title: Continuous-time Markov chain-based flux analysis in metabolism.
abstract: Metabolic flux analysis (MFA), a key technology in bioinformatics, is an effective way of analyzing the entire metabolic system by measuring fluxes. Many existing MFA approaches are based on differential equations, which are complicated to be solved mathematically. So MFA requires some simple approaches to investigate metabolism further. In this article, we applied continuous-time Markov chain to MFA, called MMFA approach, and transformed the MFA problem into a set of quadratic equations by analyzing the transition probability of each carbon atom in the entire metabolic system. Unlike the other methods, MMFA analyzes the metabolic model only through the transition probability. This approach is very generic and it could be applied to any metabolic system if all the reaction mechanisms in the system are known. The results of the MMFA approach were compared with several chemical reaction equilibrium constants from early experiments by taking pentose phosphate pathway as an example.
authors.affiliation: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University , Hung Hom, Hong Kong .
authors.forename: Yunzhang
authors.initials: Y
authors.lastname: Huo
authors.forename: Ping
authors.initials: P
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D008390:Markov Chains; D064688:Metabolic Flux Analysis / Q000379:methods*; D010427:Pentose Phosphate Pathway
keywords: continuous-time Markov chain; flux analysis; metabolism
references.citation: Appl Environ Microbiol. 2006 Jul;72(7):4743-54
references.pmid: 16820467
references.citation: Bioinformatics. 2004 Dec 12;20(18):3387-97
references.pmid: 15256408
references.citation: Anal Biochem. 2004 Feb 15;325(2):308-16
references.pmid: 14751266
references.citation: BMC Syst Biol. 2012 Jan 30;6:9
references.pmid: 22289253
references.citation: Biochem J. 1969 Dec;115(4):837-42
references.pmid: 5357024
references.citation: J Biol Chem. 1994 Feb 18;269(7):4878-82
references.pmid: 8106459
references.citation: Bioinformatics. 2011 Aug 15;27(16):2256-62
references.pmid: 21685054
references.citation: Bioinformatics. 2006 Nov 1;22(21):2681-7
references.pmid: 16940326
references.citation: Metab Eng. 2008 May-Jul;10(3-4):178-86
references.pmid: 18555197
references.citation: Biotechnol Bioeng. 1997 Jul 5;55(1):118-35
references.pmid: 18636450
references.citation: Bioinformatics. 2008 May 1;24(9):1191-7
references.pmid: 18356193
references.citation: J Biol Chem. 1986 Jan 15;261(2):690-8
references.pmid: 3079759
references.citation: J Bacteriol. 2001 Feb;183(4):1441-51
references.pmid: 11157958
references.citation: J Chromatogr A. 2007 Apr 20;1147(2):153-64
references.pmid: 17376459
references.citation: Bioinformatics. 2013 Apr 1;29(7):878-85
references.pmid: 23407359
references.citation: Biochemistry. 1988 Nov 15;27(23):8526-33
references.pmid: 2975509
references.citation: Science. 1991 Jun 21;252(5013):1668-75
references.pmid: 2047876
references.citation: PLoS One. 2013;8(1):e55137
references.pmid: 23383083
references.citation: J Biol Chem. 1985 Mar 25;260(6):3314-20
references.pmid: 3972826