Dynamical compensation in physiological circuits

Omer Karin, Avital Swisa, Benjamin Glaser, Yuval Dor, Uri Alon

Research output: Contribution to journalArticlepeer-review

Abstract

Biological systems can maintain constant steady-state output despite variation in biochemical parameters, a property known as exact adaptation. Exact adaptation is achieved using integral feedback, an engineering strategy that ensures that the output of a system robustly tracks its desired value. However, it is unclear how physiological circuits also keep their output dynamics precise-including the amplitude and response time to a changing input. Such robustness is crucial for endocrine and neuronal homeostatic circuits because they need to provide a precise dynamic response in the face of wide variation in the physiological parameters of their target tissues; how such circuits compensate their dynamics for unavoidable natural fluctuations in parameters is unknown. Here, we present a design principle that provides the desired robustness, which we call dynamical compensation (DC). We present a class of circuits that show DC by means of a nonlinear feedback loop in which the regulated variable controls the functional mass of the controlling endocrine or neuronal tissue. This mechanism applies to the control of blood glucose by insulin and explains several experimental observations on insulin resistance. We provide evidence that this mechanism may also explain compensation and organ size control in other physiological circuits.

Original languageEnglish
Article number886
Number of pages7
JournalMolecular Systems Biology
Volume12
Issue number11
DOIs
StatePublished - 1 Nov 2016

Keywords

  • calcium homeostasis
  • dynamical compensation
  • endocrine circuits
  • glucose homeostasis
  • mathematical models of disease

All Science Journal Classification (ASJC) codes

  • Information Systems
  • General Immunology and Microbiology
  • Applied Mathematics
  • General Biochemistry,Genetics and Molecular Biology
  • General Agricultural and Biological Sciences
  • Computational Theory and Mathematics

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