Simulating what cannot be simulated
نویسنده
چکیده
The background to these notes is the expectation that mathematical modelling and simulation is going to play increasingly important role in the understanding of the organization and control of genetic-, metabolic-, and signalling pathways. I am going to argue that for modelling and simulation to help our understanding of cellular dynamics, the current practice of experimental design has to change away from a ‘mining’ approach towards a signaland systems-oriented methodologies. The emergence of systems biology has therefore as much to do with the development of new techniques as it is relying on a new ‘way of thinking’ about cellular systems. This argument leads us to the fact that there exist a principal limit (or uncertainty principle) to what we can achieve in simulation and with the machine metaphor in particular. Amongst the many modelling paradigms suggested for cellular systems, it is clear that none is accurate and yet general. I am therefore to discuss a conceptual framework that generalizes a number of models (including Bayes nets, state-space models, Boolean networks) and should allow us to discuss the previous issues in a formal framework.
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