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Relevant components in critical random Boolean networks

Kaufman, Viktor ; Drossel, Barbara (2024)
Relevant components in critical random Boolean networks.
In: New Journal of Physics, 2006, 8 (10)
doi: 10.26083/tuprints-00020624
Artikel, Zweitveröffentlichung, Verlagsversion

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Kurzbeschreibung (Abstract)

Random Boolean networks (RBNs) were introduced in 1969 by Kauffman as a model for gene regulation. By combining analytical arguments and efficient numerical simulations, we evaluate the properties of relevant components of critical RBNs independently of update scheme. As known from previous study, the number of relevant components grows logarithmically with network size. We find that in most networks all relevant nodes with more than one relevant input sit in the same component, while all other relevant components are simple loops. As the proportion of nonfrozen nodes with two relevant inputs increases, the number of relevant components decreases and the size and complexity of the largest complex component grows. We evaluate the probability distribution of different types of complex components in an ensemble of networks and confirm that it becomes independent of network size in the limit of large network size. In this limit, we determine analytically the frequencies of occurrence of complex components with different topologies.

Typ des Eintrags: Artikel
Erschienen: 2024
Autor(en): Kaufman, Viktor ; Drossel, Barbara
Art des Eintrags: Zweitveröffentlichung
Titel: Relevant components in critical random Boolean networks
Sprache: Englisch
Publikationsjahr: 13 Februar 2024
Ort: Darmstadt
Publikationsdatum der Erstveröffentlichung: 9 Oktober 2006
Ort der Erstveröffentlichung: London
Verlag: IOP Publishing
Titel der Zeitschrift, Zeitung oder Schriftenreihe: New Journal of Physics
Jahrgang/Volume einer Zeitschrift: 8
(Heft-)Nummer: 10
Kollation: 16 Seiten
DOI: 10.26083/tuprints-00020624
URL / URN: https://tuprints.ulb.tu-darmstadt.de/20624
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Herkunft: Zweitveröffentlichung DeepGreen
Kurzbeschreibung (Abstract):

Random Boolean networks (RBNs) were introduced in 1969 by Kauffman as a model for gene regulation. By combining analytical arguments and efficient numerical simulations, we evaluate the properties of relevant components of critical RBNs independently of update scheme. As known from previous study, the number of relevant components grows logarithmically with network size. We find that in most networks all relevant nodes with more than one relevant input sit in the same component, while all other relevant components are simple loops. As the proportion of nonfrozen nodes with two relevant inputs increases, the number of relevant components decreases and the size and complexity of the largest complex component grows. We evaluate the probability distribution of different types of complex components in an ensemble of networks and confirm that it becomes independent of network size in the limit of large network size. In this limit, we determine analytically the frequencies of occurrence of complex components with different topologies.

ID-Nummer: Artikel-ID: 228
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-206242
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 530 Physik
Fachbereich(e)/-gebiet(e): 05 Fachbereich Physik
05 Fachbereich Physik > Institut für Physik Kondensierter Materie (IPKM)
Hinterlegungsdatum: 13 Feb 2024 10:10
Letzte Änderung: 15 Feb 2024 13:51
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