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. Our computational platform, SimPheny, is an integrated network of proprietary algorithms and methods that we are applying broadly in both internal and collaborative programs.  Unique pathways to a product of interest are identified and verified by our models as superior in terms of parameters such as yield, energy balance and redox balance. Subsequently the strain is designed through use of our 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 biosynthetic pathway and designated deletions, we implement evolutionary engineering, which is a complementary experimental approach that uses controlled selection pressure to optimize strain fitness and growth rate following genetic manipulations.  In addition to achieving superior product yield and productivities, strains generated by this approach are genetically stable and thus ideally suitable for cell recycle or continuous bioprocessing with consistent high-level production. We will describe our process for implementing this combined computational and experimental approach aimed at low-cost chemical production.