Robustness of Transcriptional Regulation in Yeast-like Model Boolean Networks

TL;DR

This study examines the robustness of gene transcription regulation in yeast using Boolean network models, revealing that simpler models with similar in-degree distributions better replicate yeast behavior than more structurally accurate models.

Contribution

It compares different Boolean network models to assess their ability to replicate yeast transcriptional regulation robustness, highlighting the importance of in-degree distribution over detailed topology.

Findings

Simpler in-degree matched models better reproduce yeast results.

Structural similarity does not guarantee better replication of dynamics.

Boolean functions significantly influence network robustness.

Abstract

We investigate the dynamical properties of the transcriptional regulation of gene expression in the yeast Saccharomyces Cerevisiae within the framework of a synchronously and deterministically updated Boolean network model. By means of a dynamically determinant subnetwork, we explore the robustness of transcriptional regulation as a function of the type of Boolean functions used in the model that mimic the influence of regulating agents on the transcription level of a gene. We compare the results obtained for the actual yeast network with those from two different model networks, one with similar in-degree distribution as the yeast and random otherwise, and another due to Balcan et al., where the global topology of the yeast network is reproduced faithfully. We, surprisingly, find that the first set of model networks better reproduce the results found with the actual yeast network, even though the Balcan et al. model networks are structurally more similar to that of yeast.