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Entropy
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Statistical Physics of Living Systems
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Statistical Physics of Living Systems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Statistical Physics".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 19956

Special Issue Editors

Prof. Dr. Amos Maritan

E-Mail Website
Guest Editor
Department of Physics and Astronomy, University of Padova, Via Marzolo 8, 35131 Padova, Italy
Interests: statistical mechanics; biological physics; ecology; complex systems; neuroscience
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Giometto

E-Mail Website
Guest Editor
Department of Physics and Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA 02138, USA
Interests: statistical physics; ecology; stochastic processes

Special Issue Information

Dear Colleagues,

Possibly more than any other branch of Physics, Statistical Physics has greatly contributed to problems beyond the traditional boundaries of the physical sciences with a return in terms of new ideas, concepts, and models. Over the past few decades, the concepts and methods of Statistical Physics have found widespread application in biology, providing a complementing approach to more traditional, reductionist approaches. Such a holistic approach is particularly suited for understanding emergent phenomena in ecology, evolution, behavior, neuroscience, and beyond. In this Special Issue, we welcome contributions that apply Statistical Physics thinking to the description of living systems, from the molecular to the ecosystem scale. We strongly encourage interdisciplinary works, possibly merging theory, experiments, and biological data sets.

Prof. Amos Maritan
Dr. Andrea Giometto
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Biological systems
  • Ecological systems
  • Emergent behavior
  • Criticality
  • Population and community dynamics
  • Fluctuations
  • Microbial interactions
  • Neural and brain modeling
  • Population genetics
  • Statistical mechanics

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Published Papers (5 papers)

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Research

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13 pages, 1036 KiB  
Article
Dynein-Inspired Multilane Exclusion Process with Open Boundary Conditions
by Riya Nandi, Uwe C. Täuber and Priyanka
Entropy 2021, 23(10), 1343; https://doi.org/10.3390/e23101343 - 14 Oct 2021
Cited by 2 | Viewed by 1883
Abstract
Motivated by the sidewise motions of dynein motors shown in experiments, we use a variant of the exclusion process to model the multistep dynamics of dyneins on a cylinder with open ends. Due to the varied step sizes of the particles in a [...] Read more.
Motivated by the sidewise motions of dynein motors shown in experiments, we use a variant of the exclusion process to model the multistep dynamics of dyneins on a cylinder with open ends. Due to the varied step sizes of the particles in a quasi-two-dimensional topology, we observe the emergence of a novel phase diagram depending on the various load conditions. Under high-load conditions, our numerical findings yield results similar to the TASEP model with the presence of all three standard TASEP phases, namely the low-density (LD), high-density (HD), and maximal-current (MC) phases. However, for medium- to low-load conditions, for all chosen influx and outflux rates, we only observe the LD and HD phases, and the maximal-current phase disappears. Further, we also measure the dynamics for a single dynein particle which is logarithmically slower than a TASEP particle with a shorter waiting time. Our results also confirm experimental observations of the dwell time distribution: The dwell time distribution for dyneins is exponential in less crowded conditions, whereas a double exponential emerges under overcrowded conditions. Full article
(This article belongs to the Special Issue Statistical Physics of Living Systems)
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27 pages, 755 KiB  
Article
The Stochastic Nature of Functional Responses
by Gian Marco Palamara, José A. Capitán and David Alonso
Entropy 2021, 23(5), 575; https://doi.org/10.3390/e23050575 - 7 May 2021
Cited by 3 | Viewed by 2946
Abstract
Functional responses are non-linear functions commonly used to describe the variation in the rate of consumption of resources by a consumer. They have been widely used in both theoretical and empirical studies, but a comprehensive understanding of their parameters at different levels of [...] Read more.
Functional responses are non-linear functions commonly used to describe the variation in the rate of consumption of resources by a consumer. They have been widely used in both theoretical and empirical studies, but a comprehensive understanding of their parameters at different levels of description remains elusive. Here, by depicting consumers and resources as stochastic systems of interacting particles, we present a minimal set of reactions for consumer resource dynamics. We rigorously derived the corresponding system of ODEs, from which we obtained via asymptotic expansions classical 2D consumer-resource dynamics, characterized by different functional responses. We also derived functional responses by focusing on the subset of reactions describing only the feeding process. This involves fixing the total number of consumers and resources, which we call chemostatic conditions. By comparing these two ways of deriving functional responses, we showed that classical functional response parameters in effective 2D consumer-resource dynamics differ from the same parameters obtained by measuring (or deriving) functional responses for typical feeding experiments under chemostatic conditions, which points to potential errors in interpreting empirical data. We finally discuss possible generalizations of our models to systems with multiple consumers and more complex population structures, including spatial dynamics. Our stochastic approach builds on fundamental ecological processes and has natural connections to basic ecological theory. Full article
(This article belongs to the Special Issue Statistical Physics of Living Systems)
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9 pages, 4211 KiB  
Article
Non-Equilibrium Living Polymers
by Davide Michieletto
Entropy 2020, 22(10), 1130; https://doi.org/10.3390/e22101130 - 6 Oct 2020
Cited by 8 | Viewed by 5243
Abstract
Systems of “living” polymers are ubiquitous in industry and are traditionally realised using surfactants. Here I first review the theoretical state-of-the-art of living polymers and then discuss non-equilibrium extensions that may be realised with advanced synthetic chemistry or DNA functionalised by proteins. These [...] Read more.
Systems of “living” polymers are ubiquitous in industry and are traditionally realised using surfactants. Here I first review the theoretical state-of-the-art of living polymers and then discuss non-equilibrium extensions that may be realised with advanced synthetic chemistry or DNA functionalised by proteins. These systems are not only interesting in order to realise novel “living” soft matter but can also shed insight into how genomes are (topologically) regulated in vivo. Full article
(This article belongs to the Special Issue Statistical Physics of Living Systems)
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24 pages, 3421 KiB  
Article
Modules or Mean-Fields?
by Thomas Parr, Noor Sajid and Karl J. Friston
Entropy 2020, 22(5), 552; https://doi.org/10.3390/e22050552 - 14 May 2020
Cited by 27 | Viewed by 5994
Abstract
The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised ‘modules’, each of which performs a specific kind of computation in isolation of [...] Read more.
The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised ‘modules’, each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is factorisation, as opposed to modularisation, that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience. Full article
(This article belongs to the Special Issue Statistical Physics of Living Systems)
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Review

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11 pages, 912 KiB  
Review
Polygenic Adaptation in a Population of Finite Size
by Wolfgang Stephan and Sona John
Entropy 2020, 22(8), 907; https://doi.org/10.3390/e22080907 - 18 Aug 2020
Cited by 6 | Viewed by 3057
Abstract
Polygenic adaptation in response to selection on quantitative traits has become an important topic in evolutionary biology. Here we review the recent literature on models of polygenic adaptation. In particular, we focus on a model that includes mutation and both directional and stabilizing [...] Read more.
Polygenic adaptation in response to selection on quantitative traits has become an important topic in evolutionary biology. Here we review the recent literature on models of polygenic adaptation. In particular, we focus on a model that includes mutation and both directional and stabilizing selection on a highly polygenic trait in a population of finite size (thus experiencing random genetic drift). Assuming that a sudden environmental shift of the fitness optimum occurs while the population is in a stochastic equilibrium, we analyze the adaptation of the trait to the new optimum. When the shift is not too large relative to the equilibrium genetic variance and this variance is determined by loci with mostly small effects, the approach of the mean phenotype to the optimum can be approximated by a rapid exponential process (whose rate is proportional to the genetic variance). During this rapid phase the underlying changes to allele frequencies, however, may depend strongly on genetic drift. While trait-increasing alleles with intermediate equilibrium frequencies are dominated by selection and contribute positively to changes of the trait mean (i.e., are aligned with the direction of the optimum shift), alleles with low or high equilibrium frequencies show more of a random dynamics, which is expected when drift is dominating. A strong effect of drift is also predicted for population size bottlenecks. Our simulations show that the presence of a bottleneck results in a larger deviation of the population mean of the trait from the fitness optimum, which suggests that more loci experience the influence of drift. Full article
(This article belongs to the Special Issue Statistical Physics of Living Systems)
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