Knowledge-driven approaches for engineering complex metabolic pathways in plants

Abstract

Plant metabolic pathways are complex and often feature multiple levels of regulation. Until recently, metabolic engineering in plants relied on the laborious testing of ad hoc modifications to achieve desirable changes in the metabolic profile. However, technological advances in data mining, modeling, multigene engineering and genome editing are now taking away much of the guesswork by allowing the impact of modifications to be predicted more accurately. In this review we discuss recent developments in knowledge-based metabolic engineering strategies, that is the gathering and mining of genomic, transcriptomic, proteomic and metabolomic data to generate models of metabolic pathways that help to define and refine optimal intervention strategies.