Critical assessment of the ability of Boolean threshold models to describe gene regulatory network dynamics.

Abstract

The inference of gene regulatory networks (GRNs) from high-throughput data constitutes a fundamental and challenging task in systems biology. Boolean networks are a popular modeling framework to understand the dynamic nature of GRNs. In the absence of reliable methods to infer the regulatory logic of Boolean GRN models, researchers frequently assume threshold logic as a default. Using the largest repository of published expert-curated Boolean GRN models as best proxy of reality, we systematically compare the ability of two popular threshold formalisms, the Ising and the 01 formalism, to truthfully recover biological functions and biological system dynamics. While Ising rules match fewer biological functions exactly than 01 rules, they yield a better average agreement. In general, more complex regulatory logic proves harder to be represented by either threshold formalism. Informed by these results and a meta-analysis of regulatory logic, we propose modified versions for both formalisms, which provide a better function-level and dynamic agreement with biological GRN models than the usual threshold formalisms. For small biological GRN models with low connectivity, corresponding threshold networks exhibit similar dynamics. However, they generally fail to recover the dynamics of large networks or highly-connected networks. In conclusion, this study provides new insights into an important question in computational systems biology: how truthfully do Boolean threshold networks capture the dynamics of GRNs? Significance statementGene regulatory networks (GRNs) describe the complex interactions between genes. GRNs control biological processes, respond to environmental changes, and contribute to diseases. An accurate understanding of the dynamics of GRNs is therefore crucial. Given insufficient data, researchers frequently employ Boolean network models with default threshold logic to study the dynamics of GRNs. We systematically assess the ability of two commonly-used types of threshold networks to recover the true regulatory logic and the dynamics of published expert-curated Boolean GRN models. Inspired by biological insights, we further propose modifications to each threshold formalism that improve their match with biological networks.