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Evolving multi-valued regulatory networks on tuneable fitness landscapes

Bull, Larry

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Authors

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor



Abstract

Random Boolean networks have been used widely to explore aspects of gene regulatory networks. As the name implies, traditionally the model has used a binary representation scheme. This paper uses a modified form of the model to systematically explore the effects of increasing the number of gene states. These random multi-valued networks are evolved within rugged fitness landscapes to explore their behavior. Results suggest the basic properties of the original model remain, regardless of the update scheme or fitness sampling method. Changes are seen in sensitivity to high levels of connectivity, the mutation rate and the ability to vary network size.

Journal Article Type Article
Acceptance Date Mar 3, 2023
Online Publication Date Dec 31, 2023
Publication Date Dec 31, 2023
Deposit Date Apr 28, 2023
Publicly Available Date Feb 1, 2024
Journal Complex Systems
Print ISSN 0891-2513
Peer Reviewed Peer Reviewed
Volume 32
Issue 3
Pages 289-307
DOI https://doi.org/10.25088/ComplexSystems.32.3.289
Keywords asynchronous; growth; mutation; NK model
Public URL https://uwe-repository.worktribe.com/output/10620997

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