Computational Modeling of Signaling Networks pp 109-118
Design of Experiments to Investigate Dynamic Cell Signaling Models
This chapter describes approaches to make use of dynamic models of cell signaling systems in order to optimize experiments in cell biology. We are particularly focusing on the question of how small molecule inhibitors or activators can best be used to get the most information out of a limited number of experiments when only a handful of molecular species can be measured. One goal addressed by this chapter is to find time course experiments to discriminate between rivaling molecular mechanisms. The other goal is to find experiments that are useful for inferring rate constants, binding affinities, concentrations, and other model parameters from time course data. Both are treated as optimal control problems in which rapid pharmacological perturbation schemes are identified in silico in order to close an experimental cycle from modeling back to the laboratory bench.
Key wordsParameter estimation Model discrimination Parameter uncertainty Experimental design Optimization Dynamical systems
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