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Related Experiment Video

Updated: Jun 22, 2026

Analysis of Autophagy in Penicillium chrysogenum by Using Starvation Pads in Combination With Fluorescence Microscopy

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Cybernetic modeling based on pathway analysis for Penicillium chrysogenum fed-batch fermentation.

Jun Geng¹, Jingqi Yuan

¹Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, People's Republic of China. jungeng@sjtu.edu.cn

Bioprocess and Biosystems Engineering

|June 23, 2009

Summary

A new cybernetic model accurately describes Penicillium chrysogenum growth and penicillin production. This macrokinetic model, validated with industrial data, enhances understanding of bioreactor dynamics for improved fermentation processes.

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Area of Science:

Biotechnology
Biochemical Engineering
Systems Biology

Background:

Understanding microbial growth and product formation is crucial for optimizing fermentation processes.
Penicillium chrysogenum is a key organism for industrial penicillin production.
Existing models may not fully capture the complex dynamics of mycelium growth and secondary metabolite synthesis.

Purpose of the Study:

To develop a macrokinetic model using cybernetic methodology for mycelium growth and penicillin production.
To integrate metabolic flux analysis and cybernetic modeling for Penicillium chrysogenum.
To link the model's reaction rates with key bioreactor state variables.

Main Methods:

Utilized a cybernetic modeling framework and metabolic flux analysis based on the complete metabolic network of Penicillium chrysogenum.
Developed an abstracted cybernetic model to describe substrate consumption, intermediate assimilation, biomass growth, and penicillin formation rates.
Integrated the cybernetic model with a bioreactor model to link reaction rates with state variables (mycelium, substrate, product concentrations).
Employed the Simplex method for sensitive parameter estimation.
Validated the model using data from 20 industrial-scale penicillin cultivation batches.

Main Results:

The cybernetic model successfully describes the transient behavior of substrate consumption, biomass growth, and penicillin production.
Parameter estimation using the Simplex method identified sensitive model parameters.
Model validation with 20 industrial batches demonstrated its predictive capability for penicillin cultivation.

Conclusions:

The proposed macrokinetic cybernetic model provides a robust framework for describing Penicillium chrysogenum fermentation.
The model enhances the understanding of the interplay between microbial metabolism, growth, and product formation in bioreactors.
This approach offers potential for optimizing industrial penicillin production through improved process modeling and control.