Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany.
Center for Mathematics, Technische Universität München, Garching 85748, Germany.
Bioinformatics. 2021 Dec 7;37(23):4493-4500. doi: 10.1093/bioinformatics/btab512.
Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence.
Here, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. In addition, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data.
The proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). pyPESTO is available at https://github.com/ICB-DCM/pyPESTO. All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613.
Supplementary data are available at Bioinformatics online.
动态模型的未知参数通常是根据实验数据估计的。然而，虽然已经提出了各种用于定量数据的高效优化和不确定性分析方法，但用于定性数据的方法很少，并且存在缩放和收敛问题。
在这里，我们提出了一种从定性数据估计常微分方程模型参数的有效且可靠的框架。在这个框架中，我们推导出了一种用于最优缩放方法的梯度计算的半分析算法，该方法是为定性数据开发的。这使得能够使用高效的基于梯度的优化算法。我们证明了使用梯度信息可以提高优化和不确定性量化在几个应用示例上的性能。平均而言，与无梯度优化相比，我们实现了超过一个数量级的加速。此外，在一些示例中，基于梯度的方法产生了显著改进的目标函数值和拟合质量。因此，所提出的框架大大改进了从定性数据进行模型参数化。
所提出的方法在开源 Python 参数估计工具包 (pyPESTO) 中实现。pyPESTO 可在 https://github.com/ICB-DCM/pyPESTO 获得。所有应用示例和重现本研究的代码都可在 https://doi.org/10.5281/zenodo.4507613 获得。
补充数据可在 Bioinformatics 在线获得。
