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Message Passing and Metabolism.

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Summary
This summary is machine-generated.

Active inference models biological systems as solving inference problems, optimizing Bayesian model evidence. This framework is extended to biochemical networks, interpreting metabolic regulation as probabilistic state transitions.

Keywords:
Bayesianmaster equationsmessage passingmetabolismnon-equilibriumstochastic

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Area of Science:

  • Theoretical Biology
  • Biochemistry
  • Computational Neuroscience

Background:

  • Active inference frames biological system dynamics as solving an inference problem.
  • Living systems tend towards steady states, maximizing Bayesian model evidence.
  • This paradigm has been applied to neuronal networks and other systems.

Purpose of the Study:

  • To apply the active inference framework to biochemical networks.
  • To interpret metabolic regulation through the lens of probabilistic inference.
  • To explore the potential for understanding metabolic disorders as inferential errors.

Main Methods:

  • Reformulating master equations of biochemical systems in terms of their steady-state distribution.
  • Viewing biochemical reactions as movements of probability mass between categorical states.
  • Utilizing the steady-state distribution as a generative model for inferential interpretation.

Main Results:

  • Biochemical networks can be modeled using active inference principles.
  • Metabolic regulation can be understood as probabilistic state transitions.
  • Metabolic disorders may be characterized as aberrant inference.

Conclusions:

  • Active inference provides a unifying framework for understanding biological systems, including biochemistry.
  • This approach offers a novel perspective on metabolic regulation and disorders.
  • The framework suggests a link between computational processes and biological function.