A Computationally-Driven Metabolic Engineering Strategy to Increase Cellulose Production in Plants

A novel approach in genome-scale metabolic flux modeling called Flux Balance Analysis with Flux Ratios (FBrAtio) has been developed to derive “fine-tuned” metabolic engineering strategies for the production of biofuels and chemicals.  In general, the FBrAtio algorithm designs metabolic flux re-direction at critical metabolite “nodes” in the metabolic network.  Based on the optimum re-distribution at several critical nodes, metabolic engineering strategies are designed using tools of (i) gene over-expression, (ii) partial knock-down by designer sRNA, and (iii) full gene knockout.  In this research, FBrAtio was applied to Arabidopsis thaliana with the purpose of increasing cellulose content of plant biomass for use in consolidated bioprocesses.  The FBrAtio approach returned a single non-intuitive gene over-expression candidate to dramatically increase cellulose synthesis.  When implemented, record levels of cellulose (~100% increase over wild-type) were achieved in plants that grew significantly larger (>20% increase) than wild-type plants and had dramatically thicker stems (2-3 fold increase).  These engineered plants represent a significant step forward towards designer crops that can provide easily accessible and abundant cellulose for consolidated bioprocessing while growing on marginal lands, in low light, or in drought conditions.  In this presentation, a full description of the FBrAtio algorithm will be presented along with instructions regarding how to use this approach to design multigenic metabolic engineering strategies in any organism of interest.  In addition, full details of the plant metabolic engineering targets and process will be given along with a full characterization of the high cellulose producing plants and a way forward for engineering additional useful traits in designer crops to be used for consolidated bioprocessing.