Dynamics of attractor transitions in Boolean networks under noise.

Byungjoon Min, Jeehye Choi, Reinhard Laubenbacher

Abstract read

In one paragraph

Article in Chaos (Woodbury, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks.NPJ systems biology and applications · 2026
Article

4 · The record

Corrections and comments

Update of
Attractor Stability of Boolean networks under noise.2025

5 · Who and what money

Authors and funding

3 authors.

Byungjoon MinDepartment of Physics, Chungbuk National University, Cheongju, Chungbuk 28644, South Korea.ORCID 0000-0003-4853-2279

Jeehye ChoiAdvanced-Basic-Convergence Research Institute, Chungbuk National University, Cheongju, Chungbuk 28644, South Korea.ORCID 0000-0002-9363-2511

Reinhard LaubenbacherDepartment of Medicine, University of Florida, Gainesville, Florida 32610, USA.ORCID 0000-0002-9143-9451

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

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.

Identifiers

PMID41729092

DOI10.1063/5.0286088

PMCPMC13629661

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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.