(5cp) Quantifying Cellular Fluxes to Drive Metabolic Engineering for the Production of Biofuels

One of the key descriptors of cellular physiology is the set of flux values that flow through the cell's metabolic pathways. With the rapid generation of genomic data, creating such metabolic models in a systematic framework becomes crucial. Once constructed, these models can be used to propose experimentally testable hypotheses and guide modifications of the organism, e.g., to improve product yield. In this poster, I present 1) methodologies for quantifying fluxes in large and genome-scale models using isotope labeling data and 2) an optimization framework that uses these flux values to suggest engineering strategies for the overproduction of the product at the desired target.

Extending the scope of isotope mapping models becomes increasingly important in order to analyze strains and drive improved product yields as more complex pathways are engineered and as secondary metabolites are used as starting points for new products. Here I present how the elementary metabolite unit (EMU) framework and flux coupling significantly decrease the computational burden of metabolic flux analysis (MFA) when applied to large-scale isotope mapping metabolic models. The combined use of EMU and flux coupling analysis leads to a ten-fold decrease in the number of variables in comparison to the original isotope distribution vector (IDV) version of the Escherichia coli model. The observed computational savings reveal the rapid progress in performing MFA with increasingly larger isotope models and enable the ultimate goal of handling genome-scale models of metabolism.

Once obtaining flux values and ranges, I present an integrated computational base to support pathway identification and strain optimization, with an emphasis on C4+ biofuels and other biochemicals. I describe the application of the OptFlux computational framework to pinpoint engineering modifications (knock-outs/up/down) that are required for the targeted biochemical overproduction. This is accomplished by classifying reactions (and combinations thereof) in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet the pre-specified overproduction target. A ?force set? can then be extracted that contains a sufficient and non-redundant set of reactions that need to be directly changed to meet the production requirements. I apply the integrated framework for the production of succinate and 1-butanol in E. coli using the most recent in silico E. coli model, iAF1260. The proposed computational workflow not only recapitulates existing pathways and engineering strategies but also reveals novel and non-intuitive ones that boost production by using and performing coordinated changes on sometimes distant pathways.