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Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments
1Biomodeling and Bioinformatics, Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands. N.A.W.v.Riel@tue.nl
Briefings in Bioinformatics
|November 17, 2006
Summary
Systems biology uses quantitative models to understand biological networks. Choosing the right model framework and using parameter analysis improves predictive power for these complex systems.
Area of Science:
- Systems biology
- Computational biology
- Biochemical modeling
Background:
- Systems biology integrates quantitative, mechanistic modeling for biological networks like genetic, signal transduction, and metabolic pathways.
- Mathematical models in biochemical networks vary significantly based on their intended purpose and application.
- Selecting an appropriate modeling framework and developing a robust model-building strategy are crucial.
Purpose of the Study:
- To discuss fundamental aspects of selecting modeling frameworks and strategies for systems biology.
- To explore the application of systems and control theory in developing computational tools for systems biology.
- To review methods for identifying critical network components and rate constants influencing system behavior.
Main Methods:
- Discusses fundamental aspects of selecting modeling frameworks and model-building strategies.
- Reviews current approaches and methods for parameter sensitivity analysis and estimation.
- Highlights the application of these methods in designing model-based experiments.
Main Results:
- Parameter sensitivity analysis and estimation are key to identifying critical network components and rate constants.
- Model-based experiments iteratively refine models, enhancing their accuracy and predictive capabilities.
- Systems and control theory offer a foundation for advanced computational tools in systems biology.
Conclusions:
- The choice of mathematical framework is dictated by the model's purpose and application in systems biology.
- Parameter sensitivity analysis and estimation are essential for understanding and improving biological network models.
- Iterative model-based experimentation leads to progressively accurate and predictive models in systems biology.
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