(352e) Metabolic Engineering in Silico Enabled by Manipulating Metabolic Pathway Flux Ratios

A new method of constraining genome-scale metabolic flux models called “flux balance analysis with flux ratios” (FBrAtio) has enabled significant progress in metabolic engineering in silico.  Despite their extraordinary promise and popularity, it is well established that genome-scale models suffer from having a large number of degrees of freedom.  Several tools have been developed to address reaction directionality and gene regulation in order to reduce the number of solutions to these models.  However, the large solution space associated with these models has thus far prevented widespread use of genome-scale models in a “predictive” capacity that is required of metabolic engineering in silico.  The novel FBrAtio approach considers a metabolite of a metabolic network as a  “node.”  At a metabolic branch point, multiple competing enzymes can consume a metabolite.  Ultimately, thermodynamics determine how this metabolite is distributed among competing enzymes, and genetic regulation (with spatial partitioning) determines the concentrations of enzymes present.  This distribution of a metabolite among competing enzymes is easily specified by the flux ratio concept.  Thus, the flux ratios at key branch points in a metabolic network can be optimized for over-production of a desired product and lead to “predictive” modeling using genome-scale metabolic flux models.  This is significant because flux ratios can be specified experimentally through regulatory engineering.  The FBrAtio algorithm returns a “fine-tuned” metabolic engineering strategy that involves targeted levels of gene over-expression and knock-down in addition to knockout.  The flux ratios approach returns a quantitative requirement of up- or down-regulation of targeted enzymes at key metabolic branch points.  In this presentation, the concepts of flux ratios are explained in detail and are applied to model systems.  Experimental validations of FBrAtio designed metabolic engineering strategies are presented for (i) the over-accumulation of cellulose by Arabidopsis, and (ii) over-production of bio-butanol and ethanol by solventogenic clostridia.  In addition, the application of FBrAtio is also presented for the case of optimizing flux through synthetic pathways installed in cyanobacteria.