Abstract
Systems Biology starts by defining the components of a biological system and collecting the relevant previous biochemical and genetic data on a global scale, using high throughput platforms, formulating an initial model of the system, systematically perturbing the components of the system, and analysing the results. By comparing the observed responses to those predicted by the model, it is then possible to iteratively refine the model so that its prediction fit best to the experimental observations. Finally, new experimental perturbations are conceived and tested in order to distinguish between the multiple competing hypotheses.We discuss the computational methods used for high throughput data collection in functional genomics, emphasizing the strong need for standardization and quality assurance. We then review the computational needs required for biological system modeling and semantic integration in the systemic framework, arguing that the Unified Modeling Language (UML) seems appropriate to support the iterative process of Systems Biology.
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