Computable biology

• Analysis of the complete datasets require it.
• We require quantitative tests of pathway hypotheses.
• Complexity make qualitative arguments difficult.
• Analysis of crosstalk, stochastic events, complex feedback....
• Even when qualitative arguments work, we wish to understand why.
• Understand the phenomenon of 'robustness' in biological systems.

Computational models of biological systems

• Static structures, systems
• Differentiation, development
• Perturbation; mutations, treatments, conditions.

Models for description and prediction

• Explain the available data, at a given level of resolution.
• Predict the behaviour of a system after a given perturbation.
• Help to suggest new experimental designs.
• Help to identify 'weak spots', points of interference.