Dynamics of attractor transitions in Boolean networks under noise

TL;DR

This paper investigates how noise influences the transitions between attractors in Boolean networks, revealing distinct effects of local and global noise on system stability, dominance, and exploration of attractor states.

Contribution

It introduces methods to compute transition probabilities at the attractor level and systematically compares local and global noise effects on attractor dynamics.

Findings

Global noise causes attractor behavior based on basin sizes.

Local noise results in structured transition patterns and broader exploration.

Transition patterns under noise are crucial for understanding attractor dynamics.

Abstract

Biological systems operate under persistent noise, which can alter system states and induce transitions between attractors. Here, we study the attractor dynamics of Boolean networks focusing on the transitions between attractors induced by noise. By computing transition probabilities between attractors, we present methods at the attractor level to determine dominance, stability, and diversity of attractors, and systematically compare local and global noise. Whereas global noise leads to attractor behavior dictated primarily by basin sizes, local noise produces structured transition patterns characterized by enhanced stability, non-trivial dominance patterns, and broader exploration of the attractor space. Our work offers insight into the dynamics of attractors, showing the importance of transition patterns under noise.