Metabolic Systems Research Team

Team Leader

Masami Yokota Hirai

Ph.D.

Masami Yokota Hirai

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ResearcherID

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 Leader, 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)

Contact

masami.hirai

Metabolic Systems Research Team,
RIKEN Center for Sustainable Resource Science

#C619 6F Central Research Building,
1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045 Japan
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Outline

Metabolic Systems Research Team
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

  1. Elucidation of the regulatory mechanism of amino acid biosynthesis
  2. Identification of genes involved in biosynthesis/degradation of plant specialized metabolites
  3. Identification of metabolic pathways regulating plant development
  4. 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