Plant Metabolomics and Integrated Approaches
Yuji Sawada
RIKEN Plant Science Center, Yokohama, Japan
Metabolomics and its application study have large advantage in the extensive detection of metabolites. Using the metabolome data with other omics data, we have identified the novel metabolic genes, e.g., transcriptional regulation factor, transporter of the metabolite, biosynthetic genes. Previously, we have established the metabolomics techniques by using multiple MS types as follows: quantitative analysis (QqQ-MS, Sawada et al., PCP 2009a-c), un-targeted tandem MS (MS/MS) analysis (QTOF-MS, Matsuda et al., Plant J. 2008) and elemental composition analysis (FT-MS, Nakabayashi et al., Anal. Chem. in press), reference MS/MS database for phytochemicals (Sawada, et al., Phytochem. 2012). These platforms powerfully promote the characterization of massive extended detectable metabolites. In this presentation, we show the case study of metabolome quantitative locus analysis by using linkage mapping and genome wide association, and the results will allow us to generate the next innovative metabolic breeding approaches.
Mathematical Modeling of Plant Metabolism Based on Metabolome Data
Masami Y. Hirai
RIKEN Plant Science Center, Yokohama, Japan
Recently, it has become possible to acquire a large metabolome dataset from high-throughput instruments. Time-series metabolome data includes important information to understand metabolism as a system. The present work proposes a new pathway-based technique for in silico analysis of a metabolic reaction network by using time-series metabolome data. In this approach, a mathematical model is constructed in the framework of Biochemical Systems Theory based on available information on metabolic pathways, which are composed of chemical reactions and feedback regulations by metabolites. The parameters in the model, namely, kinetic orders and rate constants, are estimated from actual time-series data of metabolite concentrations. The obtained mathematical model enables us to simulate metabolic behaviors and conduct the system analysis of a metabolic reaction network. In this seminar the result of our on-going study will be introduced.
