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Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

Fig 10

A comparison of the 13C-MFA flux, the flux predicted by MFlux, and the flux predicted by FBA.

FBA analysis is simulated by an E. coli iJO1366 model (latest version) with default boundary settings from the reference [54]. The default values of growth associated maintenance energy (GAM) and non-growth associated maintenance energy (NGAM) were adopted. A) E. coli fluxome of glucose metabolism was precisely measured via parallel labeling experiments (a recent paper not in our dataset) [12]. B) E. coli fluxome of glycerol and glucose co-metabolism as measured by Drs. Yao and Shimizu (unpublished data). The E. coli strain was cultured in chemostat fermentor with a working volume of 1 L(37 C). The dilution rate in the continuous culture was 0.35 h−1. [1-13C] glucose and [1, 3-13C] glycerol were used for tracer experiments. The flux calculation is based on a previous method [42]. The RMSE from FBA is 22.5, while the RMSE from MFlux (this work) is 5.1. The COBRA toolbox running on MATLAB R2012b was employed for FBA/pFBA/geometricFBA simulation, and Gurobi 5.5 was used for linear programming. Detailed information is included in S2 Table.

Fig 10

doi: https://doi.org/10.1371/journal.pcbi.1004838.g010