Guiding the Self-organization of Random Boolean Networks

Overview

Journal Theory Biosci

Specialty Biology

Date 2011 Dec 1

PMID 22127955

Citations 19

Authors

Carlos Gershenson

Affiliations

Soon will be listed here.

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.

Citing Articles

Biologically meaningful regulatory logic enhances the convergence rate in Boolean networks and bushiness of their state transition graph.

Sil P, Subbaroyan A, Kulkarni S, Martin O, Samal A Brief Bioinform. 2024; 25(3).

PMID: 38581421 PMC: 10998641. DOI: 10.1093/bib/bbae150.

Federated inference and belief sharing.

Friston K, Parr T, Heins C, Constant A, Friedman D, Isomura T Neurosci Biobehav Rev. 2023; 156:105500.

PMID: 38056542 PMC: 11139662. DOI: 10.1016/j.neubiorev.2023.105500.

The role of neuronal plasticity in cervical spondylotic myelopathy surgery: functional assessment and prognostic implication.

Bonosi L, Musso S, Cusimano L, Porzio M, Giovannini E, Benigno U Neurosurg Rev. 2023; 46(1):149.

PMID: 37358655 PMC: 10293440. DOI: 10.1007/s10143-023-02062-9.

Leveraging quantum computing for dynamic analyses of logical networks in systems biology.

Weidner F, Schwab J, Wolk S, Rupprecht F, Ikonomi N, Werle S Patterns (N Y). 2023; 4(3):100705.

PMID: 36960443 PMC: 10028428. DOI: 10.1016/j.patter.2023.100705.

Temporal, Structural, and Functional Heterogeneities Extend Criticality and Antifragility in Random Boolean Networks.

Lopez-Diaz A, Sanchez-Puig F, Gershenson C Entropy (Basel). 2023; 25(2).

PMID: 36832621 PMC: 9955688. DOI: 10.3390/e25020254.

References

Edelman G, Gally J . Degeneracy and complexity in biological systems. Proc Natl Acad Sci U S A. 2001; 98(24):13763-8. PMC: 61115. DOI: 10.1073/pnas.231499798. View

Watts D, Strogatz S . Collective dynamics of 'small-world' networks. Nature. 1998; 393(6684):440-2. DOI: 10.1038/30918. View

Balleza E, Alvarez-Buylla E, Chaos A, Kauffman S, Shmulevich I, Aldana M . Critical dynamics in genetic regulatory networks: examples from four kingdoms. PLoS One. 2008; 3(6):e2456. PMC: 2423472. DOI: 10.1371/journal.pone.0002456. View

Stern M . Emergence of homeostasis and "noise imprinting" in an evolution model. Proc Natl Acad Sci U S A. 1999; 96(19):10746-51. PMC: 17954. DOI: 10.1073/pnas.96.19.10746. View

van Nimwegen E, Crutchfield J, Huynen M . Neutral evolution of mutational robustness. Proc Natl Acad Sci U S A. 1999; 96(17):9716-20. PMC: 22276. DOI: 10.1073/pnas.96.17.9716. View

Lizier J, Pritam S, Prokopenko M . Information dynamics in small-world Boolean networks. Artif Life. 2011; 17(4):293-314. DOI: 10.1162/artl_a_00040. View

ASHBY W . Principles of the self-organizing dynamic system. J Gen Psychol. 2010; 37(2):125-8. DOI: 10.1080/00221309.1947.9918144. View

Serra R, Villani M, Semeria A . Genetic network models and statistical properties of gene expression data in knock-out experiments. J Theor Biol. 2004; 227(1):149-57. DOI: 10.1016/j.jtbi.2003.10.018. View

Wagner G, Altenberg L . PERSPECTIVE: COMPLEX ADAPTATIONS AND THE EVOLUTION OF EVOLVABILITY. Evolution. 2017; 50(3):967-976. DOI: 10.1111/j.1558-5646.1996.tb02339.x. View

10.

Wagner A . Distributed robustness versus redundancy as causes of mutational robustness. Bioessays. 2005; 27(2):176-88. DOI: 10.1002/bies.20170. View

11.

Kauffman S, Peterson C, Samuelsson B, Troein C . Genetic networks with canalyzing Boolean rules are always stable. Proc Natl Acad Sci U S A. 2004; 101(49):17102-7. PMC: 534611. DOI: 10.1073/pnas.0407783101. View

12.

Huang S, Ingber D . Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks. Exp Cell Res. 2000; 261(1):91-103. DOI: 10.1006/excr.2000.5044. View

13.

Whitacre J, Bender A . Degeneracy: a design principle for achieving robustness and evolvability. J Theor Biol. 2009; 263(1):143-53. DOI: 10.1016/j.jtbi.2009.11.008. View

14.

Kauffman S, Peterson C, Samuelsson B, Troein C . Random Boolean network models and the yeast transcriptional network. Proc Natl Acad Sci U S A. 2003; 100(25):14796-9. PMC: 299805. DOI: 10.1073/pnas.2036429100. View

15.

Kauffman S . Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol. 1969; 22(3):437-67. DOI: 10.1016/0022-5193(69)90015-0. View

16.

Riegler A . Natural or internal selection? The case of canalization in complex evolutionary systems. Artif Life. 2008; 14(3):345-62. DOI: 10.1162/artl.2008.14.3.14308. View

17.

Andrade Jr J, Herrmann H, Andrade R, da Silva L . Apollonian networks: simultaneously scale-free, small world, euclidean, space filling, and with matching graphs. Phys Rev Lett. 2005; 94(1):018702. DOI: 10.1103/PhysRevLett.94.018702. View

18.

Prokopenko M . Guided self-organization. HFSP J. 2010; 3(5):287-9. PMC: 2801529. DOI: 10.1080/19552068.2009.9635816. View

19.

Shmulevich I, Kauffman S . Activities and sensitivities in boolean network models. Phys Rev Lett. 2004; 93(4):048701. PMC: 1490311. DOI: 10.1103/PhysRevLett.93.048701. View

20.

Aldana M, Cluzel P . A natural class of robust networks. Proc Natl Acad Sci U S A. 2003; 100(15):8710-4. PMC: 166377. DOI: 10.1073/pnas.1536783100. View