TY - JOUR
T1 - Iterative experiment design guides the characterization of a light-inducible gene expression circuit
AU - Ruess, Jakob
AU - Parise, Francesca
AU - Milias-Argeitis, Andreas
AU - Khammash, Mustafa
AU - Lygeros, John
PY - 2015
Y1 - 2015
N2 - SignificanceSystem identification addresses the problem of identifying unknown model parameters from measured data of a real system. In the case of biochemical reaction networks, the available measurements are typically sparse because of technical and/or economic reasons. Therefore, it is of paramount importance to maximize the information that can be gained by each experiment. Here, we apply a systematic design scheme for single-cell experiments based on information theoretic criteria. For the considered light-inducible gene expression circuit, we show that this scheme allows one to precisely identify model parameters that were practically unidentifiable from data measured in random experiments. This result provides evidence that optimal experiment design is a key requirement for the successful identification of biochemical reaction networks. 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.
AB - SignificanceSystem identification addresses the problem of identifying unknown model parameters from measured data of a real system. In the case of biochemical reaction networks, the available measurements are typically sparse because of technical and/or economic reasons. Therefore, it is of paramount importance to maximize the information that can be gained by each experiment. Here, we apply a systematic design scheme for single-cell experiments based on information theoretic criteria. For the considered light-inducible gene expression circuit, we show that this scheme allows one to precisely identify model parameters that were practically unidentifiable from data measured in random experiments. This result provides evidence that optimal experiment design is a key requirement for the successful identification of biochemical reaction networks. 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.
UR - http://www.mendeley.com/research/iterative-experiment-design-guides-characterization-lightinducible-gene-expression-circuit
U2 - 10.1073/pnas.1423947112
DO - 10.1073/pnas.1423947112
M3 - Article
SN - 0027-8424
VL - 112
SP - 8148
EP - 8153
JO - Proceedings of the National Academy of Sciences
JF - Proceedings of the National Academy of Sciences
IS - 26
ER -