Predicting attractors from spectral properties of stylized gene regulatory networks

TL;DR

This study demonstrates that eigenvectors of the graph Laplacian can predict attractors in gene regulatory networks modeled by Boolean threshold dynamics, revealing architecture-dependent correlations and advancing understanding of network dynamics.

Contribution

It introduces a novel approach linking spectral properties of gene networks to their dynamic attractors, expanding theoretical insights into network behavior.

Findings

Eigenvectors predict attractors in Boolean gene networks

Predictive power depends on network architecture

Eigenvector-attractor correlation varies systematically with network features

Abstract

How the architecture of gene regulatory networks ultimately shapes gene expression patterns is an open question, which has been approached from a multitude of angles. The dominant strategy has been to identify non-random features in these networks and then argue for the function of these features using mechanistic modelling. Here we establish the foundation of an alternative approach by studying the correlation of eigenvectors with synthetic gene expression data simulated with a basic and popular model of gene expression dynamics -- attractors of Boolean threshold dynamics in signed directed graphs. Eigenvectors of the graph Laplacian are known to explain collective dynamical states (stationary patterns) in Turing dynamics on graphs. In this study, we show that eigenvectors can also predict collective states (attractors) for a markedly different type of dynamics, Boolean threshold dynamics, and category of graphs, signed directed graphs. However, the overall predictive power depends on details of the network architecture, in a predictable fashion. Our results are a set of statistical observations, providing the first systematic step towards a further theoretical understanding of the role of eigenvectors in dynamics on graphs.