Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
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DOI：
10.1371/journal.pcbi.1005331
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发表时间：
2017-01-01
影响因子：
4.3
通讯作者：
Hasenauer, Jan
中科院分区：
文献类型：
--
作者：
Froehlich, Fabian;Kaltenbacher, Barbara;Hasenauer, Jan
使用常微分方程（ODE）模型对生化反应网络进行机理数学建模，提高了我们对中小尺度生物过程的理解。虽然这在原则上也适用于大规模和基因组规模的过程，但到目前为止，用于分析描述数百或数千个生物化学物种和反应的ODE模型的计算方法仍然缺失。虽然个别的模拟是可行的，从实验数据的模型参数的推断是计算过于密集。在这篇文章中，我们评估了伴随灵敏度分析在大规模生化反应网络中的参数估计。我们提出了时间离散测量的方法，并将其与系统和计算生物学中使用的最先进的方法进行比较。我们的比较表明，一个显着提高计算效率和上级可扩展性的伴随灵敏度分析。计算复杂度有效地独立于参数的数量，从而能够分析大规模和基因组规模的模型。我们的研究ErbB信号的综合动力学模型表明，使用伴随灵敏度分析的参数估计需要一小部分的计算时间的既定方法。所提出的方法将有助于基因组规模的细胞过程的机械建模，在组学时代的要求。
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.