Metabolic Systems Research Team

Team Leader

Masami Yokota Hirai


Masami Yokota Hirai




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



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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Metabolic Systems Research Team
Metabolism is finely regulated in a complicated way, as it comprises the basis of life. Especially, plant’s and bacterial metabolism is an important base not only for their own lives but also for animal lives and human society by providing nutrient and functional compounds. Aiming at grasping a whole picture of metabolism, we develop metabolomics techniques, predict metabolic reaction network by mathematical modeling using omics data, and explore metabolic gene functions by molecular biology, biochemistry and molecular genetics. We also aim at improvement of plant’s and bacterial productivity of useful metabolites by use of our findings.


  1. Large-scale analysis of metabolic profiles by high-throughput metabolomics methodologies and data mining
  2. Analysis of metabolic reaction network by mathematical modeling
  3. Functional identification of genes involved in metabolism and plant production
  4. Metabolic engineering of cyanobacteria
  5. Elucidation of metabolic function in plant ontogenesis
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