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  • Book
    Samuel Bandara.
    Digital2012
    Cell signaling networks are complex dynamical systems with mechanisms for adaptation and failure-tolerance such that typical stimulus experiments are often robust to the perturbation of individual components. While such robustness is often advantageous, it also means that the information content of an experiment with respect to internal states and activities is often poor. We treat this problem formally by examining the statistical uncertainty of parameter estimates of corresponding signaling models as a function of experimental protocols that are based on live-cell imaging of fluorescent reporters. The first part of this work uses such model predictions of parameter sensitivity to design numerically optimized live-cell imaging protocols with the goal to maximally constrain the space of possible parameter values. The second part instead uses an experimental approach to define invariant parameters by correlated population estimates in order to reveal changes of regulated parameters in systematic siRNA perturbation experiments. Our results show that the effects of siRNA perturbations on reporter dynamics can be used to infer functional changes of the probed system. We exploit this feature to define not only the key molecules responsible for activity but also direct and indirect regulators.