Scientific publications
Other affiliations: Centre National de la Recherche Scientifique · Université Joseph Fourier · Andrology Gerontechnology Inflammation Modelisation · Unité de Modélisation Mathématique et Informatique des Systèmes Complexes
Jacques Demongeot* (Corresponding author)
Other affiliations: Université Joseph Fourier · Andrology Gerontechnology Inflammation Modelisation
Comptes Rendus Mathématique
Various indices of complexity are used in biological regulatory networks like the number n of their components and I of the interactions between these components, their connectance (or connectivity) equal to the ratio <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>I</mml:mi> <mml:mo stretchy="false">/</mml:mo> <mml:mi>n</mml:mi> </mml:math> , or the number of the strong connected components of their interaction graph. The stability of a biological network 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 or equilibrium 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 its equilibrium distribution after a perturbation. This article applies these notions in the case of genetic networks having a getBren structure ( i.e ., being threshold Boolean random networks) and notably those controlling the cell cycle.
