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Updated: Jul 27, 2026
08:15
Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells
Published on: September 28, 2018
Metabolic systems modeling for cell factories improvement.
Po-Wei Chen1, Matthew K Theisen1, James C Liao2
1Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States.
Current Opinion in Biotechnology
|April 8, 2017
Summary
This study reviews systems biology modeling techniques for enhancing microbial bioproduction. It highlights methods like stoichiometry with kinetics and random sampling to address pathway robustness for metabolic engineering applications.
Area of Science:
- Systems biology
- Metabolic engineering
- Biotechnology
Background:
- Microbial bioproduction modeling has a long history.
- Recent advancements focus on improving bioproduction efficiency.
- Systems biology offers diverse modeling methodologies.
Purpose of the Study:
- To survey recent literature on modeling approaches for microbial bioproduction.
- To focus on techniques that improve bioproduction outcomes.
- To discuss the role of pathway robustness and databases in metabolic engineering.
Main Methods:
- Review of literature on systems biology modeling techniques.
- Analysis of methodologies ranging from stoichiometry-only to stoichiometry with kinetics.
- Exploration of random sampling techniques for addressing unknown kinetic parameters.
Main Results:
- Modeling techniques have evolved significantly, incorporating kinetics and random sampling.
- Pathway robustness is identified as a key challenge in metabolic engineering.
- Databases play an increasingly important role in biological and biotechnological applications.
Conclusions:
- Advanced modeling techniques are crucial for optimizing microbial bioproduction.
- Addressing pathway robustness is essential for metabolic engineering success.
- Leveraging biological databases can drive innovation in biotechnology.
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