Dynamics of Unperturbed and Noisy Generalized Boolean Networks

TL;DR

This paper introduces a new update timing in Boolean network models inspired by molecular biology, improving biological plausibility and stability analysis of gene regulatory networks compared to traditional models.

Contribution

It proposes a novel update sequence for Boolean networks based on gene influence order, enhancing biological realism and stability analysis in gene regulatory network modeling.

Findings

New update sequence improves biological plausibility.

Model shows comparable or better stability under perturbations.

Results align with biological properties of gene networks.

Abstract

For years, we have been building models of gene regulatory networks, where recent advances in molecular biology shed some light on new structural and dynamical properties of such highly complex systems. In this work, we propose a novel timing of updates in Random and Scale-Free Boolean Networks, inspired by recent findings in molecular biology. This update sequence is neither fully synchronous nor asynchronous, but rather takes into account the sequence in which genes affect each other. We have used both Kauffman's original model and Aldana's extension, which takes into account the structural properties about known parts of actual GRNs, where the degree distribution is right-skewed and long-tailed. The computer simulations of the dynamics of the new model compare favorably to the original ones and show biologically plausible results both in terms of attractors number and length. We have complemented this study with a complete analysis of our systems' stability under transient perturbations, which is one of biological networks defining attribute. Results are encouraging, as our model shows comparable and usually even better behavior than preceding ones without loosing Boolean networks attractive simplicity.