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A feedback control principle common to several biological and engineered systems.

Jonathan Y Suen1, Saket Navlakha1

  • 1Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY, USA.

Journal of the Royal Society, Interface
|March 2, 2022
PubMed
Summary

Discrete-event feedback control, using additive-increase and multiplicative-decrease rules, optimizes biological and engineered systems. These strategies enhance efficiency, resource allocation, and response times in distributed systems.

Keywords:
ant coloniesbiological distributed algorithmscell sizefeedback controlnetworksneural circuits‌synaptic plasticity

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Area of Science:

  • Control Theory
  • Systems Biology
  • Distributed Computing

Background:

  • Traditional feedback control relies on continuous variable monitoring, which is resource-intensive.
  • Discrete-event feedback offers a cost-effective alternative by using event-based signals.
  • Optimizing discrete-event systems requires specialized strategies beyond traditional methods.

Purpose of the Study:

  • To investigate parallels between discrete-event feedback control in biological and engineered systems.
  • To identify common control rules employed across diverse biological and artificial systems.
  • To understand how these rules support system goals like efficiency and adaptation.

Main Methods:

  • Comparative analysis of feedback control strategies in biological and engineered systems.
  • Identification of specific control rules: additive-increase and multiplicative-decrease.
  • Case studies in harvester ant colonies, cell-size homeostasis, and neural circuits.

Main Results:

  • Two engineering rules (additive-increase, multiplicative-decrease) are prevalent in biological systems.
  • These rules are observed in ant foraging, cell-size regulation, and neural adaptation.
  • The rules effectively optimize efficiency, resource sharing, and response latency.

Conclusions:

  • Biological systems utilize discrete-event feedback control strategies analogous to engineering approaches.
  • These shared strategies highlight universal principles of adaptation and optimization in distributed systems.
  • Cross-disciplinary insights can advance both biological understanding and engineering algorithms.