Facilitate Collaborations among Synthetic Biology, Metabolic Engineering and Machine Learning. Issue 2 (4th March 2016) - Wu, Stephen Gang
Abstract: 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). SynBio has the potential to revolutionize the bio‐productions; 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, 13 C MFA (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 SynBio, but also facilitate text mining and machine learning to harness information for rational strain design. Ultimately, the data‐driven modeling and 13 C MFA will be integral components of the SynBio design‐build‐test‐learn cycle for generating novel microbial cell factories. Abstract : Synthetic biology has greatly advanced the scientific arena and offers new opportunities for industrial biotechnology. Much overlap exists between synthetic biology and metabolic engineering. By learning lessons from metabolic engineering, we may understand synthetic biology limitations and build up effective strategies to design microbial hosts for industrial applications.