PT  - JOURNAL ARTICLE
AU  - David S. Glass
AU  - Xiaofan Jin
AU  - Ingmar H. Riedel-Kruse
TI  - Nonlinear delay differential equations and their application to modeling biological network motifs
AID  - 10.1101/2020.08.02.233619
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.08.02.233619
4099  - http://biorxiv.org/content/early/2020/09/03/2020.08.02.233619.short
4100  - http://biorxiv.org/content/early/2020/09/03/2020.08.02.233619.full
AB  - Biological regulatory systems, such as transcription factor or kinase 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, conventional network motif models often ignore 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 equations (DDEs), both analytically and numerically. We find many broadly applicable results, such as the reduction in number of parameters compared to canonical descriptions via ordinary differential equations (ODE), criteria for when delays may be ignored, a complete phase space for autoregulation, explicit dependence of feedforward loops on a difference of delays, a unified framework for Hill-function logic, and conditions for oscillations and chaos. We emphasize relevance to biological function throughout our analysis, summarize key points in non-mathematical form, and conclude that explicit-delay modeling simplifies the phenomenological understanding of many biological networks and may aid in discovering new functional motifs.Competing Interest StatementThe authors have declared no competing interest.