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Light-Controlled Fermentations for Microbial Chemical and Protein Production

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Published on: March 22, 2022

Modeling fermentations with recombinant microorganisms: formulation of a structured model.

J Nielsen¹, A G Pedersen, K Strudsholm

¹Department of Biotechnology, Technical University of Denmark, DK-2800 Lyngby.

Biotechnology and Bioengineering

|April 15, 1991

Summary

A new compartment model accurately describes recombinant Escherichia coli fermentation dynamics. This model effectively captures intracellular changes and plasmid replication, including runaway scenarios.

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Area of Science:

Biotechnology
Microbial Fermentation
Systems Biology

Background:

Recombinant microorganisms, such as Escherichia coli, are crucial for biotechnological applications.
Understanding and modeling their fermentation processes is essential for optimizing production.
Dynamic changes in cellular components, particularly plasmid copy number, significantly impact fermentation outcomes.

Purpose of the Study:

To develop and validate a simple structured compartment model for recombinant Escherichia coli fermentations.
To assess the model's ability to describe observed experimental data.
To specifically evaluate the model's performance in capturing dynamic changes in plasmid copy number.

Main Methods:

Development of a compartment model with lumped intracellular variables representing biomass components.
Comparison of model predictions with experimental data from recombinant Escherichia coli fermentations.
Analysis of the model's capability to simulate dynamic changes, including plasmid replication.

Main Results:

The compartment model successfully describes a majority of experimental observations in recombinant Escherichia coli fermentations.
The model provides a biologically reasonable representation of cellular dynamics.
The model is particularly effective in describing dynamic changes in plasmid copy number, such as runaway replication.

Conclusions:

A simple structured compartment model offers a viable approach for understanding recombinant Escherichia coli fermentation.
The model's ability to predict dynamic changes, especially in plasmid copy number, is a key strength.
This modeling framework can aid in optimizing fermentation processes involving recombinant microorganisms.