Facilitate collaborations among synthetic biology, metabolic engineering and machine learning

Metabolic engineering (ME) and synthetic biology (SynBio) are two intersecting fields with different focal points. While SynBio focuses more on genomic aspects to build novel cell devices, ME emphasizes the phenotypic outputs (e.g., production). Syn-Bio has the potential to revolutionize the bioproductions; however, the introduction of synthetic devices/pathways often consumes significant cellular resources and incurs fitness costs. Currently, SynBio applications still lack guidelines in re-allocating cellular carbon and energy fluxes. To resolve this, ME principles may help the SynBio community. First, ¹³CMFA (metabolic flux analysis) can characterize the burdens of genetic infrastructures and reveal optimal strategies for distributing cellular resources. Second, novel microbial chassis should be explored to employ their unique metabolic features for product synthesis. Third, standardization and classification of bio-production papers will not only improve the communication between ME and Syn-Bio, but also facilitate text mining and machine learning to harness information for rational strain design. Ultimately, the data-driven modeling and ¹³CMFA will be integral components of the SynBio design-build-test-learn cycle for generating novel microbial cell factories.