Ruess Jakob, Parise Francesca, Milias-Argeitis Andreas, Khammash Mustafa, Lygeros John
Automatic Control Laboratory, ETH Zurich, CH-8092 Zurich, Switzerland;
Department of Biosystems Science and Engineering, ETH Zurich, CH-4058 Basel, Switzerland.
Proc Natl Acad Sci U S A. 2015 Jun 30;112(26):8148-53. doi: 10.1073/pnas.1423947112. Epub 2015 Jun 17.
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.
系统生物学基于这样一种观点，即通过建模与实验的相互作用能够更好地揭示生物复杂性。然而，这种方法的成功与否关键取决于所选实验的信息量，而这通常在事先是未知的。在此，我们提出一种基于最优实验设计、流式细胞术实验和贝叶斯参数推断迭代的系统方案，以指导随机生化反应网络情况下的发现过程。为了说明我们方法的益处，我们将其应用于酵母中工程化光诱导基因表达电路的表征，并将所得模型的性能与从非最优实验中识别出的模型进行比较。特别是，我们比较了参数后验分布以及预测未来实验结果的精度。此外，我们说明了如何使用所识别的随机模型来确定光诱导模式，以使蛋白质的平均量或细胞群体中的变异性遵循期望的分布。我们的结果表明，最优实验设计能够得出足够准确的模型，从而在较长时间内精确预测和调节异质细胞群体中的蛋白质表达。
