Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Oberschleißheim, Germany.
Department of Mathematics, Technical University Munich, Garching, Germany.
J Math Biol. 2022 May 17;84(7):56. doi: 10.1007/s00285-022-01739-x.
Mechanistic models are a powerful tool to gain insights into biological processes. The parameters of such models, e.g. kinetic rate constants, usually cannot be measured directly but need to be inferred from experimental data. In this article, we study dynamical models of the translation kinetics after mRNA transfection and analyze their parameter identifiability. That is, whether parameters can be uniquely determined from perfect or realistic data in theory and practice. Previous studies have considered ordinary differential equation (ODE) models of the process, and here we formulate a stochastic differential equation (SDE) model. For both model types, we consider structural identifiability based on the model equations and practical identifiability based on simulated as well as experimental data and find that the SDE model provides better parameter identifiability than the ODE model. Moreover, our analysis shows that even for those parameters of the ODE model that are considered to be identifiable, the obtained estimates are sometimes unreliable. Overall, our study clearly demonstrates the relevance of considering different modeling approaches and that stochastic models can provide more reliable and informative results.
机制模型是深入了解生物过程的有力工具。这些模型的参数，例如动力学速率常数，通常不能直接测量，而需要从实验数据中推断出来。在本文中，我们研究了 mRNA 转染后翻译动力学的动态模型，并分析了它们的参数可识别性。也就是说，从理论和实践上考虑完美或实际数据，参数是否可以唯一确定。以前的研究已经考虑了该过程的常微分方程 (ODE) 模型，而在这里我们提出了随机微分方程 (SDE) 模型。对于这两种模型类型，我们根据模型方程考虑结构可识别性，并根据模拟和实验数据考虑实际可识别性，发现 SDE 模型比 ODE 模型提供更好的参数可识别性。此外，我们的分析表明，即使对于 ODE 模型中被认为是可识别的那些参数，得到的估计有时也不可靠。总的来说，我们的研究清楚地表明了考虑不同建模方法的相关性，并且随机模型可以提供更可靠和信息丰富的结果。
