In silico metabolic modeling and simulation technologies greatly accelerate the pace of industrial bioprocess development by providing optimum strain designs and engineering strategies, by facilitating data interpretation, and by guiding experimental activities throughout the entire development cycle. Unique pathways to a chemical are identified and verified as superior through genome-scale metabolic models that allow prioritization in terms of parameters such as yield, energy balance and redox balance. Subsequently a strain is designed via the proprietary OptKnock algorithm, which identifies sets of genes that must be deleted in order to tightly couple product formation to growth of the organism.  Following introduction of the most attractive biosynthetic pathways and designated deletions, strains are subjected to adaptive evolution methods which use controlled selection pressure to optimize strain performance following genetic manipulations.  In addition to achieving superior product yield and productivities, evolved strains are genetically stable and thus ideally suitable for cell recycle or continuous bio-processing with consistent high-level production.  

The presentation will highlight successful implementation of this combined computational and experimental approach for engineering a microorganism that produces the industrial chemical 1,4-butanediol (BDO) directly from glucose and sucrose.