Head of Analytical and Strain R&D DSM Nutrition Products Columbia, MD, United States
Abstract: Traditional synthetic biology and metabolic engineering relies heavily on the screening of KPIs – as product titer or productivity is the most accurate and direct read-out (KPI) to design further improvements. Although significant progress has been made to enable key industrial bioderived products, without further understanding of important parameters such as pathway intermediates, microbial physiology and strain robustness, the strain and bioprocess improvements are still a black box.
Recent development in omics – especially metabolomics, lipidomics, proteomics and transcriptomics, has enabled deeper learnings in the DBTL cycle (Design-Build-Test-Learn). In industrial settings, similar learnings have also been applied into different scales to enable successful tech transfer and scale up. Such quantitative studies of microbial pathways and behavior in addition to product titers have enabled successful bioengineering of previously termed impossible targets and acceleration of DBTL cycle for KPI gains. In this talk, we will cover how omics combined with data science helped each step of the DBTL cycle of lipid production and address gaps and challenges in the industrial omics field.
*Presenter: Frank Xu, Ph.D. is the department head of analytics and strain R&D in DSM Columbia.
