Hsu Chieh-Ting Jimmy, Brouhard Gary J, François Paul
Department of Physics, McGill University, Montréal, Quebec, Canada.
Department of Physics, McGill University, Montréal, Quebec, Canada; Department of Biology, McGill University, Montréal, Quebec, Canada.
Biophys J. 2020 Mar 24;118(6):1455-1465. doi: 10.1016/j.bpj.2020.01.023. Epub 2020 Jan 29.
Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e., are sensitive to) only a subset of those parameters. This phenomenon is due to parameter space compression (PSC), in which a subset of parameters emerges as "stiff" as a function of time or space. PSC has only been used to explain analytically solvable physics models. We have generalized this result by developing a numerical approach to PSC that can be applied to any computational model. We validated our method against analytically solvable models of a random walk with drift and protein production and degradation. We then applied our method to a simple computational model of microtubule dynamic instability. We propose that numerical PSC has the potential to identify the low-dimensional structure of many computational models in biophysics. The low-dimensional structure of a model is easier to interpret and identifies the mechanisms and experiments that best characterize the system.
当生物系统的物理模型具有大量参数时，可能会变得难以解释。但实际上这些模型本身仅依赖于（即对……敏感）其中一部分参数。这种现象归因于参数空间压缩（PSC），即一部分参数会随着时间或空间的变化而呈现出“刚性”。PSC 此前仅用于解析可解的物理模型。我们通过开发一种适用于任何计算模型的 PSC 数值方法，对这一结果进行了推广。我们针对带有漂移的随机游走以及蛋白质生成与降解的解析可解模型验证了我们的方法。然后，我们将该方法应用于微管动态不稳定性的一个简单计算模型。我们提出，数值 PSC 有潜力识别生物物理学中许多计算模型的低维结构。模型的低维结构更易于解释，并且能够确定最能表征该系统的机制和实验。
