Nonlinear signaling on biological networks: the role of stochasticity and spectral clustering

TL;DR

This paper explores how the architecture of biological signaling networks influences their nonlinear, bistable behavior, revealing that spectral properties of the network determine critical transition points and activation hierarchies.

Contribution

It demonstrates that spectral analysis of network adjacency matrices predicts bistability onset and activation sequences in biological signaling networks.

Findings

Bistability emerges at a critical spectral coupling strength.

Eigenvector localization affects the nature of the bistable transition.

Spectral properties determine activation hierarchy and stochastic transition nodes.

Abstract

Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switch-like properties which are critical to their biological function. In this study, we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition, the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector.