RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan; Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.
Section of Bio-Process Design, Department of Bioscience and Biotechnology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, West #5 Bldg., Moto-oka 744, Nishi-ku, Fukuoka 819-0395, Japan.
Curr Opin Biotechnol. 2018 Dec;54:138-144. doi: 10.1016/j.copbio.2018.08.005. Epub 2018 Sep 5.
Plant metabolism is characterized by a wide diversity of metabolites, with systems far more complicated than those of microorganisms. Mathematical modeling is useful for understanding dynamic behaviors of plant metabolic systems for metabolic engineering. Time-series metabolome data has great potential for estimating kinetic model parameters to construct a genome-wide metabolic network model. However, data obtained by current metabolomics techniques does not meet the requirement for constructing accurate models. In this article, we highlight novel strategies and algorithms to handle the underlying difficulties and construct dynamic in vivo models for large-scale plant metabolic systems. The coarse but efficient modeling enables the prediction of unknown mechanisms regulating plant metabolism.
植物代谢的特点是代谢物种类繁多，其系统远比微生物系统复杂。数学建模对于理解代谢工程中的植物代谢系统的动态行为是有用的。时程代谢组学数据具有很大的潜力，可以用于估计动力学模型参数，以构建全基因组代谢网络模型。然而，当前代谢组学技术获得的数据不符合构建准确模型的要求。在本文中，我们强调了处理这些基本困难的新策略和算法，并构建了用于大规模植物代谢系统的动态体内模型。这种粗糙但有效的建模方法可以预测调节植物代谢的未知机制。
