
|
18 pages, 1 figure
| null | null | null |
q-bio.CB math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A stochastic model for the growth of a virus in a cell population is
introduced. The virus has two ways of spreading: either by allowing its host
cell to live on and duplicate, or else by multiplying in large numbers within
the host cell such that the host cell finally bursts and the viruses then have
the chance to enter new uninfected host cells. The model, and in particular the
probability of the virus population surviving, is analyzed using the theory of
Markov processes together with a coupling argument. Our analysis shows that the
optimal strategy of the virus (in terms of survival) is obtained when the virus
has no effect on the host cell's life-cycle, in agreement with experimental
data about real viruses.
|
[
{
"created": "Tue, 20 Dec 2011 14:16:19 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Oct 2012 13:57:40 GMT",
"version": "v2"
}
] |
2012-10-30
|
[
[
"Björnberg",
"Jakob E.",
""
],
[
"Britton",
"Tom",
""
],
[
"Broman",
"Erik I.",
""
],
[
"Natan",
"Eviatar",
""
]
] |
A stochastic model for the growth of a virus in a cell population is introduced. The virus has two ways of spreading: either by allowing its host cell to live on and duplicate, or else by multiplying in large numbers within the host cell such that the host cell finally bursts and the viruses then have the chance to enter new uninfected host cells. The model, and in particular the probability of the virus population surviving, is analyzed using the theory of Markov processes together with a coupling argument. Our analysis shows that the optimal strategy of the virus (in terms of survival) is obtained when the virus has no effect on the host cell's life-cycle, in agreement with experimental data about real viruses.
|
1204.1995
|
Johannes Wollbold
|
Johannes Wollbold
|
Attribute Exploration of Gene Regulatory Processes
|
111 pages, 9 figures, file size 2.1 MB, PhD thesis University of
Jena, Germany, Faculty of Mathematics and Computer Science, 2011. Online
available at http://www.db-thueringen.de/servlets/DocumentServlet?id=19601
| null | null | null |
q-bio.MN cs.CE cs.LO math.LO
|
http://creativecommons.org/licenses/by/3.0/
|
This thesis aims at the logical analysis of discrete processes, in particular
of such generated by gene regulatory networks. States, transitions and
operators from temporal logics are expressed in the language of Formal Concept
Analysis. By the attribute exploration algorithm, an expert or a computer
program is enabled to validate a minimal and complete set of implications, e.g.
by comparison of predictions derived from literature with observed data. Here,
these rules represent temporal dependencies within gene regulatory networks
including coexpression of genes, reachability of states, invariants or possible
causal relationships. This new approach is embedded into the theory of
universal coalgebras, particularly automata, Kripke structures and Labelled
Transition Systems. A comparison with the temporal expressivity of Description
Logics is made. The main theoretical results concern the integration of
background knowledge into the successive exploration of the defined data
structures (formal contexts). Applying the method a Boolean network from
literature modelling sporulation of Bacillus subtilis is examined. Finally, we
developed an asynchronous Boolean network for extracellular matrix formation
and destruction in the context of rheumatoid arthritis.
|
[
{
"created": "Mon, 9 Apr 2012 21:23:04 GMT",
"version": "v1"
}
] |
2012-04-11
|
[
[
"Wollbold",
"Johannes",
""
]
] |
This thesis aims at the logical analysis of discrete processes, in particular of such generated by gene regulatory networks. States, transitions and operators from temporal logics are expressed in the language of Formal Concept Analysis. By the attribute exploration algorithm, an expert or a computer program is enabled to validate a minimal and complete set of implications, e.g. by comparison of predictions derived from literature with observed data. Here, these rules represent temporal dependencies within gene regulatory networks including coexpression of genes, reachability of states, invariants or possible causal relationships. This new approach is embedded into the theory of universal coalgebras, particularly automata, Kripke structures and Labelled Transition Systems. A comparison with the temporal expressivity of Description Logics is made. The main theoretical results concern the integration of background knowledge into the successive exploration of the defined data structures (formal contexts). Applying the method a Boolean network from literature modelling sporulation of Bacillus subtilis is examined. Finally, we developed an asynchronous Boolean network for extracellular matrix formation and destruction in the context of rheumatoid arthritis.
|
1510.08397
|
J. C. Phillips
|
J. C. Phillips
|
Bioinformatic Scaling of Allosteric Interactions in Biomedical Isozymes
|
12 pages, 5 figures
| null |
10.1016/j.physa.2016.03.038
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Allosteric (long-range) interactions can be surprisingly strong in proteins
of biomedical interest. Here we use bioinformatic scaling to connect prior
results on nonsteroidal anti-inflammatory drugs to promising new drugs that
inhibit cancer cell metabolism. Many parallel features are apparent, which
explain how even one amino acid mutation, remote from active sites, can alter
medical results. The enzyme twins involved are cyclooxygenase (aspirin) and
isocitrate dehydrogenase (IDH). The IDH results are accurate to 1% and are
overdetermined by adjusting a single bioinformatic scaling parameter. It
appears that the final stage in optimizing protein functionality may involve
leveling of the hydrophobic cutoffs of the arms of conformational hydrophilic
hinges.
|
[
{
"created": "Mon, 19 Oct 2015 01:49:22 GMT",
"version": "v1"
}
] |
2016-05-04
|
[
[
"Phillips",
"J. C.",
""
]
] |
Allosteric (long-range) interactions can be surprisingly strong in proteins of biomedical interest. Here we use bioinformatic scaling to connect prior results on nonsteroidal anti-inflammatory drugs to promising new drugs that inhibit cancer cell metabolism. Many parallel features are apparent, which explain how even one amino acid mutation, remote from active sites, can alter medical results. The enzyme twins involved are cyclooxygenase (aspirin) and isocitrate dehydrogenase (IDH). The IDH results are accurate to 1% and are overdetermined by adjusting a single bioinformatic scaling parameter. It appears that the final stage in optimizing protein functionality may involve leveling of the hydrophobic cutoffs of the arms of conformational hydrophilic hinges.
|
0904.2668
|
Siew-Ann Cheong
|
Siew-Ann Cheong, Paul Stodghill, David J. Schneider, Samuel W.
Cartinhour, and Christopher R. Myers
|
The Context Sensitivity Problem in Biological Sequence Segmentation
|
IEEEtran class, 39 pages, 20 figures
| null | null | null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we describe the context sensitivity problem encountered in
partitioning a heterogeneous biological sequence into statistically homogeneous
segments. After showing signatures of the problem in the bacterial genomes of
Escherichia coli K-12 MG1655 and Pseudomonas syringae DC3000, when these are
segmented using two entropic segmentation schemes, we clarify the contextual
origins of these signatures through mean-field analyses of the segmentation
schemes. Finally, we explain why we believe all sequence segmentation schems
are plagued by the context sensitivity problem.
|
[
{
"created": "Fri, 17 Apr 2009 10:02:26 GMT",
"version": "v1"
}
] |
2009-04-20
|
[
[
"Cheong",
"Siew-Ann",
""
],
[
"Stodghill",
"Paul",
""
],
[
"Schneider",
"David J.",
""
],
[
"Cartinhour",
"Samuel W.",
""
],
[
"Myers",
"Christopher R.",
""
]
] |
In this paper, we describe the context sensitivity problem encountered in partitioning a heterogeneous biological sequence into statistically homogeneous segments. After showing signatures of the problem in the bacterial genomes of Escherichia coli K-12 MG1655 and Pseudomonas syringae DC3000, when these are segmented using two entropic segmentation schemes, we clarify the contextual origins of these signatures through mean-field analyses of the segmentation schemes. Finally, we explain why we believe all sequence segmentation schems are plagued by the context sensitivity problem.
|
2311.10321
|
David Black
|
David Black, Declan Byrne, Anna Walke, Sidong Liu, Antonio Di leva,
Sadahiro Kaneko, Walter Stummer, Septimiu Salcudean, Eric Suero Molina
|
Towards Machine Learning-based Quantitative Hyperspectral Image Guidance
for Brain Tumor Resection
|
22 pages, 8 figures
| null | null | null |
q-bio.TO cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Complete resection of malignant gliomas is hampered by the difficulty in
distinguishing tumor cells at the infiltration zone. Fluorescence guidance with
5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work
characterized five fluorophores' emission spectra in most human brain tumors.
In this paper, the effectiveness of these five spectra was explored for
different tumor and tissue classification tasks in 184 patients (891
hyperspectral measurements) harboring low- (n=30) and high-grade gliomas
(n=115), non-glial primary brain tumors (n=19), radiation necrosis (n=2),
miscellaneous (n=10) and metastases (n=8). Four machine learning models were
trained to classify tumor type, grade, glioma margins and IDH mutation. Using
random forests and multi-layer perceptrons, the classifiers achieved average
test accuracies of 84-87%, 96%, 86%, and 93% respectively. All five fluorophore
abundances varied between tumor margin types and tumor grades (p < 0.01). For
tissue type, at least four of the five fluorophore abundances were found to be
significantly different (p < 0.01) between all classes. These results
demonstrate the fluorophores' differing abundances in different tissue classes,
as well as the value of the five fluorophores as potential optical biomarkers,
opening new opportunities for intraoperative classification systems in
fluorescence-guided neurosurgery.
|
[
{
"created": "Fri, 17 Nov 2023 04:15:27 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Nov 2023 19:59:28 GMT",
"version": "v2"
}
] |
2023-11-28
|
[
[
"Black",
"David",
""
],
[
"Byrne",
"Declan",
""
],
[
"Walke",
"Anna",
""
],
[
"Liu",
"Sidong",
""
],
[
"Di leva",
"Antonio",
""
],
[
"Kaneko",
"Sadahiro",
""
],
[
"Stummer",
"Walter",
""
],
[
"Salcudean",
"Septimiu",
""
],
[
"Molina",
"Eric Suero",
""
]
] |
Complete resection of malignant gliomas is hampered by the difficulty in distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work characterized five fluorophores' emission spectra in most human brain tumors. In this paper, the effectiveness of these five spectra was explored for different tumor and tissue classification tasks in 184 patients (891 hyperspectral measurements) harboring low- (n=30) and high-grade gliomas (n=115), non-glial primary brain tumors (n=19), radiation necrosis (n=2), miscellaneous (n=10) and metastases (n=8). Four machine learning models were trained to classify tumor type, grade, glioma margins and IDH mutation. Using random forests and multi-layer perceptrons, the classifiers achieved average test accuracies of 84-87%, 96%, 86%, and 93% respectively. All five fluorophore abundances varied between tumor margin types and tumor grades (p < 0.01). For tissue type, at least four of the five fluorophore abundances were found to be significantly different (p < 0.01) between all classes. These results demonstrate the fluorophores' differing abundances in different tissue classes, as well as the value of the five fluorophores as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.
|
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