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  3. Toward A Modeling, Optimization, And Predictive Control Framework For Fed-batch Metabolic Cybergenetics.
  1. Home
  3. Toward A Modeling, Optimization, And Predictive Control Framework For Fed-batch Metabolic Cybergenetics.

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Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics.

Sebastián Espinel-Ríos1, Bruno Morabito2, Johannes Pohlodek3

  • 1Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.

Biotechnology and Bioengineering
|November 9, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

Cybergenetics combined with model-based control optimizes bioprocesses. This approach uses dynamic models and feedback control for improved product yield and productivity in biotechnology manufacturing.

Keywords:
constraint-based modelingdynamic metabolic controlmetabolic cybergeneticsmodel predictive controloptogeneticsstate estimation

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Area of Science:

  • Biotechnology and synthetic biology
  • Process engineering and control systems

Background:

  • Traditional bioprocess optimization relies on static methods, often failing under dynamic conditions or disturbances.
  • Extracellular factors are insufficient for optimal performance and precise product composition.
  • Cybergenetics enables dynamic gene expression control, offering new optimization possibilities.

Purpose of the Study:

  • To integrate cybergenetics with model-based optimization and predictive control for dynamic bioprocesses.
  • To develop dynamic constraint-based models incorporating metabolic reactions, resource allocation, and gene expression.
  • To enhance fed-batch process control through optimal substrate feeding rates.

Main Methods:

  • Formulating a model-based optimal control problem for process inputs.
  • Implementing model predictive control for online feedback and uncertainty management.
  • Utilizing dynamic constraint-based models for metabolic and genetic regulation.

    • Main Results:

    • Demonstrated successful optogenetic control of ATPase enzyme complex for dynamic ATP wasting.
    • Showcased adjustment of product yield and productivity through controlled metabolic modulation.
    • Validated the model-based predictive control strategy in a simulated fed-batch process.

    Conclusions:

    • The fusion of cybergenetics, model-based optimization, and predictive control offers a powerful framework for dynamic bioprocess optimization.
    • This integrated approach enhances control over gene expression and metabolic pathways, leading to improved biomanufacturing outcomes.
    • Future applications include precise control of product composition and yield in complex biotechnological systems.