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Reinforcement Learning for Robust Dynamic Metabolic Control.

Sebastián Espinel-Ríos1, River Walser2, Dongda Zhang3

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Summary
This summary is machine-generated.

This study introduces a reinforcement learning framework for dynamic metabolic control in bioprocesses. It optimizes enzyme expression for enhanced flexibility and reproducibility, overcoming challenges in complex biological systems.

Keywords:
bioprocessdynamic metabolic controlmachine learningoptimizationreinforcement learningstochasticity

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Area of Science:

  • Biotechnology
  • Metabolic Engineering
  • Control Systems

Background:

  • Dynamic metabolic control enhances bioprocess flexibility by modulating metabolic fluxes in real-time.
  • Optimizing control policies is complex due to high-dimensional spaces, metabolic burden, and stochastic dynamics.

Purpose of the Study:

  • To develop a reinforcement learning (RL) framework for deriving optimal dynamic metabolic control policies.
  • To enhance bioprocess robustness and generalizability across uncertainties using domain randomization.

Main Methods:

  • Implemented a reinforcement learning agent interacting with a surrogate dynamic model.
  • Applied domain randomization to improve policy robustness against uncertainties.
  • Utilized forward model integration, simplifying control compared to traditional model-based methods.

Main Results:

  • Demonstrated the framework's effectiveness in two Escherichia coli bioprocesses.
  • Successfully applied dynamic control for fatty-acid synthesis (acetyl-CoA carboxylase) and lactate synthesis (adenosine triphosphatase).

Conclusions:

  • The RL framework offers a powerful alternative to conventional control methods for complex bioprocesses.
  • The approach simplifies control tasks by avoiding model differentiation and relying on forward integration.