Genome-scale in silico modeling and analysis for designing synthetic terpenoid-producing microbial cell factories

Terpenoids are a large and diverse group of plant secondary metabolites with important applications in the pharmaceutical, cosmetic and food industries. However, low yields obtained from natural plant sources necessitate the search for alternative ways to increase the throughput of terpenoid production. Thus, fast-growing microbial systems, such as Escherichia coli and Saccharomyces cerevisiae, can be genetically engineered to achieve high productivity by systematically designing terpenoid synthetic routes and improving its precursor, isopentenyl diphosphate (IPP) production. To this end, we develop an in silico model-based rational framework where the cellular metabolism in natural terpenoid producer is analyzed to provide a basis for designing its synthetic pathways in microbial hosts. At the outset, we updated the genome-scale Arabidopsis thaliana metabolic model to characterize optimal metabolic utilization patterns that were subsequently incorporated into the in silico models of microbial hosts for improving terpenoid yield. We also developed a novel computational approach, cofactor modification analysis (CMA), to tackle potential limitations in terpenoid production caused by suboptimal balance of the different redox cofactors. The enzyme targets identified by CMA can potentially lead to better metabolic engineering strategies for enhancing terpenoid production in microbial systems.