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Special Issue "Biological Statistical Mechanics"

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

Deadline for manuscript submissions: 31 May 2019

Special Issue Editors

Guest Editor
Prof. Dr. Alessandro Giuliani

Environment and Health Department, Istituto Superiore di Sanità, Roma 00161, Italy
Website | E-Mail
Phone: +390649902579
Interests: multidimensional statistics; complex networks; cell fate transitions; protein structure; biocomplexity; systems biology; QSAR; physiology
Guest Editor
Prof. Mariano Bizzarri

Department of Experimental Medicine, Systems Biology Group Lab, Sapienza University of Rome, Rome, Italy
E-Mail
Phone: +39-3386125188
Interests: tumor microenvironment; systems biology approach in the integrative understanding of cell and tumour biology; proteomic and metabolomic analysis of cells in microgravity; cytoskeleton and fractal shape analysis in biology and in space biology; biophysical study of complex systems in biology; fractal and mathematical integrative analysis of biological and clinical images and dynamics data; development of micro-electronic sensor devices in biochemistry

Special Issue Information

Dear Colleagues,

Any natural entity can be imagined as a system made up of interacting elements. This allows for the development of phenomenological "laws" shared by network-like systems only dependent on their wiring architecture. We can compare this situation with the success of classical thermodynamics, even if the founding fathers of this science were erroneously convinced that heat was a fluid. The difference with classical thermodynamics is that in the case of biological systems we cannot rely on macro-parameters like volume or pressure, but we must seriously consider the particular correlation structure of the system at hand. This is why focusing on state transitions (e.g., differentiation, development, onset of diseases, ecosystem de-stabilization) in which we expect abrupt changes of the system correlation structure is probably the most fruitful direction to establish a "biological statistical mechanics". This Special Issue is devoted to the collection of statistical mechanics-inspired approaches to biological systems at any scale of definition from cell biology to ecology and epidemiology. The issue is of crucial importance given the evident failure of strictly deterministic molecular biology approaches to predicting system-level properties of biological entities.

Prof. Alessandro Giuliani
Prof. Mariano Bizzarri
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 papers will be 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 1600 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

  • network dynamics
  • cell differentiation
  • biocomplexity
  • order and organization
  • ecology
  • epidemics
  • correlation dynamics
  • complex networks, systems

Published Papers (2 papers)

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Research

Open AccessArticle Flexibility of Boolean Network Reservoir Computers in Approximating Arbitrary Recursive and Non-Recursive Binary Filters
Entropy 2018, 20(12), 954; https://doi.org/10.3390/e20120954
Received: 7 November 2018 / Revised: 6 December 2018 / Accepted: 7 December 2018 / Published: 11 December 2018
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Abstract
Reservoir computers (RCs) are biology-inspired computational frameworks for signal processing that are typically implemented using recurrent neural networks. Recent work has shown that Boolean networks (BN) can also be used as reservoirs. We analyze the performance of BN RCs, measuring their flexibility and
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Reservoir computers (RCs) are biology-inspired computational frameworks for signal processing that are typically implemented using recurrent neural networks. Recent work has shown that Boolean networks (BN) can also be used as reservoirs. We analyze the performance of BN RCs, measuring their flexibility and identifying the factors that determine the effective approximation of Boolean functions applied in a sliding-window fashion over a binary signal, both non-recursively and recursively. We train and test BN RCs of different sizes, signal connectivity, and in-degree to approximate three-bit, five-bit, and three-bit recursive binary functions, respectively. We analyze how BN RC parameters and function average sensitivity, which is a measure of function smoothness, affect approximation accuracy as well as the spread of accuracies for a single reservoir. We found that approximation accuracy and reservoir flexibility are highly dependent on RC parameters. Overall, our results indicate that not all reservoirs are equally flexible, and RC instantiation and training can be more efficient if this is taken into account. The optimum range of RC parameters opens up an angle of exploration for understanding how biological systems might be tuned to balance system restraints with processing capacity. Full article
(This article belongs to the Special Issue Biological Statistical Mechanics)
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Open AccessArticle Spatial Organization of Five-Fold Morphology as a Source of Geometrical Constraint in Biology
Entropy 2018, 20(9), 705; https://doi.org/10.3390/e20090705
Received: 28 July 2018 / Revised: 6 September 2018 / Accepted: 7 September 2018 / Published: 14 September 2018
PDF Full-text (3943 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A basic pattern in the body plan architecture of many animals, plants and some molecular and cellular systems is five-part units. This pattern has been understood as a result of genetic blueprints in development and as a widely conserved evolutionary character. Despite some
[...] Read more.
A basic pattern in the body plan architecture of many animals, plants and some molecular and cellular systems is five-part units. This pattern has been understood as a result of genetic blueprints in development and as a widely conserved evolutionary character. Despite some efforts, a definitive explanation of the abundance of pentagonal symmetry at so many levels of complexity is still missing. Based on both, a computational platform and a statistical spatial organization argument, we show that five-fold morphology is substantially different from other abundant symmetries like three-fold, four-fold and six-fold symmetries in terms of spatial interacting elements. We develop a measuring system to determine levels of spatial organization in 2D polygons (homogeneous or heterogeneous partition of defined areas) based on principles of regularity in a morphospace. We found that spatial organization of five-fold symmetry is statistically higher than all other symmetries studied here (3 to 10-fold symmetries) in terms of spatial homogeneity. The significance of our findings is based on the statistical constancy of geometrical constraints derived from spatial organization of shapes, beyond the material or complexity level of the many different systems where pentagonal symmetry occurs. Full article
(This article belongs to the Special Issue Biological Statistical Mechanics)
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Graphical abstract

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