Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
