Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.
Center for Mathematics, Technische Universität München, 85748 Garching, Germany.
Bioinformatics. 2020 Jan 15;36(2):594-602. doi: 10.1093/bioinformatics/btz581.
Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.
Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (>1000 state variables, >4000 parameters) using relative protein, phosphoprotein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements.
Supplementary code and data are available online at http://doi.org/10.5281/zenodo.3254429 and http://doi.org/10.5281/zenodo.3254441.
Supplementary data are available at Bioinformatics online.
生化反应网络的机理模型有助于定量理解生物过程和整合异质数据集。然而，一些生物过程需要考虑全面的反应网络，因此需要大规模模型。此类模型的参数估计带来了巨大的挑战，特别是当数据处于相对尺度时。
在这里，我们提出了一种新的层次方法，结合了（i）最优缩放、偏移和误差模型参数的有效分析评估，以及（ii）使用伴随灵敏度分析对目标函数梯度的可扩展评估。我们通过使用相对蛋白、磷酸蛋白和生存力测量值对泛癌常微分方程模型（>1000 个状态变量、>4000 个参数）进行参数化，评估了这些方法的性质。分层公式大大提高了优化器的性能。此外，我们表明，当没有实验估计值时，该方法可以在计算开销可忽略不计的情况下估计误差模型参数，为加权异质数据提供了一种无偏的方法。总体而言，我们的分层公式适用于广泛的模型，并允许基于异质相对测量值对大规模模型进行高效参数化。
补充代码和数据可在 http://doi.org/10.5281/zenodo.3254429 和 http://doi.org/10.5281/zenodo.3254441 在线获得。
补充数据可在生物信息学在线获得。
