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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1210.7495 | Eduardo Mizraji | Eduardo Mizraji | Illustrating a neural model of logic computations: The case of Sherlock
Holmes' old maxim | Corrected version with new references | THEORIA 31/1 (2016): 7-25 | 10.1387/theoria.13959 | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural languages can express some logical propositions that humans are able
to understand. We illustrate this fact with a famous text that Conan Doyle
attributed to Holmes: 'It is an old maxim of mine that when you have excluded
the impossible, whatever remains, however improbable, must be the truth'. This
is a subtle logical statement usually felt as an evident truth. The problem we
are trying to solve is the cognitive reason for such a feeling. We postulate
here that we accept Holmes' maxim as true because our adult brains are equipped
with neural modules that naturally perform modal logical computations.
| [
{
"created": "Sun, 28 Oct 2012 19:37:33 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Nov 2012 12:00:10 GMT",
"version": "v2"
},
{
"created": "Sat, 27 Feb 2016 14:45:52 GMT",
"version": "v3"
}
] | 2016-03-01 | [
[
"Mizraji",
"Eduardo",
""
]
] | Natural languages can express some logical propositions that humans are able to understand. We illustrate this fact with a famous text that Conan Doyle attributed to Holmes: 'It is an old maxim of mine that when you have excluded the impossible, whatever remains, however improbable, must be the truth'. This is a subtle logical statement usually felt as an evident truth. The problem we are trying to solve is the cognitive reason for such a feeling. We postulate here that we accept Holmes' maxim as true because our adult brains are equipped with neural modules that naturally perform modal logical computations. |
1411.3917 | Andrew Magyar | Andrew Magyar, John Collins | Two-population model for MTL neurons: The vast majority are almost
silent | null | Phys. Rev. E 92, 012712 (2015) | 10.1103/PhysRevE.92.012712 | null | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recordings in the human medial temporal lobe have found many neurons that
respond to pictures (and related stimuli) of just one particular person out of
those presented. It has been proposed that these are concept cells, responding
to just a single concept. However, a direct experimental test of the concept
cell idea appears impossible, because it would need the measurement of the
response of each cell to enormous numbers of other stimuli. Here we propose a
new statistical method for analysis of the data, that gives a more powerful way
to analyze how close data are to the concept-cell idea. It exploits the large
number of sampled neurons, to give sensitivity to situations where the average
response sparsity is to much less than one response for the number of presented
stimuli. We show that a conventional model where a single sparsity is
postulated for all neurons gives an extremely poor fit to the data. In contrast
a model with two dramatically different populations give an excellent fit to
data from the hippocampus and entorhinal cortex. In the hippocampus, one
population has 7% of the cells with a 2.6% sparsity. But a much larger fraction
93% respond to only 0.1% of the stimuli. This results in an extreme bias in the
reported responsive of neurons compared with a typical neuron. Finally, we show
how to allow for the fact that some of reported identified units correspond to
multiple neurons, and find that our conclusions at the neural level are
quantitatively changed but strengthened, with an even stronger difference
between the two populations.
| [
{
"created": "Fri, 14 Nov 2014 14:21:39 GMT",
"version": "v1"
}
] | 2015-07-22 | [
[
"Magyar",
"Andrew",
""
],
[
"Collins",
"John",
""
]
] | Recordings in the human medial temporal lobe have found many neurons that respond to pictures (and related stimuli) of just one particular person out of those presented. It has been proposed that these are concept cells, responding to just a single concept. However, a direct experimental test of the concept cell idea appears impossible, because it would need the measurement of the response of each cell to enormous numbers of other stimuli. Here we propose a new statistical method for analysis of the data, that gives a more powerful way to analyze how close data are to the concept-cell idea. It exploits the large number of sampled neurons, to give sensitivity to situations where the average response sparsity is to much less than one response for the number of presented stimuli. We show that a conventional model where a single sparsity is postulated for all neurons gives an extremely poor fit to the data. In contrast a model with two dramatically different populations give an excellent fit to data from the hippocampus and entorhinal cortex. In the hippocampus, one population has 7% of the cells with a 2.6% sparsity. But a much larger fraction 93% respond to only 0.1% of the stimuli. This results in an extreme bias in the reported responsive of neurons compared with a typical neuron. Finally, we show how to allow for the fact that some of reported identified units correspond to multiple neurons, and find that our conclusions at the neural level are quantitatively changed but strengthened, with an even stronger difference between the two populations. |
1809.04953 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Cluster Burst Synchronization in A Scale-Free Network of Inhibitory
Bursting Neurons | arXiv admin note: text overlap with arXiv:1803.07256,
arXiv:1708.04543 | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting
neurons, and investigate coupling-induced cluster burst synchronization by
varying the average coupling strength $J_0$. For sufficiently small $J_0$,
non-cluster desynchronized states exist. However, when passing a critical point
$J^*_c~(\simeq 0.16)$, the whole population is segregated into 3 clusters via a
constructive role of synaptic inhibition to stimulate dynamical clustering
between individual burstings, and thus 3-cluster desynchronized states appear.
As $J_0$ is further increased and passes a lower threshold $J^*_l~(\simeq
0.78)$, a transition to 3-cluster burst synchronization occurs due to another
constructive role of synaptic inhibition to favor population synchronization.
In this case, HR neurons in each cluster exhibit burst synchronization.
However, as $J_0$ passes an intermediate threshold $J^*_m~(\simeq 5.2)$, HR
neurons begin to make intermittent hoppings between the 3 clusters. Due to the
intermittent intercluster hoppings, the 3 clusters are integrated into a single
one. In spite of break-up of the 3 clusters, (non-cluster) burst
synchronization persists in the whole population, which is well visualized in
the raster plot of burst onset times where bursting stripes (composed of burst
onset times and indicating burst synchronization) appear successively. With
further increase in $J_0$, intercluster hoppings are intensified, and bursting
stripes also become smeared more and more due to a destructive role of synaptic
inhibition to spoil the burst synchronization. Eventually, when passing a
higher threshold $J^*_h~(\simeq 17.8)$ a transition to desynchronization occurs
via complete overlap between the bursting stripes. Finally, we also investigate
the effects of stochastic noise on both 3-cluster burst synchronization and
intercluster hoppings.
| [
{
"created": "Wed, 12 Sep 2018 00:40:18 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Apr 2019 13:51:14 GMT",
"version": "v2"
}
] | 2019-04-02 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced cluster burst synchronization by varying the average coupling strength $J_0$. For sufficiently small $J_0$, non-cluster desynchronized states exist. However, when passing a critical point $J^*_c~(\simeq 0.16)$, the whole population is segregated into 3 clusters via a constructive role of synaptic inhibition to stimulate dynamical clustering between individual burstings, and thus 3-cluster desynchronized states appear. As $J_0$ is further increased and passes a lower threshold $J^*_l~(\simeq 0.78)$, a transition to 3-cluster burst synchronization occurs due to another constructive role of synaptic inhibition to favor population synchronization. In this case, HR neurons in each cluster exhibit burst synchronization. However, as $J_0$ passes an intermediate threshold $J^*_m~(\simeq 5.2)$, HR neurons begin to make intermittent hoppings between the 3 clusters. Due to the intermittent intercluster hoppings, the 3 clusters are integrated into a single one. In spite of break-up of the 3 clusters, (non-cluster) burst synchronization persists in the whole population, which is well visualized in the raster plot of burst onset times where bursting stripes (composed of burst onset times and indicating burst synchronization) appear successively. With further increase in $J_0$, intercluster hoppings are intensified, and bursting stripes also become smeared more and more due to a destructive role of synaptic inhibition to spoil the burst synchronization. Eventually, when passing a higher threshold $J^*_h~(\simeq 17.8)$ a transition to desynchronization occurs via complete overlap between the bursting stripes. Finally, we also investigate the effects of stochastic noise on both 3-cluster burst synchronization and intercluster hoppings. |
0808.3873 | Gareth Hughes | Gareth Hughes | Notes on the UK Non-Native Organism Risk Assessment Scheme | New version updates the URL of the UK Non-Native Organism Risk
Assessment Scheme | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In 2004, the UK Government's Department for Environment, Food and Rural
Affairs commissioned research with the aim of developing a scheme for assessing
the risks posed to species, habitats and ecosystems in the UK by non-native
organisms. The outcome was the UK Non-Native Organism Risk Assessment Scheme.
Unfortunately, the mathematical basis of the procedure for summarising risks
deployed in the Risk Assessment Scheme, as outlined in Baker et al. (2008) and
described in more detail in the Risk Assessment Scheme's User Manual, contains
several analytical errors. These errors are outlined in the notes that follow.
| [
{
"created": "Thu, 28 Aug 2008 10:15:24 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Jun 2009 08:10:46 GMT",
"version": "v2"
}
] | 2009-06-05 | [
[
"Hughes",
"Gareth",
""
]
] | In 2004, the UK Government's Department for Environment, Food and Rural Affairs commissioned research with the aim of developing a scheme for assessing the risks posed to species, habitats and ecosystems in the UK by non-native organisms. The outcome was the UK Non-Native Organism Risk Assessment Scheme. Unfortunately, the mathematical basis of the procedure for summarising risks deployed in the Risk Assessment Scheme, as outlined in Baker et al. (2008) and described in more detail in the Risk Assessment Scheme's User Manual, contains several analytical errors. These errors are outlined in the notes that follow. |
1708.04020 | Natalia Bielczyk Ms | Natalia Z. Bielczyk, Sebo Uithol, Tim van Mourik, Paul Anderson,
Jeffrey C. Glennon, Jan K. Buitelaar | Disentangling causal webs in the brain using functional Magnetic
Resonance Imaging: A review of current approaches | null | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area.
| [
{
"created": "Mon, 14 Aug 2017 07:13:17 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Aug 2017 21:56:26 GMT",
"version": "v2"
},
{
"created": "Wed, 30 May 2018 11:38:13 GMT",
"version": "v3"
},
{
"created": "Thu, 30 May 2019 09:03:45 GMT",
"version": "v4"
}
] | 2019-05-31 | [
[
"Bielczyk",
"Natalia Z.",
""
],
[
"Uithol",
"Sebo",
""
],
[
"van Mourik",
"Tim",
""
],
[
"Anderson",
"Paul",
""
],
[
"Glennon",
"Jeffrey C.",
""
],
[
"Buitelaar",
"Jan K.",
""
]
] | In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area. |
1104.3889 | Lior Pachter | Lior Pachter | Models for transcript quantification from RNA-Seq | null | null | null | null | q-bio.GN stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | RNA-Seq is rapidly becoming the standard technology for transcriptome
analysis. Fundamental to many of the applications of RNA-Seq is the
quantification problem, which is the accurate measurement of relative
transcript abundances from the sequenced reads. We focus on this problem, and
review many recently published models that are used to estimate the relative
abundances. In addition to describing the models and the different approaches
to inference, we also explain how methods are related to each other. A key
result is that we show how inference with many of the models results in
identical estimates of relative abundances, even though model formulations can
be very different. In fact, we are able to show how a single general model
captures many of the elements of previously published methods. We also review
the applications of RNA-Seq models to differential analysis, and explain why
accurate relative transcript abundance estimates are crucial for downstream
analyses.
| [
{
"created": "Tue, 19 Apr 2011 21:46:46 GMT",
"version": "v1"
},
{
"created": "Fri, 13 May 2011 00:18:18 GMT",
"version": "v2"
}
] | 2011-05-16 | [
[
"Pachter",
"Lior",
""
]
] | RNA-Seq is rapidly becoming the standard technology for transcriptome analysis. Fundamental to many of the applications of RNA-Seq is the quantification problem, which is the accurate measurement of relative transcript abundances from the sequenced reads. We focus on this problem, and review many recently published models that are used to estimate the relative abundances. In addition to describing the models and the different approaches to inference, we also explain how methods are related to each other. A key result is that we show how inference with many of the models results in identical estimates of relative abundances, even though model formulations can be very different. In fact, we are able to show how a single general model captures many of the elements of previously published methods. We also review the applications of RNA-Seq models to differential analysis, and explain why accurate relative transcript abundance estimates are crucial for downstream analyses. |
1602.06504 | Gennadi Glinsky | Gennadi Glinsky | Malignant field signature analysis in biopsy samples at diagnosis
identifies lethal disease in patients with localized Gleason 6 and 7 prostate
cancer | 16 pages, 6 figures, 4 tables | null | null | null | q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Overtreatment of early-stage low-risk prostate cancer (PC) patients
represents a significant problem in disease management and has socio-economic
implications. Development of genetic and molecular markers of clinically
significant disease in patients diagnosed with low grade localized PC would
have a major impact in disease management. A gene expression signature (GES) is
reported for lethal PC in biopsy specimens obtained at the time of diagnosis
from patients with Gleason 6 and Gleason 7 tumors in a Swedish watchful waiting
cohort with up to 30 years follow-up. A 98-genes GES identified 89 and 100
percent of all death events 4 years after diagnosis in G7 and G6 patients,
respectively; at 6 years follow-up, 83 and 100 percent of all deaths events
were captured. Remarkably, the 98-genes GES appears to perform successfully in
patients stratification with as little as 2% of cancer cells in a specimen,
strongly indicating that it captures a malignant field effect in prostates
harboring cancer cells of different degrees of aggressiveness. In G6 and G7
tumors from PC patients of age 65 or younger, GES identified 86 percent of all
death events during the entire follow-up period. In G6 and G7 tumors from PC
patients of age 70 or younger, GES identified 90 percent of all death events 6
years after diagnosis. Classification performance of the reported in this study
98-genes GES of lethal PC appeared suitable to meet design and feasibility
requirements of a prospective 4 to 6 years clinical trial, which is essential
for regulatory approval of diagnostic and prognostic tests in clinical setting.
Prospectively validated GES of lethal PC in biopsy specimens of G6 and G7
tumors will help physicians to identify, at the time of diagnosis, patients who
should be considered for exclusion from active surveillance programs and who
would most likely benefit from immediate curative interventions.
| [
{
"created": "Sun, 21 Feb 2016 06:32:08 GMT",
"version": "v1"
}
] | 2016-02-24 | [
[
"Glinsky",
"Gennadi",
""
]
] | Overtreatment of early-stage low-risk prostate cancer (PC) patients represents a significant problem in disease management and has socio-economic implications. Development of genetic and molecular markers of clinically significant disease in patients diagnosed with low grade localized PC would have a major impact in disease management. A gene expression signature (GES) is reported for lethal PC in biopsy specimens obtained at the time of diagnosis from patients with Gleason 6 and Gleason 7 tumors in a Swedish watchful waiting cohort with up to 30 years follow-up. A 98-genes GES identified 89 and 100 percent of all death events 4 years after diagnosis in G7 and G6 patients, respectively; at 6 years follow-up, 83 and 100 percent of all deaths events were captured. Remarkably, the 98-genes GES appears to perform successfully in patients stratification with as little as 2% of cancer cells in a specimen, strongly indicating that it captures a malignant field effect in prostates harboring cancer cells of different degrees of aggressiveness. In G6 and G7 tumors from PC patients of age 65 or younger, GES identified 86 percent of all death events during the entire follow-up period. In G6 and G7 tumors from PC patients of age 70 or younger, GES identified 90 percent of all death events 6 years after diagnosis. Classification performance of the reported in this study 98-genes GES of lethal PC appeared suitable to meet design and feasibility requirements of a prospective 4 to 6 years clinical trial, which is essential for regulatory approval of diagnostic and prognostic tests in clinical setting. Prospectively validated GES of lethal PC in biopsy specimens of G6 and G7 tumors will help physicians to identify, at the time of diagnosis, patients who should be considered for exclusion from active surveillance programs and who would most likely benefit from immediate curative interventions. |
1207.3454 | Tidjani Negadi | Tidjani Negadi | The irregular (integer) tetrahedron as a warehouse of biological
information | to be published in 2012; Symmetry: Culture and Science, 2012 | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is devoted to a new classification of the twenty amino acids based
on the heronian (integer) tetrahedron.
| [
{
"created": "Sat, 14 Jul 2012 19:45:48 GMT",
"version": "v1"
}
] | 2012-07-17 | [
[
"Negadi",
"Tidjani",
""
]
] | This paper is devoted to a new classification of the twenty amino acids based on the heronian (integer) tetrahedron. |
2401.00077 | Erik Johnson | Erik C. Johnson, Thinh T. Nguyen, Benjamin K. Dichter, Frank Zappulla,
Montgomery Kosma, Kabilar Gunalan, Yaroslav O. Halchenko, Shay Q. Neufeld,
Michael Schirner, Petra Ritter, Maryann E. Martone, Brock Wester, Franco
Pestilli, Dimitri Yatsenko | A Maturity Model for Operations in Neuroscience Research | 10 pages, one figure | null | null | null | q-bio.NC cs.CY | http://creativecommons.org/licenses/by/4.0/ | Scientists are adopting new approaches to scale up their activities and
goals. Progress in neurotechnologies, artificial intelligence, automation, and
tools for collaboration promises new bursts of discoveries. However, compared
to other disciplines and the industry, neuroscience laboratories have been slow
to adopt key technologies to support collaboration, reproducibility, and
automation. Drawing on progress in other fields, we define a roadmap for
implementing automated research workflows for diverse research teams. We
propose establishing a five-level capability maturity model for operations in
neuroscience research. Achieving higher levels of operational maturity requires
new technology-enabled methodologies, which we describe as ``SciOps''. The
maturity model provides guidelines for evaluating and upgrading operations in
multidisciplinary neuroscience teams.
| [
{
"created": "Fri, 29 Dec 2023 21:37:22 GMT",
"version": "v1"
}
] | 2024-01-02 | [
[
"Johnson",
"Erik C.",
""
],
[
"Nguyen",
"Thinh T.",
""
],
[
"Dichter",
"Benjamin K.",
""
],
[
"Zappulla",
"Frank",
""
],
[
"Kosma",
"Montgomery",
""
],
[
"Gunalan",
"Kabilar",
""
],
[
"Halchenko",
"Yaroslav O.",
""
],
[
"Neufeld",
"Shay Q.",
""
],
[
"Schirner",
"Michael",
""
],
[
"Ritter",
"Petra",
""
],
[
"Martone",
"Maryann E.",
""
],
[
"Wester",
"Brock",
""
],
[
"Pestilli",
"Franco",
""
],
[
"Yatsenko",
"Dimitri",
""
]
] | Scientists are adopting new approaches to scale up their activities and goals. Progress in neurotechnologies, artificial intelligence, automation, and tools for collaboration promises new bursts of discoveries. However, compared to other disciplines and the industry, neuroscience laboratories have been slow to adopt key technologies to support collaboration, reproducibility, and automation. Drawing on progress in other fields, we define a roadmap for implementing automated research workflows for diverse research teams. We propose establishing a five-level capability maturity model for operations in neuroscience research. Achieving higher levels of operational maturity requires new technology-enabled methodologies, which we describe as ``SciOps''. The maturity model provides guidelines for evaluating and upgrading operations in multidisciplinary neuroscience teams. |
1910.03529 | Emanuele Massaro Ph.D. | Emanuele Massaro and Daniel Kondor and Carlo Ratti | Assessing the interplay between human mobility and mosquito borne
diseases in urban environments | null | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Urbanization drives the epidemiology of infectious diseases to many threats
and new challenges. In this research, we study the interplay between human
mobility and dengue outbreaks in the complex urban environment of the
city-state of Singapore. We integrate both stylized and mobile phone
data-driven mobility patterns in an agent-based transmission model in which
humans and mosquitoes are represented as agents that go through the epidemic
states of dengue. We monitor with numerical simulations the system-level
response to the epidemic by comparing our results with the observed cases
reported during the 2013 and 2014 outbreaks. Our results show that human
mobility is a major factor in the spread of vector-borne diseases such as
dengue even on the short scale corresponding to intra-city distances. We
finally discuss the advantages and the limits of mobile phone data and
potential alternatives for assessing valuable mobility patterns for modeling
vector-borne diseases outbreaks in cities.
| [
{
"created": "Tue, 8 Oct 2019 16:36:43 GMT",
"version": "v1"
}
] | 2019-10-09 | [
[
"Massaro",
"Emanuele",
""
],
[
"Kondor",
"Daniel",
""
],
[
"Ratti",
"Carlo",
""
]
] | Urbanization drives the epidemiology of infectious diseases to many threats and new challenges. In this research, we study the interplay between human mobility and dengue outbreaks in the complex urban environment of the city-state of Singapore. We integrate both stylized and mobile phone data-driven mobility patterns in an agent-based transmission model in which humans and mosquitoes are represented as agents that go through the epidemic states of dengue. We monitor with numerical simulations the system-level response to the epidemic by comparing our results with the observed cases reported during the 2013 and 2014 outbreaks. Our results show that human mobility is a major factor in the spread of vector-borne diseases such as dengue even on the short scale corresponding to intra-city distances. We finally discuss the advantages and the limits of mobile phone data and potential alternatives for assessing valuable mobility patterns for modeling vector-borne diseases outbreaks in cities. |
2208.02344 | Vincent Zaballa | Vincent D. Zaballa and Elliot E. Hui | An Optimal Likelihood Free Method for Biological Model Selection | 2022 International Conference on Machine Learning Workshop on
Computational Biology | null | null | null | q-bio.QM stat.ML | http://creativecommons.org/licenses/by/4.0/ | Systems biology seeks to create math models of biological systems to reduce
inherent biological complexity and provide predictions for applications such as
therapeutic development. However, it remains a challenge to determine which
math model is correct and how to arrive optimally at the answer. We present an
algorithm for automated biological model selection using mathematical models of
systems biology and likelihood free inference methods. Our algorithm shows
improved performance in arriving at correct models without a priori information
over conventional heuristics used in experimental biology and random search.
This method shows promise to accelerate biological basic science and drug
discovery.
| [
{
"created": "Wed, 3 Aug 2022 21:05:20 GMT",
"version": "v1"
}
] | 2022-08-05 | [
[
"Zaballa",
"Vincent D.",
""
],
[
"Hui",
"Elliot E.",
""
]
] | Systems biology seeks to create math models of biological systems to reduce inherent biological complexity and provide predictions for applications such as therapeutic development. However, it remains a challenge to determine which math model is correct and how to arrive optimally at the answer. We present an algorithm for automated biological model selection using mathematical models of systems biology and likelihood free inference methods. Our algorithm shows improved performance in arriving at correct models without a priori information over conventional heuristics used in experimental biology and random search. This method shows promise to accelerate biological basic science and drug discovery. |
2312.07026 | Sarah Brueningk | John Metzcar, Catherine R. Jutzeler, Paul Macklin, Alvaro
K\"ohn-Luque, Sarah C. Br\"uningk | A review of mechanistic learning in mathematical oncology | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mechanistic learning, the synergistic combination of knowledge-driven and
data-driven modeling, is an emerging field. In particular, in mathematical
oncology, the application of mathematical modeling to cancer biology and
oncology, the use of mechanistic learning is growing. This review aims to
capture the current state of the field and provide a perspective on how
mechanistic learning may further progress in mathematical oncology. We
highlight the synergistic potential of knowledge-driven mechanistic
mathematical modeling and data-driven modeling, such as machine and deep
learning. We point out similarities and differences regarding model complexity,
data requirements, outputs generated, and interpretability of the algorithms
and their results. Then, organizing combinations of knowledge- and data-driven
modeling into four categories (sequential, parallel, intrinsic, and extrinsic
mechanistic learning), we summarize a variety of approaches at the interface
between purely data- and knowledge-driven models. Using examples predominantly
from oncology, we discuss a range of techniques including physics-informed
neural networks, surrogate model learning, and digital twins. We see that
mechanistic learning, with its intentional leveraging of the strengths of both
knowledge and data-driven modeling, can greatly impact the complex problems of
oncology. Given the increasing ubiquity and impact of machine learning, it is
critical to incorporate it into the study of mathematical oncology with
mechanistic learning providing a path to that end. As the field of mechanistic
learning advances, we aim for this review and proposed categorization framework
to foster additional collaboration between the data- and knowledge-driven
modeling fields. Further collaboration will help address difficult issues in
oncology such as limited data availability, requirements of model transparency,
and complex input data
| [
{
"created": "Tue, 12 Dec 2023 07:24:43 GMT",
"version": "v1"
}
] | 2023-12-13 | [
[
"Metzcar",
"John",
""
],
[
"Jutzeler",
"Catherine R.",
""
],
[
"Macklin",
"Paul",
""
],
[
"Köhn-Luque",
"Alvaro",
""
],
[
"Brüningk",
"Sarah C.",
""
]
] | Mechanistic learning, the synergistic combination of knowledge-driven and data-driven modeling, is an emerging field. In particular, in mathematical oncology, the application of mathematical modeling to cancer biology and oncology, the use of mechanistic learning is growing. This review aims to capture the current state of the field and provide a perspective on how mechanistic learning may further progress in mathematical oncology. We highlight the synergistic potential of knowledge-driven mechanistic mathematical modeling and data-driven modeling, such as machine and deep learning. We point out similarities and differences regarding model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Then, organizing combinations of knowledge- and data-driven modeling into four categories (sequential, parallel, intrinsic, and extrinsic mechanistic learning), we summarize a variety of approaches at the interface between purely data- and knowledge-driven models. Using examples predominantly from oncology, we discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. We see that mechanistic learning, with its intentional leveraging of the strengths of both knowledge and data-driven modeling, can greatly impact the complex problems of oncology. Given the increasing ubiquity and impact of machine learning, it is critical to incorporate it into the study of mathematical oncology with mechanistic learning providing a path to that end. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data |
1503.01843 | Brooks Emerick | Brooks Emerick, Abhyudai Singh | Host-feeding enhances stability of discrete-time host-parasitoid
population dynamic models | 18 pages, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Discrete-time models are the traditional approach for capturing population
dynamics of a host-parasitoid system. Recent work has introduced a
semi-discrete framework for obtaining model update functions that connect
host-parasitoid population levels from year-to-year. In particular, this
framework uses differential equations to describe the hosts-parasitoid
interaction during the time of year where they come in contact, allowing
specific behaviors to be mechanistically incorporated into the model. We use
the semi-discrete approach to study the effects of host-feeding, which occurs
when a parasitoid consumes a potential host larva without ovipositing. Our
results show that host-feeding by itself cannot stabilize the system, and both
the host and parasitoid populations exhibit diverging oscillations similar to
the Nicholson-Bailey model. However, when combined with other stabilizing
mechanisms such as density-dependent host mortality or density-dependent
parasitoid attack rate, host-feeding expands the region of parameter space that
allows for a stable host-parasitoid equilibrium. Finally, our results show that
host-feeding causes inefficiency in the parasitoid population, which yields a
higher population of hosts per generation. This suggests that host-feeding may
have limited long-term impact in terms of suppressing host levels for
biological control applications.
| [
{
"created": "Fri, 6 Mar 2015 03:52:14 GMT",
"version": "v1"
}
] | 2015-03-09 | [
[
"Emerick",
"Brooks",
""
],
[
"Singh",
"Abhyudai",
""
]
] | Discrete-time models are the traditional approach for capturing population dynamics of a host-parasitoid system. Recent work has introduced a semi-discrete framework for obtaining model update functions that connect host-parasitoid population levels from year-to-year. In particular, this framework uses differential equations to describe the hosts-parasitoid interaction during the time of year where they come in contact, allowing specific behaviors to be mechanistically incorporated into the model. We use the semi-discrete approach to study the effects of host-feeding, which occurs when a parasitoid consumes a potential host larva without ovipositing. Our results show that host-feeding by itself cannot stabilize the system, and both the host and parasitoid populations exhibit diverging oscillations similar to the Nicholson-Bailey model. However, when combined with other stabilizing mechanisms such as density-dependent host mortality or density-dependent parasitoid attack rate, host-feeding expands the region of parameter space that allows for a stable host-parasitoid equilibrium. Finally, our results show that host-feeding causes inefficiency in the parasitoid population, which yields a higher population of hosts per generation. This suggests that host-feeding may have limited long-term impact in terms of suppressing host levels for biological control applications. |
2002.08802 | Norichika Ogata | Tomoko Matsuda, Hikoyu Suzuki, Norichika Ogata | Phylogenetic analyses of the severe acute respiratory syndrome
coronavirus 2 reflected the several routes of introduction to Taiwan, the
United States, and Japan | 9 pages, 4 figures and 4 tables | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Worldwide Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
infection is disrupting in the economy and anxiety of people. The public
anxiety has increased the psychological burden on government and healthcare
professionals, resulting in a government worker suicide in Japan. The terrified
people are asking the government for border measures. However, are border
measures possible for this virus? By analyzing 48 almost complete virus genome
sequences, we found out that the viruses that invaded Taiwan, the United
States, and Japan were introduced independently. We identified thirteen
parsimony-informative sites and three groups (CTC, TCC, and TCT). Viruses found
outside China did not form a monophyletic clade, opposite to previous study.
These results suggest the difficulty of implementing effective border measures
against this virus.
| [
{
"created": "Thu, 20 Feb 2020 15:29:37 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Feb 2020 16:08:35 GMT",
"version": "v2"
}
] | 2020-03-02 | [
[
"Matsuda",
"Tomoko",
""
],
[
"Suzuki",
"Hikoyu",
""
],
[
"Ogata",
"Norichika",
""
]
] | Worldwide Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is disrupting in the economy and anxiety of people. The public anxiety has increased the psychological burden on government and healthcare professionals, resulting in a government worker suicide in Japan. The terrified people are asking the government for border measures. However, are border measures possible for this virus? By analyzing 48 almost complete virus genome sequences, we found out that the viruses that invaded Taiwan, the United States, and Japan were introduced independently. We identified thirteen parsimony-informative sites and three groups (CTC, TCC, and TCT). Viruses found outside China did not form a monophyletic clade, opposite to previous study. These results suggest the difficulty of implementing effective border measures against this virus. |
q-bio/0611021 | Reidun Twarock Dr | N. Jonoska and R. Twarock | A Note on Genome Organisation in RNA Viruses with Icosahedral Symmetry | 8 pages, 8 figures | null | null | null | q-bio.BM | null | The structural organisation of the viral genome within its protein container,
called the viral capsid, is an important aspect of virus architecture. Many
single-stranded (ss) RNA viruses organise a significant part of their genome in
a dodecahedral cage as a RNA duplex structure that mirrors the symmetry of the
capsid. Bruinsma and Rudnick have suggested a model for the structural
organisation of the RNA in these cages. It is the purpose of this paper to
further develop their approach based on results from the areas of graph theory
and DNA network engineering. We start by suggesting a scenario for pariacoto
virus, a representative of this class of viruses, that is energetically more
favorable than those derived previously. We then show that it is a
representative of a whole family of cage structures that abide to the same
construction principle, and then derive the energetically optimal configuration
for a second family of cage structures along similar lines. Finally, we give
reasons for the conjecture that these two families are more likely to occur in
nature than other scenarios.
| [
{
"created": "Mon, 6 Nov 2006 18:50:41 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Jonoska",
"N.",
""
],
[
"Twarock",
"R.",
""
]
] | The structural organisation of the viral genome within its protein container, called the viral capsid, is an important aspect of virus architecture. Many single-stranded (ss) RNA viruses organise a significant part of their genome in a dodecahedral cage as a RNA duplex structure that mirrors the symmetry of the capsid. Bruinsma and Rudnick have suggested a model for the structural organisation of the RNA in these cages. It is the purpose of this paper to further develop their approach based on results from the areas of graph theory and DNA network engineering. We start by suggesting a scenario for pariacoto virus, a representative of this class of viruses, that is energetically more favorable than those derived previously. We then show that it is a representative of a whole family of cage structures that abide to the same construction principle, and then derive the energetically optimal configuration for a second family of cage structures along similar lines. Finally, we give reasons for the conjecture that these two families are more likely to occur in nature than other scenarios. |
0911.0406 | Per Arne Rikvold | Per Arne Rikvold (Florida State University) | Degree Correlations in a Dynamically Generated Model Food Web | 4 pages | In Proceedings of CSP09, edited by D.P. Landau, S.P. Lewis, and
H.-B. Sch\"uttler, Physics Procedia 3, 1487-1492 (2010). | 10.1016/j.phpro.2010.01.210 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore aspects of the community structures generated by a simple
predator-prey model of biological coevolution, using large-scale kinetic Monte
Carlo simulations. The model accounts for interspecies and intraspecies
competition for resources, as well as adaptive foraging behavior. It produces a
metastable low-diversity phase and a stable high-diversity phase. The
structures and joint indegree-outdegree distributions of the food webs
generated in the latter phase are discussed.
| [
{
"created": "Mon, 2 Nov 2009 23:24:45 GMT",
"version": "v1"
}
] | 2010-02-18 | [
[
"Rikvold",
"Per Arne",
"",
"Florida State University"
]
] | We explore aspects of the community structures generated by a simple predator-prey model of biological coevolution, using large-scale kinetic Monte Carlo simulations. The model accounts for interspecies and intraspecies competition for resources, as well as adaptive foraging behavior. It produces a metastable low-diversity phase and a stable high-diversity phase. The structures and joint indegree-outdegree distributions of the food webs generated in the latter phase are discussed. |
1405.3902 | Benjamin Good | Benjamin H Good and Michael M Desai | Deleterious passengers in adapting populations | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most new mutations are deleterious and are eventually eliminated by natural
selection. But in an adapting population, the rapid amplification of beneficial
mutations can hinder the removal of deleterious variants in nearby regions of
the genome, altering the patterns of sequence evolution. Here, we analyze the
interactions between beneficial "driver" mutations and linked deleterious
"passengers" during the course of adaptation. We derive analytical expressions
for the substitution rate of a deleterious mutation as a function of its
fitness cost, as well as the reduction in the beneficial substitution rate due
to the genetic load of the passengers. We find that the fate of each
deleterious mutation varies dramatically with the rate and spectrum of
beneficial mutations, with a non-monotonic dependence on both the population
size and the rate of adaptation. By quantifying this dependence, our results
allow us to estimate which deleterious mutations will be likely to fix, and how
many of these mutations must arise before the progress of adaptation is
significantly reduced.
| [
{
"created": "Thu, 15 May 2014 16:29:05 GMT",
"version": "v1"
}
] | 2014-05-16 | [
[
"Good",
"Benjamin H",
""
],
[
"Desai",
"Michael M",
""
]
] | Most new mutations are deleterious and are eventually eliminated by natural selection. But in an adapting population, the rapid amplification of beneficial mutations can hinder the removal of deleterious variants in nearby regions of the genome, altering the patterns of sequence evolution. Here, we analyze the interactions between beneficial "driver" mutations and linked deleterious "passengers" during the course of adaptation. We derive analytical expressions for the substitution rate of a deleterious mutation as a function of its fitness cost, as well as the reduction in the beneficial substitution rate due to the genetic load of the passengers. We find that the fate of each deleterious mutation varies dramatically with the rate and spectrum of beneficial mutations, with a non-monotonic dependence on both the population size and the rate of adaptation. By quantifying this dependence, our results allow us to estimate which deleterious mutations will be likely to fix, and how many of these mutations must arise before the progress of adaptation is significantly reduced. |
2311.08546 | Yanying Wu | Yanying Wu | A Category of Genes | 13 pages, 6 figures, 1 table | null | null | null | q-bio.OT math.CT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Understanding how genes interact and relate to each other is a fundamental
question in biology. However, current practices for describing these
relationships, such as drawing diagrams or graphs in a somewhat arbitrary
manner, limit our ability to integrate various aspects of the gene functions
and view the genome holistically. To overcome these limitations, we need a more
appropriate way to describe the intricate relationships between genes.
Interestingly, category theory, an abstract field of mathematics seemingly
unrelated to biology, has emerged as a powerful language for describing
relations in general. We propose that category theory could provide a framework
for unifying our knowledge of genes and their relationships.
As a starting point, we construct a category of genes, with its morphisms
abstracting various aspects of the relationships betweens genes. These
relationships include, but not limited to, the order of genes on the
chromosomes, the physical or genetic interactions, the signalling pathways, the
gene ontology causal activity models (GO-CAM) and gene groups. Previously, they
were encoded by miscellaneous networks or graphs, while our work unifies them
in a consistent manner as a category. By doing so, we hope to view the
relationships between genes systematically. In the long run, this paves a
promising way for us to understand the fundamental principles that govern gene
regulation and function.
| [
{
"created": "Tue, 14 Nov 2023 21:19:14 GMT",
"version": "v1"
}
] | 2023-11-16 | [
[
"Wu",
"Yanying",
""
]
] | Understanding how genes interact and relate to each other is a fundamental question in biology. However, current practices for describing these relationships, such as drawing diagrams or graphs in a somewhat arbitrary manner, limit our ability to integrate various aspects of the gene functions and view the genome holistically. To overcome these limitations, we need a more appropriate way to describe the intricate relationships between genes. Interestingly, category theory, an abstract field of mathematics seemingly unrelated to biology, has emerged as a powerful language for describing relations in general. We propose that category theory could provide a framework for unifying our knowledge of genes and their relationships. As a starting point, we construct a category of genes, with its morphisms abstracting various aspects of the relationships betweens genes. These relationships include, but not limited to, the order of genes on the chromosomes, the physical or genetic interactions, the signalling pathways, the gene ontology causal activity models (GO-CAM) and gene groups. Previously, they were encoded by miscellaneous networks or graphs, while our work unifies them in a consistent manner as a category. By doing so, we hope to view the relationships between genes systematically. In the long run, this paves a promising way for us to understand the fundamental principles that govern gene regulation and function. |
2304.10736 | Zitong Lu | Zitong Lu and Julie D. Golomb | Generate your neural signals from mine: individual-to-individual EEG
converters | Proceedings of the 45th Annual Meeting of the Cognitive Science
Society (CogSci 2023) | null | null | null | q-bio.NC cs.CV cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most models in cognitive and computational neuroscience trained on one
subject do not generalize to other subjects due to individual differences. An
ideal individual-to-individual neural converter is expected to generate real
neural signals of one subject from those of another one, which can overcome the
problem of individual differences for cognitive and computational models. In
this study, we propose a novel individual-to-individual EEG converter, called
EEG2EEG, inspired by generative models in computer vision. We applied THINGS
EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to
72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to
effectively learn the mapping of neural representations in EEG signals from one
subject to another and achieve high conversion performance. Additionally, the
generated EEG signals contain clearer representations of visual information
than that can be obtained from real data. This method establishes a novel and
state-of-the-art framework for neural conversion of EEG signals, which can
realize a flexible and high-performance mapping from individual to individual
and provide insight for both neural engineering and cognitive neuroscience.
| [
{
"created": "Fri, 21 Apr 2023 04:13:16 GMT",
"version": "v1"
}
] | 2023-04-24 | [
[
"Lu",
"Zitong",
""
],
[
"Golomb",
"Julie D.",
""
]
] | Most models in cognitive and computational neuroscience trained on one subject do not generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision. We applied THINGS EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to 72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to effectively learn the mapping of neural representations in EEG signals from one subject to another and achieve high conversion performance. Additionally, the generated EEG signals contain clearer representations of visual information than that can be obtained from real data. This method establishes a novel and state-of-the-art framework for neural conversion of EEG signals, which can realize a flexible and high-performance mapping from individual to individual and provide insight for both neural engineering and cognitive neuroscience. |
1802.07338 | Chandre Dharma-wardana | M.W.C. Dharma-wardana | Fertilizer usage and cadmium in soils, crops and food | 14 pages, two figures | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phosphate fertilizers were first implicated by Schroeder and Balassa in 1963
for increasing the Cd concentration in cultivated soils and crops. This
suggestion has become a part of the accepted paradigm on soil toxicity.
Consequently, stringent fertilizer control programs to monitor Cd have been
launched. Attempts to link Cd toxicity and fertilizers to chronic diseases are
common. A re-assessment of this "accepted" paradigm is timely, given the larger
body of data available today. The data show that both the input and output of
Cd per hectare from fertilizers are negligibly small compared to the total
amount of Cd/hectare usually present in the soil itself. Calculations based on
current agricultural practices are used to show that it will take about 18
centuries to double the ambient soil-cadmium level, and about 8 centuries to
double the soil-fluoride level, even after neglecting leaching and other
removal effects. Hence the concern of long-term agriculture should be the
depletion of available phosphate fertilizers, rather than the contamination of
the soil by trace metals or fluoride. This conclusion is confirmed by showing
that the claimed correlations between fertilizer input and cadmium accumulation
in crops are not robust. Alternative scenarios that explain the data are
examined. Thus soil acidulation on fertilizer loading, and the effect of
magnesium, zinc, and fluoride ions contained in fertilizers are considered
using recent Cd$^{2+}$, Mg$^{2+}$ and F$^-$ ion-association theories. The
protective role of ions like Zn, Se, Fe, etc., is emphasized, and the question
of cadmium toxicity in the presence of other ions is considered. These help to
clarify and rectify difficulties found in the standard point of view. This
analysis does not modify the accepted views on Cd contamination by airborne
delivery, smoking, and industrial activity, or P-contamination causing algal
blooms.
| [
{
"created": "Wed, 21 Feb 2018 18:40:07 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Feb 2018 20:16:33 GMT",
"version": "v2"
},
{
"created": "Tue, 5 Jun 2018 23:48:49 GMT",
"version": "v3"
}
] | 2018-06-07 | [
[
"Dharma-wardana",
"M. W. C.",
""
]
] | Phosphate fertilizers were first implicated by Schroeder and Balassa in 1963 for increasing the Cd concentration in cultivated soils and crops. This suggestion has become a part of the accepted paradigm on soil toxicity. Consequently, stringent fertilizer control programs to monitor Cd have been launched. Attempts to link Cd toxicity and fertilizers to chronic diseases are common. A re-assessment of this "accepted" paradigm is timely, given the larger body of data available today. The data show that both the input and output of Cd per hectare from fertilizers are negligibly small compared to the total amount of Cd/hectare usually present in the soil itself. Calculations based on current agricultural practices are used to show that it will take about 18 centuries to double the ambient soil-cadmium level, and about 8 centuries to double the soil-fluoride level, even after neglecting leaching and other removal effects. Hence the concern of long-term agriculture should be the depletion of available phosphate fertilizers, rather than the contamination of the soil by trace metals or fluoride. This conclusion is confirmed by showing that the claimed correlations between fertilizer input and cadmium accumulation in crops are not robust. Alternative scenarios that explain the data are examined. Thus soil acidulation on fertilizer loading, and the effect of magnesium, zinc, and fluoride ions contained in fertilizers are considered using recent Cd$^{2+}$, Mg$^{2+}$ and F$^-$ ion-association theories. The protective role of ions like Zn, Se, Fe, etc., is emphasized, and the question of cadmium toxicity in the presence of other ions is considered. These help to clarify and rectify difficulties found in the standard point of view. This analysis does not modify the accepted views on Cd contamination by airborne delivery, smoking, and industrial activity, or P-contamination causing algal blooms. |
2203.04671 | Tim Esser | Paul Fremdling, Tim K. Esser, Bodhisattwa Saha, Alexander Makarov,
Kyle Fort, Maria Reinhardt-Szyba, Joseph Gault, and Stephan Rauschenbach | A preparative mass spectrometer to deposit intact large native protein
complexes | null | null | 10.1021/acsnano.2c04831 | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Electrospray ion-beam deposition (ES-IBD) is a versatile tool to study
structure and reactivity of molecules from small metal clusters to large
protein assemblies. It brings molecules gently into the gas phase where they
can be accurately manipulated and purified, followed by controlled deposition
onto various substrates. In combination with imaging techniques, direct
structural information of well-defined molecules can be obtained, which is
essential to test and interpret results from indirect mass spectrometry
techniques. To date, ion-beam deposition experiments are limited to a small
number of custom instruments worldwide, and there are no commercial
alternatives. Here we present a module that adds ion-beam deposition
capabilities to a popular commercial MS platform (Thermo
Scientific$^{\mathrm{TM}}$ Q Exactive$^{\mathrm{TM}}$ UHMR). This combination
significantly reduces the overhead associated with custom instruments, while
benefiting from established high performance and reliability. We present
current performance characteristics including beam intensity, landing-energy
control, and deposition spot size for a broad range of molecules. In
combination with atomic force microscopy (AFM) and transmission electron
microscopy (TEM), we distinguish near-native from unfolded proteins and show
retention of native shape of protein assemblies after dehydration and
deposition. Further, we use an enzymatic assay to quantify activity of an
non-covalent protein complex after deposition an a dry surface. Together, these
results indicate a great potential of ES-IBD for applications in structural
biology, but also outline the challenges that need to be solved for it to reach
its full potential.
| [
{
"created": "Wed, 9 Mar 2022 12:24:23 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Mar 2022 11:19:42 GMT",
"version": "v2"
},
{
"created": "Mon, 21 Mar 2022 16:41:45 GMT",
"version": "v3"
}
] | 2022-09-14 | [
[
"Fremdling",
"Paul",
""
],
[
"Esser",
"Tim K.",
""
],
[
"Saha",
"Bodhisattwa",
""
],
[
"Makarov",
"Alexander",
""
],
[
"Fort",
"Kyle",
""
],
[
"Reinhardt-Szyba",
"Maria",
""
],
[
"Gault",
"Joseph",
""
],
[
"Rauschenbach",
"Stephan",
""
]
] | Electrospray ion-beam deposition (ES-IBD) is a versatile tool to study structure and reactivity of molecules from small metal clusters to large protein assemblies. It brings molecules gently into the gas phase where they can be accurately manipulated and purified, followed by controlled deposition onto various substrates. In combination with imaging techniques, direct structural information of well-defined molecules can be obtained, which is essential to test and interpret results from indirect mass spectrometry techniques. To date, ion-beam deposition experiments are limited to a small number of custom instruments worldwide, and there are no commercial alternatives. Here we present a module that adds ion-beam deposition capabilities to a popular commercial MS platform (Thermo Scientific$^{\mathrm{TM}}$ Q Exactive$^{\mathrm{TM}}$ UHMR). This combination significantly reduces the overhead associated with custom instruments, while benefiting from established high performance and reliability. We present current performance characteristics including beam intensity, landing-energy control, and deposition spot size for a broad range of molecules. In combination with atomic force microscopy (AFM) and transmission electron microscopy (TEM), we distinguish near-native from unfolded proteins and show retention of native shape of protein assemblies after dehydration and deposition. Further, we use an enzymatic assay to quantify activity of an non-covalent protein complex after deposition an a dry surface. Together, these results indicate a great potential of ES-IBD for applications in structural biology, but also outline the challenges that need to be solved for it to reach its full potential. |
q-bio/0610009 | Tao Hu | Tao Hu, Rui Zhang, B. I. Shklovskii | Electrostatic theory of viral self-assembly: a toy model | 4 pages, 2 figures | Physica A 387, 3059 (2008) | 10.1016/j.physa.2008.01.010 | null | q-bio.BM cond-mat.soft | null | Viruses self-assemble from identical capsid proteins and their genome
consisting, for example, of a long single stranded (ss) RNA. For a big class of
T = 3 viruses capsid proteins have long positive N-terminal tails. We explore
the role played by the Coulomb interaction between the brush of positive
N-terminal tails rooted at the inner surface of the capsid and the negative ss
RNA molecule. We show that viruses are most stable when the total contour
length of ss RNA is close to the total length of the tails. For such a
structure the absolute value of the total RNA charge is approximately twice
larger than the charge of the capsid. This conclusion agrees with structural
data.
| [
{
"created": "Tue, 3 Oct 2006 23:17:54 GMT",
"version": "v1"
},
{
"created": "Tue, 10 Oct 2006 16:00:08 GMT",
"version": "v2"
},
{
"created": "Thu, 28 Dec 2006 19:16:44 GMT",
"version": "v3"
},
{
"created": "Fri, 2 Feb 2007 17:14:53 GMT",
"version": "v4"
}
] | 2015-06-26 | [
[
"Hu",
"Tao",
""
],
[
"Zhang",
"Rui",
""
],
[
"Shklovskii",
"B. I.",
""
]
] | Viruses self-assemble from identical capsid proteins and their genome consisting, for example, of a long single stranded (ss) RNA. For a big class of T = 3 viruses capsid proteins have long positive N-terminal tails. We explore the role played by the Coulomb interaction between the brush of positive N-terminal tails rooted at the inner surface of the capsid and the negative ss RNA molecule. We show that viruses are most stable when the total contour length of ss RNA is close to the total length of the tails. For such a structure the absolute value of the total RNA charge is approximately twice larger than the charge of the capsid. This conclusion agrees with structural data. |
1511.00262 | Konrad Kording | Konrad Paul Kording | The geometry of Tempotronlike problems | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the discrete Tempotron learning problem a neuron receives time varying
inputs and for a set of such input sequences ($\mathcal S_-$ set) the neuron
must be sub-threshold for all times while for some other sequences ($\mathcal
S_+$ set) the neuron must be super threshold for at least one time. Here we
present a graphical treatment of a slight reformulation of the tempotron
problem. We show that the problem's general form is equivalent to the question
if a polytope, specified by a set of inequalities, is contained in the union of
a set of equally defined polytopes. Using recent results from computational
geometry, we show that the problem is W[1]-hard. This phrasing gives some new
insights into the nature of gradient based learning algorithms. A sampling
based approach can, under certain circumstances provide an approximation in
polynomial time. Other problems, related to hierarchical neural networks may
share some topological structure.
| [
{
"created": "Sun, 1 Nov 2015 15:49:46 GMT",
"version": "v1"
}
] | 2015-11-03 | [
[
"Kording",
"Konrad Paul",
""
]
] | In the discrete Tempotron learning problem a neuron receives time varying inputs and for a set of such input sequences ($\mathcal S_-$ set) the neuron must be sub-threshold for all times while for some other sequences ($\mathcal S_+$ set) the neuron must be super threshold for at least one time. Here we present a graphical treatment of a slight reformulation of the tempotron problem. We show that the problem's general form is equivalent to the question if a polytope, specified by a set of inequalities, is contained in the union of a set of equally defined polytopes. Using recent results from computational geometry, we show that the problem is W[1]-hard. This phrasing gives some new insights into the nature of gradient based learning algorithms. A sampling based approach can, under certain circumstances provide an approximation in polynomial time. Other problems, related to hierarchical neural networks may share some topological structure. |
2402.00484 | Satori Tsuzuki Ph.D | Satori Tsuzuki | Extreme value statistics of nerve transmission delay | null | null | null | null | q-bio.NC math-ph math.MP stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nerve transmission delay is an important topic in neuroscience. Spike signals
fired or received at the dendrites of a neuron travel from the axon to the
presynaptic cell. The spike signal triggers a chemical reaction at the synapse,
wherein a presynaptic cell transfers neurotransmitters to the postsynaptic
cell, and regenerates electrical signals by a chemical reaction process through
ion channels and transmits it to neighboring neurons. In the context of
describing the complex physiological reaction process as a stochastic process,
this study aimed to show that the distribution of the maximum time interval of
spike signals follows extreme order statistics. By considering the statistical
variance in the time constant of the Leaky Integrate-and-Fire model, which is a
deterministic time evolution model of spike signals, we enabled randomness in
the time interval of spike signals. When the time constant follows an
exponential distribution function, the time interval of the spike signal also
follows an exponential distribution. In this case, our theory and simulations
confirmed that the histogram of the maximum time interval follows the Gumbel
distribution, which is one of the three types of extreme value statistics. We
also confirmed that the histogram of the maximum time interval follows a
Fr\'{e}chet distribution when the time interval of the spike signal follows a
Pareto distribution. These findings confirm that nerve transmission delay can
be described using extreme value statistics and could, therefore, be used as a
new indicator for transmission delay.
| [
{
"created": "Thu, 1 Feb 2024 10:40:42 GMT",
"version": "v1"
},
{
"created": "Tue, 21 May 2024 19:55:50 GMT",
"version": "v2"
}
] | 2024-05-24 | [
[
"Tsuzuki",
"Satori",
""
]
] | Nerve transmission delay is an important topic in neuroscience. Spike signals fired or received at the dendrites of a neuron travel from the axon to the presynaptic cell. The spike signal triggers a chemical reaction at the synapse, wherein a presynaptic cell transfers neurotransmitters to the postsynaptic cell, and regenerates electrical signals by a chemical reaction process through ion channels and transmits it to neighboring neurons. In the context of describing the complex physiological reaction process as a stochastic process, this study aimed to show that the distribution of the maximum time interval of spike signals follows extreme order statistics. By considering the statistical variance in the time constant of the Leaky Integrate-and-Fire model, which is a deterministic time evolution model of spike signals, we enabled randomness in the time interval of spike signals. When the time constant follows an exponential distribution function, the time interval of the spike signal also follows an exponential distribution. In this case, our theory and simulations confirmed that the histogram of the maximum time interval follows the Gumbel distribution, which is one of the three types of extreme value statistics. We also confirmed that the histogram of the maximum time interval follows a Fr\'{e}chet distribution when the time interval of the spike signal follows a Pareto distribution. These findings confirm that nerve transmission delay can be described using extreme value statistics and could, therefore, be used as a new indicator for transmission delay. |
1409.1946 | Stephan Schiffels | Stephan Schiffels, Michael L\"assig, and Ville Mustonen | Rate and cost of adaptation in the Drosophila Genome | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies have consistently inferred high rates of adaptive molecular
evolution between Drosophila species. At the same time, the Drosophila genome
evolves under different rates of recombination, which results in partial
genetic linkage between alleles at neighboring genomic loci. Here we analyze
how linkage correlations affect adaptive evolution. We develop a new inference
method for adaptation that takes into account the effect on an allele at a
focal site caused by neighboring deleterious alleles (background selection) and
by neighboring adaptive substitutions (hitchhiking). Using complete genome
sequence data and fine-scale recombination maps, we infer a highly
heterogeneous scenario of adaptation in Drosophila. In high-recombining
regions, about 50% of all amino acid substitutions are adaptive, together with
about 20% of all substitutions in proximal intergenic regions. In
low-recombining regions, only a small fraction of the amino acid substitutions
are adaptive, while hitchhiking accounts for the majority of these changes.
Hitchhiking of deleterious alleles generates a substantial collateral cost of
adaptation, leading to a fitness decline of about 30/2N per gene and per
million years in the lowest-recombining regions. Our results show how
recombination shapes rate and efficacy of the adaptive dynamics in eukaryotic
genomes.
| [
{
"created": "Fri, 5 Sep 2014 21:09:48 GMT",
"version": "v1"
}
] | 2014-09-09 | [
[
"Schiffels",
"Stephan",
""
],
[
"Lässig",
"Michael",
""
],
[
"Mustonen",
"Ville",
""
]
] | Recent studies have consistently inferred high rates of adaptive molecular evolution between Drosophila species. At the same time, the Drosophila genome evolves under different rates of recombination, which results in partial genetic linkage between alleles at neighboring genomic loci. Here we analyze how linkage correlations affect adaptive evolution. We develop a new inference method for adaptation that takes into account the effect on an allele at a focal site caused by neighboring deleterious alleles (background selection) and by neighboring adaptive substitutions (hitchhiking). Using complete genome sequence data and fine-scale recombination maps, we infer a highly heterogeneous scenario of adaptation in Drosophila. In high-recombining regions, about 50% of all amino acid substitutions are adaptive, together with about 20% of all substitutions in proximal intergenic regions. In low-recombining regions, only a small fraction of the amino acid substitutions are adaptive, while hitchhiking accounts for the majority of these changes. Hitchhiking of deleterious alleles generates a substantial collateral cost of adaptation, leading to a fitness decline of about 30/2N per gene and per million years in the lowest-recombining regions. Our results show how recombination shapes rate and efficacy of the adaptive dynamics in eukaryotic genomes. |
1804.07406 | Soham De | Soham De, Dana S. Nau, Xinyue Pan, Michele J. Gelfand | Tipping Points for Norm Change in Human Cultures | SBP-BRiMS 2018 | null | 10.1007/978-3-319-93372-6_7 | null | q-bio.PE cs.CY cs.GT cs.MA physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans interact with each other on a daily basis by developing and
maintaining various social norms and it is critical to form a deeper
understanding of how such norms develop, how they change, and how fast they
change. In this work, we develop an evolutionary game-theoretic model based on
research in cultural psychology that shows that humans in various cultures
differ in their tendencies to conform with those around them. Using this model,
we analyze the evolutionary relationships between the tendency to conform and
how quickly a population reacts when conditions make a change in norm
desirable. Our analysis identifies conditions when a tipping point is reached
in a population, causing norms to change rapidly.
| [
{
"created": "Thu, 19 Apr 2018 23:43:28 GMT",
"version": "v1"
},
{
"created": "Mon, 2 Jul 2018 01:05:42 GMT",
"version": "v2"
}
] | 2018-07-03 | [
[
"De",
"Soham",
""
],
[
"Nau",
"Dana S.",
""
],
[
"Pan",
"Xinyue",
""
],
[
"Gelfand",
"Michele J.",
""
]
] | Humans interact with each other on a daily basis by developing and maintaining various social norms and it is critical to form a deeper understanding of how such norms develop, how they change, and how fast they change. In this work, we develop an evolutionary game-theoretic model based on research in cultural psychology that shows that humans in various cultures differ in their tendencies to conform with those around them. Using this model, we analyze the evolutionary relationships between the tendency to conform and how quickly a population reacts when conditions make a change in norm desirable. Our analysis identifies conditions when a tipping point is reached in a population, causing norms to change rapidly. |
1906.08881 | Vijay Singh | Vijay Singh and Ilya Nemenman | Universal properties of concentration sensing in large ligand-receptor
networks | 5 pages, 3 figures, 2 supplementary figures | Phys. Rev. Lett. 124, 028101 (2020) | 10.1103/PhysRevLett.124.028101 | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cells estimate concentrations of chemical ligands in their environment using
a limited set of receptors. Recent work has shown that the temporal sequence of
binding and unbinding events on just a single receptor can be used to estimate
the concentrations of multiple ligands. Here, for a network of many ligands and
many receptors, we show that such temporal sequences can be used to estimate
the concentration of a few times as many ligand species as there are receptors.
Crucially, we show that the spectrum of the inverse covariance matrix of these
estimates has several universal properties, which we trace to properties of
Vandermonde matrices. We argue that this can be used by cells in realistic
biochemical decoding networks.
| [
{
"created": "Thu, 20 Jun 2019 22:29:17 GMT",
"version": "v1"
}
] | 2020-01-22 | [
[
"Singh",
"Vijay",
""
],
[
"Nemenman",
"Ilya",
""
]
] | Cells estimate concentrations of chemical ligands in their environment using a limited set of receptors. Recent work has shown that the temporal sequence of binding and unbinding events on just a single receptor can be used to estimate the concentrations of multiple ligands. Here, for a network of many ligands and many receptors, we show that such temporal sequences can be used to estimate the concentration of a few times as many ligand species as there are receptors. Crucially, we show that the spectrum of the inverse covariance matrix of these estimates has several universal properties, which we trace to properties of Vandermonde matrices. We argue that this can be used by cells in realistic biochemical decoding networks. |
2009.02156 | Andreas Kamilaris | P. Papademas, E. Kamilari, M. Aspri, D. A Anagnostopoulos, P.
Mousikos, A. Kamilaris and D. Tsaltas | Investigation of the Cyprus donkey milk bacterial diversity by 16SrDNA
high-throughput sequencing in a Cyprus donkey farm | Accepted for publication at Journal of Dairy Science | null | null | null | q-bio.QM cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The interest in milk originating from donkeys is growing worldwide due to its
claimed functional and nutritional properties, especially for sensitive
population groups, such as infants with cow milk protein allergy. The current
study aimed to assess the microbiological quality of donkey milk produced in a
donkey farm in Cyprus using cultured-based and high-throughput sequencing (HTS)
techniques. The culture-based microbiological analysis showed very low
microbial counts, while important food-borne pathogens were not detected in any
sample. In addition, HTS was applied to characterize the bacterial communities
of donkey milk samples. Donkey milk was mostly comprised of: Gram-negative
Proteobacteria, including Sphingomonas, Pseudomonas Mesorhizobium and
Acinetobacter; lactic acid bacteria, including Lactobacillus and Streptococcus;
the endospores forming Clostridium; and the environmental genera Flavobacterium
and Ralstonia, detected in lower relative abundances. The results of the study
support existing findings that donkey milk contains mostly Gram-negative
bacteria. Moreover, it raises questions regarding the contribution: a) of
antimicrobial agents (i.e. lysozyme, peptides) in shaping the microbial
communities and b) of the bacterial microbiota to the functional value of
donkey milk.
| [
{
"created": "Fri, 4 Sep 2020 12:42:54 GMT",
"version": "v1"
}
] | 2020-09-07 | [
[
"Papademas",
"P.",
""
],
[
"Kamilari",
"E.",
""
],
[
"Aspri",
"M.",
""
],
[
"Anagnostopoulos",
"D. A",
""
],
[
"Mousikos",
"P.",
""
],
[
"Kamilaris",
"A.",
""
],
[
"Tsaltas",
"D.",
""
]
] | The interest in milk originating from donkeys is growing worldwide due to its claimed functional and nutritional properties, especially for sensitive population groups, such as infants with cow milk protein allergy. The current study aimed to assess the microbiological quality of donkey milk produced in a donkey farm in Cyprus using cultured-based and high-throughput sequencing (HTS) techniques. The culture-based microbiological analysis showed very low microbial counts, while important food-borne pathogens were not detected in any sample. In addition, HTS was applied to characterize the bacterial communities of donkey milk samples. Donkey milk was mostly comprised of: Gram-negative Proteobacteria, including Sphingomonas, Pseudomonas Mesorhizobium and Acinetobacter; lactic acid bacteria, including Lactobacillus and Streptococcus; the endospores forming Clostridium; and the environmental genera Flavobacterium and Ralstonia, detected in lower relative abundances. The results of the study support existing findings that donkey milk contains mostly Gram-negative bacteria. Moreover, it raises questions regarding the contribution: a) of antimicrobial agents (i.e. lysozyme, peptides) in shaping the microbial communities and b) of the bacterial microbiota to the functional value of donkey milk. |
2404.09947 | Katherine Meyer | Benjamin Hafner and Katherine Meyer | Bounding seed loss from isolated habitat patches | 25 pages, 10 figures | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dispersal of propagules (seeds, spores) from a geographically isolated
population into an uninhabitable matrix can threaten population persistence if
it prevents new growth from keeping pace with mortality. Quantifying propagule
loss can thus inform restoration and conservation of vulnerable populations in
fragmented landscapes. To model propagule loss in detail, one can integrate
dispersal kernels (probabilistic descriptions of dispersal) and plant
densities. However, one might lack the detailed spatial information and
computational tools needed by such integral models. Here we derive two upper
bounds on the probability of propagule loss--one assuming rotational symmetry
of dispersal and the other not--that require only habitat area, habitat
perimeter, and the mean dispersal distance of a propagule. We compare the
bounds to simulations of integral models for the population of Asclepias
syriaca (common milkweed) at McKnight Prairie--a 13.7 hectare reserve
surrounded by agricultural fields in Goodhue County, Minnesota--and identify
conditions under which the bounds closely estimate propagule loss.
| [
{
"created": "Mon, 15 Apr 2024 17:21:23 GMT",
"version": "v1"
}
] | 2024-04-16 | [
[
"Hafner",
"Benjamin",
""
],
[
"Meyer",
"Katherine",
""
]
] | Dispersal of propagules (seeds, spores) from a geographically isolated population into an uninhabitable matrix can threaten population persistence if it prevents new growth from keeping pace with mortality. Quantifying propagule loss can thus inform restoration and conservation of vulnerable populations in fragmented landscapes. To model propagule loss in detail, one can integrate dispersal kernels (probabilistic descriptions of dispersal) and plant densities. However, one might lack the detailed spatial information and computational tools needed by such integral models. Here we derive two upper bounds on the probability of propagule loss--one assuming rotational symmetry of dispersal and the other not--that require only habitat area, habitat perimeter, and the mean dispersal distance of a propagule. We compare the bounds to simulations of integral models for the population of Asclepias syriaca (common milkweed) at McKnight Prairie--a 13.7 hectare reserve surrounded by agricultural fields in Goodhue County, Minnesota--and identify conditions under which the bounds closely estimate propagule loss. |
2305.01059 | Jeferson J. Arenzon | Jeferson J. Arenzon and Luca Peliti | Emergent cooperative behavior in transient compartments | 7 pages, 5 figures | Phys. Rev. E 108 (2023) 034409 | 10.1103/PhysRevE.108.034409 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a minimal model of multilevel selection on structured
populations, considering the interplay between game theory and population
dynamics. Through a bottleneck process, finite groups are formed with
cooperators and defectors sampled from an infinite pool. After the
fragmentation, these transient compartments grow until the carrying capacity is
attained. Eventually, all compartments are merged, well mixed and the whole
process is repeated. We show that cooperators, even if interacting only through
mean-field intra-group interactions that favor defectors, may perform well
because of the inter-group competition and the size diversity among the
compartments. These cycles of isolation and coalescence may therefore be
important in maintaining diversity among different species or strategies and
may help to understand the underlying mechanisms of the scaffolding processes
in the transition to multicellularity.
| [
{
"created": "Mon, 1 May 2023 19:45:33 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Sep 2023 00:35:20 GMT",
"version": "v2"
}
] | 2023-10-02 | [
[
"Arenzon",
"Jeferson J.",
""
],
[
"Peliti",
"Luca",
""
]
] | We introduce a minimal model of multilevel selection on structured populations, considering the interplay between game theory and population dynamics. Through a bottleneck process, finite groups are formed with cooperators and defectors sampled from an infinite pool. After the fragmentation, these transient compartments grow until the carrying capacity is attained. Eventually, all compartments are merged, well mixed and the whole process is repeated. We show that cooperators, even if interacting only through mean-field intra-group interactions that favor defectors, may perform well because of the inter-group competition and the size diversity among the compartments. These cycles of isolation and coalescence may therefore be important in maintaining diversity among different species or strategies and may help to understand the underlying mechanisms of the scaffolding processes in the transition to multicellularity. |
1910.04048 | Mohsen Annabestani | Mohsen Annabestani, Sina Azizmohseni, Pouria Esmaeili-Dokht, Nahal
Bagheri, Afarin Aghassizadeh and, Mahdi Fardmanesh | Multiphysics analysis and practical implementation of an ionic soft
actuator-based microfluidic device toward the design of a POCT compatible
active micromixer | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electroactive-Polymers (EAPs) are one of the best soft materials with great
applications in active microfluidics. Ionic ones (i-EAPs) have more promising
features for being appropriate candidates to use in active microfluidic
devices. Here, as a case study, we have designed and fabricated a microfluidic
micromixer using an i-EAP named Ionic Polymer-Metal Composite (IPMC). In
microfluidics, active devices have more functionality but due to their required
facilities are less effective for Point of Care Tests (POCTs). In the direction
of solving this paradox, we should use some active components that they need
minimum facilities. IPMC can be one of these components, hence by integrating
the IPMC actuator into a microfluidic channel, a micromixer chip was designed
and put to the simulation and experimental tests. The result showed that the
proposed micromixer is able to mix the micro fluids properly and IPMC actuator
has adequate potential to be an active component for POCT-based microfluidic
chips.
| [
{
"created": "Tue, 8 Oct 2019 11:57:20 GMT",
"version": "v1"
}
] | 2019-10-10 | [
[
"Annabestani",
"Mohsen",
""
],
[
"Azizmohseni",
"Sina",
""
],
[
"Esmaeili-Dokht",
"Pouria",
""
],
[
"Bagheri",
"Nahal",
""
],
[
"and",
"Afarin Aghassizadeh",
""
],
[
"Fardmanesh",
"Mahdi",
""
]
] | Electroactive-Polymers (EAPs) are one of the best soft materials with great applications in active microfluidics. Ionic ones (i-EAPs) have more promising features for being appropriate candidates to use in active microfluidic devices. Here, as a case study, we have designed and fabricated a microfluidic micromixer using an i-EAP named Ionic Polymer-Metal Composite (IPMC). In microfluidics, active devices have more functionality but due to their required facilities are less effective for Point of Care Tests (POCTs). In the direction of solving this paradox, we should use some active components that they need minimum facilities. IPMC can be one of these components, hence by integrating the IPMC actuator into a microfluidic channel, a micromixer chip was designed and put to the simulation and experimental tests. The result showed that the proposed micromixer is able to mix the micro fluids properly and IPMC actuator has adequate potential to be an active component for POCT-based microfluidic chips. |
1411.4108 | Li Zhang | Li Zhang, Xuejun Liu, Songcan Chen | Detecting Differential Expression from RNA-seq Data with Expression
Measurement Uncertainty | 20 pages, 9 figures | null | null | null | q-bio.GN cs.CE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High-throughput RNA sequencing (RNA-seq) has emerged as a revolutionary and
powerful technology for expression profiling. Most proposed methods for
detecting differentially expressed (DE) genes from RNA-seq are based on
statistics that compare normalized read counts between conditions. However,
there are few methods considering the expression measurement uncertainty into
DE detection. Moreover, most methods are only capable of detecting DE genes,
and few methods are available for detecting DE isoforms. In this paper, a
Bayesian framework (BDSeq) is proposed to detect DE genes and isoforms with
consideration of expression measurement uncertainty. This expression
measurement uncertainty provides useful information which can help to improve
the performance of DE detection. Three real RAN-seq data sets are used to
evaluate the performance of BDSeq and results show that the inclusion of
expression measurement uncertainty improves accuracy in detection of DE genes
and isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline to
facilitate users, which is freely available at the website
http://parnec.nuaa.edu.cn/liux/GSBD/GamSeq-BDSeq.html.
| [
{
"created": "Sat, 15 Nov 2014 03:43:01 GMT",
"version": "v1"
}
] | 2014-11-18 | [
[
"Zhang",
"Li",
""
],
[
"Liu",
"Xuejun",
""
],
[
"Chen",
"Songcan",
""
]
] | High-throughput RNA sequencing (RNA-seq) has emerged as a revolutionary and powerful technology for expression profiling. Most proposed methods for detecting differentially expressed (DE) genes from RNA-seq are based on statistics that compare normalized read counts between conditions. However, there are few methods considering the expression measurement uncertainty into DE detection. Moreover, most methods are only capable of detecting DE genes, and few methods are available for detecting DE isoforms. In this paper, a Bayesian framework (BDSeq) is proposed to detect DE genes and isoforms with consideration of expression measurement uncertainty. This expression measurement uncertainty provides useful information which can help to improve the performance of DE detection. Three real RAN-seq data sets are used to evaluate the performance of BDSeq and results show that the inclusion of expression measurement uncertainty improves accuracy in detection of DE genes and isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline to facilitate users, which is freely available at the website http://parnec.nuaa.edu.cn/liux/GSBD/GamSeq-BDSeq.html. |
2309.01663 | Brandon Legried | Brandon Legried | Anomaly zones for uniformly sampled gene trees under the gene
duplication and loss model | 32 pages, 3 pages of references, 8 figures, Appendix with 8 pages | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Recently, there has been interest in extending long-known results about the
multispecies coalescent tree to other models of gene trees. Results about the
gene duplication and loss (GDL) tree have mathematical proofs, including
species tree identifiability, estimability, and sample complexity of popular
algorithms like ASTRAL. Here, this work is continued by characterizing the
anomaly zones of uniformly sampled gene trees. The anomaly zone for species
trees is the set of parameters where some discordant gene tree occurs with the
maximal probability. The detection of anomalous gene trees is an important
problem in phylogenomics, as their presence renders effective estimation
methods to being positively misleading. Under the multispecies coalescent,
anomaly zones are known to exist for rooted species trees with as few as four
species.
The gene duplication and loss process is a generalization of the generalized
linear-birth death process to the rooted species tree, where each edge is
treated as a single timeline with exponential-rate duplication and loss. The
methods and results come from a detailed probabilistic analysis of trajectories
observed from this stochastic process. It is shown that anomaly zones do not
exist for rooted GDL balanced trees on four species, but do exist for rooted
caterpillar trees, as with the multispecies coalescent.
| [
{
"created": "Mon, 4 Sep 2023 15:30:52 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Feb 2024 02:16:32 GMT",
"version": "v2"
},
{
"created": "Thu, 28 Mar 2024 23:00:08 GMT",
"version": "v3"
}
] | 2024-04-01 | [
[
"Legried",
"Brandon",
""
]
] | Recently, there has been interest in extending long-known results about the multispecies coalescent tree to other models of gene trees. Results about the gene duplication and loss (GDL) tree have mathematical proofs, including species tree identifiability, estimability, and sample complexity of popular algorithms like ASTRAL. Here, this work is continued by characterizing the anomaly zones of uniformly sampled gene trees. The anomaly zone for species trees is the set of parameters where some discordant gene tree occurs with the maximal probability. The detection of anomalous gene trees is an important problem in phylogenomics, as their presence renders effective estimation methods to being positively misleading. Under the multispecies coalescent, anomaly zones are known to exist for rooted species trees with as few as four species. The gene duplication and loss process is a generalization of the generalized linear-birth death process to the rooted species tree, where each edge is treated as a single timeline with exponential-rate duplication and loss. The methods and results come from a detailed probabilistic analysis of trajectories observed from this stochastic process. It is shown that anomaly zones do not exist for rooted GDL balanced trees on four species, but do exist for rooted caterpillar trees, as with the multispecies coalescent. |
1502.03135 | M\'at\'e Lengyel | M\'at\'e Lengyel, \'Ad\'am Koblinger, Marjena Popovi\'c, J\'ozsef
Fiser | On the role of time in perceptual decision making | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | According to the dominant view, time in perceptual decision making is used
for integrating new sensory evidence. Based on a probabilistic framework, we
investigated the alternative hypothesis that time is used for gradually
refining an internal estimate of uncertainty, that is to obtain an increasingly
accurate approximation of the posterior distribution through collecting samples
from it. In the context of a simple orientation estimation task, we
analytically derived predictions of how humans should behave under the two
hypotheses, and identified the across-trial correlation between error and
subjective uncertainty as a proper assay to distinguish between them. Next, we
developed a novel experimental paradigm that could be used to reliably measure
these quantities, and tested the predictions derived from the two hypotheses.
We found that in our task, humans show clear evidence that they use time mostly
for probabilistic sampling and not for evidence integration. These results
provide the first empirical support for iteratively improving probabilistic
representations in perceptual decision making, and open the way to reinterpret
the role of time in the cortical processing of complex sensory information.
| [
{
"created": "Tue, 10 Feb 2015 22:09:41 GMT",
"version": "v1"
}
] | 2015-02-12 | [
[
"Lengyel",
"Máté",
""
],
[
"Koblinger",
"Ádám",
""
],
[
"Popović",
"Marjena",
""
],
[
"Fiser",
"József",
""
]
] | According to the dominant view, time in perceptual decision making is used for integrating new sensory evidence. Based on a probabilistic framework, we investigated the alternative hypothesis that time is used for gradually refining an internal estimate of uncertainty, that is to obtain an increasingly accurate approximation of the posterior distribution through collecting samples from it. In the context of a simple orientation estimation task, we analytically derived predictions of how humans should behave under the two hypotheses, and identified the across-trial correlation between error and subjective uncertainty as a proper assay to distinguish between them. Next, we developed a novel experimental paradigm that could be used to reliably measure these quantities, and tested the predictions derived from the two hypotheses. We found that in our task, humans show clear evidence that they use time mostly for probabilistic sampling and not for evidence integration. These results provide the first empirical support for iteratively improving probabilistic representations in perceptual decision making, and open the way to reinterpret the role of time in the cortical processing of complex sensory information. |
2012.15647 | Franz Franchetti | Yoko Franchetti, Thomas D. Nolin, Franz Franchetti | Indirect Measurement of Hepatic Drug Clearance by Fitting Dynamical
Models | This preprint is based on Chapter 2 of the PhD dissertation of Y
Franchetti. The dissertation thesis is available at
http://d-scholarship.pitt.edu/id/eprint/39885 | null | null | University of Pittsburgh ETD 39885 | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an indirect signal processing-based measurement method for
biological quantities in humans that cannot be directly measured. We develop
the method by focusing on estimating hepatic enzyme and drug transporter
activity through breath-biopsy samples clinically obtained via the erythromycin
breath test (EBT): a small dose of radio-labeled drug is injected and the
subsequent content of radio-labeled CO$_2$ is measured repeatedly in exhaled
breath; the resulting time series is analyzed. To model EBT we developed a
14-variable non-linear reduced order dynamical model that describes the
behavior of the drug and its metabolites in the human body well enough to
capture all biological phenomena of interest. Based on this system of coupled
non-linear ordinary differential equations (ODEs) we treat the measurement
problem as inverse problem: we estimate the ODE parameters of individual
patients from the measured EBT time series. These estimates then provide a
measurement of the liver activity of interest. The parameters are hard to
estimate as the ODEs are stiff and the problem needs to be regularized to
ensure stable convergence. We develop a formal operator framework to capture
and treat the specific non-linearities present, and perform perturbation
analysis to establish properties of the estimation procedure and its solution.
Development of the method required 150,000 CPU hours at a supercomputing
center, and a single production run takes CPU 24 hours. We introduce and
analyze the method in the context of future precision dosing of drugs for
vulnerable patients (e.g., oncology, nephrology, or pediatrics) to eventually
ensure efficacy and avoid toxicity.
| [
{
"created": "Thu, 31 Dec 2020 15:09:21 GMT",
"version": "v1"
}
] | 2021-01-01 | [
[
"Franchetti",
"Yoko",
""
],
[
"Nolin",
"Thomas D.",
""
],
[
"Franchetti",
"Franz",
""
]
] | We present an indirect signal processing-based measurement method for biological quantities in humans that cannot be directly measured. We develop the method by focusing on estimating hepatic enzyme and drug transporter activity through breath-biopsy samples clinically obtained via the erythromycin breath test (EBT): a small dose of radio-labeled drug is injected and the subsequent content of radio-labeled CO$_2$ is measured repeatedly in exhaled breath; the resulting time series is analyzed. To model EBT we developed a 14-variable non-linear reduced order dynamical model that describes the behavior of the drug and its metabolites in the human body well enough to capture all biological phenomena of interest. Based on this system of coupled non-linear ordinary differential equations (ODEs) we treat the measurement problem as inverse problem: we estimate the ODE parameters of individual patients from the measured EBT time series. These estimates then provide a measurement of the liver activity of interest. The parameters are hard to estimate as the ODEs are stiff and the problem needs to be regularized to ensure stable convergence. We develop a formal operator framework to capture and treat the specific non-linearities present, and perform perturbation analysis to establish properties of the estimation procedure and its solution. Development of the method required 150,000 CPU hours at a supercomputing center, and a single production run takes CPU 24 hours. We introduce and analyze the method in the context of future precision dosing of drugs for vulnerable patients (e.g., oncology, nephrology, or pediatrics) to eventually ensure efficacy and avoid toxicity. |
0902.0980 | Ping Ao | P Ao | Global View of Bionetwork Dynamics: Adaptive Landscape | 16 pages | Journal of Genetics and Genomics. V.36, 63-73 (2009) Ping Ao.
Global View of Bionetwork Dynamics: Adaptive Landscape | null | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantifying the adaptive landscape in a given dynamical processes has been
one of most important goals in theoretical biology. It can have immediate
implications for many dynamical properties, such as robustness and plasticity.
Based on recent work here I give a nontechnical brief review of this powerful
quantitative concept in biology. This concept was initially proposed by S
Wright 70 years ago, re-introduced by one of the founders of molecular biology
and by others in different biological contexts. It was apparently forgotten by
mainstream modern biologists for many years. Currently, this concept has found
its increasingly important role in the development of systems biology and the
modeling of bionetwork dynamics, from phage lambda genetic switch to endogenous
network of cancer genesis and progression. It is an ideal quantify to describe
the robustness and stability of bionetworks. I will first introduce five
landmark proposals in biology on this concept, to demonstrate the important
common thread in its theoretical biology development. Then I will discuss a few
recent results, focusing on the work showing the logical consistency of
adaptive landscape. From the perspective of a working scientist and of what
needed for a dynamical theory when confronting empirical data, the adaptive
landscape is useful both metaphorically and quantitatively and has captured an
essential aspect of biological dynamical processes. Still, many important open
problems remain to be solved. Having this important problem under control, we
may expect that we are on the right road to quantitatively formulate the
evolutionary dynamics discovered by Darwin and Wallace.
| [
{
"created": "Thu, 5 Feb 2009 21:25:11 GMT",
"version": "v1"
}
] | 2009-02-09 | [
[
"Ao",
"P",
""
]
] | Quantifying the adaptive landscape in a given dynamical processes has been one of most important goals in theoretical biology. It can have immediate implications for many dynamical properties, such as robustness and plasticity. Based on recent work here I give a nontechnical brief review of this powerful quantitative concept in biology. This concept was initially proposed by S Wright 70 years ago, re-introduced by one of the founders of molecular biology and by others in different biological contexts. It was apparently forgotten by mainstream modern biologists for many years. Currently, this concept has found its increasingly important role in the development of systems biology and the modeling of bionetwork dynamics, from phage lambda genetic switch to endogenous network of cancer genesis and progression. It is an ideal quantify to describe the robustness and stability of bionetworks. I will first introduce five landmark proposals in biology on this concept, to demonstrate the important common thread in its theoretical biology development. Then I will discuss a few recent results, focusing on the work showing the logical consistency of adaptive landscape. From the perspective of a working scientist and of what needed for a dynamical theory when confronting empirical data, the adaptive landscape is useful both metaphorically and quantitatively and has captured an essential aspect of biological dynamical processes. Still, many important open problems remain to be solved. Having this important problem under control, we may expect that we are on the right road to quantitatively formulate the evolutionary dynamics discovered by Darwin and Wallace. |
2101.00304 | Shahabeddin Sotudian | Shahabeddin Sotudian and Mohammad Hossein Fazel Zarandi | Interval Type-2 Enhanced Possibilistic Fuzzy C-Means Clustering for Gene
Expression Data Analysis | null | null | null | null | q-bio.GN cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Both FCM and PCM clustering methods have been widely applied to pattern
recognition and data clustering. Nevertheless, FCM is sensitive to noise and
PCM occasionally generates coincident clusters. PFCM is an extension of the PCM
model by combining FCM and PCM, but this method still suffers from the
weaknesses of PCM and FCM. In the current paper, the weaknesses of the PFCM
algorithm are corrected and the enhanced possibilistic fuzzy c-means (EPFCM)
clustering algorithm is presented. EPFCM can still be sensitive to noise.
Therefore, we propose an interval type-2 enhanced possibilistic fuzzy c-means
(IT2EPFCM) clustering method by utilizing two fuzzifiers $(m_1, m_2)$ for fuzzy
memberships and two fuzzifiers $({\theta}_1, {\theta}_2)$ for possibilistic
typicalities. Our computational results show the superiority of the proposed
approaches compared with several state-of-the-art techniques in the literature.
Finally, the proposed methods are implemented for analyzing microarray gene
expression data.
| [
{
"created": "Fri, 1 Jan 2021 19:29:24 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Nov 2021 06:52:01 GMT",
"version": "v2"
}
] | 2021-11-25 | [
[
"Sotudian",
"Shahabeddin",
""
],
[
"Zarandi",
"Mohammad Hossein Fazel",
""
]
] | Both FCM and PCM clustering methods have been widely applied to pattern recognition and data clustering. Nevertheless, FCM is sensitive to noise and PCM occasionally generates coincident clusters. PFCM is an extension of the PCM model by combining FCM and PCM, but this method still suffers from the weaknesses of PCM and FCM. In the current paper, the weaknesses of the PFCM algorithm are corrected and the enhanced possibilistic fuzzy c-means (EPFCM) clustering algorithm is presented. EPFCM can still be sensitive to noise. Therefore, we propose an interval type-2 enhanced possibilistic fuzzy c-means (IT2EPFCM) clustering method by utilizing two fuzzifiers $(m_1, m_2)$ for fuzzy memberships and two fuzzifiers $({\theta}_1, {\theta}_2)$ for possibilistic typicalities. Our computational results show the superiority of the proposed approaches compared with several state-of-the-art techniques in the literature. Finally, the proposed methods are implemented for analyzing microarray gene expression data. |
1701.08043 | Magne Aldrin | Magne Aldrin, Ragnar Bang Huseby, Audun Stien, Randi Nygaard
Gr{\o}ntvedt, Hildegunn Viljugrein and Peder Andreas Jansen | A stage-structured Bayesian hierarchical model for salmon lice
populations at individual salmon farms - Estimated from multiple farm data
sets | null | Ecological Modelling, 2017 | 10.1016/j.ecolmodel.2017.05.019 | null | q-bio.PE q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Salmon farming has become a prosperous international industry over the last
decades. Along with growth in the production farmed salmon, however, an
increasing threat by pathogens has emerged. Of special concern is the
propagation and spread of the salmon louse, Lepeophtheirus salmonis. In order
to gain insight into this parasites population dynamics in large scale salmon
farming system, we present a fully mechanistic stage-structured population
model for the salmon louse, also allowing for complexities involved in the
hierarchical structure of full scale salmon farming. The model estimates
parameters controlling a wide range of processes, including temperature
dependent demographic rates, fish size and abundance effects on louse
transmission rates, effects sizes of various salmon louse control measures, and
distance based between farm transmission rates. Model parameters were estimated
from data including 32 salmon farms, except the last production months for five
farms which were used to evaluate model predictions. We used a Bayesian
estimation approach, combining the prior distributions and the data likelihood
into a joint posterior distribution for all model parameters. The model
generated expected values that fitted the observed infection levels of the
chalimus, adult female and other mobile stages of salmon lice, reasonably well.
Predictions for the time periods not used for fitting the model were also
consistent with the observational data. We argue that the present model for the
population dynamics of the salmon louse in aquaculture farm systems may
contribute to resolve the complexity of processes that drive that drive this
host-parasite relationship, and hence may improve strategies to control the
parasite in this production system.
| [
{
"created": "Fri, 27 Jan 2017 13:07:49 GMT",
"version": "v1"
}
] | 2018-08-22 | [
[
"Aldrin",
"Magne",
""
],
[
"Huseby",
"Ragnar Bang",
""
],
[
"Stien",
"Audun",
""
],
[
"Grøntvedt",
"Randi Nygaard",
""
],
[
"Viljugrein",
"Hildegunn",
""
],
[
"Jansen",
"Peder Andreas",
""
]
] | Salmon farming has become a prosperous international industry over the last decades. Along with growth in the production farmed salmon, however, an increasing threat by pathogens has emerged. Of special concern is the propagation and spread of the salmon louse, Lepeophtheirus salmonis. In order to gain insight into this parasites population dynamics in large scale salmon farming system, we present a fully mechanistic stage-structured population model for the salmon louse, also allowing for complexities involved in the hierarchical structure of full scale salmon farming. The model estimates parameters controlling a wide range of processes, including temperature dependent demographic rates, fish size and abundance effects on louse transmission rates, effects sizes of various salmon louse control measures, and distance based between farm transmission rates. Model parameters were estimated from data including 32 salmon farms, except the last production months for five farms which were used to evaluate model predictions. We used a Bayesian estimation approach, combining the prior distributions and the data likelihood into a joint posterior distribution for all model parameters. The model generated expected values that fitted the observed infection levels of the chalimus, adult female and other mobile stages of salmon lice, reasonably well. Predictions for the time periods not used for fitting the model were also consistent with the observational data. We argue that the present model for the population dynamics of the salmon louse in aquaculture farm systems may contribute to resolve the complexity of processes that drive that drive this host-parasite relationship, and hence may improve strategies to control the parasite in this production system. |
1311.3236 | Erik Wijnker | Erik Wijnker, Geo Velikkakam James, Jia Ding, Frank Becker, Jonas R.
Klasen, Vimal Rawat, Beth A. Rowan, Daniel F. de Jong, C. Bastiaan de Snoo,
Luis Zapata, Bruno Huettel, Hans de Jong, Stephan Ossowski, Detlef Weigel,
Maarten Koornneef, Joost J.B. Keurentjes and Korbinian Schneeberger | The genomic landscape of meiotic crossovers and gene conversions in
Arabidopsis thaliana | 44 pages, 5 figures with figure supplements | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge of the exact distribution of meiotic crossovers (COs) and gene
conversions (GCs) is essential for understanding many aspects of population
genetics and evolution, from haplotype structure and long-distance genetic
linkage to the generation of new allelic variants of genes. To this end, we
resequenced the four products of 13 meiotic tetrads along with 10 doubled
haploids derived from Arabidopsis thaliana hybrids. GC detection through short
reads has previously been confounded by genomic rearrangements. Rigid filtering
for misaligned reads allowed GC identification at high accuracy and revealed an
~80-kb transposition, which undergoes copy-number changes mediated by meiotic
recombination. Non-crossover associated GCs were extremely rare most likely due
to their short average length of ~25-50 bp, which is significantly shorter than
the length of CO associated GCs. Overall, recombination preferentially targeted
non-methylated nucleosome-free regions at gene promoters, which showed
significant enrichment of two sequence motifs.
| [
{
"created": "Wed, 13 Nov 2013 18:12:04 GMT",
"version": "v1"
}
] | 2013-11-14 | [
[
"Wijnker",
"Erik",
""
],
[
"James",
"Geo Velikkakam",
""
],
[
"Ding",
"Jia",
""
],
[
"Becker",
"Frank",
""
],
[
"Klasen",
"Jonas R.",
""
],
[
"Rawat",
"Vimal",
""
],
[
"Rowan",
"Beth A.",
""
],
[
"de Jong",
"Daniel F.",
""
],
[
"de Snoo",
"C. Bastiaan",
""
],
[
"Zapata",
"Luis",
""
],
[
"Huettel",
"Bruno",
""
],
[
"de Jong",
"Hans",
""
],
[
"Ossowski",
"Stephan",
""
],
[
"Weigel",
"Detlef",
""
],
[
"Koornneef",
"Maarten",
""
],
[
"Keurentjes",
"Joost J. B.",
""
],
[
"Schneeberger",
"Korbinian",
""
]
] | Knowledge of the exact distribution of meiotic crossovers (COs) and gene conversions (GCs) is essential for understanding many aspects of population genetics and evolution, from haplotype structure and long-distance genetic linkage to the generation of new allelic variants of genes. To this end, we resequenced the four products of 13 meiotic tetrads along with 10 doubled haploids derived from Arabidopsis thaliana hybrids. GC detection through short reads has previously been confounded by genomic rearrangements. Rigid filtering for misaligned reads allowed GC identification at high accuracy and revealed an ~80-kb transposition, which undergoes copy-number changes mediated by meiotic recombination. Non-crossover associated GCs were extremely rare most likely due to their short average length of ~25-50 bp, which is significantly shorter than the length of CO associated GCs. Overall, recombination preferentially targeted non-methylated nucleosome-free regions at gene promoters, which showed significant enrichment of two sequence motifs. |
2204.03354 | Patrick Krauss | Achim Schilling, William Sedley, Richard Gerum, Claus Metzner,
Konstantin Tziridis, Andreas Maier, Holger Schulze, Fan-Gang Zeng, Karl J.
Friston, Patrick Krauss | Predictive coding and stochastic resonance as fundamental principles of
auditory perception | arXiv admin note: substantial text overlap with arXiv:2010.01914 | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | How is information processed in the brain during perception? Mechanistic
insight is achieved only when experiments are employed to test formal or
computational models. In analogy to lesion studies, phantom perception may
serve as a vehicle to understand the fundamental processing principles
underlying auditory perception. With a special focus on tinnitus -- as the
prime example of auditory phantom perception -- we review recent work at the
intersection of artificial intelligence, psychology, and neuroscience. In
particular, we discuss why everyone with tinnitus suffers from hearing loss,
but not everyone with hearing loss suffers from tinnitus. We argue that the
increase of sensory precision due to Bayesian inference could be caused by
intrinsic neural noise and lead to a prediction error in the cerebral cortex.
Hence, two fundamental processing principles - being ubiquitous in the brain -
provide the most explanatory power for the emergence of tinnitus: predictive
coding as a top-down, and stochastic resonance as a complementary bottom-up
mechanism. We conclude that both principles play a crucial role in healthy
auditory perception.
| [
{
"created": "Thu, 7 Apr 2022 10:47:58 GMT",
"version": "v1"
},
{
"created": "Mon, 23 May 2022 09:14:52 GMT",
"version": "v2"
}
] | 2022-05-24 | [
[
"Schilling",
"Achim",
""
],
[
"Sedley",
"William",
""
],
[
"Gerum",
"Richard",
""
],
[
"Metzner",
"Claus",
""
],
[
"Tziridis",
"Konstantin",
""
],
[
"Maier",
"Andreas",
""
],
[
"Schulze",
"Holger",
""
],
[
"Zeng",
"Fan-Gang",
""
],
[
"Friston",
"Karl J.",
""
],
[
"Krauss",
"Patrick",
""
]
] | How is information processed in the brain during perception? Mechanistic insight is achieved only when experiments are employed to test formal or computational models. In analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying auditory perception. With a special focus on tinnitus -- as the prime example of auditory phantom perception -- we review recent work at the intersection of artificial intelligence, psychology, and neuroscience. In particular, we discuss why everyone with tinnitus suffers from hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that the increase of sensory precision due to Bayesian inference could be caused by intrinsic neural noise and lead to a prediction error in the cerebral cortex. Hence, two fundamental processing principles - being ubiquitous in the brain - provide the most explanatory power for the emergence of tinnitus: predictive coding as a top-down, and stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles play a crucial role in healthy auditory perception. |
2012.02361 | Storm Slivkoff | Storm Slivkoff, Jack L. Gallant | Design of Complex Experiments Using Mixed Integer Linear Programming | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over the past few decades, neuroscience experiments have become increasingly
complex and naturalistic. Experimental design has in turn become more
challenging, as experiments must conform to an ever-increasing diversity of
design constraints. In this article we demonstrate how this design process can
be greatly assisted using an optimization tool known as Mixed Integer Linear
Programming (MILP). MILP provides a rich framework for incorporating many types
of real-world design constraints into a neuroimaging experiment. We introduce
the mathematical foundations of MILP, compare MILP to other experimental design
techniques, and provide four case studies of how MILP can be used to solve
complex experimental design challenges.
| [
{
"created": "Fri, 4 Dec 2020 01:49:43 GMT",
"version": "v1"
}
] | 2020-12-07 | [
[
"Slivkoff",
"Storm",
""
],
[
"Gallant",
"Jack L.",
""
]
] | Over the past few decades, neuroscience experiments have become increasingly complex and naturalistic. Experimental design has in turn become more challenging, as experiments must conform to an ever-increasing diversity of design constraints. In this article we demonstrate how this design process can be greatly assisted using an optimization tool known as Mixed Integer Linear Programming (MILP). MILP provides a rich framework for incorporating many types of real-world design constraints into a neuroimaging experiment. We introduce the mathematical foundations of MILP, compare MILP to other experimental design techniques, and provide four case studies of how MILP can be used to solve complex experimental design challenges. |
1607.08694 | Alma Dal Co | Alma Dal Co, Marco Cosentino Lagomarsino, Michele Caselle, Matteo
Osella | Stochastic timing in gene expression for simple regulatory strategies | 10 pages, 5 figures | Nucleic Acids Res 45 (3): 1069-1078 (2017) | 10.1093/nar/gkw1235 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Timing is essential for many cellular processes, from cellular responses to
external stimuli to the cell cycle and circadian clocks. Many of these
processes are based on gene expression. For example, an activated gene may be
required to reach in a precise time a threshold level of expression that
triggers a specific downstream process. However, gene expression is subject to
stochastic fluctuations, naturally inducing an uncertainty in this
threshold-crossing time with potential consequences on biological functions and
phenotypes. Here, we consider such "timing fluctuations", and we ask how they
can be controlled. Our analytical estimates and simulations show that, for an
induced gene, timing variability is minimal if the threshold level of
expression is approximately half of the steady-state level. Timing fuctuations
can be reduced by increasing the transcription rate, while they are insensitive
to the translation rate. In presence of self-regulatory strategies, we show
that self-repression reduces timing noise for threshold levels that have to be
reached quickly, while selfactivation is optimal at long times. These results
lay a framework for understanding stochasticity of endogenous systems such as
the cell cycle, as well as for the design of synthetic trigger circuits.
| [
{
"created": "Fri, 29 Jul 2016 06:35:05 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Feb 2017 07:52:32 GMT",
"version": "v2"
}
] | 2017-02-24 | [
[
"Co",
"Alma Dal",
""
],
[
"Lagomarsino",
"Marco Cosentino",
""
],
[
"Caselle",
"Michele",
""
],
[
"Osella",
"Matteo",
""
]
] | Timing is essential for many cellular processes, from cellular responses to external stimuli to the cell cycle and circadian clocks. Many of these processes are based on gene expression. For example, an activated gene may be required to reach in a precise time a threshold level of expression that triggers a specific downstream process. However, gene expression is subject to stochastic fluctuations, naturally inducing an uncertainty in this threshold-crossing time with potential consequences on biological functions and phenotypes. Here, we consider such "timing fluctuations", and we ask how they can be controlled. Our analytical estimates and simulations show that, for an induced gene, timing variability is minimal if the threshold level of expression is approximately half of the steady-state level. Timing fuctuations can be reduced by increasing the transcription rate, while they are insensitive to the translation rate. In presence of self-regulatory strategies, we show that self-repression reduces timing noise for threshold levels that have to be reached quickly, while selfactivation is optimal at long times. These results lay a framework for understanding stochasticity of endogenous systems such as the cell cycle, as well as for the design of synthetic trigger circuits. |
1910.03934 | Anyou Wang | Anyou Wang, Hai Rong | Noncoding RNAs serve as the deadliest regulators for cancer | null | null | null | null | q-bio.GN q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cancer is one of the leading causes of human death. Many efforts have made to
understand its mechanism and have further identified many proteins and DNA
sequence variations as suspected targets for therapy. However, drugs targeting
these targets have low success rates, suggesting the basic mechanism still
remains unclear. Here, we develop a computational software combining Cox
proportional-hazards model and stability-selection to unearth an overlooked,
yet the most important cancer drivers hidden in massive data from The Cancer
Genome Atlas (TCGA), including 11,574 RNAseq samples and clinic data.
Generally, noncoding RNAs primarily regulate cancer deaths and work as the
deadliest cancer inducers and repressors, in contrast to proteins as
conventionally thought. Especially, processed-pseudogenes serve as the primary
cancer inducers, while lincRNA and antisense RNAs dominate the repressors.
Strikingly, noncoding RNAs serves as the universal strongest regulators for all
cancer types although personal clinic variables such as alcohol and smoking
significantly alter cancer genome. Furthermore, noncoding RNAs also work as
central hubs in cancer regulatory network and as biomarkers to discriminate
cancer types. Therefore, noncoding RNAs overall serve as the deadliest cancer
regulators, which refreshes the basic concept of cancer mechanism and builds a
novel basis for cancer research and therapy. Biological functions of
pseudogenes have rarely been recognized. Here we reveal them as the most
important cancer drivers for all cancer types from big data, breaking a wall to
explore their biological potentials.
| [
{
"created": "Tue, 8 Oct 2019 05:42:36 GMT",
"version": "v1"
}
] | 2019-10-10 | [
[
"Wang",
"Anyou",
""
],
[
"Rong",
"Hai",
""
]
] | Cancer is one of the leading causes of human death. Many efforts have made to understand its mechanism and have further identified many proteins and DNA sequence variations as suspected targets for therapy. However, drugs targeting these targets have low success rates, suggesting the basic mechanism still remains unclear. Here, we develop a computational software combining Cox proportional-hazards model and stability-selection to unearth an overlooked, yet the most important cancer drivers hidden in massive data from The Cancer Genome Atlas (TCGA), including 11,574 RNAseq samples and clinic data. Generally, noncoding RNAs primarily regulate cancer deaths and work as the deadliest cancer inducers and repressors, in contrast to proteins as conventionally thought. Especially, processed-pseudogenes serve as the primary cancer inducers, while lincRNA and antisense RNAs dominate the repressors. Strikingly, noncoding RNAs serves as the universal strongest regulators for all cancer types although personal clinic variables such as alcohol and smoking significantly alter cancer genome. Furthermore, noncoding RNAs also work as central hubs in cancer regulatory network and as biomarkers to discriminate cancer types. Therefore, noncoding RNAs overall serve as the deadliest cancer regulators, which refreshes the basic concept of cancer mechanism and builds a novel basis for cancer research and therapy. Biological functions of pseudogenes have rarely been recognized. Here we reveal them as the most important cancer drivers for all cancer types from big data, breaking a wall to explore their biological potentials. |
2109.15308 | Michael Arcidiacono | Michael Arcidiacono and David Ryan Koes | MOLUCINATE: A Generative Model for Molecules in 3D Space | Camera-ready submission to NeurIPS 2020 MLSB workshop. 6 pages and 2
figures | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Recent advances in machine learning have enabled generative models for both
optimization and de novo generation of drug candidates with desired properties.
Previous generative models have focused on producing SMILES strings or 2D
molecular graphs, while attempts at producing molecules in 3D have focused on
reinforcement learning (RL), distance matrices, and pure atom density grids.
Here we present MOLUCINATE (MOLecUlar ConvolutIoNal generATive modEl), a novel
architecture that simultaneously generates topological and 3D atom position
information. We demonstrate the utility of this method by using it to optimize
molecules for desired radius of gyration. In the future, this model can be used
for more useful optimization such as binding affinity for a protein target.
| [
{
"created": "Thu, 30 Sep 2021 17:51:50 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Nov 2021 03:56:07 GMT",
"version": "v2"
}
] | 2021-11-25 | [
[
"Arcidiacono",
"Michael",
""
],
[
"Koes",
"David Ryan",
""
]
] | Recent advances in machine learning have enabled generative models for both optimization and de novo generation of drug candidates with desired properties. Previous generative models have focused on producing SMILES strings or 2D molecular graphs, while attempts at producing molecules in 3D have focused on reinforcement learning (RL), distance matrices, and pure atom density grids. Here we present MOLUCINATE (MOLecUlar ConvolutIoNal generATive modEl), a novel architecture that simultaneously generates topological and 3D atom position information. We demonstrate the utility of this method by using it to optimize molecules for desired radius of gyration. In the future, this model can be used for more useful optimization such as binding affinity for a protein target. |
0902.4152 | Sergei Mukhin I | Sergei I. Mukhin, Boris B. Kheyfets | Analytical derivation of thermodynamic properties of bilayer membrane
with interdigitation | 20 pages, 12 figures, 1 table, Biophysical Society Annual Meeting
2009, Boston, USA | null | null | null | q-bio.QM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a model of bilayer lipid membrane with interdigitation, in which
the lipid tails of the opposite monolayers interpenetrate. The interdigitation
is modeled by linking tails of the hydrophobic chains in the opposite
monolayers within bilayer as a first approximation. A number of thermodynamical
characteristics are calculated analytically and compared with the ones of a
regular membrane without interdigitation. Striking difference between lateral
pressure profiles at the layers interface for linked and regular bilayer models
is found. In the linked case, the lateral pressure mid-plane peak disappears,
while the free energy per chain increases. Within our model we found that in
case of elongation of the chains inside a nucleus of e.g. liquid-condensed
phase, homogeneous interdigitation would be more costly for the membrane's free
energy than energy of the hydrophobic mismatch between the elongated chains and
the liquid-expanded surrounding. Nonetheless, an inhomogeneous interdigitation
along the nucleous boundary may occur inside a ``belt'' of a width that varies
approximately with the hydrophobic mismatch amplitude.
| [
{
"created": "Tue, 24 Feb 2009 13:59:31 GMT",
"version": "v1"
}
] | 2009-02-25 | [
[
"Mukhin",
"Sergei I.",
""
],
[
"Kheyfets",
"Boris B.",
""
]
] | We consider a model of bilayer lipid membrane with interdigitation, in which the lipid tails of the opposite monolayers interpenetrate. The interdigitation is modeled by linking tails of the hydrophobic chains in the opposite monolayers within bilayer as a first approximation. A number of thermodynamical characteristics are calculated analytically and compared with the ones of a regular membrane without interdigitation. Striking difference between lateral pressure profiles at the layers interface for linked and regular bilayer models is found. In the linked case, the lateral pressure mid-plane peak disappears, while the free energy per chain increases. Within our model we found that in case of elongation of the chains inside a nucleus of e.g. liquid-condensed phase, homogeneous interdigitation would be more costly for the membrane's free energy than energy of the hydrophobic mismatch between the elongated chains and the liquid-expanded surrounding. Nonetheless, an inhomogeneous interdigitation along the nucleous boundary may occur inside a ``belt'' of a width that varies approximately with the hydrophobic mismatch amplitude. |
q-bio/0507001 | Luciano da Fontoura Costa | Luciano da Fontoura Costa, Fernando Rocha and Silene Araujo de Lima | Characterizing Polygonality in Biological Structures | 14 pages, 11 figures | Phys. Rev. E 73, 011913 (2006) | 10.1103/PhysRevE.73.011913 | null | q-bio.TO cond-mat.dis-nn | null | Several systems involve spatial arrangements of elements such as molecules or
cells, the characterization of which bears important implications to biological
and physical investigations. Traditional approaches to quantify spatial order
and regularity have relied on nearest neighbor distances or the number of sides
of cells. The current work shows that superior features can be achieved by
considering angular regularity. Voronoi tessellations are obtained for each
basic element and the angular regularity is then estimated from the differences
between the angles defined by adjacent cells and a reference angle. In case
this angle is 60 degrees, the measurement quantifies the hexagonality of the
system. Other reference angles can be considered in order to quantify other
types of spatial symmetries. The performance of the angular regularity is
compared with other measurements including the conformity ratio (based on
nearest neighbor distances) and the number of sides of the cells, confirming
its improved sensitivity and discrimination power. The superior performance of
the haxagonality measurement is illustrated also with respect to a real
application concerning the characterization of retinal mosaics.
| [
{
"created": "Thu, 30 Jun 2005 22:05:47 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Oct 2005 17:41:55 GMT",
"version": "v2"
}
] | 2007-09-19 | [
[
"Costa",
"Luciano da Fontoura",
""
],
[
"Rocha",
"Fernando",
""
],
[
"de Lima",
"Silene Araujo",
""
]
] | Several systems involve spatial arrangements of elements such as molecules or cells, the characterization of which bears important implications to biological and physical investigations. Traditional approaches to quantify spatial order and regularity have relied on nearest neighbor distances or the number of sides of cells. The current work shows that superior features can be achieved by considering angular regularity. Voronoi tessellations are obtained for each basic element and the angular regularity is then estimated from the differences between the angles defined by adjacent cells and a reference angle. In case this angle is 60 degrees, the measurement quantifies the hexagonality of the system. Other reference angles can be considered in order to quantify other types of spatial symmetries. The performance of the angular regularity is compared with other measurements including the conformity ratio (based on nearest neighbor distances) and the number of sides of the cells, confirming its improved sensitivity and discrimination power. The superior performance of the haxagonality measurement is illustrated also with respect to a real application concerning the characterization of retinal mosaics. |
2106.07713 | Jason Zwicker | Jason Zwicker, Francois Rivest | Interval Timing: Modeling the break-run-break pattern using start/stop
threshold-less drift-diffusion model | null | null | 10.1016/j.jmp.2022.102663 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Animal interval timing is often studied through the peak interval (PI)
procedure. In this procedure, the animal is rewarded for the first response
after a fixed delay from the stimulus onset, but on some trials, the stimulus
remains and no reward is given. The common methods and models to analyse the
response pattern describe it as break-run-break, a period of low rate response
followed by rapid responding, followed by a low rate of response. The study of
the pattern has found correlations between start, stop, and duration of the run
period that hold across species and experiment.
It is commonly assumed that in order to achieve the statistics with a
pacemaker accumulator model it is necessary to have start and stop thresholds.
In this paper we will develop a new model that varies response rate in relation
to the likelihood of event occurrence, as opposed to a threshold, for changing
the response rate. The new model reproduced the start and stop statistics that
have been observed in 14 different PI experiments from 3 different papers. The
developed model is also compared to the Time-adaptive Drift-diffusion Model
(TDDM), the latest accumulator model subsuming the scalar expectancy theory
(SET), on all 14 data-sets. The results show that it is unnecessary to have
explicit start and stop thresholds or an internal equivalent to break-run-break
states to reproduce the individual trials statistics and population behaviour
and get the same break-run-break analysis results. The new model also produces
more realistic individual trials compared to TDDM.
| [
{
"created": "Mon, 14 Jun 2021 19:07:21 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Apr 2022 16:05:49 GMT",
"version": "v2"
}
] | 2022-04-05 | [
[
"Zwicker",
"Jason",
""
],
[
"Rivest",
"Francois",
""
]
] | Animal interval timing is often studied through the peak interval (PI) procedure. In this procedure, the animal is rewarded for the first response after a fixed delay from the stimulus onset, but on some trials, the stimulus remains and no reward is given. The common methods and models to analyse the response pattern describe it as break-run-break, a period of low rate response followed by rapid responding, followed by a low rate of response. The study of the pattern has found correlations between start, stop, and duration of the run period that hold across species and experiment. It is commonly assumed that in order to achieve the statistics with a pacemaker accumulator model it is necessary to have start and stop thresholds. In this paper we will develop a new model that varies response rate in relation to the likelihood of event occurrence, as opposed to a threshold, for changing the response rate. The new model reproduced the start and stop statistics that have been observed in 14 different PI experiments from 3 different papers. The developed model is also compared to the Time-adaptive Drift-diffusion Model (TDDM), the latest accumulator model subsuming the scalar expectancy theory (SET), on all 14 data-sets. The results show that it is unnecessary to have explicit start and stop thresholds or an internal equivalent to break-run-break states to reproduce the individual trials statistics and population behaviour and get the same break-run-break analysis results. The new model also produces more realistic individual trials compared to TDDM. |
1810.09935 | Markus D Schirmer | Markus D. Schirmer and Ai Wern Chung and P. Ellen Grant and Natalia S.
Rost | Network Structural Dependency in the Human Connectome Across the
Life-Span | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Principles of network topology have been widely studied in the human
connectome. Of particular interest is the modularity of the human brain, where
the connectome is divided into subnetworks and subsequently changes with
development, aging or disease are investigated. We present a weighted network
measure, the Network Dependency Index (NDI), to identify an individual region's
importance to the global functioning of the network. Importantly, we utilize
NDI to differentiate four subnetworks (Tiers) in the human connectome following
Gaussian Mixture Model fitting. We analyze the topological aspects of each
subnetwork with respect to age and compare it to rich-club based subnetworks
(rich-club, feeder and seeder). Our results first demonstrate the efficacy of
NDI to identify more consistent, central nodes of the connectome across
age-groups, when compared to the rich-club framework. Stratifying the
connectome by NDI led to consistent subnetworks across the life-span revealing
distinct patterns associated with age where, e.g., the key relay nuclei and
cortical regions are contained in a subnetwork with highest NDI. The divisions
of the human connectome derived from our data-driven NDI framework have the
potential to reveal topological alterations described by network measures
through the life-span.
| [
{
"created": "Tue, 23 Oct 2018 16:01:23 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Oct 2018 17:44:37 GMT",
"version": "v2"
},
{
"created": "Sat, 19 Jan 2019 01:40:14 GMT",
"version": "v3"
},
{
"created": "Sat, 2 Feb 2019 20:38:24 GMT",
"version": "v4"
}
] | 2019-02-05 | [
[
"Schirmer",
"Markus D.",
""
],
[
"Chung",
"Ai Wern",
""
],
[
"Grant",
"P. Ellen",
""
],
[
"Rost",
"Natalia S.",
""
]
] | Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks and subsequently changes with development, aging or disease are investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region's importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian Mixture Model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich-club based subnetworks (rich-club, feeder and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age-groups, when compared to the rich-club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span revealing distinct patterns associated with age where, e.g., the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span. |
1504.03622 | Alexander Huth | Alexander G. Huth, Thomas L. Griffiths, Frederic E. Theunissen, Jack
L. Gallant | PrAGMATiC: a Probabilistic and Generative Model of Areas Tiling the
Cortex | null | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Much of the human cortex seems to be organized into topographic cortical
maps. Yet few quantitative methods exist for characterizing these maps. To
address this issue we developed a modeling framework that can reveal
group-level cortical maps based on neuroimaging data. PrAGMATiC, a
probabilistic and generative model of areas tiling the cortex, is a
hierarchical Bayesian generative model of cortical maps. This model assumes
that the cortical map in each individual subject is a sample from a single
underlying probability distribution. Learning the parameters of this
distribution reveals the properties of a cortical map that are common across a
group of subjects while avoiding the potentially lossy step of co-registering
each subject into a group anatomical space. In this report we give a
mathematical description of PrAGMATiC, describe approximations that make it
practical to use, show preliminary results from its application to a real
dataset, and describe a number of possible future extensions.
| [
{
"created": "Tue, 14 Apr 2015 16:52:31 GMT",
"version": "v1"
}
] | 2015-04-15 | [
[
"Huth",
"Alexander G.",
""
],
[
"Griffiths",
"Thomas L.",
""
],
[
"Theunissen",
"Frederic E.",
""
],
[
"Gallant",
"Jack L.",
""
]
] | Much of the human cortex seems to be organized into topographic cortical maps. Yet few quantitative methods exist for characterizing these maps. To address this issue we developed a modeling framework that can reveal group-level cortical maps based on neuroimaging data. PrAGMATiC, a probabilistic and generative model of areas tiling the cortex, is a hierarchical Bayesian generative model of cortical maps. This model assumes that the cortical map in each individual subject is a sample from a single underlying probability distribution. Learning the parameters of this distribution reveals the properties of a cortical map that are common across a group of subjects while avoiding the potentially lossy step of co-registering each subject into a group anatomical space. In this report we give a mathematical description of PrAGMATiC, describe approximations that make it practical to use, show preliminary results from its application to a real dataset, and describe a number of possible future extensions. |
1906.02710 | Gao-De Li Dr | Gao-De Li | Flexible Cancer-Associated Chromatin Configuration (CACC) Might Be the
Fundamental Reason Why Cancer Is So Difficult to Cure | 8 pages | null | null | null | q-bio.SC q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We once proposed that cell-type-associated chromatin configurations determine
cell types and that cancer cell type is determined by cancer-associated
chromatin configuration (CACC). In this paper, we hypothesize that flexible
cell-type-associated chromatin configuration is associated with cell potency
and has an advantage over inflexible one in regulating genome related
activities, such as DNA replication, DNA transcription, DNA repair, and DNA
mutagenesis. The reason why cancer is so difficult to treat is because CACC is
flexible, which enables cancer cells not only to produce heterogeneous
subclones through limited cell differentiation, but also to maximally and
efficiently use genome related resources to survive environmental changes.
Therefore, to beat cancer, more efforts should be made to restrict the
flexibility of CACC or to change CACC so that cancer cells can be turned back
to normal or become less malignant.
| [
{
"created": "Thu, 6 Jun 2019 17:24:32 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Jun 2019 08:34:44 GMT",
"version": "v2"
},
{
"created": "Sun, 16 Jun 2019 13:01:21 GMT",
"version": "v3"
},
{
"created": "Fri, 28 Jun 2019 16:42:23 GMT",
"version": "v4"
}
] | 2019-07-01 | [
[
"Li",
"Gao-De",
""
]
] | We once proposed that cell-type-associated chromatin configurations determine cell types and that cancer cell type is determined by cancer-associated chromatin configuration (CACC). In this paper, we hypothesize that flexible cell-type-associated chromatin configuration is associated with cell potency and has an advantage over inflexible one in regulating genome related activities, such as DNA replication, DNA transcription, DNA repair, and DNA mutagenesis. The reason why cancer is so difficult to treat is because CACC is flexible, which enables cancer cells not only to produce heterogeneous subclones through limited cell differentiation, but also to maximally and efficiently use genome related resources to survive environmental changes. Therefore, to beat cancer, more efforts should be made to restrict the flexibility of CACC or to change CACC so that cancer cells can be turned back to normal or become less malignant. |
1512.01088 | Hector Zenil | Narsis A. Kiani, Hector Zenil, Jakub Olczak and Jesper Tegn\'er | Evaluating Network Inference Methods in Terms of Their Ability to
Preserve the Topology and Complexity of Genetic Networks | main part: 18 pages. 21 pages with Sup Inf. Forthcoming in the
journal of Seminars in Cell and Developmental Biology | null | null | null | q-bio.MN cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network inference is a rapidly advancing field, with new methods being
proposed on a regular basis. Understanding the advantages and limitations of
different network inference methods is key to their effective application in
different circumstances. The common structural properties shared by diverse
networks naturally pose a challenge when it comes to devising accurate
inference methods, but surprisingly, there is a paucity of comparison and
evaluation methods. Historically, every new methodology has only been tested
against \textit{gold standard} (true values) purpose-designed synthetic and
real-world (validated) biological networks. In this paper we aim to assess the
impact of taking into consideration aspects of topological and information
content in the evaluation of the final accuracy of an inference procedure.
Specifically, we will compare the best inference methods, in both
graph-theoretic and information-theoretic terms, for preserving topological
properties and the original information content of synthetic and biological
networks. New methods for performance comparison are introduced by borrowing
ideas from gene set enrichment analysis and by applying concepts from
algorithmic complexity. Experimental results show that no individual algorithm
outperforms all others in all cases, and that the challenging and non-trivial
nature of network inference is evident in the struggle of some of the
algorithms to turn in a performance that is superior to random guesswork.
Therefore special care should be taken to suit the method to the purpose at
hand. Finally, we show that evaluations from data generated using different
underlying topologies have different signatures that can be used to better
choose a network reconstruction method.
| [
{
"created": "Thu, 3 Dec 2015 14:25:04 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Dec 2015 18:54:09 GMT",
"version": "v2"
},
{
"created": "Wed, 14 Sep 2016 18:18:09 GMT",
"version": "v3"
}
] | 2016-09-15 | [
[
"Kiani",
"Narsis A.",
""
],
[
"Zenil",
"Hector",
""
],
[
"Olczak",
"Jakub",
""
],
[
"Tegnér",
"Jesper",
""
]
] | Network inference is a rapidly advancing field, with new methods being proposed on a regular basis. Understanding the advantages and limitations of different network inference methods is key to their effective application in different circumstances. The common structural properties shared by diverse networks naturally pose a challenge when it comes to devising accurate inference methods, but surprisingly, there is a paucity of comparison and evaluation methods. Historically, every new methodology has only been tested against \textit{gold standard} (true values) purpose-designed synthetic and real-world (validated) biological networks. In this paper we aim to assess the impact of taking into consideration aspects of topological and information content in the evaluation of the final accuracy of an inference procedure. Specifically, we will compare the best inference methods, in both graph-theoretic and information-theoretic terms, for preserving topological properties and the original information content of synthetic and biological networks. New methods for performance comparison are introduced by borrowing ideas from gene set enrichment analysis and by applying concepts from algorithmic complexity. Experimental results show that no individual algorithm outperforms all others in all cases, and that the challenging and non-trivial nature of network inference is evident in the struggle of some of the algorithms to turn in a performance that is superior to random guesswork. Therefore special care should be taken to suit the method to the purpose at hand. Finally, we show that evaluations from data generated using different underlying topologies have different signatures that can be used to better choose a network reconstruction method. |
0912.5315 | Peter Waddell | Peter J. Waddell and Timothy Herston | Expectation-Maximization (EM) Algorithms for Mapping Short Reads
Illustrated with FAIRE data and the TP53-WRAP53 Gene Region | 17 pages, 3 figures, 1 table all incorporated in one pdf | null | null | null | q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Huge numbers of short reads are being generated for mapping back to the
genome to discover the frequency of transcripts, miRNAs, DNAase hypersensitive
sites, FAIRE regions, nucleosome occupancy, etc. Since these reads are
typically short (e.g., 36 base pairs) and since many eukaryotic genomes,
including humans, have highly repetitive sequences then many of these reads map
to two or more locations in the genome. Current mapping of these reads, grading
them according to 0, 1 or 2 mismatches wastes a great deal of information.
These short sequences are typically mapped with no account of the accuracy of
the sequence, even in company software when per base error rates are being
reported by another part of the machine. Further, multiply mapping locations
are frequently discarded altogether or allocated with no regard to where other
reads are accumulating. Here we show how to combine probabilistic mapping of
reads with an EM algorithm to iteratively improve the empirical likelihood of
the allocation of short reads. Mapping using LAST takes into account the per
base accuracy of the read, plus insertions and deletions, plus anticipated
occasional errors or SNPs with respect to the parent genome. The probabilistic
EM algorithm iteratively allocates reads based on the proportion of reads
mapping within windows on the previous cycle, along with any prior information
on where the read best maps. The methods are illustrated with FAIRE ENCODE data
looking at the very important head-to-head gene combination of TP53 and WRAP
53.
| [
{
"created": "Tue, 29 Dec 2009 15:27:24 GMT",
"version": "v1"
}
] | 2009-12-31 | [
[
"Waddell",
"Peter J.",
""
],
[
"Herston",
"Timothy",
""
]
] | Huge numbers of short reads are being generated for mapping back to the genome to discover the frequency of transcripts, miRNAs, DNAase hypersensitive sites, FAIRE regions, nucleosome occupancy, etc. Since these reads are typically short (e.g., 36 base pairs) and since many eukaryotic genomes, including humans, have highly repetitive sequences then many of these reads map to two or more locations in the genome. Current mapping of these reads, grading them according to 0, 1 or 2 mismatches wastes a great deal of information. These short sequences are typically mapped with no account of the accuracy of the sequence, even in company software when per base error rates are being reported by another part of the machine. Further, multiply mapping locations are frequently discarded altogether or allocated with no regard to where other reads are accumulating. Here we show how to combine probabilistic mapping of reads with an EM algorithm to iteratively improve the empirical likelihood of the allocation of short reads. Mapping using LAST takes into account the per base accuracy of the read, plus insertions and deletions, plus anticipated occasional errors or SNPs with respect to the parent genome. The probabilistic EM algorithm iteratively allocates reads based on the proportion of reads mapping within windows on the previous cycle, along with any prior information on where the read best maps. The methods are illustrated with FAIRE ENCODE data looking at the very important head-to-head gene combination of TP53 and WRAP 53. |
1909.02297 | Pedro Mediano | Pedro A.M. Mediano, Fernando Rosas, Robin L. Carhart-Harris, Anil K.
Seth, Adam B. Barrett | Beyond integrated information: A taxonomy of information dynamics
phenomena | null | null | null | null | q-bio.NC physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most information dynamics and statistical causal analysis frameworks rely on
the common intuition that causal interactions are intrinsically pairwise --
every 'cause' variable has an associated 'effect' variable, so that a 'causal
arrow' can be drawn between them. However, analyses that depict
interdependencies as directed graphs fail to discriminate the rich variety of
modes of information flow that can coexist within a system. This, in turn,
creates problems with attempts to operationalise the concepts of 'dynamical
complexity' or `integrated information.' To address this shortcoming, we
combine concepts of partial information decomposition and integrated
information, and obtain what we call Integrated Information Decomposition, or
$\Phi$ID. We show how $\Phi$ID paves the way for more detailed analyses of
interdependencies in multivariate time series, and sheds light on collective
modes of information dynamics that have not been reported before. Additionally,
$\Phi$ID reveals that what is typically referred to as 'integration' is
actually an aggregate of several heterogeneous phenomena. Furthermore, $\Phi$ID
can be used to formulate new, tailored measures of integrated information, as
well as to understand and alleviate the limitations of existing measures.
| [
{
"created": "Thu, 5 Sep 2019 10:11:00 GMT",
"version": "v1"
}
] | 2019-09-06 | [
[
"Mediano",
"Pedro A. M.",
""
],
[
"Rosas",
"Fernando",
""
],
[
"Carhart-Harris",
"Robin L.",
""
],
[
"Seth",
"Anil K.",
""
],
[
"Barrett",
"Adam B.",
""
]
] | Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise -- every 'cause' variable has an associated 'effect' variable, so that a 'causal arrow' can be drawn between them. However, analyses that depict interdependencies as directed graphs fail to discriminate the rich variety of modes of information flow that can coexist within a system. This, in turn, creates problems with attempts to operationalise the concepts of 'dynamical complexity' or `integrated information.' To address this shortcoming, we combine concepts of partial information decomposition and integrated information, and obtain what we call Integrated Information Decomposition, or $\Phi$ID. We show how $\Phi$ID paves the way for more detailed analyses of interdependencies in multivariate time series, and sheds light on collective modes of information dynamics that have not been reported before. Additionally, $\Phi$ID reveals that what is typically referred to as 'integration' is actually an aggregate of several heterogeneous phenomena. Furthermore, $\Phi$ID can be used to formulate new, tailored measures of integrated information, as well as to understand and alleviate the limitations of existing measures. |
2101.06294 | Halim Maaroufi Hal | Halim Maaroufi | Interactions of SARS-CoV-2 spike protein and transient receptor
potential (TRP) cation channels could explain smell, taste, and/or
chemesthesis disorders | 46 pages, 2 tables, 4 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A significant subset of patients infected by SARS-CoV-2 presents olfactory,
taste, and/or chemesthesis (OTC) disorders (OTCD). These patients recover
rapidly, eliminating damage of sensory nerves. Discovering that S protein
contains two ankyrin repeat binding motifs (S-ARBMs) and some TRP cation
channels, implicated in OTC, have ankyrin repeat domains (TRPs-ARDs), I
hypothesized that interaction of S-ARBMs and TRPs-ARDs could dysregulate the
function of the latter and thus explains OTCD. Of note, some TRPs-ARDs are
expressed in the olfactory epithelium, taste buds, trigeminal neurons in the
oronasal cavity and vagal neurons in the trachea/lungs. Furthermore, this
hypothesis is supported by studies that have shown: (i) respiratory viruses
interact with TRPA1 and TRPV1 on sensory nerves and epithelial cells in the
airways, (ii) the respiratory pathophysiology in COVID-19 patients is similar
to lungs injuries produced by the sensitization of TRPV1 and TRPV4, and (iii)
resolvin D1 and D2 shown to reduce SARS-CoV-2-induced inflammation, directly
inhibit TRPA1, TRPV1, TRPV3 and TRPV4. Herein, results of blind dockings of
S-ARBMs, 408-RQIAPG-413 (in RBD but distal from the ACE-2 binding region) and
905-RFNGIG-910 (in HR1), into TRPA1, TRPV1 and TRPV4 suggest that S-ARBMs
interact with ankyrin repeat 6 of TRPA1 near an active site, and ankyrin repeat
3-4 of TRPV1 near cysteine 258 supposed to be implicated in the formation of
inter-subunits disulfide bond. These findings suggest that S-ARBMs affect
TRPA1, TRPV1 and TRPV4 function by interfering with channel assembly and
trafficking. After an experimental confirmation of these interactions, among
possible preventive treatments against COVID-19, the use of pharmacological
manipulation (probably inhibition) of TRPs-ARDs to control or mitigate
sustained pro-inflammatory response.
| [
{
"created": "Fri, 15 Jan 2021 20:29:30 GMT",
"version": "v1"
}
] | 2021-01-19 | [
[
"Maaroufi",
"Halim",
""
]
] | A significant subset of patients infected by SARS-CoV-2 presents olfactory, taste, and/or chemesthesis (OTC) disorders (OTCD). These patients recover rapidly, eliminating damage of sensory nerves. Discovering that S protein contains two ankyrin repeat binding motifs (S-ARBMs) and some TRP cation channels, implicated in OTC, have ankyrin repeat domains (TRPs-ARDs), I hypothesized that interaction of S-ARBMs and TRPs-ARDs could dysregulate the function of the latter and thus explains OTCD. Of note, some TRPs-ARDs are expressed in the olfactory epithelium, taste buds, trigeminal neurons in the oronasal cavity and vagal neurons in the trachea/lungs. Furthermore, this hypothesis is supported by studies that have shown: (i) respiratory viruses interact with TRPA1 and TRPV1 on sensory nerves and epithelial cells in the airways, (ii) the respiratory pathophysiology in COVID-19 patients is similar to lungs injuries produced by the sensitization of TRPV1 and TRPV4, and (iii) resolvin D1 and D2 shown to reduce SARS-CoV-2-induced inflammation, directly inhibit TRPA1, TRPV1, TRPV3 and TRPV4. Herein, results of blind dockings of S-ARBMs, 408-RQIAPG-413 (in RBD but distal from the ACE-2 binding region) and 905-RFNGIG-910 (in HR1), into TRPA1, TRPV1 and TRPV4 suggest that S-ARBMs interact with ankyrin repeat 6 of TRPA1 near an active site, and ankyrin repeat 3-4 of TRPV1 near cysteine 258 supposed to be implicated in the formation of inter-subunits disulfide bond. These findings suggest that S-ARBMs affect TRPA1, TRPV1 and TRPV4 function by interfering with channel assembly and trafficking. After an experimental confirmation of these interactions, among possible preventive treatments against COVID-19, the use of pharmacological manipulation (probably inhibition) of TRPs-ARDs to control or mitigate sustained pro-inflammatory response. |
1203.0072 | Michael Woodhams | Michael Woodhams, Dorothy A. Steane, Rebecca C. Jones, Dean Nicolle,
Vincent Moulton, Barbara R. Holland | Novel Distances for Dollo Data | null | null | null | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate distances on binary (presence/absence) data in the context of
a Dollo process, where a trait can only arise once on a phylogenetic tree but
may be lost many times. We introduce a novel distance, the Additive Dollo
Distance (ADD), which is consistent for data generated under a Dollo model, and
show that it has some useful theoretical properties including an intriguing
link to the LogDet distance. Simulations of Dollo data are used to compare a
number of binary distances including ADD, LogDet, Nei Li and some simple, but
to our knowledge previously unstudied, variations on common binary distances.
The simulations suggest that ADD outperforms other distances on Dollo data.
Interestingly, we found that the LogDet distance performs poorly in the context
of a Dollo process, which may have implications for its use in connection with
conditioned genome reconstruction. We apply the ADD to two Diversity Arrays
Technology (DArT) datasets, one that broadly covers Eucalyptus species and one
that focuses on the Eucalyptus series Adnataria. We also reanalyse gene family
presence/absence data on bacteria from the COG database and compare the results
to previous phylogenies estimated using the conditioned genome reconstruction
approach.
| [
{
"created": "Thu, 1 Mar 2012 01:50:59 GMT",
"version": "v1"
}
] | 2012-03-02 | [
[
"Woodhams",
"Michael",
""
],
[
"Steane",
"Dorothy A.",
""
],
[
"Jones",
"Rebecca C.",
""
],
[
"Nicolle",
"Dean",
""
],
[
"Moulton",
"Vincent",
""
],
[
"Holland",
"Barbara R.",
""
]
] | We investigate distances on binary (presence/absence) data in the context of a Dollo process, where a trait can only arise once on a phylogenetic tree but may be lost many times. We introduce a novel distance, the Additive Dollo Distance (ADD), which is consistent for data generated under a Dollo model, and show that it has some useful theoretical properties including an intriguing link to the LogDet distance. Simulations of Dollo data are used to compare a number of binary distances including ADD, LogDet, Nei Li and some simple, but to our knowledge previously unstudied, variations on common binary distances. The simulations suggest that ADD outperforms other distances on Dollo data. Interestingly, we found that the LogDet distance performs poorly in the context of a Dollo process, which may have implications for its use in connection with conditioned genome reconstruction. We apply the ADD to two Diversity Arrays Technology (DArT) datasets, one that broadly covers Eucalyptus species and one that focuses on the Eucalyptus series Adnataria. We also reanalyse gene family presence/absence data on bacteria from the COG database and compare the results to previous phylogenies estimated using the conditioned genome reconstruction approach. |
1411.2820 | Sharon Lee | Sharon X. Lee, Geoffrey J. McLachlan, Saumyadipta Pyne | Supervised Classification of Flow Cytometric Samples via the Joint
Clustering and Matching (JCM) Procedure | null | null | null | null | q-bio.QM stat.ME stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the use of the Joint Clustering and Matching (JCM) procedure for
the supervised classification of a flow cytometric sample with respect to a
number of predefined classes of such samples. The JCM procedure has been
proposed as a method for the unsupervised classification of cells within a
sample into a number of clusters and in the case of multiple samples, the
matching of these clusters across the samples. The two tasks of clustering and
matching of the clusters are performed simultaneously within the JCM framework.
In this paper, we consider the case where there is a number of distinct classes
of samples whose class of origin is known, and the problem is to classify a new
sample of unknown class of origin to one of these predefined classes. For
example, the different classes might correspond to the types of a particular
disease or to the various health outcomes of a patient subsequent to a course
of treatment. We show and demonstrate on some real datasets how the JCM
procedure can be used to carry out this supervised classification task. A
mixture distribution is used to model the distribution of the expressions of a
fixed set of markers for each cell in a sample with the components in the
mixture model corresponding to the various populations of cells in the
composition of the sample. For each class of samples, a class template is
formed by the adoption of random-effects terms to model the inter-sample
variation within a class. The classification of a new unclassified sample is
undertaken by assigning the unclassified sample to the class that minimizes the
Kullback-Leibler distance between its fitted mixture density and each class
density provided by the class templates.
| [
{
"created": "Tue, 11 Nov 2014 14:22:32 GMT",
"version": "v1"
}
] | 2014-11-12 | [
[
"Lee",
"Sharon X.",
""
],
[
"McLachlan",
"Geoffrey J.",
""
],
[
"Pyne",
"Saumyadipta",
""
]
] | We consider the use of the Joint Clustering and Matching (JCM) procedure for the supervised classification of a flow cytometric sample with respect to a number of predefined classes of such samples. The JCM procedure has been proposed as a method for the unsupervised classification of cells within a sample into a number of clusters and in the case of multiple samples, the matching of these clusters across the samples. The two tasks of clustering and matching of the clusters are performed simultaneously within the JCM framework. In this paper, we consider the case where there is a number of distinct classes of samples whose class of origin is known, and the problem is to classify a new sample of unknown class of origin to one of these predefined classes. For example, the different classes might correspond to the types of a particular disease or to the various health outcomes of a patient subsequent to a course of treatment. We show and demonstrate on some real datasets how the JCM procedure can be used to carry out this supervised classification task. A mixture distribution is used to model the distribution of the expressions of a fixed set of markers for each cell in a sample with the components in the mixture model corresponding to the various populations of cells in the composition of the sample. For each class of samples, a class template is formed by the adoption of random-effects terms to model the inter-sample variation within a class. The classification of a new unclassified sample is undertaken by assigning the unclassified sample to the class that minimizes the Kullback-Leibler distance between its fitted mixture density and each class density provided by the class templates. |
1311.6950 | Sanjiv Dwivedi | Sarika Jalan and Sanjiv K. Dwivedi | Balanced condition in networks leads to Weibull statistics | 7 pages, 10 figures | Phys. Rev. E 89, 062718 (2014) | 10.1103/PhysRevE.89.062718 | 10.903/PhysRevE.89.062718 | q-bio.NC cond-mat.dis-nn nlin.AO physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The importance of the balance in inhibitory and excitatory couplings in the
brain has increasingly been realized. Despite the key role played by
inhibitory-excitatory couplings in the functioning of brain networks, the
impact of a balanced condition on the stability properties of underlying
networks remains largely unknown. We investigate properties of the largest
eigenvalues of networks having such couplings, and find that they follow
completely different statistics when in the balanced situation. Based on
numerical simulations, we demonstrate that the transition from Weibull to
Fr\'echet via the Gumbel distribution can be controlled by the variance of the
column sum of the adjacency matrix, which depends monotonically on the
denseness of the underlying network. As a balanced condition is imposed, the
largest real part of the eigenvalue emulates a transition to the generalized
extreme value statistics, independent of the inhibitory connection probability.
Furthermore, the transition to the Weibull statistics and the small-world
transition occur at the same rewiring probability, reflecting a more stable
system.
| [
{
"created": "Wed, 27 Nov 2013 12:23:57 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Jalan",
"Sarika",
""
],
[
"Dwivedi",
"Sanjiv K.",
""
]
] | The importance of the balance in inhibitory and excitatory couplings in the brain has increasingly been realized. Despite the key role played by inhibitory-excitatory couplings in the functioning of brain networks, the impact of a balanced condition on the stability properties of underlying networks remains largely unknown. We investigate properties of the largest eigenvalues of networks having such couplings, and find that they follow completely different statistics when in the balanced situation. Based on numerical simulations, we demonstrate that the transition from Weibull to Fr\'echet via the Gumbel distribution can be controlled by the variance of the column sum of the adjacency matrix, which depends monotonically on the denseness of the underlying network. As a balanced condition is imposed, the largest real part of the eigenvalue emulates a transition to the generalized extreme value statistics, independent of the inhibitory connection probability. Furthermore, the transition to the Weibull statistics and the small-world transition occur at the same rewiring probability, reflecting a more stable system. |
q-bio/0411039 | Albert Diaz-Guilera | Luis A.N. Amaral (1), Albert Diaz-Guilera (1,2,3), Andre A. Moreira
(1), Ary L. Goldberger (2), Lewis A. Lipsitz (4) ((1) Department of Chemical
and Biological Engineering, Northwestern University (2) Cardiovascular
Division, Beth Israel Deaconess Medical Center, Harvard Medical School (3)
Dept. Fisica Fonamental, Universitat de Barcelona (4) Hebrew Rehabilitation
Center for the Aged, Harvard Medical School) | Emergence of Complex Dynamics in a Simple Model of Signaling Networks | null | Proc. Nat. Acad. Sci. USA 101 (2004) 15551-15555 | 10.1073/pnas.0404843101 | null | q-bio.OT cond-mat.soft physics.bio-ph | null | A variety of physical, social and biological systems generate complex
fluctuations with correlations across multiple time scales. In physiologic
systems, these long-range correlations are altered with disease and aging. Such
correlated fluctuations in living systems have been attributed to the
interaction of multiple control systems; however, the mechanisms underlying
this behavior remain unknown. Here, we show that a number of distinct classes
of dynamical behaviors, including correlated fluctuations characterized by
$1/f$-scaling of their power spectra, can emerge in networks of simple
signaling units. We find that under general conditions, complex dynamics can be
generated by systems fulfilling two requirements: i) a ``small-world'' topology
and ii) the presence of noise. Our findings support two notable conclusions:
first, complex physiologic-like signals can be modeled with a minimal set of
components; and second, systems fulfilling conditions (i) and (ii) are robust
to some degree of degradation, i.e., they will still be able to generate
$1/f$-dynamics.
| [
{
"created": "Fri, 19 Nov 2004 09:35:08 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Amaral",
"Luis A. N.",
""
],
[
"Diaz-Guilera",
"Albert",
""
],
[
"Moreira",
"Andre A.",
""
],
[
"Goldberger",
"Ary L.",
""
],
[
"Lipsitz",
"Lewis A.",
""
]
] | A variety of physical, social and biological systems generate complex fluctuations with correlations across multiple time scales. In physiologic systems, these long-range correlations are altered with disease and aging. Such correlated fluctuations in living systems have been attributed to the interaction of multiple control systems; however, the mechanisms underlying this behavior remain unknown. Here, we show that a number of distinct classes of dynamical behaviors, including correlated fluctuations characterized by $1/f$-scaling of their power spectra, can emerge in networks of simple signaling units. We find that under general conditions, complex dynamics can be generated by systems fulfilling two requirements: i) a ``small-world'' topology and ii) the presence of noise. Our findings support two notable conclusions: first, complex physiologic-like signals can be modeled with a minimal set of components; and second, systems fulfilling conditions (i) and (ii) are robust to some degree of degradation, i.e., they will still be able to generate $1/f$-dynamics. |
2405.07772 | Samuel Gornard-Laidet | Samuel Gornard (EGCE), Pascaline Venon, Florian Lasfont, Thierry
Balliau, Laure Marie-Paule Kaiser-Arnauld, Florence Mougel | Characterizing virulence differences in a parasitoid wasp through
comparative transcriptomic and proteomic | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Two strains of the endoparasitoid Cotesia typhae present a
differential parasitism success on the host, Sesamia nonagrioides. One is
virulent on both permissive and resistant host populations, and the other only
on the permissive host. This interaction provides a very interesting frame for
studying virulence factors. Here, we used a combination of comparative
transcriptomic and proteomic analyses to unravel the molecular basis underlying
virulence differences between the strains.Results: First, we report that
virulence genes are mostly expressed during the nymphal stage of the
parasitoid. Especially, proviral genes are broadly up-regulated at this stage,
while their expression is only expected in the host. Parasitoid gene expression
in the host increases with time, indicating the production of more virulence
factors. Secondly, comparison between strains reveals differences in venom
composition, with 12 proteins showing differential abundance. Proviral
expression in the host displays a strong temporal variability, along with
differential patterns between strains. Notably, a subset of proviral genes
including protein-tyrosine phosphatases is specifically over-expressed in the
resistant host parasitized by the less virulent strain, 24 hours after
parasitism. This result particularly hints at host modulation of proviral
expression.Conclusions: This study sheds light on the temporal expression of
virulence factors of Cotesia typhae, both in the host and in the parasitoid. It
also identifies potential molecular candidates driving differences in
parasitism success between two strains. Together, those findings provide a path
for further exploration of virulence mechanisms in parasitoid wasps, and offer
insights into host-parasitoid coevolution.
| [
{
"created": "Mon, 13 May 2024 14:17:35 GMT",
"version": "v1"
}
] | 2024-05-14 | [
[
"Gornard",
"Samuel",
"",
"EGCE"
],
[
"Venon",
"Pascaline",
""
],
[
"Lasfont",
"Florian",
""
],
[
"Balliau",
"Thierry",
""
],
[
"Kaiser-Arnauld",
"Laure Marie-Paule",
""
],
[
"Mougel",
"Florence",
""
]
] | Background: Two strains of the endoparasitoid Cotesia typhae present a differential parasitism success on the host, Sesamia nonagrioides. One is virulent on both permissive and resistant host populations, and the other only on the permissive host. This interaction provides a very interesting frame for studying virulence factors. Here, we used a combination of comparative transcriptomic and proteomic analyses to unravel the molecular basis underlying virulence differences between the strains.Results: First, we report that virulence genes are mostly expressed during the nymphal stage of the parasitoid. Especially, proviral genes are broadly up-regulated at this stage, while their expression is only expected in the host. Parasitoid gene expression in the host increases with time, indicating the production of more virulence factors. Secondly, comparison between strains reveals differences in venom composition, with 12 proteins showing differential abundance. Proviral expression in the host displays a strong temporal variability, along with differential patterns between strains. Notably, a subset of proviral genes including protein-tyrosine phosphatases is specifically over-expressed in the resistant host parasitized by the less virulent strain, 24 hours after parasitism. This result particularly hints at host modulation of proviral expression.Conclusions: This study sheds light on the temporal expression of virulence factors of Cotesia typhae, both in the host and in the parasitoid. It also identifies potential molecular candidates driving differences in parasitism success between two strains. Together, those findings provide a path for further exploration of virulence mechanisms in parasitoid wasps, and offer insights into host-parasitoid coevolution. |
2305.10472 | Vincent Miele | Marine Desprez, Vincent Miele and Olivier Gimenez | Nine tips for ecologists using machine learning | null | null | null | null | q-bio.PE cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Due to their high predictive performance and flexibility, machine learning
models are an appropriate and efficient tool for ecologists. However,
implementing a machine learning model is not yet a trivial task and may seem
intimidating to ecologists with no previous experience in this area. Here we
provide a series of tips to help ecologists in implementing machine learning
models. We focus on classification problems as many ecological studies aim to
assign data into predefined classes such as ecological states or biological
entities. Each of the nine tips identifies a common error, trap or challenge in
developing machine learning models and provides recommendations to facilitate
their use in ecological studies.
| [
{
"created": "Wed, 17 May 2023 15:41:08 GMT",
"version": "v1"
},
{
"created": "Fri, 26 May 2023 07:38:55 GMT",
"version": "v2"
}
] | 2023-05-29 | [
[
"Desprez",
"Marine",
""
],
[
"Miele",
"Vincent",
""
],
[
"Gimenez",
"Olivier",
""
]
] | Due to their high predictive performance and flexibility, machine learning models are an appropriate and efficient tool for ecologists. However, implementing a machine learning model is not yet a trivial task and may seem intimidating to ecologists with no previous experience in this area. Here we provide a series of tips to help ecologists in implementing machine learning models. We focus on classification problems as many ecological studies aim to assign data into predefined classes such as ecological states or biological entities. Each of the nine tips identifies a common error, trap or challenge in developing machine learning models and provides recommendations to facilitate their use in ecological studies. |
1405.1260 | Xiang-Ping Jia | Xiang-Ping Jia and Hong Sun | Recognizing Cancer via Somatic and Organic Evolution | 28 pages, 5 figures | null | null | null | q-bio.PE q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fitness of somatic cells of metazoan, the ability of proliferation and
survival, depends on microenvironment. In somatic evolution, a mutated cell in
a tissue clonally expands abnormally because of its high fitness as normal
cells in a corresponding microenvironment. In this study, we propose the cancer
cell hypothesis that cancer cells are the mutated cells with two
characteristics: clonal expansion and damaging the microenvironment through the
behaviours such as producing more poison in metabolism than normal cells. This
model provides an explanation for the nature of invasion and metastasis, which
are still controversial. In addition, we theoretically reasoned out that normal
cells have almost the highest fitness in healthy microenvironments as a result
of long-term organic evolution. This inspires a new kind of therapy of cancer,
which improving microenvironment to make cancer cells lower in fitness than
normal cells and then halt the growth of tumours. This general therapy relies
on a mechanism differing from chemotherapy and targeted therapy.
| [
{
"created": "Tue, 6 May 2014 13:22:26 GMT",
"version": "v1"
},
{
"created": "Fri, 4 Sep 2015 11:24:05 GMT",
"version": "v2"
}
] | 2015-09-07 | [
[
"Jia",
"Xiang-Ping",
""
],
[
"Sun",
"Hong",
""
]
] | The fitness of somatic cells of metazoan, the ability of proliferation and survival, depends on microenvironment. In somatic evolution, a mutated cell in a tissue clonally expands abnormally because of its high fitness as normal cells in a corresponding microenvironment. In this study, we propose the cancer cell hypothesis that cancer cells are the mutated cells with two characteristics: clonal expansion and damaging the microenvironment through the behaviours such as producing more poison in metabolism than normal cells. This model provides an explanation for the nature of invasion and metastasis, which are still controversial. In addition, we theoretically reasoned out that normal cells have almost the highest fitness in healthy microenvironments as a result of long-term organic evolution. This inspires a new kind of therapy of cancer, which improving microenvironment to make cancer cells lower in fitness than normal cells and then halt the growth of tumours. This general therapy relies on a mechanism differing from chemotherapy and targeted therapy. |
2405.10486 | Patrick Vincent Lubenia | Patrick Vincent N. Lubenia, Eduardo R. Mendoza, Angelyn R. Lao | Comparison of reaction networks of insulin signaling | 18 pages, 0 figure | null | null | null | q-bio.MN | http://creativecommons.org/publicdomain/zero/1.0/ | Understanding the insulin signaling cascade provides insights on the
underlying mechanisms of biological phenomena such as insulin resistance,
diabetes, Alzheimer's disease, and cancer. For this reason, previous studies
utilized chemical reaction network theory to perform comparative analyses of
reaction networks of insulin signaling in healthy (INSMS: INSulin Metabolic
Signaling) and diabetic cells (INRES: INsulin RESistance). This study extends
these analyses using various methods which give further insights regarding
insulin signaling. Using embedded networks, we discuss evidence of the presence
of a structural "bifurcation" in the signaling process between INSMS and INRES.
Concordance profiles of INSMS and INRES show that both have a high propensity
to remain monostationary. Moreover, the concordance properties allow us to
present heuristic evidence that INRES has a higher level of stability beyond
its monostationarity. Finally, we discuss a new way of analyzing reaction
networks through network translation. This method gives rise to three new
insights: (i) each stoichiometric class of INSMS and INRES contains a unique
positive equilibrium; (ii) any positive equilibrium of INSMS is exponentially
stable and is a global attractor in its stoichiometric class; and (iii) any
positive equilibrium of INRES is locally asymptotically stable. These results
open up opportunities for collaboration with experimental biologists to
understand insulin signaling better.
| [
{
"created": "Fri, 17 May 2024 01:19:51 GMT",
"version": "v1"
}
] | 2024-05-20 | [
[
"Lubenia",
"Patrick Vincent N.",
""
],
[
"Mendoza",
"Eduardo R.",
""
],
[
"Lao",
"Angelyn R.",
""
]
] | Understanding the insulin signaling cascade provides insights on the underlying mechanisms of biological phenomena such as insulin resistance, diabetes, Alzheimer's disease, and cancer. For this reason, previous studies utilized chemical reaction network theory to perform comparative analyses of reaction networks of insulin signaling in healthy (INSMS: INSulin Metabolic Signaling) and diabetic cells (INRES: INsulin RESistance). This study extends these analyses using various methods which give further insights regarding insulin signaling. Using embedded networks, we discuss evidence of the presence of a structural "bifurcation" in the signaling process between INSMS and INRES. Concordance profiles of INSMS and INRES show that both have a high propensity to remain monostationary. Moreover, the concordance properties allow us to present heuristic evidence that INRES has a higher level of stability beyond its monostationarity. Finally, we discuss a new way of analyzing reaction networks through network translation. This method gives rise to three new insights: (i) each stoichiometric class of INSMS and INRES contains a unique positive equilibrium; (ii) any positive equilibrium of INSMS is exponentially stable and is a global attractor in its stoichiometric class; and (iii) any positive equilibrium of INRES is locally asymptotically stable. These results open up opportunities for collaboration with experimental biologists to understand insulin signaling better. |
1008.0523 | Tsvi Tlusty | Dror Sagi, Tsvi Tlusty and Joel Stavans | High fidelity of RecA-catalyzed recombination: a watchdog of genetic
diversity | http://www.weizmann.ac.il/complex/tlusty/papers/NuclAcidRes2006.pdf
http://nar.oxfordjournals.org/cgi/content/short/34/18/5021 | Nucleic Acids Research, 2006, Vol. 34, No. 18 5021-5031 | 10.1093/nar/gkl586 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Homologous recombination plays a key role in generating genetic diversity,
while maintaining protein functionality. The mechanisms by which RecA enables a
single-stranded segment of DNA to recognize a homologous tract within a whole
genome are poorly understood. The scale by which homology recognition takes
place is of a few tens of base pairs, after which the quest for homology is
over. To study the mechanism of homology recognition, RecA-promoted homologous
recombination between short DNA oligomers with different degrees of heterology
was studied in vitro, using fluorescence resonant energy transfer. RecA can
detect single mismatches at the initial stages of recombination, and the
efficiency of recombination is strongly dependent on the location and
distribution of mismatches. Mismatches near the 5' end of the incoming strand
have a minute effect, whereas mismatches near the 3' end hinder strand exchange
dramatically. There is a characteristic DNA length above which the sensitivity
to heterology decreases sharply. Experiments with competitor sequences with
varying degrees of homology yield information about the process of homology
search and synapse lifetime. The exquisite sensitivity to mismatches and the
directionality in the exchange process support a mechanism for homology
recognition that can be modeled as a kinetic proofreading cascade.
| [
{
"created": "Tue, 3 Aug 2010 11:24:16 GMT",
"version": "v1"
}
] | 2010-08-04 | [
[
"Sagi",
"Dror",
""
],
[
"Tlusty",
"Tsvi",
""
],
[
"Stavans",
"Joel",
""
]
] | Homologous recombination plays a key role in generating genetic diversity, while maintaining protein functionality. The mechanisms by which RecA enables a single-stranded segment of DNA to recognize a homologous tract within a whole genome are poorly understood. The scale by which homology recognition takes place is of a few tens of base pairs, after which the quest for homology is over. To study the mechanism of homology recognition, RecA-promoted homologous recombination between short DNA oligomers with different degrees of heterology was studied in vitro, using fluorescence resonant energy transfer. RecA can detect single mismatches at the initial stages of recombination, and the efficiency of recombination is strongly dependent on the location and distribution of mismatches. Mismatches near the 5' end of the incoming strand have a minute effect, whereas mismatches near the 3' end hinder strand exchange dramatically. There is a characteristic DNA length above which the sensitivity to heterology decreases sharply. Experiments with competitor sequences with varying degrees of homology yield information about the process of homology search and synapse lifetime. The exquisite sensitivity to mismatches and the directionality in the exchange process support a mechanism for homology recognition that can be modeled as a kinetic proofreading cascade. |
2401.02989 | Robin Zbinden | Robin Zbinden, Nina van Tiel, Benjamin Kellenberger, Lloyd Hughes,
Devis Tuia | On the selection and effectiveness of pseudo-absences for species
distribution modeling with deep learning | null | Ecological Informatics, Volume 81, 2024, 102623 | 10.1016/j.ecoinf.2024.102623 | null | q-bio.QM cs.LG q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Species distribution modeling is a highly versatile tool for understanding
the intricate relationship between environmental conditions and species
occurrences. However, the available data often lacks information on confirmed
species absence and is limited to opportunistically sampled, presence-only
observations. To overcome this limitation, a common approach is to employ
pseudo-absences, which are specific geographic locations designated as negative
samples. While pseudo-absences are well-established for single-species
distribution models, their application in the context of multi-species neural
networks remains underexplored. Notably, the significant class imbalance
between species presences and pseudo-absences is often left unaddressed.
Moreover, the existence of different types of pseudo-absences (e.g., random and
target-group background points) adds complexity to the selection process.
Determining the optimal combination of pseudo-absences types is difficult and
depends on the characteristics of the data, particularly considering that
certain types of pseudo-absences can be used to mitigate geographic biases. In
this paper, we demonstrate that these challenges can be effectively tackled by
integrating pseudo-absences in the training of multi-species neural networks
through modifications to the loss function. This adjustment involves assigning
different weights to the distinct terms of the loss function, thereby
addressing both the class imbalance and the choice of pseudo-absence types.
Additionally, we propose a strategy to set these loss weights using spatial
block cross-validation with presence-only data. We evaluate our approach using
a benchmark dataset containing independent presence-absence data from six
different regions and report improved results when compared to competing
approaches.
| [
{
"created": "Wed, 3 Jan 2024 16:06:30 GMT",
"version": "v1"
}
] | 2024-06-18 | [
[
"Zbinden",
"Robin",
""
],
[
"van Tiel",
"Nina",
""
],
[
"Kellenberger",
"Benjamin",
""
],
[
"Hughes",
"Lloyd",
""
],
[
"Tuia",
"Devis",
""
]
] | Species distribution modeling is a highly versatile tool for understanding the intricate relationship between environmental conditions and species occurrences. However, the available data often lacks information on confirmed species absence and is limited to opportunistically sampled, presence-only observations. To overcome this limitation, a common approach is to employ pseudo-absences, which are specific geographic locations designated as negative samples. While pseudo-absences are well-established for single-species distribution models, their application in the context of multi-species neural networks remains underexplored. Notably, the significant class imbalance between species presences and pseudo-absences is often left unaddressed. Moreover, the existence of different types of pseudo-absences (e.g., random and target-group background points) adds complexity to the selection process. Determining the optimal combination of pseudo-absences types is difficult and depends on the characteristics of the data, particularly considering that certain types of pseudo-absences can be used to mitigate geographic biases. In this paper, we demonstrate that these challenges can be effectively tackled by integrating pseudo-absences in the training of multi-species neural networks through modifications to the loss function. This adjustment involves assigning different weights to the distinct terms of the loss function, thereby addressing both the class imbalance and the choice of pseudo-absence types. Additionally, we propose a strategy to set these loss weights using spatial block cross-validation with presence-only data. We evaluate our approach using a benchmark dataset containing independent presence-absence data from six different regions and report improved results when compared to competing approaches. |
2011.06124 | Matthew Zalesak | Matthew Zalesak and Samitha Samaranayake (Cornell University) | SEIR-Campus: Modeling Infectious Diseases on University Campuses | 18 pages, 10 figures | null | null | null | q-bio.PE cs.MA cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a Python package for modeling and studying the spread of
infectious diseases using an agent-based SEIR style epidemiological model with
a focus on university campuses. This document explains the epidemiological
model used in the package and gives examples highlighting the ways that the
package can be used.
| [
{
"created": "Wed, 11 Nov 2020 23:50:32 GMT",
"version": "v1"
}
] | 2020-11-13 | [
[
"Zalesak",
"Matthew",
"",
"Cornell University"
],
[
"Samaranayake",
"Samitha",
"",
"Cornell University"
]
] | We introduce a Python package for modeling and studying the spread of infectious diseases using an agent-based SEIR style epidemiological model with a focus on university campuses. This document explains the epidemiological model used in the package and gives examples highlighting the ways that the package can be used. |
q-bio/0611066 | Tao Hu | Tao Hu, B. I. Shklovskii | Kinetics of viral self-assembly: the role of ss RNA antenna | 4 pages, 3 figures, several experiments are proposed, a new idea of
experiment is added | Phys. Rev. E 75, 051901 (2007) | 10.1103/PhysRevE.75.051901 | null | q-bio.BM cond-mat.soft | null | A big class of viruses self-assemble from a large number of identical capsid
proteins with long flexible N-terminal tails and ss RNA. We study the role of
the strong Coulomb interaction of positive N-terminal tails with ss RNA in the
kinetics of the in vitro virus self-assembly. Capsid proteins stick to
unassembled chain of ss RNA (which we call "antenna") and slide on it towards
the assembly site. We show that at excess of capsid proteins such
one-dimensional diffusion accelerates self-assembly more than ten times. On the
other hand at excess of ss RNA, antenna slows self-assembly down. Several
experiments are proposed to verify the role of ss RNA antenna.
| [
{
"created": "Mon, 20 Nov 2006 20:27:14 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Dec 2006 19:32:34 GMT",
"version": "v2"
},
{
"created": "Fri, 15 Dec 2006 22:43:07 GMT",
"version": "v3"
},
{
"created": "Fri, 2 Feb 2007 20:32:22 GMT",
"version": "v4"
}
] | 2009-11-13 | [
[
"Hu",
"Tao",
""
],
[
"Shklovskii",
"B. I.",
""
]
] | A big class of viruses self-assemble from a large number of identical capsid proteins with long flexible N-terminal tails and ss RNA. We study the role of the strong Coulomb interaction of positive N-terminal tails with ss RNA in the kinetics of the in vitro virus self-assembly. Capsid proteins stick to unassembled chain of ss RNA (which we call "antenna") and slide on it towards the assembly site. We show that at excess of capsid proteins such one-dimensional diffusion accelerates self-assembly more than ten times. On the other hand at excess of ss RNA, antenna slows self-assembly down. Several experiments are proposed to verify the role of ss RNA antenna. |
0707.2573 | F\`elix Campelo | F. Campelo and A. Hernandez-Machado | Model for curvature-driven pearling instability in membranes | Accepted for publication in Phys. Rev. Lett | Phys. Rev. Lett. 99, 088101 (2007) | 10.1103/PhysRevLett.99.088101 | null | q-bio.QM cond-mat.soft q-bio.CB | null | A phase-field model for dealing with dynamic instabilities in membranes is
presented. We use it to study curvature-driven pearling instability in vesicles
induced by the anchorage of amphiphilic polymers on the membrane. Within this
model, we obtain the morphological changes reported in recent experiments. The
formation of a homogeneous pearled structure is achieved by consequent pearling
of an initial cylindrical tube from the tip. For high enough concentration of
anchors, we show theoretically that the homogeneous pearled shape is
energetically less favorable than an inhomogeneous one, with a large sphere
connected to an array of smaller spheres.
| [
{
"created": "Tue, 17 Jul 2007 17:22:16 GMT",
"version": "v1"
}
] | 2011-11-10 | [
[
"Campelo",
"F.",
""
],
[
"Hernandez-Machado",
"A.",
""
]
] | A phase-field model for dealing with dynamic instabilities in membranes is presented. We use it to study curvature-driven pearling instability in vesicles induced by the anchorage of amphiphilic polymers on the membrane. Within this model, we obtain the morphological changes reported in recent experiments. The formation of a homogeneous pearled structure is achieved by consequent pearling of an initial cylindrical tube from the tip. For high enough concentration of anchors, we show theoretically that the homogeneous pearled shape is energetically less favorable than an inhomogeneous one, with a large sphere connected to an array of smaller spheres. |
2008.10246 | Trinh Xuan Hoang | Phuong Thuy Bui and Trinh Xuan Hoang | Protein escape at the ribosomal exit tunnel: Effect of the tunnel shape | 12 pages, 11 figures, with supplementary materials | J. Chem. Phys. 153, 045105 (2020) | 10.1063/5.0008292 | null | q-bio.BM cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the post-translational escape of nascent proteins at the ribosomal
exit tunnel with the consideration of a real shape atomistic tunnel based on
the Protein Data Bank (PDB) structure of the large ribosome subunit of archeon
Haloarcula marismortui. Molecular dynamics simulations employing the Go-like
model for the proteins show that at intermediate and high temperatures,
including a presumable physiological temperature, the protein escape process at
the atomistic tunnel is quantitatively similar to that at a cylinder tunnel of
length L = 72 {\AA} and diameter d = 16 {\AA}. At low temperatures, the
atomistic tunnel, however, yields an increased probability of protein trapping
inside the tunnel while the cylinder tunnel does not cause the trapping.
All-$\beta$ proteins tend to escape faster than all-$\alpha$ proteins but this
difference is blurred on increasing the protein's chain length. A 29-residue
zinc-finger domain is shown to be severely trapped inside the tunnel. Most of
the single-domain proteins considered, however, can escape efficiently at the
physiological temperature with the escape time distribution following the
diffusion model proposed in our previous works. An extrapolation of the
simulation data to a realistic value of the friction coefficient for amino
acids indicates that the escape times of globular proteins are at the
sub-millisecond scale. It is argued that this time scale is short enough for
the smooth functioning of the ribosome by not allowing nascent proteins to jam
the ribosome tunnel.
| [
{
"created": "Mon, 24 Aug 2020 08:18:50 GMT",
"version": "v1"
}
] | 2020-08-25 | [
[
"Bui",
"Phuong Thuy",
""
],
[
"Hoang",
"Trinh Xuan",
""
]
] | We study the post-translational escape of nascent proteins at the ribosomal exit tunnel with the consideration of a real shape atomistic tunnel based on the Protein Data Bank (PDB) structure of the large ribosome subunit of archeon Haloarcula marismortui. Molecular dynamics simulations employing the Go-like model for the proteins show that at intermediate and high temperatures, including a presumable physiological temperature, the protein escape process at the atomistic tunnel is quantitatively similar to that at a cylinder tunnel of length L = 72 {\AA} and diameter d = 16 {\AA}. At low temperatures, the atomistic tunnel, however, yields an increased probability of protein trapping inside the tunnel while the cylinder tunnel does not cause the trapping. All-$\beta$ proteins tend to escape faster than all-$\alpha$ proteins but this difference is blurred on increasing the protein's chain length. A 29-residue zinc-finger domain is shown to be severely trapped inside the tunnel. Most of the single-domain proteins considered, however, can escape efficiently at the physiological temperature with the escape time distribution following the diffusion model proposed in our previous works. An extrapolation of the simulation data to a realistic value of the friction coefficient for amino acids indicates that the escape times of globular proteins are at the sub-millisecond scale. It is argued that this time scale is short enough for the smooth functioning of the ribosome by not allowing nascent proteins to jam the ribosome tunnel. |
1908.03332 | Nicolas Blondeau | Tauskela Joseph S., Blondeau Nicolas (IPMC) | Requirement for preclinical prioritization of neuroprotective strategies
in stroke: Incorporation of preconditioning | null | Conditioning Medicine, 2018 | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Acute neuroprotection in numerous human clinical trials has been an abject
failure. Major systemic-and procedural-based issues have subsequently been
identified in both clinical trials and preclinical animal model
experimentation. As well, issues related to the neuroprotective moiety itself
have contributed to clinical trial failures, including late delivery,
mono-targeting, low potency and poor tolerability. Conditioning (pre-or post-)
strategies can potentially address these issues and are therefore gaining
increasing attention as approaches to protect the brain from cerebral ischemia.
In principle, conditioning can address concerns of timing (preconditioning
could be pre-emptively applied in high-risk patients, and post-conditioning
after patients experience an unannounced brain infarction) and signaling
(multi-modal). However, acute neuroprotection and conditioning strategies face
a common translational issue: a myriad of possibilities exist, but with no
strategy to select optimal candidates. In this review, we argue that what is
required is a neuroprotective framework to identify the "best" agent(s), at the
earliest investigational stage possible. This may require switching mindsets
from identifying how neuroprotection can be achieved to determining how
neuroprotection can fail, for the vast majority of candidates. Understanding
the basis for failure can in turn guide supplementary treatment, thereby
forming an evidence-based rationale for selecting combinations of therapies. An
appropriately designed in vitro (neuron culture, brain slices) approach, based
on increasing the harshness of the ischemic-like insult, can be useful in
identifying the "best" conditioner or acute neuroprotective therapy, as well as
how the two modalities can be combined to overcome individual limitations. This
would serve as a base from which to launch further investigation into therapies
required to protect the neurovascular unit in in vivo animal models of cerebral
ischemia. Based on these respective approaches, our laboratories suggest that
there is merit in examining synaptic activity-and nutraceutical-based
preconditioning / acute neuroprotection.
| [
{
"created": "Fri, 9 Aug 2019 06:46:19 GMT",
"version": "v1"
}
] | 2019-08-12 | [
[
"S.",
"Tauskela Joseph",
"",
"IPMC"
],
[
"Nicolas",
"Blondeau",
"",
"IPMC"
]
] | Acute neuroprotection in numerous human clinical trials has been an abject failure. Major systemic-and procedural-based issues have subsequently been identified in both clinical trials and preclinical animal model experimentation. As well, issues related to the neuroprotective moiety itself have contributed to clinical trial failures, including late delivery, mono-targeting, low potency and poor tolerability. Conditioning (pre-or post-) strategies can potentially address these issues and are therefore gaining increasing attention as approaches to protect the brain from cerebral ischemia. In principle, conditioning can address concerns of timing (preconditioning could be pre-emptively applied in high-risk patients, and post-conditioning after patients experience an unannounced brain infarction) and signaling (multi-modal). However, acute neuroprotection and conditioning strategies face a common translational issue: a myriad of possibilities exist, but with no strategy to select optimal candidates. In this review, we argue that what is required is a neuroprotective framework to identify the "best" agent(s), at the earliest investigational stage possible. This may require switching mindsets from identifying how neuroprotection can be achieved to determining how neuroprotection can fail, for the vast majority of candidates. Understanding the basis for failure can in turn guide supplementary treatment, thereby forming an evidence-based rationale for selecting combinations of therapies. An appropriately designed in vitro (neuron culture, brain slices) approach, based on increasing the harshness of the ischemic-like insult, can be useful in identifying the "best" conditioner or acute neuroprotective therapy, as well as how the two modalities can be combined to overcome individual limitations. This would serve as a base from which to launch further investigation into therapies required to protect the neurovascular unit in in vivo animal models of cerebral ischemia. Based on these respective approaches, our laboratories suggest that there is merit in examining synaptic activity-and nutraceutical-based preconditioning / acute neuroprotection. |
1406.2447 | Peter Gawthrop | Peter J. Gawthrop and Edmund J. Crampin | Energy-based Analysis of Biochemical Cycles using Bond Graphs | null | Proc. R. Soc. A 2014 470 20140459 | 10.1098/rspa.2014.0459 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Thermodynamic aspects of chemical reactions have a long history in the
Physical Chemistry literature. In particular, biochemical cycles - the
building-blocks of biochemical systems - require a source of energy to
function. However, although fundamental, the role of chemical potential and
Gibb's free energy in the analysis of biochemical systems is often overlooked
leading to models which are physically impossible. The bond graph approach was
developed for modelling engineering systems where energy generation, storage
and transmission are fundamental. The method focuses on how power flows between
components and how energy is stored, transmitted or dissipated within
components. Based on early ideas of network thermodynamics, we have applied
this approach to biochemical systems to generate models which automatically
obey the laws of thermodynamics. We illustrate the method with examples of
biochemical cycles. We have found that thermodynamically compliant models of
simple biochemical cycles can easily be developed using this approach. In
particular, both stoichiometric information and simulation models can be
developed directly from the bond graph. Furthermore, model reduction and
approximation while retaining structural and thermodynamic properties is
facilitated. Because the bond graph approach is also modular and scaleable, we
believe that it provides a secure foundation for building thermodynamically
compliant models of large biochemical networks.
| [
{
"created": "Tue, 10 Jun 2014 07:25:10 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Sep 2014 05:34:40 GMT",
"version": "v2"
}
] | 2018-08-14 | [
[
"Gawthrop",
"Peter J.",
""
],
[
"Crampin",
"Edmund J.",
""
]
] | Thermodynamic aspects of chemical reactions have a long history in the Physical Chemistry literature. In particular, biochemical cycles - the building-blocks of biochemical systems - require a source of energy to function. However, although fundamental, the role of chemical potential and Gibb's free energy in the analysis of biochemical systems is often overlooked leading to models which are physically impossible. The bond graph approach was developed for modelling engineering systems where energy generation, storage and transmission are fundamental. The method focuses on how power flows between components and how energy is stored, transmitted or dissipated within components. Based on early ideas of network thermodynamics, we have applied this approach to biochemical systems to generate models which automatically obey the laws of thermodynamics. We illustrate the method with examples of biochemical cycles. We have found that thermodynamically compliant models of simple biochemical cycles can easily be developed using this approach. In particular, both stoichiometric information and simulation models can be developed directly from the bond graph. Furthermore, model reduction and approximation while retaining structural and thermodynamic properties is facilitated. Because the bond graph approach is also modular and scaleable, we believe that it provides a secure foundation for building thermodynamically compliant models of large biochemical networks. |
1610.06421 | Hao Dong | Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews and Yike
Guo | Mixed Neural Network Approach for Temporal Sleep Stage Classification | THIS ARTICLE HAS BEEN PUBLISHED IN IEEE TRANSACTIONS ON NEURAL
SYSTEMS AND REHABILITATION ENGINEERING | null | 10.1109/TNSRE.2017.2733220 | null | q-bio.NC cs.CV cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a practical approach to addressing limitations posed by
use of single active electrodes in applications for sleep stage classification.
Electroencephalography (EEG)-based characterizations of sleep stage progression
contribute the diagnosis and monitoring of the many pathologies of sleep.
Several prior reports have explored ways of automating the analysis of sleep
EEG and of reducing the complexity of the data needed for reliable
discrimination of sleep stages in order to make it possible to perform sleep
studies at lower cost in the home (rather than only in specialized clinical
facilities). However, these reports have involved recordings from electrodes
placed on the cranial vertex or occiput, which can be uncomfortable or
difficult for subjects to position. Those that have utilized single EEG
channels which contain less sleep information, have showed poor classification
performance. We have taken advantage of Rectifier Neural Network for feature
detection and Long Short-Term Memory (LSTM) network for sequential data
learning to optimize classification performance with single electrode
recordings. After exploring alternative electrode placements, we found a
comfortable configuration of a single-channel EEG on the forehead and have
shown that it can be integrated with additional electrodes for simultaneous
recording of the electroocuolgram (EOG). Evaluation of data from 62 people
(with 494 hours sleep) demonstrated better performance of our analytical
algorithm for automated sleep classification than existing approaches using
vertex or occipital electrode placements. Use of this recording configuration
with neural network deconvolution promises to make clinically indicated home
sleep studies practical.
| [
{
"created": "Sat, 15 Oct 2016 18:48:00 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Jul 2017 17:39:53 GMT",
"version": "v2"
},
{
"created": "Thu, 3 Aug 2017 15:00:48 GMT",
"version": "v3"
}
] | 2017-08-04 | [
[
"Dong",
"Hao",
""
],
[
"Supratak",
"Akara",
""
],
[
"Pan",
"Wei",
""
],
[
"Wu",
"Chao",
""
],
[
"Matthews",
"Paul M.",
""
],
[
"Guo",
"Yike",
""
]
] | This paper proposes a practical approach to addressing limitations posed by use of single active electrodes in applications for sleep stage classification. Electroencephalography (EEG)-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports have explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages in order to make it possible to perform sleep studies at lower cost in the home (rather than only in specialized clinical facilities). However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which can be uncomfortable or difficult for subjects to position. Those that have utilized single EEG channels which contain less sleep information, have showed poor classification performance. We have taken advantage of Rectifier Neural Network for feature detection and Long Short-Term Memory (LSTM) network for sequential data learning to optimize classification performance with single electrode recordings. After exploring alternative electrode placements, we found a comfortable configuration of a single-channel EEG on the forehead and have shown that it can be integrated with additional electrodes for simultaneous recording of the electroocuolgram (EOG). Evaluation of data from 62 people (with 494 hours sleep) demonstrated better performance of our analytical algorithm for automated sleep classification than existing approaches using vertex or occipital electrode placements. Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical. |
2202.00507 | George A Kevrekidis | G.A. Kevrekidis, Z. Rapti, Y. Drossinos, P.G. Kevrekidis, M.A.
Barmann, Q.Y. Chen, J. Cuevas-Maraver | Backcasting COVID-19: A Physics-Informed Estimate for Early Case
Incidence | null | null | null | null | q-bio.QM physics.soc-ph q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | It is widely accepted that the number of reported cases during the first
stages of the COVID-19 pandemic severely underestimates the number of actual
cases. We leverage delay embedding theorems of Whitney and Takens and use
Gaussian Process regression to estimate the number of cases during the first
2020 wave based on the second wave of the epidemic in several European
countries, South Korea, and Brazil. We assume that the second wave was more
accurately monitored and hence that it can be trusted. We then construct a
manifold diffeomorphic to that of the implied original dynamical system, using
fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to
the reported cases coordinate of the dynamical system. Our main finding is that
in the European countries studied, the actual cases are under-reported by as
much as 50\%. On the other hand, in South Korea -- which had an exemplary and
proactive mitigation approach -- a far smaller discrepancy between the actual
and reported cases is predicted, with an approximately 17\% predicted
under-estimation. We believe that our backcasting framework is applicable to
other epidemic outbreaks where (due to limited or poor quality data) there is
uncertainty around the actual cases.
| [
{
"created": "Mon, 31 Jan 2022 04:19:58 GMT",
"version": "v1"
}
] | 2022-02-02 | [
[
"Kevrekidis",
"G. A.",
""
],
[
"Rapti",
"Z.",
""
],
[
"Drossinos",
"Y.",
""
],
[
"Kevrekidis",
"P. G.",
""
],
[
"Barmann",
"M. A.",
""
],
[
"Chen",
"Q. Y.",
""
],
[
"Cuevas-Maraver",
"J.",
""
]
] | It is widely accepted that the number of reported cases during the first stages of the COVID-19 pandemic severely underestimates the number of actual cases. We leverage delay embedding theorems of Whitney and Takens and use Gaussian Process regression to estimate the number of cases during the first 2020 wave based on the second wave of the epidemic in several European countries, South Korea, and Brazil. We assume that the second wave was more accurately monitored and hence that it can be trusted. We then construct a manifold diffeomorphic to that of the implied original dynamical system, using fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to the reported cases coordinate of the dynamical system. Our main finding is that in the European countries studied, the actual cases are under-reported by as much as 50\%. On the other hand, in South Korea -- which had an exemplary and proactive mitigation approach -- a far smaller discrepancy between the actual and reported cases is predicted, with an approximately 17\% predicted under-estimation. We believe that our backcasting framework is applicable to other epidemic outbreaks where (due to limited or poor quality data) there is uncertainty around the actual cases. |
2302.11378 | The Tien Mai | T. Tien Mai, Gerry Tonkin-Hill, John A. Lees, Jukka Corander | Quantifying the common genetic variability of bacterial traits | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | The study of common heritability, or co-heritability, among multiple traits
has been widely established in quantitative and molecular genetics. However, in
bacteria, genome-based estimation of heritability has only been considered very
recently and no methods are currently available for considering
co-heritability. Here we introduce such a method and demonstrate its usefulness
by multi-trait analyses of the three major human pathogens \textit{Escherichia
coli}, \textit{Neisseria gonorrhoeae} and \textit{Streprococcus pneumoniae}. We
anticipate that the increased availability of high-throughput genomic and
phenotypic screens of bacterial populations will spawn ample future
opportunities to understand the common molecular basis of different traits in
bacteria.
| [
{
"created": "Wed, 22 Feb 2023 13:52:40 GMT",
"version": "v1"
}
] | 2023-02-23 | [
[
"Mai",
"T. Tien",
""
],
[
"Tonkin-Hill",
"Gerry",
""
],
[
"Lees",
"John A.",
""
],
[
"Corander",
"Jukka",
""
]
] | The study of common heritability, or co-heritability, among multiple traits has been widely established in quantitative and molecular genetics. However, in bacteria, genome-based estimation of heritability has only been considered very recently and no methods are currently available for considering co-heritability. Here we introduce such a method and demonstrate its usefulness by multi-trait analyses of the three major human pathogens \textit{Escherichia coli}, \textit{Neisseria gonorrhoeae} and \textit{Streprococcus pneumoniae}. We anticipate that the increased availability of high-throughput genomic and phenotypic screens of bacterial populations will spawn ample future opportunities to understand the common molecular basis of different traits in bacteria. |
1911.05531 | Iddo Drori | Iddo Drori, Darshan Thaker, Arjun Srivatsa, Daniel Jeong, Yueqi Wang,
Linyong Nan, Fan Wu, Dimitri Leggas, Jinhao Lei, Weiyi Lu, Weilong Fu, Yuan
Gao, Sashank Karri, Anand Kannan, Antonio Moretti, Mohammed AlQuraishi, Chen
Keasar, Itsik Pe'er | Accurate Protein Structure Prediction by Embeddings and Deep Learning
Representations | null | Machine Learning in Computational Biology, 2019 | null | null | q-bio.BM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proteins are the major building blocks of life, and actuators of almost all
chemical and biophysical events in living organisms. Their native structures in
turn enable their biological functions which have a fundamental role in drug
design. This motivates predicting the structure of a protein from its sequence
of amino acids, a fundamental problem in computational biology. In this work,
we demonstrate state-of-the-art protein structure prediction (PSP) results
using embeddings and deep learning models for prediction of backbone atom
distance matrices and torsion angles. We recover 3D coordinates of backbone
atoms and reconstruct full atom protein by optimization. We create a new gold
standard dataset of proteins which is comprehensive and easy to use. Our
dataset consists of amino acid sequences, Q8 secondary structures, position
specific scoring matrices, multiple sequence alignment co-evolutionary
features, backbone atom distance matrices, torsion angles, and 3D coordinates.
We evaluate the quality of our structure prediction by RMSD on the latest
Critical Assessment of Techniques for Protein Structure Prediction (CASP) test
data and demonstrate competitive results with the winning teams and AlphaFold
in CASP13 and supersede the results of the winning teams in CASP12. We make our
data, models, and code publicly available.
| [
{
"created": "Sat, 9 Nov 2019 00:21:17 GMT",
"version": "v1"
}
] | 2019-11-14 | [
[
"Drori",
"Iddo",
""
],
[
"Thaker",
"Darshan",
""
],
[
"Srivatsa",
"Arjun",
""
],
[
"Jeong",
"Daniel",
""
],
[
"Wang",
"Yueqi",
""
],
[
"Nan",
"Linyong",
""
],
[
"Wu",
"Fan",
""
],
[
"Leggas",
"Dimitri",
""
],
[
"Lei",
"Jinhao",
""
],
[
"Lu",
"Weiyi",
""
],
[
"Fu",
"Weilong",
""
],
[
"Gao",
"Yuan",
""
],
[
"Karri",
"Sashank",
""
],
[
"Kannan",
"Anand",
""
],
[
"Moretti",
"Antonio",
""
],
[
"AlQuraishi",
"Mohammed",
""
],
[
"Keasar",
"Chen",
""
],
[
"Pe'er",
"Itsik",
""
]
] | Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biological functions which have a fundamental role in drug design. This motivates predicting the structure of a protein from its sequence of amino acids, a fundamental problem in computational biology. In this work, we demonstrate state-of-the-art protein structure prediction (PSP) results using embeddings and deep learning models for prediction of backbone atom distance matrices and torsion angles. We recover 3D coordinates of backbone atoms and reconstruct full atom protein by optimization. We create a new gold standard dataset of proteins which is comprehensive and easy to use. Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates. We evaluate the quality of our structure prediction by RMSD on the latest Critical Assessment of Techniques for Protein Structure Prediction (CASP) test data and demonstrate competitive results with the winning teams and AlphaFold in CASP13 and supersede the results of the winning teams in CASP12. We make our data, models, and code publicly available. |
1805.11851 | Simone Carlo Surace | Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner, Johanni
Brea | On the choice of metric in gradient-based theories of brain function | Revised version; 14 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The idea that the brain functions so as to minimize certain costs pervades
theoretical neuroscience. Since a cost function by itself does not predict how
the brain finds its minima, additional assumptions about the optimization
method need to be made to predict the dynamics of physiological quantities. In
this context, steepest descent (also called gradient descent) is often
suggested as an algorithmic principle of optimization potentially implemented
by the brain. In practice, researchers often consider the vector of partial
derivatives as the gradient. However, the definition of the gradient and the
notion of a steepest direction depend on the choice of a metric. Since the
choice of the metric involves a large number of degrees of freedom, the
predictive power of models that are based on gradient descent must be called
into question, unless there are strong constraints on the choice of the metric.
Here we provide a didactic review of the mathematics of gradient descent,
illustrate common pitfalls of using gradient descent as a principle of brain
function with examples from the literature and propose ways forward to
constrain the metric.
| [
{
"created": "Wed, 30 May 2018 08:21:41 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Jun 2018 21:35:49 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Dec 2018 14:12:10 GMT",
"version": "v3"
}
] | 2018-12-24 | [
[
"Surace",
"Simone Carlo",
""
],
[
"Pfister",
"Jean-Pascal",
""
],
[
"Gerstner",
"Wulfram",
""
],
[
"Brea",
"Johanni",
""
]
] | The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Since a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method need to be made to predict the dynamics of physiological quantities. In this context, steepest descent (also called gradient descent) is often suggested as an algorithmic principle of optimization potentially implemented by the brain. In practice, researchers often consider the vector of partial derivatives as the gradient. However, the definition of the gradient and the notion of a steepest direction depend on the choice of a metric. Since the choice of the metric involves a large number of degrees of freedom, the predictive power of models that are based on gradient descent must be called into question, unless there are strong constraints on the choice of the metric. Here we provide a didactic review of the mathematics of gradient descent, illustrate common pitfalls of using gradient descent as a principle of brain function with examples from the literature and propose ways forward to constrain the metric. |
2112.01002 | Sammy Sambu | Sammy Sambu | Attaining scalable storage-expansion dualism for bioartificial tissues | 10 pages, 5 figures | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | The untenable dependence of cryopreservation on cytotoxic cryoprotectants has
motivated the mining of biochemical libraries to identify molecular features
marking cryoprotectants that may prevent ice crystal growth. It is hypothesized
that such molecules may be useful across all temperatures eliminating cellular
destruction due to equilibration cytotoxicity before and after
cryopreservation. By probing the biochemical space using solvation-associated
molecular topology and partition-distribution measures we developed an analytic
formalism for ice crystal inhibition. By probing the union between a heat-shock
protein cluster and an anti-freeze glycoprotein cluster, the model development
process generated distinct regions of interest for anti-freeze glycoprotein and
proved robust across different classes of proteins. These results confirm that
there is a chemical space within which efficacious Ice Crystal Inhibitor
molecular libraries can be constructed. These spaces contain latent projections
drawn from solvent accessibility, hydrogen bonding capacity and molecular
geometry. They also showed that molecular design can be a useful tool in the
development of new-age cryoactive matrices to enable the smooth transition
across culture, preservation and expansion. These chemical spaces provide low
cytotoxicity since such amphipathic molecules occupy a continuum between
solubizing and membrane stabilizing regions as shown by the free energy of
translocation calculations. These biochemical design spaces are fundamentally
the solution to efficient scale-up and deployment of cell therapies.
Consequently, this article proposes the use of a molecular knowledge-mining
approach in the development of a class of non-cytotoxic cryoprotective agents
in the class of Ice Crystal Inhibitor compatible with continuous cryothermic
and normothermic cell storage and expansion.
| [
{
"created": "Thu, 2 Dec 2021 06:28:23 GMT",
"version": "v1"
}
] | 2021-12-03 | [
[
"Sambu",
"Sammy",
""
]
] | The untenable dependence of cryopreservation on cytotoxic cryoprotectants has motivated the mining of biochemical libraries to identify molecular features marking cryoprotectants that may prevent ice crystal growth. It is hypothesized that such molecules may be useful across all temperatures eliminating cellular destruction due to equilibration cytotoxicity before and after cryopreservation. By probing the biochemical space using solvation-associated molecular topology and partition-distribution measures we developed an analytic formalism for ice crystal inhibition. By probing the union between a heat-shock protein cluster and an anti-freeze glycoprotein cluster, the model development process generated distinct regions of interest for anti-freeze glycoprotein and proved robust across different classes of proteins. These results confirm that there is a chemical space within which efficacious Ice Crystal Inhibitor molecular libraries can be constructed. These spaces contain latent projections drawn from solvent accessibility, hydrogen bonding capacity and molecular geometry. They also showed that molecular design can be a useful tool in the development of new-age cryoactive matrices to enable the smooth transition across culture, preservation and expansion. These chemical spaces provide low cytotoxicity since such amphipathic molecules occupy a continuum between solubizing and membrane stabilizing regions as shown by the free energy of translocation calculations. These biochemical design spaces are fundamentally the solution to efficient scale-up and deployment of cell therapies. Consequently, this article proposes the use of a molecular knowledge-mining approach in the development of a class of non-cytotoxic cryoprotective agents in the class of Ice Crystal Inhibitor compatible with continuous cryothermic and normothermic cell storage and expansion. |
2404.01514 | Nicholas Williams | Nicholas Williams and Kara E. Rudolph | A drug classification pipeline for Medicaid claims using RxNorm | null | null | null | null | q-bio.QM cs.DB | http://creativecommons.org/licenses/by/4.0/ | Objective: Freely preprocess drug codes recorded in electronic health records
and insurance claims to drug classes that may then be used in biomedical
research.
Materials and Methods: We developed a drug classification pipeline for
linking National Drug Codes to the World Health Organization Anatomical
Therapeutic Chemical classification. To implement our solution, we created an R
package interface to the National Library of Medicine's RxNorm API.
Results: Using the classification pipeline, 59.4% of all unique NDC were
linked to an ATC, resulting in 95.5% of all claims being successfully linked to
a drug classification. We identified 12,004 unique NDC codes that were
classified as being an opioid or non-opioid prescription for treating pain.
Discussion: Our proposed pipeline performed similarly well to other NDC
classification routines using commercial databases. A check of a small, random
sample of non-active NDC found the pipeline to be accurate for classifying
these codes.
Conclusion: The RxNorm NDC classification pipeline is a practical and
reliable tool for categorizing drugs in large-scale administrative claims data.
| [
{
"created": "Mon, 1 Apr 2024 22:39:18 GMT",
"version": "v1"
}
] | 2024-04-03 | [
[
"Williams",
"Nicholas",
""
],
[
"Rudolph",
"Kara E.",
""
]
] | Objective: Freely preprocess drug codes recorded in electronic health records and insurance claims to drug classes that may then be used in biomedical research. Materials and Methods: We developed a drug classification pipeline for linking National Drug Codes to the World Health Organization Anatomical Therapeutic Chemical classification. To implement our solution, we created an R package interface to the National Library of Medicine's RxNorm API. Results: Using the classification pipeline, 59.4% of all unique NDC were linked to an ATC, resulting in 95.5% of all claims being successfully linked to a drug classification. We identified 12,004 unique NDC codes that were classified as being an opioid or non-opioid prescription for treating pain. Discussion: Our proposed pipeline performed similarly well to other NDC classification routines using commercial databases. A check of a small, random sample of non-active NDC found the pipeline to be accurate for classifying these codes. Conclusion: The RxNorm NDC classification pipeline is a practical and reliable tool for categorizing drugs in large-scale administrative claims data. |
2104.10957 | Chen-Gia Tsai | Chia-Wei Li and Chen-Gia Tsai | Differential brain connectivity patterns while listening to breakup and
rebellious songs: A functional magnetic resonance imaging study | 14 pages, 4 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Song appreciation involves a broad range of mental processes, and different
neural networks may be activated by different song types. The aim of the
present study was to show differential functional connectivity of the
prefrontal cortices while listening to breakup and rebellious songs. Breakup
songs describe romance and longing, whereas rebellious songs convey criticism
of conventional ideas or socio-cultural norms. We hypothesized that the medial
and lateral prefrontal cortices may interact with different brain regions in
response to these two song types. Functional magnetic resonance imaging data of
fifteen participants were collected while they were listening to two complete
breakup songs and two complete rebellious songs currently popular in Taiwan.
The results showed that listening to the breakup songs, compared to the
rebellious songs, enhanced the coupling between the medial prefrontal cortex
and several emotion-related regions, including the thalamus, caudate, amygdala,
hippocampus, middle orbitofrontal cortex, and right inferior frontal gyrus.
This coupling may reflect the neural processes of pain empathy, reward
processing, compassion, and reappraisal in response to longing and sorrow
expressed by the breakup songs. Compared to the breakup songs, listening to the
rebellious songs was associated with enhanced coupling between subregions in
the prefrontal and orbitofrontal cortices. These areas might work in concert to
support re-evaluation of conventional ideas or socio-cultural norms as
suggested by the rebellious songs. This study advanced our understanding of the
integration of brain functions while processing complex information.
| [
{
"created": "Thu, 22 Apr 2021 09:37:07 GMT",
"version": "v1"
},
{
"created": "Fri, 4 Jun 2021 07:19:19 GMT",
"version": "v2"
}
] | 2021-06-07 | [
[
"Li",
"Chia-Wei",
""
],
[
"Tsai",
"Chen-Gia",
""
]
] | Song appreciation involves a broad range of mental processes, and different neural networks may be activated by different song types. The aim of the present study was to show differential functional connectivity of the prefrontal cortices while listening to breakup and rebellious songs. Breakup songs describe romance and longing, whereas rebellious songs convey criticism of conventional ideas or socio-cultural norms. We hypothesized that the medial and lateral prefrontal cortices may interact with different brain regions in response to these two song types. Functional magnetic resonance imaging data of fifteen participants were collected while they were listening to two complete breakup songs and two complete rebellious songs currently popular in Taiwan. The results showed that listening to the breakup songs, compared to the rebellious songs, enhanced the coupling between the medial prefrontal cortex and several emotion-related regions, including the thalamus, caudate, amygdala, hippocampus, middle orbitofrontal cortex, and right inferior frontal gyrus. This coupling may reflect the neural processes of pain empathy, reward processing, compassion, and reappraisal in response to longing and sorrow expressed by the breakup songs. Compared to the breakup songs, listening to the rebellious songs was associated with enhanced coupling between subregions in the prefrontal and orbitofrontal cortices. These areas might work in concert to support re-evaluation of conventional ideas or socio-cultural norms as suggested by the rebellious songs. This study advanced our understanding of the integration of brain functions while processing complex information. |
1503.05927 | Guo-Wei Wei | Kelin Xia, Kristopher Opron and Guo-Wei Wei | Correlation function based Gaussian network models | 4 figures | null | null | null | q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gaussian network model (GNM) is one of the most accurate and efficient
methods for biomolecular flexibility analysis. However, the systematic
generalization of the GNM has been elusive. We show that the GNM Kirchhoff
matrix can be built from the ideal low-pass filter, which is a special case of
a wide class of correlation functions underpinning the linear scaling
flexibility-rigidity index (FRI) method. Based on the mathematical structure of
correlation functions, we propose a unified framework to construct generalized
Kirchhoff matrices whose matrix inverse leads to correlation function based
GNMs, whereas, the direct inverse of the diagonal elements gives rise to FRI
method. We illustrate that correlation function based GNMs outperform the
original GNM in the B-factor prediction of a set of 364 proteins. We
demonstrate that for any given correlation function, FRI and GNM methods
provide essentially identical B-factor predictions when the scale value in the
correlation function is sufficiently large.
| [
{
"created": "Mon, 2 Mar 2015 00:56:56 GMT",
"version": "v1"
}
] | 2015-03-23 | [
[
"Xia",
"Kelin",
""
],
[
"Opron",
"Kristopher",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Gaussian network model (GNM) is one of the most accurate and efficient methods for biomolecular flexibility analysis. However, the systematic generalization of the GNM has been elusive. We show that the GNM Kirchhoff matrix can be built from the ideal low-pass filter, which is a special case of a wide class of correlation functions underpinning the linear scaling flexibility-rigidity index (FRI) method. Based on the mathematical structure of correlation functions, we propose a unified framework to construct generalized Kirchhoff matrices whose matrix inverse leads to correlation function based GNMs, whereas, the direct inverse of the diagonal elements gives rise to FRI method. We illustrate that correlation function based GNMs outperform the original GNM in the B-factor prediction of a set of 364 proteins. We demonstrate that for any given correlation function, FRI and GNM methods provide essentially identical B-factor predictions when the scale value in the correlation function is sufficiently large. |
2010.14366 | Maria Soledad Aronna | Felipe J.P. Antunes and M. Soledad Aronna and Cl\'audia T. Code\c{c}o | Modeling and control of malaria dynamics in fish farming regions | To appear in SIAM Journal of Applied Dynamical Systems | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we propose a model that represents the relation between fish
ponds, the mosquito population and the transmission of malaria.
It has been observed that in the Amazonic region of Acre, in the North of
Brazil, fish farming is correlated to the transmission of malaria when carried
out in artificial ponds that become breeding sites. Evidence has been found
indicating that cleaning the vegetation from the edges of the crop tanks helps
to control the size of the mosquito population.
We use our model to determine the effective contribution of fish farming
practices on malaria transmission dynamics. The model consists of a nonlinear
system of ordinary differential equations with jumps at the cleaning time,
which act as impulsive controls. We study the asymptotic behaviour of the
system in function of the intensity and periodicity of the cleaning, and the
value of the parameters. In particular, we state sufficient conditions under
which the mosquito population is eliminated or persists, and under which the
malaria is eliminated or becomes endemic. We prove our conditions by applying
results for cooperative systems with concave nonlinearities.
| [
{
"created": "Tue, 27 Oct 2020 15:22:39 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Nov 2022 13:56:55 GMT",
"version": "v2"
},
{
"created": "Wed, 29 Mar 2023 15:32:56 GMT",
"version": "v3"
},
{
"created": "Wed, 26 Apr 2023 13:47:37 GMT",
"version": "v4"
}
] | 2023-04-27 | [
[
"Antunes",
"Felipe J. P.",
""
],
[
"Aronna",
"M. Soledad",
""
],
[
"Codeço",
"Cláudia T.",
""
]
] | In this work we propose a model that represents the relation between fish ponds, the mosquito population and the transmission of malaria. It has been observed that in the Amazonic region of Acre, in the North of Brazil, fish farming is correlated to the transmission of malaria when carried out in artificial ponds that become breeding sites. Evidence has been found indicating that cleaning the vegetation from the edges of the crop tanks helps to control the size of the mosquito population. We use our model to determine the effective contribution of fish farming practices on malaria transmission dynamics. The model consists of a nonlinear system of ordinary differential equations with jumps at the cleaning time, which act as impulsive controls. We study the asymptotic behaviour of the system in function of the intensity and periodicity of the cleaning, and the value of the parameters. In particular, we state sufficient conditions under which the mosquito population is eliminated or persists, and under which the malaria is eliminated or becomes endemic. We prove our conditions by applying results for cooperative systems with concave nonlinearities. |
2107.03475 | Pantea Moghimi | Pantea Moghimi, Anh The Dang, Theoden I. Netoff, Kelvin O. Lim,
Gowtham Atluri | A Review on MR Based Human Brain Parcellation Methods | 31 pages, 3 figures, 2 tables | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Brain parcellations play a ubiquitous role in the analysis of magnetic
resonance imaging (MRI) datasets. Over 100 years of research has been conducted
in pursuit of an ideal brain parcellation. Different methods have been
developed and studied for constructing brain parcellations using different
imaging modalities. More recently, several data-driven parcellation methods
have been adopted from data mining, machine learning, and statistics
communities. With contributions from different scientific fields, there is a
rich body of literature that needs to be examined to appreciate the breadth of
existing research and the gaps that need to be investigated. In this work, we
review the large body of in vivo brain parcellation research spanning different
neuroimaging modalities and methods. A key contribution of this work is a
semantic organization of this large body of work into different taxonomies,
making it easy to understand the breadth and depth of the brain parcellation
literature. Specifically, we categorized the existing parcellations into three
groups: Anatomical parcellations, functional parcellations, and structural
parcellations which are constructed using T1-weighted MRI, functional MRI
(fMRI), and diffusion-weighted imaging (DWI) datasets, respectively. We provide
a multi-level taxonomy of different methods studied in each of these
categories, compare their relative strengths and weaknesses, and highlight the
challenges currently faced for the development of brain parcellations.
| [
{
"created": "Wed, 7 Jul 2021 20:55:51 GMT",
"version": "v1"
}
] | 2021-07-09 | [
[
"Moghimi",
"Pantea",
""
],
[
"Dang",
"Anh The",
""
],
[
"Netoff",
"Theoden I.",
""
],
[
"Lim",
"Kelvin O.",
""
],
[
"Atluri",
"Gowtham",
""
]
] | Brain parcellations play a ubiquitous role in the analysis of magnetic resonance imaging (MRI) datasets. Over 100 years of research has been conducted in pursuit of an ideal brain parcellation. Different methods have been developed and studied for constructing brain parcellations using different imaging modalities. More recently, several data-driven parcellation methods have been adopted from data mining, machine learning, and statistics communities. With contributions from different scientific fields, there is a rich body of literature that needs to be examined to appreciate the breadth of existing research and the gaps that need to be investigated. In this work, we review the large body of in vivo brain parcellation research spanning different neuroimaging modalities and methods. A key contribution of this work is a semantic organization of this large body of work into different taxonomies, making it easy to understand the breadth and depth of the brain parcellation literature. Specifically, we categorized the existing parcellations into three groups: Anatomical parcellations, functional parcellations, and structural parcellations which are constructed using T1-weighted MRI, functional MRI (fMRI), and diffusion-weighted imaging (DWI) datasets, respectively. We provide a multi-level taxonomy of different methods studied in each of these categories, compare their relative strengths and weaknesses, and highlight the challenges currently faced for the development of brain parcellations. |
2406.01627 | Zicheng Liu | Zicheng Liu, Jiahui Li, Siyuan Li, Zelin Zang, Cheng Tan, Yufei Huang,
Yajing Bai, and Stan Z. Li | GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic
Foundation Models | null | null | null | null | q-bio.GN cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Genomic Foundation Model (GFM) paradigm is expected to facilitate the
extraction of generalizable representations from massive genomic data, thereby
enabling their application across a spectrum of downstream applications.
Despite advancements, a lack of evaluation framework makes it difficult to
ensure equitable assessment due to experimental settings, model intricacy,
benchmark datasets, and reproducibility challenges. In the absence of
standardization, comparative analyses risk becoming biased and unreliable. To
surmount this impasse, we introduce GenBench, a comprehensive benchmarking
suite specifically tailored for evaluating the efficacy of Genomic Foundation
Models. GenBench offers a modular and expandable framework that encapsulates a
variety of state-of-the-art methodologies. Through systematic evaluations of
datasets spanning diverse biological domains with a particular emphasis on both
short-range and long-range genomic tasks, firstly including the three most
important DNA tasks covering Coding Region, Non-Coding Region, Genome
Structure, etc. Moreover, We provide a nuanced analysis of the interplay
between model architecture and dataset characteristics on task-specific
performance. Our findings reveal an interesting observation: independent of the
number of parameters, the discernible difference in preference between the
attention-based and convolution-based models on short- and long-range tasks may
provide insights into the future design of GFM.
| [
{
"created": "Sat, 1 Jun 2024 08:01:05 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Jun 2024 10:51:22 GMT",
"version": "v2"
}
] | 2024-06-06 | [
[
"Liu",
"Zicheng",
""
],
[
"Li",
"Jiahui",
""
],
[
"Li",
"Siyuan",
""
],
[
"Zang",
"Zelin",
""
],
[
"Tan",
"Cheng",
""
],
[
"Huang",
"Yufei",
""
],
[
"Bai",
"Yajing",
""
],
[
"Li",
"Stan Z.",
""
]
] | The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application across a spectrum of downstream applications. Despite advancements, a lack of evaluation framework makes it difficult to ensure equitable assessment due to experimental settings, model intricacy, benchmark datasets, and reproducibility challenges. In the absence of standardization, comparative analyses risk becoming biased and unreliable. To surmount this impasse, we introduce GenBench, a comprehensive benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. Through systematic evaluations of datasets spanning diverse biological domains with a particular emphasis on both short-range and long-range genomic tasks, firstly including the three most important DNA tasks covering Coding Region, Non-Coding Region, Genome Structure, etc. Moreover, We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance. Our findings reveal an interesting observation: independent of the number of parameters, the discernible difference in preference between the attention-based and convolution-based models on short- and long-range tasks may provide insights into the future design of GFM. |
1602.05877 | Ivo Siekmann | Ivo Siekmann, Mark Fackrell, Edmund J. Crampin and Peter Taylor | Modelling modal gating of ion channels with hierarchical Markov models | 28 pages, 8 figures, 3 tables | Proc. R. Soc. A 2016 472 20160122 | 10.1098/rspa.2016.0122 | null | q-bio.QM math.PR q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many ion channels spontaneously switch between different levels of activity.
Although this behaviour known as modal gating has been observed for a long time
it is currently not well understood. Despite the fact that appropriately
representing activity changes is essential for accurately capturing time course
data from ion channels, systematic approaches for modelling modal gating are
currently not available. In this paper, we develop a modular approach for
building such a model in an iterative process. First, stochastic switching
between modes and stochastic opening and closing within modes are represented
in separate aggregated Markov models. Second, the continuous-time hierarchical
Markov model, a new modelling framework proposed here, then enables us to
combine these components so that in the integrated model both mode switching as
well as the kinetics within modes are appropriately represented. A mathematical
analysis reveals that the behaviour of the hierarchical Markov model naturally
depends on the properties of its components. We also demonstrate how a
hierarchical Markov model can be parameterised using experimental data and show
that it provides a better representation than a previous model of the same data
set. Because evidence is increasing that modal gating reflects underlying
molecular properties of the channel protein, it is likely that biophysical
processes are better captured by our new approach than in earlier models.
| [
{
"created": "Thu, 18 Feb 2016 16:58:42 GMT",
"version": "v1"
}
] | 2018-08-14 | [
[
"Siekmann",
"Ivo",
""
],
[
"Fackrell",
"Mark",
""
],
[
"Crampin",
"Edmund J.",
""
],
[
"Taylor",
"Peter",
""
]
] | Many ion channels spontaneously switch between different levels of activity. Although this behaviour known as modal gating has been observed for a long time it is currently not well understood. Despite the fact that appropriately representing activity changes is essential for accurately capturing time course data from ion channels, systematic approaches for modelling modal gating are currently not available. In this paper, we develop a modular approach for building such a model in an iterative process. First, stochastic switching between modes and stochastic opening and closing within modes are represented in separate aggregated Markov models. Second, the continuous-time hierarchical Markov model, a new modelling framework proposed here, then enables us to combine these components so that in the integrated model both mode switching as well as the kinetics within modes are appropriately represented. A mathematical analysis reveals that the behaviour of the hierarchical Markov model naturally depends on the properties of its components. We also demonstrate how a hierarchical Markov model can be parameterised using experimental data and show that it provides a better representation than a previous model of the same data set. Because evidence is increasing that modal gating reflects underlying molecular properties of the channel protein, it is likely that biophysical processes are better captured by our new approach than in earlier models. |
2304.09239 | John Vandermeer | John Vandermeer, Ivette Perfecto | The ghost of ecology in chaos, combining intransitive and higher order
effects | 29 pages, 15 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Historically, musings about the structure of ecological communities has
revolved around the structure of pairwise interactions, competition, predation,
mutualism, etc. . . Recently a growing literature acknowledges that the
baseline assumption that the pair of species is not necessarily the
metaphorical molecule of community ecology, and that certain structures
containing three or more species may not be usefully divisible into pairwise
components. Two examples are intransitive competition (species A dominates
species B dominates species C dominates species A), and nonlinear higher-order
effects. While these two processes have been discussed extensively, the
explicit analysis of how the two of them behave when simultaneously part of the
same dynamic system has not yet appeared in the literature. A concrete
situation exists on coffee farms in Puerto Rico in which three ant species, at
least on some farms, form an intransitive competitive triplet, and that triplet
is strongly influenced, nonlinearly, by a fly parasitoid that modifies the
competitive ability of one of the species in the triplet. Using this
arrangement as a template we explore the dynamical consequences with a simple
ODE model. Results are complicated and include include alternative periodic and
chaotic attractors. The qualitative structures of those complications, however,
may be retrieved easily from a reflection on the basic natural history of the
system.
| [
{
"created": "Tue, 18 Apr 2023 18:57:03 GMT",
"version": "v1"
}
] | 2023-04-20 | [
[
"Vandermeer",
"John",
""
],
[
"Perfecto",
"Ivette",
""
]
] | Historically, musings about the structure of ecological communities has revolved around the structure of pairwise interactions, competition, predation, mutualism, etc. . . Recently a growing literature acknowledges that the baseline assumption that the pair of species is not necessarily the metaphorical molecule of community ecology, and that certain structures containing three or more species may not be usefully divisible into pairwise components. Two examples are intransitive competition (species A dominates species B dominates species C dominates species A), and nonlinear higher-order effects. While these two processes have been discussed extensively, the explicit analysis of how the two of them behave when simultaneously part of the same dynamic system has not yet appeared in the literature. A concrete situation exists on coffee farms in Puerto Rico in which three ant species, at least on some farms, form an intransitive competitive triplet, and that triplet is strongly influenced, nonlinearly, by a fly parasitoid that modifies the competitive ability of one of the species in the triplet. Using this arrangement as a template we explore the dynamical consequences with a simple ODE model. Results are complicated and include include alternative periodic and chaotic attractors. The qualitative structures of those complications, however, may be retrieved easily from a reflection on the basic natural history of the system. |
2205.04877 | Samrat Mondal | Joy Das Bairagya, Samrat Sohel Mondal, Debashish Chowdhury, Sagar
Chakraborty | Eco-evolutionary games for harvesting self-renewing common resource:
Effect of growing harvester population | 10 pages, 3 figures | null | null | null | q-bio.PE nlin.AO physics.bio-ph physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The tragedy of the commons (TOC) is a ubiquitous social dilemma witnessed in
interactions between a population of living entities and shared resources
available to them: The individuals in the population tend to selfishly
overexploit a common resource as it is arguably the rational choice, or in case
of non-human beings, it may be an evolutionarily uninvadable action. How to
avert the TOC is a significant problem related to the conservation of
resources. It is not hard to envisage situations where the resource could be
self-renewing and the size of the population may be dependent on the state of
the resource through the fractions of the population employing different
exploitation rates. If the self-renewal rate of the resource lies between the
maximum and the minimum exploitation rates, it is not a priori obvious under
what conditions the TOC can be averted. In this paper, we address this question
analytically and numerically using the setup of an evolutionary game
theoretical replicator equation that models the Darwinian tenet of natural
selection. Through the replicator equation, while we investigate how a
population of replicators exploit the shared resource, the latter's dynamical
feedback on the former is also not ignored. We also present a transparent
bottom-up derivation of the game-resource feedback model to facilitate future
studies on the stochastic effects on the findings presented herein.
| [
{
"created": "Tue, 10 May 2022 13:23:20 GMT",
"version": "v1"
}
] | 2022-05-11 | [
[
"Bairagya",
"Joy Das",
""
],
[
"Mondal",
"Samrat Sohel",
""
],
[
"Chowdhury",
"Debashish",
""
],
[
"Chakraborty",
"Sagar",
""
]
] | The tragedy of the commons (TOC) is a ubiquitous social dilemma witnessed in interactions between a population of living entities and shared resources available to them: The individuals in the population tend to selfishly overexploit a common resource as it is arguably the rational choice, or in case of non-human beings, it may be an evolutionarily uninvadable action. How to avert the TOC is a significant problem related to the conservation of resources. It is not hard to envisage situations where the resource could be self-renewing and the size of the population may be dependent on the state of the resource through the fractions of the population employing different exploitation rates. If the self-renewal rate of the resource lies between the maximum and the minimum exploitation rates, it is not a priori obvious under what conditions the TOC can be averted. In this paper, we address this question analytically and numerically using the setup of an evolutionary game theoretical replicator equation that models the Darwinian tenet of natural selection. Through the replicator equation, while we investigate how a population of replicators exploit the shared resource, the latter's dynamical feedback on the former is also not ignored. We also present a transparent bottom-up derivation of the game-resource feedback model to facilitate future studies on the stochastic effects on the findings presented herein. |
2207.09671 | Aminur Rahman | Aminur Rahman, Angela Peace, Ramesh Kesawan, Souparno Ghosh | Spatio-temporal models of infectious disease with high rates of
asymptomatic transmission | 8 figures | null | null | null | q-bio.PE math.DS physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The surprisingly mercurial Covid-19 pandemic has highlighted the need to not
only accelerate research on infectious disease, but to also study them using
novel techniques and perspectives. A major contributor to the difficulty of
containing the current pandemic is due to the highly asymptomatic nature of the
disease. In this investigation, we develop a modeling framework to study the
spatio-temporal evolution of diseases with high rates of asymptomatic
transmission, and we apply this framework to a hypothetical country with
mathematically tractable geography; namely, square counties uniformly organized
into a rectangle. We first derive a model for the temporal dynamics of
susceptible, infected, and recovered populations, which is applied at the
county level. Next we use likelihood-based parameter estimation to derive
temporally varying disease transmission parameters on the state-wide level.
While these two methods give us some spatial structure and show the effects of
behavioral and policy changes, they miss the evolution of hot zones that have
caused significant difficulties in resource allocation during the current
pandemic. It is evident that the distribution of cases will not be stagnantly
based on the population density, as with many other diseases, but will
continuously evolve. We model this as a diffusive process where the diffusivity
is spatially varying based on the population distribution, and temporally
varying based on the current number of simulated asymptomatic cases. With this
final addition coupled to the SIR model with temporally varying transmission
parameters, we capture the evolution of "hot zones" in our hypothetical setup.
| [
{
"created": "Wed, 20 Jul 2022 06:02:02 GMT",
"version": "v1"
}
] | 2022-07-21 | [
[
"Rahman",
"Aminur",
""
],
[
"Peace",
"Angela",
""
],
[
"Kesawan",
"Ramesh",
""
],
[
"Ghosh",
"Souparno",
""
]
] | The surprisingly mercurial Covid-19 pandemic has highlighted the need to not only accelerate research on infectious disease, but to also study them using novel techniques and perspectives. A major contributor to the difficulty of containing the current pandemic is due to the highly asymptomatic nature of the disease. In this investigation, we develop a modeling framework to study the spatio-temporal evolution of diseases with high rates of asymptomatic transmission, and we apply this framework to a hypothetical country with mathematically tractable geography; namely, square counties uniformly organized into a rectangle. We first derive a model for the temporal dynamics of susceptible, infected, and recovered populations, which is applied at the county level. Next we use likelihood-based parameter estimation to derive temporally varying disease transmission parameters on the state-wide level. While these two methods give us some spatial structure and show the effects of behavioral and policy changes, they miss the evolution of hot zones that have caused significant difficulties in resource allocation during the current pandemic. It is evident that the distribution of cases will not be stagnantly based on the population density, as with many other diseases, but will continuously evolve. We model this as a diffusive process where the diffusivity is spatially varying based on the population distribution, and temporally varying based on the current number of simulated asymptomatic cases. With this final addition coupled to the SIR model with temporally varying transmission parameters, we capture the evolution of "hot zones" in our hypothetical setup. |
2310.17896 | Ayan Paul | Arunava Patra, Supratim Sengupta, Ayan Paul, Sagar Chakraborty | Inferring to C or not to C: Evolutionary games with Bayesian inferential
strategies | 13 pages, 9 figures | null | null | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Strategies for sustaining cooperation and preventing exploitation by selfish
agents in repeated games have mostly been restricted to Markovian strategies
where the response of an agent depends on the actions in the previous round.
Such strategies are characterized by lack of learning. However, learning from
accumulated evidence over time and using the evidence to dynamically update our
response is a key feature of living organisms. Bayesian inference provides a
framework for such evidence-based learning mechanisms. It is therefore
imperative to understand how strategies based on Bayesian learning fare in
repeated games with Markovian strategies. Here, we consider a scenario where
the Bayesian player uses the accumulated evidence of the opponent's actions
over several rounds to continuously update her belief about the reactive
opponent's strategy. The Bayesian player can then act on her inferred belief in
different ways. By studying repeated Prisoner's dilemma games with such
Bayesian inferential strategies, both in infinite and finite populations, we
identify the conditions under which such strategies can be evolutionarily
stable. We find that a Bayesian strategy that is less altruistic than the
inferred belief about the opponent's strategy can outperform a larger set of
reactive strategies, whereas one that is more generous than the inferred belief
is more successful when the benefit-to-cost ratio of mutual cooperation is
high. Our analysis reveals how learning the opponent's strategy through
Bayesian inference, as opposed to utility maximization, can be beneficial in
the long run, in preventing exploitation and eventual invasion by reactive
strategies.
| [
{
"created": "Fri, 27 Oct 2023 05:06:34 GMT",
"version": "v1"
}
] | 2023-10-30 | [
[
"Patra",
"Arunava",
""
],
[
"Sengupta",
"Supratim",
""
],
[
"Paul",
"Ayan",
""
],
[
"Chakraborty",
"Sagar",
""
]
] | Strategies for sustaining cooperation and preventing exploitation by selfish agents in repeated games have mostly been restricted to Markovian strategies where the response of an agent depends on the actions in the previous round. Such strategies are characterized by lack of learning. However, learning from accumulated evidence over time and using the evidence to dynamically update our response is a key feature of living organisms. Bayesian inference provides a framework for such evidence-based learning mechanisms. It is therefore imperative to understand how strategies based on Bayesian learning fare in repeated games with Markovian strategies. Here, we consider a scenario where the Bayesian player uses the accumulated evidence of the opponent's actions over several rounds to continuously update her belief about the reactive opponent's strategy. The Bayesian player can then act on her inferred belief in different ways. By studying repeated Prisoner's dilemma games with such Bayesian inferential strategies, both in infinite and finite populations, we identify the conditions under which such strategies can be evolutionarily stable. We find that a Bayesian strategy that is less altruistic than the inferred belief about the opponent's strategy can outperform a larger set of reactive strategies, whereas one that is more generous than the inferred belief is more successful when the benefit-to-cost ratio of mutual cooperation is high. Our analysis reveals how learning the opponent's strategy through Bayesian inference, as opposed to utility maximization, can be beneficial in the long run, in preventing exploitation and eventual invasion by reactive strategies. |
2304.04713 | Masahito Ohue | Kairi Furui, Masahito Ohue | Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free
Energy Perturbation | null | null | null | null | q-bio.BM cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, free energy perturbation (FEP) calculations have garnered
increasing attention as tools to support drug discovery. The lead optimization
mapper (Lomap) was proposed as an algorithm to calculate the relative free
energy between ligands efficiently. However, Lomap requires checking whether
each edge in the FEP graph is removable, which necessitates checking the
constraints for all edges. Consequently, conventional Lomap requires
significant computation time, at least several hours for cases involving
hundreds of compounds, and is impractical for cases with more than tens of
thousands of edges. In this study, we aimed to reduce the computational cost of
Lomap to enable the construction of FEP graphs for hundreds of compounds. We
can reduce the overall number of constraint checks required from an amount
dependent on the number of edges to one dependent on the number of nodes by
using the chunk check process to check the constraints for as many edges as
possible simultaneously. Moreover, the output graph is equivalent to that
obtained using conventional Lomap, enabling direct replacement of the original
Lomap with our method. With our improvement, the execution was tens to hundreds
of times faster than that of the original Lomap.
https://github.com/ohuelab/FastLomap
| [
{
"created": "Mon, 10 Apr 2023 17:14:19 GMT",
"version": "v1"
}
] | 2023-04-11 | [
[
"Furui",
"Kairi",
""
],
[
"Ohue",
"Masahito",
""
]
] | In recent years, free energy perturbation (FEP) calculations have garnered increasing attention as tools to support drug discovery. The lead optimization mapper (Lomap) was proposed as an algorithm to calculate the relative free energy between ligands efficiently. However, Lomap requires checking whether each edge in the FEP graph is removable, which necessitates checking the constraints for all edges. Consequently, conventional Lomap requires significant computation time, at least several hours for cases involving hundreds of compounds, and is impractical for cases with more than tens of thousands of edges. In this study, we aimed to reduce the computational cost of Lomap to enable the construction of FEP graphs for hundreds of compounds. We can reduce the overall number of constraint checks required from an amount dependent on the number of edges to one dependent on the number of nodes by using the chunk check process to check the constraints for as many edges as possible simultaneously. Moreover, the output graph is equivalent to that obtained using conventional Lomap, enabling direct replacement of the original Lomap with our method. With our improvement, the execution was tens to hundreds of times faster than that of the original Lomap. https://github.com/ohuelab/FastLomap |
1405.2780 | Michael Sadovsky | Michael G. Sadovsky, Maria Yu. Senashova | Reflexive spatial behaviour does not guarantee evolution advantage in
prey--predator communities | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the model of spatially distributed population consisting of two
species with "\textsl{predator\,--\,prey}" interaction; each of the species
occupies two stations. Transfer of individuals between the stations (migration)
is not random and yields the maximization of a net reproduction of each
species. Besides, each species implements reflexive behavior strategy to
determine the optimal migration flow.
| [
{
"created": "Mon, 12 May 2014 14:35:30 GMT",
"version": "v1"
}
] | 2014-05-13 | [
[
"Sadovsky",
"Michael G.",
""
],
[
"Senashova",
"Maria Yu.",
""
]
] | We consider the model of spatially distributed population consisting of two species with "\textsl{predator\,--\,prey}" interaction; each of the species occupies two stations. Transfer of individuals between the stations (migration) is not random and yields the maximization of a net reproduction of each species. Besides, each species implements reflexive behavior strategy to determine the optimal migration flow. |
2402.07949 | Risto Miikkulainen | Ashok Khanna, Olivier Francon, Risto Miikkulainen | Optimizing the Design of an Artificial Pancreas to Improve Diabetes
Management | null | null | null | null | q-bio.QM cs.AI cs.LG cs.NE | http://creativecommons.org/licenses/by/4.0/ | Diabetes, a chronic condition that impairs how the body turns food into
energy, i.e. blood glucose, affects 38 million people in the US alone. The
standard treatment is to supplement carbohydrate intake with an artificial
pancreas, i.e. a continuous insulin pump (basal shots), as well as occasional
insulin injections (bolus shots). The goal of the treatment is to keep blood
glucose at the center of an acceptable range, as measured through a continuous
glucose meter. A secondary goal is to minimize injections, which are unpleasant
and difficult for some patients to implement. In this study, neuroevolution was
used to discover an optimal strategy for the treatment. Based on a dataset of
30 days of treatment and measurements of a single patient, a random forest was
first trained to predict future glucose levels. A neural network was then
evolved to prescribe carbohydrates, basal pumping levels, and bolus injections.
Evolution discovered a Pareto front that reduced deviation from the target and
number of injections compared to the original data, thus improving patients'
quality of life. To make the system easier to adopt, a language interface was
developed with a large language model. Thus, these technologies not only
improve patient care but also adoption in a broader population.
| [
{
"created": "Sat, 10 Feb 2024 00:49:46 GMT",
"version": "v1"
}
] | 2024-02-14 | [
[
"Khanna",
"Ashok",
""
],
[
"Francon",
"Olivier",
""
],
[
"Miikkulainen",
"Risto",
""
]
] | Diabetes, a chronic condition that impairs how the body turns food into energy, i.e. blood glucose, affects 38 million people in the US alone. The standard treatment is to supplement carbohydrate intake with an artificial pancreas, i.e. a continuous insulin pump (basal shots), as well as occasional insulin injections (bolus shots). The goal of the treatment is to keep blood glucose at the center of an acceptable range, as measured through a continuous glucose meter. A secondary goal is to minimize injections, which are unpleasant and difficult for some patients to implement. In this study, neuroevolution was used to discover an optimal strategy for the treatment. Based on a dataset of 30 days of treatment and measurements of a single patient, a random forest was first trained to predict future glucose levels. A neural network was then evolved to prescribe carbohydrates, basal pumping levels, and bolus injections. Evolution discovered a Pareto front that reduced deviation from the target and number of injections compared to the original data, thus improving patients' quality of life. To make the system easier to adopt, a language interface was developed with a large language model. Thus, these technologies not only improve patient care but also adoption in a broader population. |
0708.1781 | Eduardo Candelario-Jalil | A. Gonzalez-Falcon, E. Candelario-Jalil, M. Garcia-Cabrera, O. S. Leon | Effects of pyruvate administration on infarct volume and neurological
deficits following permanent focal cerebral ischemia in rats | null | Brain Research 990(1-2): 1-7 (2003) | null | null | q-bio.TO | null | Recent experimental evidences indicate that pyruvate, the final metabolite of
glycolysis, has a remarkable protective effect against different types of brain
injury. The purpose of this study was to assess the neuroprotective effect and
the neurological outcome after pyruvate administration in a model of ischemic
stroke induced by permanent middle cerebral artery occlusion (pMCAO) in rats.
Three doses of pyruvate (250, 500 and 1000 mg/kg, i.p.) or vehicle were
administered intraperitoneally 30 min after pMCAO. In other set of experiments,
pyruvate was given either before, immediately after ischemia or in a long-term
administration paradigm. Functional outcome, mortality and infarct volume were
determined 24 h after stroke. Even when the lowest doses of pyruvate reduced
mortality and neurological deficits, no concomitant reduction in infarct volume
was observed. The highest dose of pyruvate increased cortical infarction by 27%
when administered 30 min after pMCAO. In addition, when pyruvate was given
before pMCAO, a significant increase in neurological deficits was noticed.
Surprisingly, on the contrary of what was found in the case of transient global
ischemia, present findings do not support a great neuroprotective role for
pyruvate in permanent focal cerebral ischemia, suggesting two distinct
mechanisms involved in the effects of this glycolytic metabolite in the
ischemic brain.
| [
{
"created": "Mon, 13 Aug 2007 22:10:46 GMT",
"version": "v1"
}
] | 2007-08-15 | [
[
"Gonzalez-Falcon",
"A.",
""
],
[
"Candelario-Jalil",
"E.",
""
],
[
"Garcia-Cabrera",
"M.",
""
],
[
"Leon",
"O. S.",
""
]
] | Recent experimental evidences indicate that pyruvate, the final metabolite of glycolysis, has a remarkable protective effect against different types of brain injury. The purpose of this study was to assess the neuroprotective effect and the neurological outcome after pyruvate administration in a model of ischemic stroke induced by permanent middle cerebral artery occlusion (pMCAO) in rats. Three doses of pyruvate (250, 500 and 1000 mg/kg, i.p.) or vehicle were administered intraperitoneally 30 min after pMCAO. In other set of experiments, pyruvate was given either before, immediately after ischemia or in a long-term administration paradigm. Functional outcome, mortality and infarct volume were determined 24 h after stroke. Even when the lowest doses of pyruvate reduced mortality and neurological deficits, no concomitant reduction in infarct volume was observed. The highest dose of pyruvate increased cortical infarction by 27% when administered 30 min after pMCAO. In addition, when pyruvate was given before pMCAO, a significant increase in neurological deficits was noticed. Surprisingly, on the contrary of what was found in the case of transient global ischemia, present findings do not support a great neuroprotective role for pyruvate in permanent focal cerebral ischemia, suggesting two distinct mechanisms involved in the effects of this glycolytic metabolite in the ischemic brain. |
1501.04732 | Aditya Hernowo | Aditya Tri Hernowo and R. Haryo Yudono | Strabismic syndromes and syndromic strabismus - a brief review | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Strabismus can be found in association with congenital heart diseases, for
examples, in velocardiofacial (DiGeorge) syndrome, Down syndrome, mild
dysmorphic features, in CHARGE association, Turner syndrome, Ullrich-Turner
syndrome, cardiofaciocutaneous syndrome.1-4 Some types of strabismus is
heritable (e.g. infantile esotropia syndrome), particularly the ones associated
with multisystem disorders, e.g. Moebius syndrome, Prader-Willi syndrome,
craniofacial dysostoses, and mitochondrial myopathies.5 Due to the complexities
that a case of strabismus may pertain to, it is worthwhile to get to know more
about the strabismic syndromes and syndromic strabismus. This brief review -as
its name implied- does not attempt to cover every angle of the syndromic
conditions, but offers a refreshment on our knowledge about more prevalent
strabismus-related syndromes.
| [
{
"created": "Tue, 20 Jan 2015 08:27:20 GMT",
"version": "v1"
}
] | 2015-01-21 | [
[
"Hernowo",
"Aditya Tri",
""
],
[
"Yudono",
"R. Haryo",
""
]
] | Strabismus can be found in association with congenital heart diseases, for examples, in velocardiofacial (DiGeorge) syndrome, Down syndrome, mild dysmorphic features, in CHARGE association, Turner syndrome, Ullrich-Turner syndrome, cardiofaciocutaneous syndrome.1-4 Some types of strabismus is heritable (e.g. infantile esotropia syndrome), particularly the ones associated with multisystem disorders, e.g. Moebius syndrome, Prader-Willi syndrome, craniofacial dysostoses, and mitochondrial myopathies.5 Due to the complexities that a case of strabismus may pertain to, it is worthwhile to get to know more about the strabismic syndromes and syndromic strabismus. This brief review -as its name implied- does not attempt to cover every angle of the syndromic conditions, but offers a refreshment on our knowledge about more prevalent strabismus-related syndromes. |
0711.3253 | Hiroki Sayama | Jonathan P. Newman and Hiroki Sayama | The Effect of Sensory Blind Zones on Milling Behavior in a Dynamic
Self-Propelled Particle Model | 12 pages, 4 figures | Phys. Rev. E 78, 011913 (2008) | 10.1103/PhysRevE.78.011913 | null | q-bio.PE | null | Emergent pattern formation in self-propelled particle (SPP) systems is
extensively studied because it addresses a range of swarming phenomena which
occur without leadership. Here we present a dynamic SPP model in which a
sensory blind zone is introduced into each particle's zone of interaction.
Using numerical simulations we discovered that the degradation of milling
patterns with increasing blind zone ranges undergoes two distinct transitions,
including a new, spatially nonhomogeneous transition that involves cessation of
particles' motion caused by broken symmetries in their interaction fields. Our
results also show the necessity of nearly complete panoramic sensory ability
for milling behavior to emerge in dynamic SPP models, suggesting a possible
relationship between collective behavior and sensory systems of biological
organisms.
| [
{
"created": "Wed, 21 Nov 2007 02:27:12 GMT",
"version": "v1"
},
{
"created": "Sat, 22 Mar 2008 23:08:36 GMT",
"version": "v2"
}
] | 2008-07-23 | [
[
"Newman",
"Jonathan P.",
""
],
[
"Sayama",
"Hiroki",
""
]
] | Emergent pattern formation in self-propelled particle (SPP) systems is extensively studied because it addresses a range of swarming phenomena which occur without leadership. Here we present a dynamic SPP model in which a sensory blind zone is introduced into each particle's zone of interaction. Using numerical simulations we discovered that the degradation of milling patterns with increasing blind zone ranges undergoes two distinct transitions, including a new, spatially nonhomogeneous transition that involves cessation of particles' motion caused by broken symmetries in their interaction fields. Our results also show the necessity of nearly complete panoramic sensory ability for milling behavior to emerge in dynamic SPP models, suggesting a possible relationship between collective behavior and sensory systems of biological organisms. |
1308.3542 | Ross Williamson | Ross S. Williamson, Maneesh Sahani, Jonathan W. Pillow | The equivalence of information-theoretic and likelihood-based methods
for neural dimensionality reduction | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stimulus dimensionality-reduction methods in neuroscience seek to identify a
low-dimensional space of stimulus features that affect a neuron's probability
of spiking. One popular method, known as maximally informative dimensions
(MID), uses an information-theoretic quantity known as "single-spike
information" to identify this space. Here we examine MID from a model-based
perspective. We show that MID is a maximum-likelihood estimator for the
parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical
single-spike information corresponds to the normalized log-likelihood under a
Poisson model. This equivalence implies that MID does not necessarily find
maximally informative stimulus dimensions when spiking is not well described as
Poisson. We provide several examples to illustrate this shortcoming, and derive
a lower bound on the information lost when spiking is Bernoulli in discrete
time bins. To overcome this limitation, we introduce model-based dimensionality
reduction methods for neurons with non-Poisson firing statistics, and show that
they can be framed equivalently in likelihood-based or information-theoretic
terms. Finally, we show how to overcome practical limitations on the number of
stimulus dimensions that MID can estimate by constraining the form of the
non-parametric nonlinearity in an LNP model. We illustrate these methods with
simulations and data from primate visual cortex.
| [
{
"created": "Fri, 16 Aug 2013 03:47:19 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Feb 2015 22:29:56 GMT",
"version": "v2"
}
] | 2015-02-26 | [
[
"Williamson",
"Ross S.",
""
],
[
"Sahani",
"Maneesh",
""
],
[
"Pillow",
"Jonathan W.",
""
]
] | Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex. |
2110.12221 | Giovanni Carmantini | Giovanni Sirio Carmantini, Fabio Schittler Neves, Marc Timme, Serafim
Rodrigues | Stochastic facilitation in heteroclinic communication channels | null | Chaos 31, 093130 (2021) | 10.1063/5.0054485 | null | q-bio.NC cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological neural systems encode and transmit information as patterns of
activity tracing complex trajectories in high-dimensional state-spaces,
inspiring alternative paradigms of information processing. Heteroclinic
networks, naturally emerging in artificial neural systems, are networks of
saddles in state-space that provide a transparent approach to generate complex
trajectories via controlled switches among interconnected saddles. External
signals induce specific switching sequences, thus dynamically encoding inputs
as trajectories. Recent works have focused either on computational aspects of
heteroclinic networks, i.e. Heteroclinic Computing, or their stochastic
properties under noise. Yet, how well such systems may transmit information
remains an open question. Here we investigate the information transmission
properties of heteroclinic networks, studying them as communication channels.
Choosing a tractable but representative system exhibiting a heteroclinic
network, we investigate the mutual information rate (MIR) between input signals
and the resulting sequences of states as the level of noise varies.
Intriguingly, MIR does not decrease monotonically with increasing noise.
Intermediate noise levels indeed maximize the information transmission capacity
by promoting an increased yet controlled exploration of the underlying network
of states. Complementing standard stochastic resonance, these results highlight
the constructive effect of stochastic facilitation (i.e. noise-enhanced
information transfer) on heteroclinic communication channels and possibly on
more general dynamical systems exhibiting complex trajectories in state-space.
| [
{
"created": "Sat, 23 Oct 2021 13:50:16 GMT",
"version": "v1"
}
] | 2021-10-26 | [
[
"Carmantini",
"Giovanni Sirio",
""
],
[
"Neves",
"Fabio Schittler",
""
],
[
"Timme",
"Marc",
""
],
[
"Rodrigues",
"Serafim",
""
]
] | Biological neural systems encode and transmit information as patterns of activity tracing complex trajectories in high-dimensional state-spaces, inspiring alternative paradigms of information processing. Heteroclinic networks, naturally emerging in artificial neural systems, are networks of saddles in state-space that provide a transparent approach to generate complex trajectories via controlled switches among interconnected saddles. External signals induce specific switching sequences, thus dynamically encoding inputs as trajectories. Recent works have focused either on computational aspects of heteroclinic networks, i.e. Heteroclinic Computing, or their stochastic properties under noise. Yet, how well such systems may transmit information remains an open question. Here we investigate the information transmission properties of heteroclinic networks, studying them as communication channels. Choosing a tractable but representative system exhibiting a heteroclinic network, we investigate the mutual information rate (MIR) between input signals and the resulting sequences of states as the level of noise varies. Intriguingly, MIR does not decrease monotonically with increasing noise. Intermediate noise levels indeed maximize the information transmission capacity by promoting an increased yet controlled exploration of the underlying network of states. Complementing standard stochastic resonance, these results highlight the constructive effect of stochastic facilitation (i.e. noise-enhanced information transfer) on heteroclinic communication channels and possibly on more general dynamical systems exhibiting complex trajectories in state-space. |
1307.0757 | Yi Ming Zou | Yi Ming Zou | Dynamics of Boolean Networks | null | Discrete and Continuous Dynamical Systems Series S, Vol 4 (6),
2011, 1629-1640 | 10.3934/dcdss.2011.4.1629 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Boolean networks are special types of finite state time-discrete dynamical
systems. A Boolean network can be described by a function from an n-dimensional
vector space over the field of two elements to itself. A fundamental problem in
studying these dynamical systems is to link their long term behaviors to the
structures of the functions that define them. In this paper, a method for
deriving a Boolean network's dynamical information via its disjunctive normal
form is explained. For a given Boolean network, a matrix with entries 0 and 1
is associated with the polynomial function that represents the network, then
the information on the fixed points and the limit cycles is derived by
analyzing the matrix. The described method provides an algorithm for the
determination of the fixed points from the polynomial expression of a Boolean
network. The method can also be used to construct Boolean networks with
prescribed limit cycles and fixed points. Examples are provided to explain the
algorithm.
| [
{
"created": "Tue, 2 Jul 2013 16:49:00 GMT",
"version": "v1"
}
] | 2013-07-03 | [
[
"Zou",
"Yi Ming",
""
]
] | Boolean networks are special types of finite state time-discrete dynamical systems. A Boolean network can be described by a function from an n-dimensional vector space over the field of two elements to itself. A fundamental problem in studying these dynamical systems is to link their long term behaviors to the structures of the functions that define them. In this paper, a method for deriving a Boolean network's dynamical information via its disjunctive normal form is explained. For a given Boolean network, a matrix with entries 0 and 1 is associated with the polynomial function that represents the network, then the information on the fixed points and the limit cycles is derived by analyzing the matrix. The described method provides an algorithm for the determination of the fixed points from the polynomial expression of a Boolean network. The method can also be used to construct Boolean networks with prescribed limit cycles and fixed points. Examples are provided to explain the algorithm. |
1908.08464 | Niv DeMalach | Man Qi, Niv DeMalach, Tao Sun, Hailin Zhang | Coexistence under hierarchical resource exploitation: the role of
R*-preemption tradeoff | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Resource competition theory predicts coexistence and exclusion patterns based
on species R*s, the minimum resource values required for a species to persist.
A central assumption of the theory is that all species have equal access to
resources. However, many systems are characterized by preemption exploitation,
where some species deplete resources before their competitors can access them
(e.g., asymmetric light competition, contest competition among animals). We
hypothesize that coexistence under preemption requires an R*-preemption
tradeoff, i.e., the species with the priority access should have a higher R*
(lower efficiency). Thus, we developed an extension of resource competition
theory to investigate partial and total preemption (in the latter, the
preemptor is unaffected by species with lower preemption rank). We found that
an R*-preemption tradeoff is a necessary condition for coexistence in all
models. Moreover, under total preemption, the tradeoff alone is sufficient for
coexistence. In contrast, under partial preemption, more conditions are needed,
which restricts the parameter space of coexistence. Finally, we discussed the
implications of our finding for seemingly distinct tradeoffs, which we view as
special cases of R*-preemption tradeoff. These tradeoffs include the
digger-grazer, the competition-colonization, and tradeoffs related to light
competition between trees and understories.
| [
{
"created": "Thu, 22 Aug 2019 15:49:06 GMT",
"version": "v1"
},
{
"created": "Thu, 20 May 2021 13:01:42 GMT",
"version": "v2"
},
{
"created": "Tue, 16 Nov 2021 14:46:05 GMT",
"version": "v3"
}
] | 2021-11-17 | [
[
"Qi",
"Man",
""
],
[
"DeMalach",
"Niv",
""
],
[
"Sun",
"Tao",
""
],
[
"Zhang",
"Hailin",
""
]
] | Resource competition theory predicts coexistence and exclusion patterns based on species R*s, the minimum resource values required for a species to persist. A central assumption of the theory is that all species have equal access to resources. However, many systems are characterized by preemption exploitation, where some species deplete resources before their competitors can access them (e.g., asymmetric light competition, contest competition among animals). We hypothesize that coexistence under preemption requires an R*-preemption tradeoff, i.e., the species with the priority access should have a higher R* (lower efficiency). Thus, we developed an extension of resource competition theory to investigate partial and total preemption (in the latter, the preemptor is unaffected by species with lower preemption rank). We found that an R*-preemption tradeoff is a necessary condition for coexistence in all models. Moreover, under total preemption, the tradeoff alone is sufficient for coexistence. In contrast, under partial preemption, more conditions are needed, which restricts the parameter space of coexistence. Finally, we discussed the implications of our finding for seemingly distinct tradeoffs, which we view as special cases of R*-preemption tradeoff. These tradeoffs include the digger-grazer, the competition-colonization, and tradeoffs related to light competition between trees and understories. |
1202.2219 | Liang Wu | Liang Wu, Chengcheng Ji, Sishuo Wang, Jianhao Lv | The advantages of the pentameral symmetry of the starfish | 17 pages, 10 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Starfish typically show pentameral symmetry, and they are typically similar
in shape to a pentagram. Although starfish can evolve and live with other
numbers of arms, the dominant species always show pentameral symmetry. We used
mathematical and physical methods to analyze the superiority of starfish with
five arms in comparison with those with a different number of arms with respect
to detection, turning over, autotomy and adherence. In this study, we
determined that starfish with five arms, although slightly inferior to others
in one or two aspects, exhibit the best performance when the four
aforementioned factors are considered together. In addition, five-armed
starfish perform best on autotomy, which is crucially important for starfish
survival. This superiority contributes to the dominance of five-armed starfish
in evolution, which is consistent with the practical situation. Nevertheless,
we can see some flexibility in the number and conformation of arms. The
analyses performed in our research will be of great help in unraveling the
mysteries of dominant shapes and structures.
| [
{
"created": "Fri, 10 Feb 2012 09:34:31 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Feb 2012 04:54:48 GMT",
"version": "v2"
}
] | 2012-02-22 | [
[
"Wu",
"Liang",
""
],
[
"Ji",
"Chengcheng",
""
],
[
"Wang",
"Sishuo",
""
],
[
"Lv",
"Jianhao",
""
]
] | Starfish typically show pentameral symmetry, and they are typically similar in shape to a pentagram. Although starfish can evolve and live with other numbers of arms, the dominant species always show pentameral symmetry. We used mathematical and physical methods to analyze the superiority of starfish with five arms in comparison with those with a different number of arms with respect to detection, turning over, autotomy and adherence. In this study, we determined that starfish with five arms, although slightly inferior to others in one or two aspects, exhibit the best performance when the four aforementioned factors are considered together. In addition, five-armed starfish perform best on autotomy, which is crucially important for starfish survival. This superiority contributes to the dominance of five-armed starfish in evolution, which is consistent with the practical situation. Nevertheless, we can see some flexibility in the number and conformation of arms. The analyses performed in our research will be of great help in unraveling the mysteries of dominant shapes and structures. |
1810.12397 | Thomas Shultz | Daniel R. Shultz, Marcel Montrey, Thomas R. Shultz | Comparing fitness and drift explanations of Neanderthal replacement | 28 pages, 9 figures | Proceedings of the Royal Society B 286: 20190907 (2019) 1-8 | 10.1098/rspb.2019.0907 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is a general consensus among archaeologists that replacement of
Neanderthals by anatomically modern humans in Europe occurred around 40K to 35K
YBP. However, the causal mechanism for this replacement continues to be
debated. Searching for specific fitness advantages in the archaeological record
has proven difficult, as these may be obscured, absent, or subject to
interpretation. Proposed models have therefore featured either fitness
advantages in favor of anatomically modern humans, or invoked neutral drift
under various preconditions. To bridge this gap, we rigorously compare the
system-level properties of fitness- and drift-based explanations of Neanderthal
replacement. Our stochastic simulations and analytical predictions show that,
although both fitness and drift can produce fixation, they present important
differences in 1) required initial conditions, 2) reliability, 3) time to
replacement, and 4) path to replacement (population histories). These results
present useful opportunities for comparison with archaeological and genetic
data. We find far greater agreement between the available empirical evidence
and the system-level properties of replacement by differential fitness, rather
than by neutral drift.
| [
{
"created": "Mon, 29 Oct 2018 20:40:07 GMT",
"version": "v1"
}
] | 2021-07-01 | [
[
"Shultz",
"Daniel R.",
""
],
[
"Montrey",
"Marcel",
""
],
[
"Shultz",
"Thomas R.",
""
]
] | There is a general consensus among archaeologists that replacement of Neanderthals by anatomically modern humans in Europe occurred around 40K to 35K YBP. However, the causal mechanism for this replacement continues to be debated. Searching for specific fitness advantages in the archaeological record has proven difficult, as these may be obscured, absent, or subject to interpretation. Proposed models have therefore featured either fitness advantages in favor of anatomically modern humans, or invoked neutral drift under various preconditions. To bridge this gap, we rigorously compare the system-level properties of fitness- and drift-based explanations of Neanderthal replacement. Our stochastic simulations and analytical predictions show that, although both fitness and drift can produce fixation, they present important differences in 1) required initial conditions, 2) reliability, 3) time to replacement, and 4) path to replacement (population histories). These results present useful opportunities for comparison with archaeological and genetic data. We find far greater agreement between the available empirical evidence and the system-level properties of replacement by differential fitness, rather than by neutral drift. |
2111.15184 | Durham Smith | Durham Smith and Grigory Tikhomirov | small: A Programmatic Nanostructure Design and Modelling Environment | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Structural DNA nanotechnology has advanced to the extent that extremely
complex structures can be designed. Much of this advancement has been due to
the development of automated DNA design and simulation tools. Typically, the
tools (e.g. NUPAK, cadnano, OxDNA) are created for a specific task. Ideally,
there would be an environment that can integrate all such DNA tools, also with
non-DNA tools - for example for modelling electromagnetic field along a
zero-mode waveguide made of gold nanoparticles organized on a DNA breadboard.
Such an environment would streamline design in DNA nanotechnology and enable
applying DNA nanotechnology principles to construct high performance materials
and devices from non-DNA components.
Here we present small a programmatic tool that is a step towards building
such an environment for designing arbitrary nanostructures. In particular we
showcase how small has been used to create an integrated computational
materials engineering (ICME) framework for DNA nanotechnology, allowing the
hierarchical design, simulation and visualization of arbitrary DNA
nanostructures. Furthermore we demonstrate the design and modeling of the mode
profiles and band structure of hybrid DNA-nanoparticle materials through the
integration of small with Maxwell solvers.
| [
{
"created": "Tue, 30 Nov 2021 07:49:08 GMT",
"version": "v1"
}
] | 2021-12-01 | [
[
"Smith",
"Durham",
""
],
[
"Tikhomirov",
"Grigory",
""
]
] | Structural DNA nanotechnology has advanced to the extent that extremely complex structures can be designed. Much of this advancement has been due to the development of automated DNA design and simulation tools. Typically, the tools (e.g. NUPAK, cadnano, OxDNA) are created for a specific task. Ideally, there would be an environment that can integrate all such DNA tools, also with non-DNA tools - for example for modelling electromagnetic field along a zero-mode waveguide made of gold nanoparticles organized on a DNA breadboard. Such an environment would streamline design in DNA nanotechnology and enable applying DNA nanotechnology principles to construct high performance materials and devices from non-DNA components. Here we present small a programmatic tool that is a step towards building such an environment for designing arbitrary nanostructures. In particular we showcase how small has been used to create an integrated computational materials engineering (ICME) framework for DNA nanotechnology, allowing the hierarchical design, simulation and visualization of arbitrary DNA nanostructures. Furthermore we demonstrate the design and modeling of the mode profiles and band structure of hybrid DNA-nanoparticle materials through the integration of small with Maxwell solvers. |
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