Entropy of complex relevant components of Boolean networks

TL;DR

This paper investigates the entropy and dynamical properties of Boolean networks, showing how basin entropy relates to network connectivity and can be estimated from data, applicable to both deterministic and non-deterministic models.

Contribution

It introduces numerical analysis of basin entropy in Boolean networks and demonstrates its estimation from time-series data, extending applicability to non-deterministic models.

Findings

Basin entropy correlates with network connectivity.

Basin entropy can be estimated from time-series data.

Entropy analysis reveals information storage capacity of networks.

Abstract

Boolean network models of strongly connected modules are capable of capturing the high regulatory complexity of many biological gene regulatory circuits. We study numerically the previously introduced basin entropy, a parameter for the dynamical uncertainty or information storage capacity of a network as well as the average transient time in random relevant components as a function of their connectivity. We also demonstrate that basin entropy can be estimated from time-series data and is therefore also applicable to non-deterministic networks models.