Graphical characterizations of robust stability in biological interaction networks

TL;DR

This paper introduces graphical criteria to assess the robust stability of biological interaction networks, enabling intuitive and scalable analysis of large nonlinear systems without complex computations.

Contribution

It provides novel graphical methods for stability analysis, including criteria for stability certificates, non-degeneracy, persistence, and global stability, applicable to large biological networks.

Findings

Graphical criteria effectively determine stability in large networks.

Stability-preserving graph modifications include enzymatic motifs.

Applications to biological motifs demonstrate practical utility.

Abstract

Previous studies have inferred robust stability of reaction networks by utilizing linear programs or iterative algorithms. Such algorithms become tedious or computationally infeasible for large networks. In addition, they operate like black-boxes without offering intuition for the structures that are necessary to maintain stability. In this work, we provide several graphical criteria for constructing robust stability certificates, checking robust non-degeneracy, {verifying persistence}, and establishing global stability. By characterizing a set of stability-preserving graph modifications that includes the enzymatic modification motif, we show that the stability of arbitrarily large nonlinear networks can be examined by simple visual inspection. We show applications of this technique to ubiquitous motifs in systems biology such as Post-Translational Modification (PTM) cycles, the Ribosome Flow Model (RFM), T-cell kinetic proofreading and others.