Metabolic Systems Research Team

Understanding the mechanisms and physiology of plant metabolism and improving production of useful materials

Team Director

Masami Yokota Hirai Ph.D.

1994: Ph.D., Agriculture, University of Tokyo
1994: JSPS Postdoctoral Fellow, University of Tokyo
1997: Research Associate, Chiba University
2001: CREST Postdoctoral Fellow, Japan Science and Technology Agency
2005: Unit Leader, Metabolic Systems Research Unit, RIKEN Plant Science Center
2005: Adjunct Associate Professor, School of Agricultural Sciences and Graduate School of Bioagricultural Sciences, Nagoya University
2006: Visiting Associate Professor, University of Tokyo
2007: Adjunct Professor, Nagoya University (-current)
2008: Team Leader, Metabolic Systems Research Team, RIKEN Plant Science Center
2010: Visiting professor, Alkali Soil Natural Environmental Science Center, Northeast Forestry University, China
2013: Team Director, Metabolic Systems Research Team, RIKEN Center for Sustainable Resource Science (-current)
2020: Unit Leader, Mass Spectrometry and Microscopy Unit, RIKEN Center for Sustainable Resource Science (-current)

Main Research Fields :	Biological Sciences
Related Research Fields :	Biology / Agricultural Sciences
Keywords :	Metabolism / Metabolomics / Mathematical modelling / Plant / Secondary metabolism
Strategic Program :	Sustainable Bioproduction

Outline

Metabolism is the basis of life and is finely regulated. Plant metabolism and its regulation are complicated, because plants produce primary metabolites as well as diverse specialized metabolites. Since ancient times, humans have used plant metabolites for nutrients, medicine, flavors, etc. We aim to understand the mechanisms and physiology of plant metabolism and improve plant productivity of useful metabolites based on our findings. We identify genes involved in biosynthesis/degradation of amino acids and their derivative specialized metabolites and elucidate regulatory mechanism. We also develop metabolomics techniques and exploit mathematical modelling and machine learning for data mining from metabolome data.

Subjects

Elucidation of the regulatory mechanism of amino acid biosynthesis
Identification of genes involved in biosynthesis/degradation of plant specialized metabolites
Identification of metabolic pathways regulating plant development
Data mining from metabolome data through machine learning and mathematical modeling

Image of prediction of metabolic reaction network based on time-series metabolite data: We established a novel algorithm for prediction of metabolic reaction network. The algorithm is comprised of 4 steps: smoothing of time-series metabolite data, determination of causal relationship by Granger causality test, modeling of metabolic reaction network by biochemical system theory, and estimation of parameters by nonlinear least-square method. (Sriyudthsak et al. PLOS ONE 2013)

Identification of a key transcription factor regulating glucosinolate biosynthesis: Based on coexpression analysis using the transcriptome data, we identified candidate genes involved in glucosinolate biosynthesis, and confirmed the predicted function of the candidates by widely targeted metabolomics. （Hirai et al. PNAS 2007）