Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle

PLoS One. 2014 Nov 14;9(11):e112524. doi: 10.1371/journal.pone.0112524. eCollection 2014.


Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Escherichia coli
  • Gene Expression Profiling / methods*
  • Humans
  • Metabolic Flux Analysis / methods*
  • Metabolic Networks and Pathways / physiology*
  • Models, Biological*
  • RNA, Messenger / metabolism*
  • Saccharomyces cerevisiae


  • RNA, Messenger

Grant support

This work was funded as part of the U.S. Army Network Science Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.