This repository contains code and data supporting the study of attractor-centric robustness in Boolean network models of gene regulatory systems. While most classical analyses assess robustness using random states (e.g., via Derrida plots or basin coherence), biological systems operate primarily at attractor states, which correspond to phenotypes or cell types. This work formalizes and investigates attractor coherence, a Kauffman-inspired stability measure quantifying the probability that a small perturbation at an attractor changes the system’s long-term behavior.
All figures underlying the corresponding publication "Attractors are less stable than their basins: Canalization creates a coherence gap in gene regulatory networks" by S. Bavisetty, M. Wheeler and C. Kadelka can be created using the code in this repository.