Abstract
The nascent field of systems biology ambitiously proposes to integrate information from large-scale biology projects to create computational models that are, in some sense, complete. However, the details of what would constitute a complete systems-level model of an organism are far from clear. To provide a framework for this difficult question it is useful to define a model as a set of rules that maps a set of inputs (e.g. descriptions of the cell's environment) to a set of outputs (e.g. the concentrations of all its RNAs and proteins). We show how the properties of a model affect the required experimental sampling and estimate the number of experiments needed to ‘complete’ a particular model. Based on these estimates, we suggest that the complete determination of a biological system is a concrete, achievable goal.
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