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Updated: May 8, 2026
13:19
The Use of Chemostats in Microbial Systems Biology
Published on: October 14, 2013
Systematic methodology for the development of mathematical models for biological processes.
1Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, UK.
Methods in Molecular Biology (Clifton, N.J.)
|September 3, 2013
Summary
This study introduces a systematic methodology for creating reliable predictive models in synthetic biology. It reduces experimental costs for in silico simulation and optimization of cellular functions.
Area of Science:
- Synthetic biology
- Systems biology
- Biotechnology
Background:
- Synthetic biology enables rational redesign of cellular functions.
- Model-based tools are crucial for designing biological networks.
- Model reliability is essential for successful synthetic biology applications.
Purpose of the Study:
- To present a systematic methodology for developing reliable predictive models.
- To enable efficient in silico simulation and optimization of engineered biological systems.
- To minimize experimental costs in model development.
Main Methods:
- Model formulation and consideration of engineering principles.
- Global sensitivity analysis for model understanding.
- Model reduction for complex systems or limited data.
- Optimal experimental design for parameter estimation.
- Predictive capability checking for model validation.
Main Results:
- Demonstrated efficacy and validity of the methodology using a bioprocessing example.
- Systematized the development of reliable mathematical models.
- Enabled in silico simulation and optimization with reduced experimental effort.
Conclusions:
- The proposed methodology enhances the reliability of predictive models in synthetic biology.
- This approach facilitates cost-effective development and application of computational models.
- It supports the rational design and optimization of engineered cellular functions.
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