Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany.
Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany.
Nat Commun. 2022 Jan 10;13(1):34. doi: 10.1038/s41467-021-27374-6.
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.
定量动态模型被广泛用于研究细胞信号处理。在建模过程中，一个关键步骤是从实验数据中估计未知的模型参数。随着模型规模和数据集的不断增长，针对机械模型的既定参数优化方法在计算上变得极具挑战性。在深度学习中使用的小批量优化方法具有更好的扩展性能。在这项工作中，我们对常微分方程（ODE）模型进行了小批量优化的调整、应用和基准测试，从而在动态建模和机器学习之间建立了直接联系。在我们的主要应用示例——癌症信号的大规模模型中，我们将小批量优化与已有的方法进行了基准测试，实现了更好的优化结果，并将计算量减少了一个数量级以上。我们期望我们的工作将成为针对 ODE 模型的小批量优化的第一步，并使建模能够涵盖比当前更大型和更复杂的系统。
