SS-mPMG and SS-GA: Tools for Finding Pathways and Dynamic Simulation of Metabolic Networks | Zendy

Tetsuo Katsuragi | Zendy; Naoaki Ono | Zendy; Keiichi Yasumoto | Zendy; Md. AltafUlAmin | Zendy; Masami Yokota Hirai | Zendy; Kansuporn Sriyudthsak | Zendy; Yuji Sawada | Zendy; Yui Yamashita | Zendy; Yukako Chiba | Zendy; Hitoshi Onouchi | Zendy; Toru Fujiwara | Zendy; Satoshi Naito | Zendy; Fumihidé Shiraishi | Zendy; Shigehiko Kanaya | Zendy

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SS-mPMG and SS-GA: Tools for Finding Pathways and Dynamic Simulation of Metabolic Networks

Author(s) -

Tetsuo Katsuragi,

Naoaki Ono,

Keiichi Yasumoto,

Md. AltafUlAmin,

Masami Yokota Hirai,

Kansuporn Sriyudthsak,

Yuji Sawada,

Yui Yamashita,

Yukako Chiba,

Hitoshi Onouchi,

Toru Fujiwara,

Satoshi Naito,

Fumihidé Shiraishi,

Shigehiko Kanaya

Publication year - 2013

Publication title -

plant and cell physiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.975

H-Index - 152

eISSN - 1471-9053

pISSN - 0032-0781

DOI - 10.1093/pcp/pct052

Subject(s) - metabolic network , metabolomics , computer science , metabolic pathway , generator (circuit theory) , construct (python library) , computational biology , metabolite , genetic algorithm , biological system , bioinformatics , chemistry , biology , genetics , biochemistry , gene , machine learning , physics , power (physics) , quantum mechanics , programming language

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.

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