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

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.

Engineering high-level production of fatty alcohols by Saccharomyces cerevisiae from lignocellulosic feedstocks

“…Metabolic flux analysis was performed as previously reported (García Martín et al, 2015;Ghosh et al, 2016). Briefly, strains were grown in 250-ml shake flasks in 25 ml medium containing 13 C-labeled glucose (sigma cat.…”

“…Sampling included filtering media for high-performance liquid chromatography (HPLC) to quantify extracellular metabolites, ethyl acetate extraction followed by gas chromatography-mass spectrometry (GC-MS) to quantify fatty acid products, and methanol/chloroform extraction followed by liquid chromatography-mass spectrometry (LC-MS/MS) to analyze 13 C labeling in metabolites. These 13 C labeling data were used to constrain the S. cerevisiae genome-scale model iMM904 (Mo et al, 2009) using two-scale 13 C Metabolic Flux Analysis (García Martín et al, 2015;Ghosh et al, 2016) with the open-source, python-based JBEI Quantitative Metabolic Modeling library (https://github.com/JBEI/jqmm) to model the metabolic flux distribution.…”

“…To model the metabolic flux distribution in yL401, we measured metabolite levels and 13 C labeling patterns and used the JBEI Quantitative Metabolic Modeling library (García Martín et al, 2015) to model flux through genome-scale model iMM904 (Mo et al, 2009), adding fatty acyl thioesterase reactions (EC number 3.1.2.2). The reconstruction shows carbon from glucose mostly following glycolysis, then the pyruvate dehydrogenase (PDH) bypass from pyruvate to acetaldehyde, and lastly to ethanol (Fig.…”

“…2). Of the four (Runguphan and Keasling, 2013) was analyzed by shotgun proteomics and metabolic flux analysis (García Martín et al, 2015) to identify targets for engineering increased production. The black dotted line demarcates the cell.…”

Modelling and multiobjective optimization for simulation of cyanobacterial metabolism

“…number of molecules traversing each metabolic reaction per unit time), have an important role in investigating cellular physiology, as they show how the available resources (e.g. carbon, reducing equivalents and chemical energy) flow through the metabolism to enable cell function (García Martín et al, 2015). Metabolic phenotypes 4 can be defined in terms of flux distributions through a metabolic network, which can be interpreted and predicted using mathematical modelling and computer simulation (Edwards et al, 2002a).…”