Homeostasis in networks with multiple inputs

Research output: Contribution to journalArticlepeer-review

Abstract

Homeostasis, also known as adaptation, refers to the ability of a system to counteract persistent external disturbances and tightly control the output of a key observable. Existing studies on homeostasis in network dynamics have mainly focused on ‘perfect adaptation’ in deterministic single-input single-output networks where the disturbances are scalar and affect the network dynamics via a pre-specified input node. In this paper we provide a full classification of all possible network topologies capable of generating infinitesimal homeostasis in arbitrarily large and complex multiple inputs networks. Working in the framework of ‘infinitesimal homeostasis’ allows us to make no assumption about how the components are interconnected and the functional form of the associated differential equations, apart from being compatible with the network architecture. Remarkably, we show that there are just three distinct ‘mechanisms’ that generate infinitesimal homeostasis. Each of these three mechanisms generates a rich class of well-defined network topologies—called homeostasis subnetworks. More importantly, we show that these classes of homeostasis subnetworks provides a topological basis for the classification of ‘homeostasis types’: the full set of all possible multiple inputs networks can be uniquely decomposed into these special homeostasis subnetworks. We illustrate our results with some simple abstract examples and a biologically realistic model for the co-regulation of calcium (Ca) and phosphate (PO 4) in the rat. Furthermore, we identify a new phenomenon that occurs in the multiple input setting, that we call homeostasis mode interaction, in analogy with the well-known characteristic of multiparameter bifurcation theory.

Original languageEnglish
Article number17
Number of pages40
JournalJournal of Mathematical Biology
Volume89
Issue number2
Early online date20 Jun 2024
DOIs
Publication statusE-pub ahead of print - 20 Jun 2024

Keywords

  • 37N25
  • 92C42 (primary)
  • 92C45
  • Biochemical networks
  • Combinatorial matrix theory
  • Coupled systems
  • Homeostasis
  • Input–output networks
  • Perfect adaptation

ASJC Scopus subject areas

  • Applied Mathematics
  • Agricultural and Biological Sciences (miscellaneous)
  • Modelling and Simulation

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