Abstract
Metabolic engineering modifies cellular function to address various biochemical applications. Underlying metabolic engineering efforts are a host of tools and knowledge that are integrated to enable successful outcomes. Concurrent development of computational and experimental tools has enabled different approaches to metabolic engineering. One approach is to leverage knowledge and computational tools to prospectively predict designs to achieve the desired outcome. An alternative approach is to utilize combinatorial experimental tools to empirically explore the range of cellular function and to screen for desired traits. This mini-review focuses on computational systems biology and synthetic biology tools that can be used in combination for prospective in silico strain design.