Publications scientifiques
Autres affiliations : Centre National de la Recherche Scientifique · Université Joseph Fourier · Andrology Gerontechnology Inflammation Modelisation · Unité de Modélisation Mathématique et Informatique des Systèmes Complexes · Université Grenoble Alpes
Jacques Demongeot* (Auteur correspondant)
Autres affiliations : Université Joseph Fourier · Andrology Gerontechnology Inflammation Modelisation · Université Grenoble Alpes
Comptes Rendus Mathématique
Numerous indices of complexity are used in biological regulatory networks like the number of their components, their connectance (or connectivity), or the number of the strong connected components of their interaction graph. Concerning the stability of a biological network, it corresponds to its ability to recover from dynamical or parametric disturbance. Complexity is here quantified by the evolutionary entropy, which describes the way the asymptotic presence distribution of the corresponding dynamical system is spread over the state space and the stability (or robustness) is characterized by the rate at which the system returns to this equilibrium distribution after a perturbation. This article shows the mathematical relationships between entropy and stability rate in the general framework of a Markov chain.
