(732d) A Constraint-Based Method for Integrating Omics Datasets to Improve Flux Predictions

Transcriptomic and/or proteomic data has been integrated into constraint-based models to enhance flux predictions. However, it has been observed that for E.coli and S. cerevisiae, the predictions obtained by parsimonious flux balance analysis (pFBA) (1) are as good or better than those integrating transcriptomic and/or proteomic data (2). We developed a constraint-based method called Linear Bound Flux Balance Analysis (LBFBA). The method integrates transcriptomic and/or proteomic data to predict the flux distribution. It requires a training set containing transcriptomic and/or proteomic and fluxomic data under different experimental conditions. These data in the training set are used to further tighten the upper and lower bounds for reactions’ flux.

We applied LBFBA to E.coli and S. cerevisiae and compared its predictions to those from pFBA. For E.coli, LBFBA predictions (normalized errors around 20%) were more accurate than pFBA predictions (normalized errors around 40%), demonstrating LBFBA’s power to more accurately predict the flux distribution. For S. cerevisiae, LBFBA improved the flux prediction and had a smaller error range for every condition, indicating smaller flux variability across alternate optimal solutions. LBFBA predicts flux distribution more precisely since it uses reaction-specific bounds to limit individual fluxes instead of using the same bound for every flux like pFBA or using a global assumption for all reactions (e.g. highly expressed reactions have high flux). Sensitivity analysis on training set size showed that four or five experimental conditions with measured omics datasets was enough to improve LBFBA predictions. LBFBA is the first constraint-based method which has been shown to improve flux predictions by integrating proteomic and/or transcriptomic data.

1. N. E. Lewis et al., Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 6(2010), doi:10.1038/msb.2010.47.

2. D. Machado, M. Herrgård, Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism. PLoS Comput. Biol. 10, e1003580 (2014).