Fröhlich Fabian, Loos Carolin, Hasenauer Jan
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
Center for Mathematics, Technische Universität München, Garching, Germany.
Methods Mol Biol. 2019;1883:385-422. doi: 10.1007/978-1-4939-8882-2_16.
Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.
常微分方程模型已成为生化过程机理描述的标准工具。如果从实验数据中推断参数，这种机理模型可以对潜在变量的行为或新实验条件下的过程提供准确预测。作为补充，模型结构推断可用于从一组候选模型中识别最合理的模型结构，从而获得新的生物学见解。有几个工具箱可以直接推断中小型机理模型的参数和结构。然而，针对高度多路复用数据集的模型可能需要数百到数千个状态变量和参数。对于此类大规模模型的分析，大多数算法需要高得难以处理的计算时间。本章概述了用于参数和模型推断的最新方法，重点是可扩展性。
