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Cited by 68Cellular Automata and ApplicationsGene Regulatory Network AnalysisComplex Network Analysis Techniques

Guiding the self-organization of random Boolean networks

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Gershenson, Carlos·2012·Theory in Biosciences
DOI 10.1007/s12064-011-0144-xPMC PMC3414703
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Abstract

Random Boolean networks (RBNs) are models of genetic regulatory networks. It is useful to describe RBNs as self-organizing systems to study how changes in the nodes and connections affect the global network dynamics. This article reviews eight different methods for guiding the self-organization of RBNs. In particular, the article is focused on guiding RBNs toward the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases. The properties and advantages of the critical regime for life, computation, adaptability, evolvability, and robustness are reviewed. The guidance methods of RBNs can be used for engineering systems with the features of the critical regime, as well as for studying how natural selection evolved living systems, which are also critical.

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  • A successful controller should have A > 1, although the controller will be more efficient if A approaches 1.

    “Thus, a successful controller should have A > 1 (at multiple scales (Gershenson, 2011)), although the controller will be more efficient if A → 1, assuming that higher complexities have higher costs.”

    From Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis (2013) · cites this paper as Gershenson, 2011

    The paper’s “Why criticality?” section discusses the benefits of balancing stability and change in random Boolean networks. Searches for controllers, A > 1, multiple scales, and complexity costs found no passage establishing the cited controller threshold or the claimed efficiency as A approaches 1. (LLM, conf=0.76)

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Authors
Gershenson, C.
Affiliations
Universidad Nacional Autónoma de México (MX)
Corresponding author
Gershenson, Carlos
ORCID
Gershenson, Carlos
Venue
Theory in Biosciences
Publisher
Springer Science and Business Media LLC
Published
2011-11-30
Volume
131
Issue
3
Pages
181-191
ISSN
  • 1431-7613 (ISSN-L)
  • 1611-7530
DOI
10.1007/s12064-011-0144-x
PMC
PMC3414703
OpenAlex
W1997521991
Cited by
68
References
5
Citations by year
2026: 1 · 2025: 4 · 2024: 5 · 2023: 7 · 2022: 8 · 2021: 2 · 2020: 5 · 2019: 5 · 2018: 4 · 2017: 1 · 2016: 2 · 2015: 3 · 2014: 8 · 2013: 7 · 2012: 3
FWCI
0.62
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Theory in Biosciences (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Cellular Automata and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
random Boolean networks, self-organization, gene regulatory networks, phase transitions, network dynamics, evolvability, robustness, adaptability, natural selection
MeSH terms
Models, Genetic, Selection, Genetic, Gene Regulatory Networks

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