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

Author(s): Ruess, Jakob; Parise, Francesca; Milias-Argeitis, Andreas; Khammash, Mustafa H; Lygeros, John
Article Title: Iterative experiment design guides the characterization of a light-inducible gene expression circuit
Affiliation IST Austria
Abstract: Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. 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 characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show 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.
Keywords: Parameter inference; In vivo control; Light-induced gene expression; Optimal experiment design; Stochastic kinetic models
Journal Title: PNAS
Volume: 112
Issue 26
ISSN: 1091-6490
Publisher: National Academy of Sciences  
Date Published: 2015-06-30
Start Page: 8148
End Page: 8153
DOI: 10.1073/pnas.1423947112
Notes: J.R., F.P., and J.L. acknowledge support from the European Commission under the Network of Excellence HYCON2 (highly-complex and networked control systems) and under the SignalX Project. J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges support from Human Frontier Science Program Grant RP0061/2011 (
Open access: yes (repository)