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Iterative experiment design guides the characterization of a light-inducible gene expression circuit


J. Ruess, F. Parise, A. Milias, M. Khammash, J. Lygeros

Proceedings of the National Academy of Sciences

Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimen- tation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here we propose a systematic scheme, based on iterations of optimal experiment design, flow cytometry experiments and Bayesian parameter inference, to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characteriza- tion of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from non-optimal experiments. In particular, we compare the parameter posterior distribution and the precision to which the outcome of future experiments can be predicted. Moreover, we illus- trate how the identified stochastic model can be used to determine light-induction patterns that make either the average amount of pro- tein or the variability in a population of cells follow a desired profile. Our results demonstrate that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations, over extended periods of time.


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% Autogenerated BibTeX entry
@Article { RueEtal:2015:IFA_5192,
    author={J. Ruess and F. Parise and A. Milias and M. Khammash and J. Lygeros},
    title={{Iterative experiment design guides the characterization of
	  a light-inducible gene expression circuit}},
    journal={Proceedings of the National Academy of Sciences},
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