Upper bound for the stability of Boolean networks

Boolean networks, inspired by gene regulatory networks, were developed to understand the complex behaviors observed in biological systems, with network attractors corresponding to biological phenotypes or cell types. In this article, we present a proof for a conjecture by Williadsen, Triesch and Wiles about upper bounds for the stability of basins of attraction in Boolean networks. We further extend this result from a single basin of attraction to the entire network. Specifically, we demonstrate that the asymptotic upper bound for the robustness and the basin entropy of a Boolean network are negatively linearly related.

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Publication Details

Journal
Physical review. E
Published
2025-09-08
DOI
https://doi.org/10.1103/1862-2fj1
Citations
1
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
0.81
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article

Upper bound for the stability of Boolean networks

Claus Kadelka, Reinhard Laubenbacher, Matthew Wheeler, Venkata Sai Narayana Bavisetty
1 citations
Physical review. E
Gene Regulatory Network Analysis
0.81
article

Upper bound for the stability of Boolean networks

Claus Kadelka, Reinhard Laubenbacher, Matthew Wheeler, Venkata Sai Narayana Bavisetty
article en
1 citations

Abstract

Boolean networks, inspired by gene regulatory networks, were developed to understand the complex behaviors observed in biological systems, with network attractors corresponding to biological phenotypes or cell types. In this article, we present a proof for a conjecture by Williadsen, Triesch and Wiles about upper bounds for the stability of basins of attraction in Boolean networks. We further extend this result from a single basin of attraction to the entire network. Specifically, we demonstrate that the asymptotic upper bound for the robustness and the basin entropy of a Boolean network are negatively linearly related.

Physical review. EVol. 112(4)
Iowa State University (US), University of Florida (US)
Openalex Percentile: Top 23%
Gene Regulatory Network Analysis
0.81
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