ArticleJournal of mathematical biology2022
A homeostasis criterion for limit cycle systems based on infinitesimal shape response curves.
Article in Journal of mathematical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Tradeoffs in the energetic value of neuromodulation in a closed-loop neuromechanical system.Journal of theoretical biology · 2025Article
- Variational analysis of sensory feedback mechanisms in powerstroke-recovery systems.Biological cybernetics · 2024Article
- Homeostasis in networks with multiple inputs.Journal of mathematical biology · 2024Article
- Reliability and robustness of oscillations in some slow-fast chaotic systems.Chaos (Woodbury, N.Y.) · 2023Article
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2 authors.
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Abstract
Homeostasis occurs in a control system when a quantity remains approximately constant as a parameter, representing an external perturbation, varies over some range. Golubitsky and Stewart (J Math Biol 74(1-2):387-407, 2017) developed a notion of infinitesimal homeostasis for equilibrium systems using singularity theory. Rhythmic physiological systems (breathing, locomotion, feeding) maintain homeostasis through control of large-amplitude limit cycles rather than equilibrium points. Here we take an initial step to study (infinitesimal) homeostasis for limit-cycle systems in terms of the average of a quantity taken around the limit cycle. We apply the "infinitesimal shape response curve" (iSRC) introduced by Wang et al. (SIAM J Appl Dyn Syst 82(7):1-43, 2021) to study infinitesimal homeostasis for limit-cycle systems in terms of the mean value of a quantity of interest, averaged around the limit cycle. Using the iSRC, which captures the linearized shape displacement of an oscillator upon a static perturbation, we provide a formula for the derivative of the averaged quantity with respect to the control parameter. Our expression allows one to identify homeostasis points for limit cycle systems in the averaging sense. We demonstrate in the Hodgkin-Huxley model and in a metabolic regulatory network model that the iSRC-based method provides an accurate representation of the sensitivity of averaged quantities.
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