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Updated: Jul 11, 2025

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

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Published on: December 15, 2017

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

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 modeling dynamic metabolic control metabolic cybergenetics model predictive control optogenetics state 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.