Continuous-time markov chain-based flux analysis in metabolism

Yunzhang Huo, Ping Ji

Research output: Journal article publicationJournal articleAcademic researchpeer-review


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.
Original languageEnglish
Pages (from-to)691-698
Number of pages8
JournalJournal of Computational Biology
Issue number9
Publication statusPublished - 1 Sep 2014


  • continuous-time Markov chain
  • flux analysis
  • metabolism

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Medicine(all)

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