Evolving multi-valued regulatory networks on tuneable fitness landscapes

Bull, Larry

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