Section of Bio-Process Design, Department of Bioscience and Biotechnology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, 6-10-1, Hakozaki, Higashi-Ku, Fukuoka 820-8581, Japan; Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Section of Bio-Process Design, Department of Bioscience and Biotechnology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, 6-10-1, Hakozaki, Higashi-Ku, Fukuoka 820-8581, Japan.
Math Biosci. 2018 Jul;301:21-31. doi: 10.1016/j.mbs.2018.01.010. Epub 2018 Feb 2.
In a mathematical model, estimation of parameters from time-series data of metabolic concentrations in cells is a challenging task. However, it seems that a promising approach for such estimation has not yet been established. Biochemical Systems Theory (BST) is a powerful methodology to construct a power-law type model for a given metabolic reaction system and to then characterize it efficiently. In this paper, we discuss the use of an S-system root-finding method (S-system method) to estimate parameters from time-series data of metabolite concentrations. We demonstrate that the S-system method is superior to the Newton-Raphson method in terms of the convergence region and iteration number. We also investigate the usefulness of a translocation technique and a complex-step differentiation method toward the practical application of the S-system method. The results indicate that the S-system method is useful to construct mathematical models for a variety of metabolic reaction networks.
在数学模型中,从细胞代谢浓度的时间序列数据中估计参数是一项具有挑战性的任务。然而,似乎尚未建立用于此类估计的有前途的方法。生化系统理论(BST)是构建给定代谢反应系统的幂律模型并对其进行有效表征的强大方法。在本文中,我们讨论了使用 S 系统求根方法(S 系统方法)从代谢物浓度的时间序列数据中估计参数。我们证明了 S 系统方法在收敛区域和迭代次数方面优于牛顿-拉普森方法。我们还研究了易位技术和复步差分法在 S 系统方法的实际应用中的有用性。结果表明,S 系统方法可用于构建各种代谢反应网络的数学模型。