Exploiting transcriptomic data for metabolic engineering: toward a systematic strain design

Abstract

Transcriptomics is now recognized as a primary tool for metabolic engineering as it can be used for identifying new strain designs by diagnosing current states of microbial cells. This review summarizes current application of transcriptomic data for strain design. Along with a few successful examples, limitations of conventionally used differentially expressed gene-based strain design approaches have been discussed, which have been major reasons why transcriptomic data are considerably underutilized. Recently, integrative network-based approaches interpreting transcriptomic data in the context of biological networks were invented to provide complimentary solutions for metabolic engineering by overcoming the limitations of conventional approaches. Here, we highlight recent pioneering studies in which integrative network-based methods have been used for providing novel strain designs.