期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 112, 期 26, 页码 8148-8153出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1423947112
关键词
stochastic kinetic models; optimal experiment design; in vivo control; parameter inference; light-induced gene expression
资金
- European Commission under the Network of Excellence HYCON2 (highly-complex and networked control systems)
- SystemsX.ch under the SignalX Project
- People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7 under REA (Research Executive Agency) [291734]
- Human Frontier Science Program Grant [RP0061/2011]
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.
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