Determining important regulatory relations of amino acids from dynamic network analysis of plasma amino acids
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The changes in the concentrations of plasma amino acids do not always follow the flow-based metabolic pathway network. We have previously shown that there is a control-based network structure among plasma amino acids besides the metabolic pathway map. Based on this network structure, in this study, we performed dynamic analysis using time-course data of the plasma samples of rats fed single essential amino acid deficient diet. Using S-system model (conceptual mathematical model represented by power-law formalism), we inferred the dynamic network structure which reproduces the actual time-courses within the error allowance of 13.17%. By performing sensitivity analysis, three of the most dominant relations in this network were selected; the control paths from leucine to valine, from methionine to threonine, and from leucine to isoleucine. This result is in good agreement with the biological knowledge regarding branched-chain amino acids, and suggests the biological importance of the effect from methionine to threonine.
KeywordsPlasma amino acids Relation Regulation Network Amino acid deficiency Dynamic
This work was partially supported by Grants-in-Aid for Scientific Research (c) [No. 18500228(YM)] and Scientific Research on Priority Areas, ‘New IT Infrastructure for the Information-explosion Era’ [No. 18049073(MO)] from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
- Brindle JT, Antti H, Holmes E, Tranter G, Nicholson JK, Bethell HW, Clarke S, Schofield PM, McKilligin E, Mosedale DE, Grainger DJ (2002) Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med 8:1439–1444. doi: 10.1038/nm802 CrossRefPubMedGoogle Scholar
- Cooper GF, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9:309Google Scholar
- Imoto S, Goto T, Miyano S (2002) Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. Pac Symp Biocomput, pp 175–186Google Scholar
- Maki Y, Tominaga D, Okamoto M, Watanabe S, Eguchi Y (2001) Development of a system for the inference of large scale genetic networks. Pac Symp Biocomput, pp 446–458Google Scholar
- Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29:1181–1189. doi: 10.1080/004982599238047 CrossRefPubMedGoogle Scholar
- Ono I, Kobayashi S (1997) A real-coded genetic algorithm for function optimization using unimodal distribution crossover. 7th ICGA pp 249–253Google Scholar
- Sato H, Ono I, Kobayashi S (1997) A new generation alternation model of genetic algorithm and its assessment. J. Jpn Soc Artif Intell 12:734–744Google Scholar
- Savageau MA (1976) Biochemical systems analysis: a study of function and design in molecular biology. Addison-Wesley, ReadingGoogle Scholar
- Savageau MA (1998) Rules for the evolution of gene circuitry. Pac Symp Biocomput, pp 54–65Google Scholar
- Somogyi R, Sniegoski CA (1996) Modeling the complexity of genetic networks: understanding multigenetic and pleiotropic regulation. Complexity 1:45–63Google Scholar
- Ueda T, Koga N, Ono I, Okamoto M (2002) Efficient numerical optimization technique based on real-coded genetic altorithm for inverse problem. 7th international symposium on artificial life and robotics (AROB 7th ‘02), 290–293Google Scholar