Attractor Stability of Boolean networks under noise.

Byungjoon Min, Jeehye Choi, Reinhard Laubenbacher

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Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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0 citing papers in PubMed.

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4 · The record

Corrections and comments

Updated by
Dynamics of attractor transitions in Boolean networks under noise.2026

5 · Who and what money

Authors and funding

3 authors.

Byungjoon MinDepartment of Physics, Chungbuk National University, Cheongju, Chungbuk 28644, Korea.

Jeehye ChoiAdvanced-Basic-Convergence Research Institute, Chungbuk National University, Cheongju, Chungbuk 28644, Korea.

Reinhard LaubenbacherDepartment of Medicine, University of Florida, Gainesville, FL 32610, USA.

Funding

Multiscale modeling of the role of heme during invasive pulmonary aspergillosisR01AI135128 · NIAID · UNIVERSITY OF FLORIDA · PI REINHARD LAUBENBACHER, Borna Mehrad · 2018 to 2026

$4.9M

Mechanistic modeling of the innate immune responses of the human lung to understand the inter-individual heterogeneity of COVID-19 pneumoniaR01HL169974 · NHLBI · UNIVERSITY OF FLORIDA · PI REINHARD LAUBENBACHER, Borna Mehrad · 2023 to 2026

$2.9M

NHLBI NIH HHS R01 HL169974NIAID NIH HHS R01 AI135128

6 · The paper itself

Abstract

We study the impact of noise on attractor dynamics in Boolean networks, focusing on their stability and transition behaviors. By constructing attractor matrices based on single-node perturbations, we propose a framework to quantify attractor stability and identify dominant attractors. We find that attractors are more stable than predicted by basin sizes, showing the importance of dynamical structure in noisy environments. In addition, under global perturbations, basin sizes dictate long-term behavior; under local noise, however, attractor dominance is determined by noise-induced transition patterns rather than basin sizes. Our results show that transition dynamics induced by stochastic perturbations provide an efficient and quantitative description for the attractor stability and dynamics in Boolean networks under noise.

Identifiers

PMID40980768

PMCPMC12447722

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.