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Entropy
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Biological Statistical Mechanics II
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Biological Statistical Mechanics II

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

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 9375

Special Issue Editors

Prof. Oleg Naimark

E-Mail Website
Guest Editor
Institute of Continuous Media Mechanics UB RAS, 614000 Perm, Russia
Interests: mechanobiology; physics of condensed matter; out-of-equilibriun critical phenomena
Prof. Dr. Alessandro Giuliani

E-Mail Website
Guest Editor
Environment and Health Department, Istituto Superiore di Sanità, 00161 Rome, Italy
Interests: data analysis; complex systems; systems biology; statistical mechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Mariano Bizzarri

E-Mail Website
Guest Editor
Department of Experimental Medicine, Systems Biology Group Lab, Sapienza University of Rome, Via A. Scarpa 16, 00163 Rome, Italy
Interests: inositol in endocrine control and carcinogenesis; tumor reversion through epigenetic and microenvironment modification; biophysical control of morphogenesis and differentiation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

The interaction of a multiplicity of scales in both time and space is a fundamental feature of biological systems. The complementation of macroscopic (entire organism) and microscopic (molecular biology) views with a mesoscopic level of analysis able to connect the different planes of investigation is urgently needed. This will allow us to both obtain a general frame of reference for rationalizing the burden of data coming from high throughput technologies and to derive effective operational views on biological systems. To reach this goal, we need a “biological statistical mechanics” taking into account the peculiar nature of biological systems.

Biological statistical mechanics is strongly linked to the generalization of statistical approaches for out-of-equilibrium mesoscopic systems as the background for the extension of thermodynamics for biological systems revealing specific types of criticality responsible for DNA and cell transformation. This way can be promising for defining meaningful internal variables and out of equilibrium thermodynamic potentials related to the epigenetic landscape of biological system kinetics and “thermalization” conditions. The definition of internal variables in the statistics and thermodynamics of biological systems is related also to the symmetry changes responsible for critical dynamics of biological transformation. The statistical mechanics approach is correlated with the method of analysis of biological critical systems to establish the qualitative changes of the spatial–temporal scaling properties in the transformation of biological systems.

This implies we must use a sensible approach when transferring established physical concepts into the biological realm, and thus, an “attractor-like” behavior in cell biology will correspond to a typical gene expression profile over many thousands of genes and can be recognized in terms of Pearson correlation between different samples of the same cell kind while escaping a rigorous mathematical description in terms of differential equations. We are convinced Entropy is the right place to host scientific works that dare pay serious attention to biology without considering biological problems only as an occasion for interesting applications of physical concepts.

Keywords

  • tipping-point
  • phase transitions
  • complex networks
  • non-linear dynamics
  • protein structure and dynamics
  • scaling
  • biological evolution
  • cell biology, physiology

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Published Papers (3 papers)

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16 pages, 3300 KiB  
Article
A Statistical Journey through the Topological Determinants of the β2 Adrenergic Receptor Dynamics
by Luisa Di Paola, Humanath Poudel, Mauro Parise, Alessandro Giuliani and David M. Leitner
Entropy 2022, 24(7), 998; https://doi.org/10.3390/e24070998 - 19 Jul 2022
Cited by 6 | Viewed by 2265
Abstract
Activation of G-protein-coupled receptors (GPCRs) is mediated by molecular switches throughout the transmembrane region of the receptor. In this work, we continued along the path of a previous computational study wherein energy transport in the β2 Adrenergic Receptor (β2-AR) was examined and allosteric [...] Read more.
Activation of G-protein-coupled receptors (GPCRs) is mediated by molecular switches throughout the transmembrane region of the receptor. In this work, we continued along the path of a previous computational study wherein energy transport in the β2 Adrenergic Receptor (β2-AR) was examined and allosteric switches were identified in the molecular structure through the reorganization of energy transport networks during activation. In this work, we further investigated the allosteric properties of β2-AR, using Protein Contact Networks (PCNs). In this paper, we report an extensive statistical analysis of the topological and structural properties of β2-AR along its molecular dynamics trajectory to identify the activation pattern of this molecular system. The results show a distinct character to the activation that both helps to understand the allosteric switching previously identified and confirms the relevance of the network formalism to uncover relevant functional features of protein molecules. Full article
(This article belongs to the Special Issue Biological Statistical Mechanics II)
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32 pages, 413 KiB  
Article
Toward a Logic of the Organism: A Process Philosophical Consideration
by Spyridon A. Koutroufinis
Entropy 2022, 24(1), 66; https://doi.org/10.3390/e24010066 - 30 Dec 2021
Viewed by 3333
Abstract
Mathematical models applied in contemporary theoretical and systems biology are based on some implicit ontological assumptions about the nature of organisms. This article aims to show that real organisms reveal a logic of internal causality transcending the tacit logic of biological modeling. Systems [...] Read more.
Mathematical models applied in contemporary theoretical and systems biology are based on some implicit ontological assumptions about the nature of organisms. This article aims to show that real organisms reveal a logic of internal causality transcending the tacit logic of biological modeling. Systems biology has focused on models consisting of static systems of differential equations operating with fixed control parameters that are measured or fitted to experimental data. However, the structure of real organisms is a highly dynamic process, the internal causality of which can only be captured by continuously changing systems of equations. In addition, in real physiological settings kinetic parameters can vary by orders of magnitude, i.e., organisms vary the value of internal quantities that in models are represented by fixed control parameters. Both the plasticity of organisms and the state dependence of kinetic parameters adds indeterminacy to the picture and asks for a new statistical perspective. This requirement could be met by the arising Biological Statistical Mechanics project, which promises to do more justice to the nature of real organisms than contemporary modeling. This article concludes that Biological Statistical Mechanics allows for a wider range of organismic ontologies than does the tacitly followed ontology of contemporary theoretical and systems biology, which are implicitly and explicitly based on systems theory. Full article
(This article belongs to the Special Issue Biological Statistical Mechanics II)
12 pages, 671 KiB  
Article
Network Dynamics in Elemental Assimilation and Metabolism
by Austen Curtin, Christine Austin, Alessandro Giuliani, Manuel Ruiz Marín, Francheska Merced-Nieves, Martha M. Téllez-Rojo, Robert O. Wright, Manish Arora and Paul Curtin
Entropy 2021, 23(12), 1633; https://doi.org/10.3390/e23121633 - 4 Dec 2021
Viewed by 2650
Abstract
Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by [...] Read more.
Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by exposures to non-essential elements which may be toxic. In this study, we applied laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to reconstruct longitudinal exposure profiles of essential and non-essential elements throughout prenatal and early post-natal development. We applied cross-recurrence quantification analysis (CRQA) to characterize dynamics involved in elemental integration, and to construct a graph-theory based analysis of elemental metabolism. Our findings show how exposure to lead, a well-characterized toxicant, perturbs the metabolism of essential elements. In particular, our findings indicate that high levels of lead exposure dysregulate global aspects of metabolic network connectivity. For example, the magnitude of each element’s degree was increased in children exposed to high lead levels. Similarly, high lead exposure yielded discrete effects on specific essential elements, particularly zinc and magnesium, which showed reduced network metrics compared to other elements. In sum, this approach presents a new, systems-based perspective on the dynamics involved in elemental metabolism during critical periods of human development. Full article
(This article belongs to the Special Issue Biological Statistical Mechanics II)
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