Bioprocess development for biofuels and biochemicals typically requires several rounds of metabolic engineering to meet process targets including product yield, titer and productivity, all of which impact the process economics. Advances in computational modeling techniques have allowed the development of genome-scale models of metabolism in several organisms. Such models have been the basis of several algorithms that couple growth to metabolic production resulting in hundreds if not thousands of strain designs. Such a plethora of strain designs leads to the open question of prioritizing these designs for experimental implementation. Synthetic biology tools have accelerated the build stage of the classic design-build-test-learn cycle enabling the realization of hundreds-thousands of strain designs and their subsequent characterization. Such data allows for the application of modeling, optimization and machine learning methods for metabolic engineering. In this talk, I will describe the overall workflow for bioprocess development and where tools such as modeling and synthetic biology can have maximum impact. I will present some examples of how these tools have been applied drawn from our work and others in the field.