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Computational systems biology in drug discovery and development: methods and applications
Wayne Materi1, David S Wishart
1National Research Council, National Institute for Nanotechnology (NINT) Edmonton, Alberta, Canada T6G 2E8.
Drug Discovery Today
|March 31, 2007
Summary
Computational systems biology uses diverse simulation techniques to model biological processes from genomic data. These computational methods aid in understanding complex biological systems and accelerate drug discovery and development.
Area of Science:
- Computational systems biology
- Biological simulation
- Multiscale modeling
Background:
- Emerging field integrating experimental data (genomics, proteomics, metabolomics) for biological simulation.
- Need for modeling complex temporal and spatiotemporal biological processes across various scales.
Purpose of the Study:
- Review and assess various computational modeling techniques.
- Highlight applications of these methods in drug discovery and development.
Main Methods:
- Systems of differential equations
- Petri nets
- Cellular automata simulators
- Agent-based models
- Pi calculus
Main Results:
- Summary and assessment of key computational systems biology approaches.
- Examples of successful application in drug discovery pipelines.
Conclusions:
- Computational systems biology offers powerful tools for biological simulation.
- These modeling techniques are crucial for advancing drug discovery and development.
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