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1. Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

Author

Jacques Demongeot, Mariem Jelassi, Hana Hazgui, Slimane Ben Miled, Narjes Bellamine Ben Saoud, and Carla Taramasco

Subjects
biological networks, dynamic entropy, isochronal entropy, attractor entropy, entropy centrality, robustness, Science, Astrophysics, QB460-466, Physics, QC1-999
Abstract

Networks used in biological applications at different scales (molecule, cell and population) are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system) as well as in their discrete Boolean versions (e.g., non-linear Hopfield system); in both cases, the notion of interaction graph G(J) associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J), kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i) attractor entropy, (ii) isochronal entropy and (iii) entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.

Published
2018
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2. Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

Author

Carla Taramasco, Mariem Jelassi, Hana Hazgui, Narjès Bellamine Ben Saoud, Slimane Ben Miled, Jacques Demongeot, AGeing and IMagery (AGIM), Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), Universidad de Valparaiso [Chile], Laboratoire de Bioinformatique, biomathématiques, biostatistiques (BIMS) (LR11IPT09), Université de Tunis El Manar (UTM)-Institut Pasteur de Tunis, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] (RIADI), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), We are indebted to the Projects PHC Maghreb SCIM (Complex Systems and Medical Engineering), ANR e-swallhome, and H3ABioNet for providing research grants and administrative support., and ANR-13-TECS-0011,e-SwallHome,Déglutition & Respiration : Modélisation & e-santé à Domicile(2013)

Subjects
0301 basic medicine, Theoretical computer science, Computer science, [SDV]Life Sciences [q-bio], Population, Genetic network, General Physics and Astronomy, lcsh:Astrophysics, biological networks, dynamic entropy, isochronal entropy, attractor entropy, entropy centrality, robustness, Article, 03 medical and health sciences, symbols.namesake, Attractor, lcsh:QB460-466, Entropy (information theory), education, lcsh:Science, education.field_of_study, Artificial neural network, lcsh:QC1-999, 030104 developmental biology, Jacobian matrix and determinant, symbols, lcsh:Q, Centrality, Biological network, lcsh:Physics
Abstract

International audience; Networks used in biological applications at different scales (molecule, cell and population) are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system) as well as in their discrete Boolean versions (e.g., non-linear Hopfield system); in both cases, the notion of interaction graph G(J) associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J), kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i) attractor entropy, (ii) isochronal entropy and (iii) entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.

Published
2018
Full Text
View/download PDF

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