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Systematic integration of experimental data and models in systems biology
Peter Li1, Joseph O Dada, Daniel Jameson
1School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk
BMC Bioinformatics
|December 1, 2010
Summary
Automated Taverna workflows integrate diverse data to build quantitative metabolic network models. This facilitates rapid analysis of biochemical systems, aiding systems biology research.
Area of Science:
- Systems Biology
- Computational Biology
- Biochemistry
Background:
- Mathematical models are crucial for understanding biological systems.
- Model construction requires integrating diverse data on components, reactions, and parameters.
- Automating model assembly necessitates robust data integration and resource interoperability.
Purpose of the Study:
- To develop automated workflows for constructing quantitative, parameterized metabolic networks in Systems Biology Markup Language (SBML).
- To evaluate the workflows' effectiveness in building a model of yeast glycolysis.
Main Methods:
- Developed Taverna workflows for automated SBML model assembly.
- Utilized MIRIAM-compliant yeast metabolism data for qualitative network construction.
- Integrated experimental data from SABIO-RK and quantitative results databases for parameterization.
- Employed COPASIWS web services for model calibration and simulation.
Main Results:
- Successfully constructed a parameterized SBML model of yeast glycolysis using the developed workflows.
- Demonstrated the systematic assembly of metabolic networks from qualitative to quantitative stages.
- Validated the integration of diverse data sources and computational tools.
Conclusions:
- Standardized metabolic reaction data (MIRIAM) enables automated construction of quantitative systems biology models.
- Taverna workflows facilitate efficient data integration for rapid biochemical system analysis.
- This approach provides a scalable method for building and analyzing complex biological models.
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