Attractor Transitions in Boolean Networks

Drs. Byungjoon Min and Reinhard Laubenbacher are primary and senior author on a research article with Jeehye Choi in the journal Chaos.

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. Although 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.

Boolean networks under noise
(a) Wiring diagram of a Boolean network and its Boolean function. (b) State space of the Boolean network and its attractors. (c) Probabilities of remaining within the same attractor or transitioning to a different attractor due to local noise.