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Related Experiment Videos

Dynamic compensation, parameter identifiability, and equivariances.

Eduardo D Sontag1

  • 1Department of Mathematics and Center for Quantitative Biology, Hill Center, Rutgers University, Piscataway, New Jersey, United States of America.

Plos Computational Biology
|April 7, 2017
PubMed
Summary

Dynamical compensation (DC) in biological systems is linked to parameter non-identifiability. This study reformulates DC testing using systems biology tools, offering new insights into physiological regulation.

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Area of Science:

  • Systems Biology
  • Control Theory
  • Mathematical Biology

Background:

  • Dynamical compensation (DC) is a key property in biological circuits, crucial for physiological regulation like glucose homeostasis.
  • Karin et al. previously defined DC and proposed a sufficient condition for its identification.
  • The existing methods for testing DC may lack broader theoretical integration.

Purpose of the Study:

  • To reformulate dynamical compensation (DC) using the established concept of parameter structural non-identifiability.
  • To leverage advanced theoretical and computational tools from systems biology, statistics, and control theory for DC analysis.
  • To provide a unified framework connecting DC with system equivalence and fold-change detection.

Main Methods:

  • Formulating dynamical compensation (DC) as a problem of parameter structural non-identifiability.

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  • Applying established techniques from systems biology, statistics, and control theory to analyze biological systems.
  • Deriving a general condition for DC and relating it to existing criteria.

    • Main Results:

    • Demonstrated that dynamical compensation (DC) is equivalent to parameter structural non-identifiability in biological systems.
    • Recovered the sufficient criterion proposed by Karin et al. as a special case of the new formulation.
    • Established connections between DC, system equivalence, and fold-change detection properties.

    Conclusions:

    • Parameter structural non-identifiability offers a powerful and versatile framework for analyzing dynamical compensation (DC).
    • This reformulation facilitates the application of advanced computational and theoretical tools to study biological regulation.
    • The findings deepen the understanding of physiological control mechanisms and their mathematical underpinnings.