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PLoS Comput Biol. 2015 Sep 17;11(9):e1004363. doi: 10.1371/journal.pcbi.1004363. eCollection 2015 Sep.

A Method to Constrain Genome-Scale Models with 13C Labeling Data.

Author information

1
Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America; Joint BioEnergy Institute, Emeryville, United States of America.
2
Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America; Department of Bioengineering, University of California, Berkeley, Berkely, United States of America.
3
Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States of America; Joint BioEnergy Institute, Emeryville, United States of America; Department of Bioengineering, University of California, Berkeley, Berkely, United States of America; Department of Chemical Engineering, University of California, Berkeley, Berkeley, United States of America.

Abstract

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.

PMID:
26379153
PMCID:
PMC4574858
DOI:
10.1371/journal.pcbi.1004363
[Indexed for MEDLINE]
Free PMC Article

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