Journal
BIOINFORMATICS
Volume 34, Issue 22, Pages 3882-3888Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty445
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Funding
- Office of Science (BER), U.S. Department of Energy [DE-SC0008103]
- U.S. Department of Energy Great Lakes Bioenergy Research Center (DOE BER Office of Science) [DE-FC02-07ER64494]
- National Institute of Allergy and Infectious Diseases, National Institutes of Health [U19AI106772]
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [U19AI106772] Funding Source: NIH RePORTER
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Motivation: Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models. Results: In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA.
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