[Plantsci] REMINDER: School of Plant Sciences Seminar Series Presents: Drs. Masami Hirai and Yuji Sawada, 01/11/13

[Gabriel Rodriguez]

Seminar Abstracts

 

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.

 

 

 

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