On the feasibility of dynamical analysis of network models of biochemical regulation

TL;DR

This paper argues that dynamical analysis of large biochemical regulation network models is feasible and offers scalable methods, countering claims that such analysis is impractical due to system size.

Contribution

The paper clarifies misconceptions by demonstrating that scalable dynamical methods exist for analyzing large automata network models in systems biology.

Findings

Dynamical analysis of large automata networks is feasible.

Graph-based static analysis alone is insufficient for large models.

Scalable dynamical methods can effectively analyze biochemical regulation networks.

Abstract

A recent article by Weidner et al. [2021] presents a method to extract graph properties that are predictive of the dynamical behavior of multivariate, discrete models of biochemical regulation. In other words, a method that uses only features from the structure of network interactions to predict which nodes are most involved in automata network dynamics. However, the authors claim that dynamical analysis of large automata network models is "not even feasible." To make sure that others are not discouraged from working on this problem, it is important to clarify that effective dynamical analysis of automata network models, to the contrary, is feasible. Unlike what is suggested in the article, graph-based analysis of static features is not the only analytical avenue for large systems biology models of regulation and signaling dynamics because there are dynamical methods that are, indeed, scalable.