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SysBioMed report: advancing systems biology for medical applications
O Wolkenhauer1, D Fell, P De Meyts
1University of Rostock, Rostock, Germany. olaf.wolkenhauer@informatik.uni-rostock.de
IET Systems Biology
|May 20, 2009
Summary
Mathematical modelling and computational models are crucial for understanding complex biological systems and diseases. Integrating data through these approaches requires better infrastructure, training, and funding, especially in medical sciences.
Area of Science:
- Systems Biology
- Computational Biology
- Medical Informatics
Background:
- Systems biology utilizes mathematical modelling and bioinformatics to understand complex biological phenomena.
- Dynamical systems theory provides a framework for investigating nonlinear biological systems.
- Integrating diverse data sources is essential for creating comprehensive computational models.
Purpose of the Study:
- To summarize conclusions and recommendations from workshops on mathematical modelling in systems biology.
- To highlight the importance of mathematical modelling for integrating data and gaining insights into complex diseases.
- To identify key areas for action in medical systems biology.
Main Methods:
- Mathematical modelling and simulation of subcellular, cellular, and macro-scale phenomena using dynamical systems theory.
- Integration of mathematical models with bioinformatics resources to create computational models.
- Analysis of existing European Medical Systems Biology projects to identify needs and challenges.
Main Results:
- Mathematical modelling aids hypothesis generation, reveals hidden patterns, and integrates biological/clinical information.
- A gap exists in the adoption of mathematical modelling and interdisciplinary collaboration within medical sciences compared to biological sciences.
- Current academic funding schemes and pharmaceutical industry investment require reconsideration for advancing Medical Systems Biology.
Conclusions:
- Mathematical modelling is a vital tool for advancing medical systems biology and understanding complex diseases.
- Enhanced training, infrastructure, and funding are necessary to support mathematical modelling in medical sciences.
- Greater recognition and promotion of mathematical modelling by medical journals and industry are crucial for progress.
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