SS-mPMG and SS-GA: Tools for finding pathways and dynamic simulation of metabolic networks

Tetsuo Katsuragi, Naoaki Ono, Keiichi Yasumoto, Md Altaf-Ul-Amin, Masami Y. Hirai, Kansuporn Sriyudthsak, Yuji Sawada, Yui Yamashita, Yukako Chiba, Hitoshi Onouchi, Toru Fujiwara, Satoshi Naito, Fumihide Shiraishi, Shigehiko Kanaya

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.

Original languageEnglish
Pages (from-to)728-739
Number of pages12
JournalPlant and Cell Physiology
Volume54
Issue number5
DOIs
Publication statusPublished - May 2013

All Science Journal Classification (ASJC) codes

  • Physiology
  • Plant Science
  • Cell Biology

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