Weber Lisa, Raymond William, Munsky Brian
Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States of America.
Phys Biol. 2018 May 18;15(5):055001. doi: 10.1088/1478-3975/aabc31.
In quantitative analyses of biological processes, one may use many different scales of models (e.g. spatial or non-spatial, deterministic or stochastic, time-varying or at steady-state) or many different approaches to match models to experimental data (e.g. model fitting or parameter uncertainty/sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. We use a simplified gene regulation model to illustrate many of these concerns, especially for ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we employ Matlab and Python software to consider a time-dependent input signal (e.g. a kinase nuclear translocation) and several model hypotheses, along with simulated single-cell data. We illustrate different approaches (e.g. deterministic and stochastic) to identify the mechanisms and parameters of the same model from the same simulated data. For each approach, we explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design. We conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast (Neuert et al 2013 Science 339 584-7) and human cells (Senecal et al 2014 Cell Rep. 8 75-83).
在生物过程的定量分析中，人们可以使用许多不同尺度的模型（例如空间或非空间、确定性或随机性、随时间变化或处于稳态），或者使用许多不同的方法将模型与实验数据进行匹配（例如模型拟合或采用不同实验设计进行参数不确定性/不精确性量化）。即使应用于相同的数据和相同的模型，这些不同的分析也可能导致惊人不同的结果。我们使用一个简化的基因调控模型来说明其中的许多问题，特别是对于确定性过程的常微分方程分析、异质过程的化学主方程和有限状态投影分析以及随机模拟。对于每种分析，我们使用Matlab和Python软件来考虑一个随时间变化的输入信号（例如激酶核转位）、几个模型假设以及模拟的单细胞数据。我们说明了从相同的模拟数据中识别同一模型的机制和参数的不同方法（例如确定性和随机性方法）。对于每种方法，我们探讨参数空间中的不确定性如何随所选的分析方法或特定的实验设计而变化。我们最后讨论了我们的模拟结果如何与实验和计算研究的整合相关，以探索酵母（Neuert等人，2013年，《科学》339卷，584 - 7页）和人类细胞（Senecal等人，2014年，《细胞报告》8卷，75 - 83页）中的信号激活基因表达模型。
