Nonlinear delay differential equations and their application to modeling biological network motifs

David S. Glass, Xiaofan Jin and Ingmar H. Riedel-Kruse ()
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David S. Glass: Weizmann Institute of Science
Xiaofan Jin: Gladstone Institutes
Ingmar H. Riedel-Kruse: University of Arizona

Nature Communications, 2021, vol. 12, issue 1, 1-19

Abstract: Abstract Biological regulatory systems, such as cell signaling networks, nervous systems and ecological webs, consist of complex dynamical interactions among many components. Network motif models focus on small sub-networks to provide quantitative insight into overall behavior. However, such models often overlook time delays either inherent to biological processes or associated with multi-step interactions. Here we systematically examine explicit-delay versions of the most common network motifs via delay differential equation (DDE) models, both analytically and numerically. We find many broadly applicable results, including parameter reduction versus canonical ordinary differential equation (ODE) models, analytical relations for converting between ODE and DDE models, criteria for when delays may be ignored, a complete phase space for autoregulation, universal behaviors of feedforward loops, a unified Hill-function logic framework, and conditions for oscillations and chaos. We conclude that explicit-delay modeling simplifies the phenomenology of many biological networks and may aid in discovering new functional motifs.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21700-8

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DOI: 10.1038/s41467-021-21700-8

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