Fox Zachary R, Neuert Gregor, Munsky Brian
Inria Saclay Ile-de-France, Palaiseau 91120, France Institut Pasteur, USR 3756 IP CNRS Paris, 75015, France School of Biomedical Engineering, Colorado State University Fort Collins, CO 80523, USA.
Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232, USA.
Complexity. 2020;2020. doi: 10.1155/2020/8536365. Epub 2020 Jun 13.
Modern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological behaviors or to infer biological parameters. Such quantitative analyses can also help to improve experiment designs for particular goals, such as to learn more about specific model mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment design is to use the Fisher information matrix (FIM), which quantifies the expected information a particular experiment will reveal about model parameters. The Finite State Projection based FIM (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory systems, whose complex response distributions do not satisfy standard assumptions of Gaussian variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response genes in under time-varying MAPK induction. We verify this FSP-FIM analysis and use it to optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters. We then extend the FSP-FIM approach to explore how different measurement times or genetic modifications help to minimize uncertainty in the sensing of extracellular environments, and we experimentally validate the FSP-FIM to rank single-cell experiments for their abilities to minimize estimation uncertainty of NaCl concentrations during yeast osmotic shock. This work demonstrates the potential of quantitative models to not only make sense of modern biological data sets, but to close the loop between quantitative modeling and experimental data collection.
现代生物学实验正变得越来越复杂，设计这些实验以获得尽可能多的定量见解是一个开放性挑战。越来越多的复杂随机生物系统的计算模型被用于理解和预测生物行为或推断生物学参数。这种定量分析还可以帮助改进针对特定目标的实验设计，比如更多地了解特定模型机制或在某些情况下减少预测误差。一种经典的实验设计方法是使用费希尔信息矩阵（FIM），它量化了特定实验将揭示的关于模型参数的预期信息。基于有限状态投影的FIM（FSP-FIM）最近被开发出来，用于计算离散随机基因调控系统的FIM，其复杂的响应分布不满足高斯变化的标准假设。在这项工作中，我们针对在时变丝裂原活化蛋白激酶（MAPK）诱导下应激反应基因的随机模型开展了FSP-FIM分析。我们验证了这种FSP-FIM分析，并利用它来优化在特定时间应量化的细胞数量，以便尽可能多地了解模型参数。然后，我们扩展了FSP-FIM方法，以探索不同的测量时间或基因修饰如何有助于最小化细胞外环境感知中的不确定性，并且我们通过实验验证了FSP-FIM，以便对单细胞实验在酵母渗透休克期间最小化NaCl浓度估计不确定性的能力进行排名。这项工作证明了定量模型不仅有潜力理解现代生物学数据集，而且有潜力在定量建模和实验数据收集之间形成闭环。
