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Facilitate Collaborations among Synthetic Biology, Metabolic Engineering and Machine Learning
Author(s) -
Wu Stephen Gang,
Shimizu Kazuyuki,
Tang Joseph KuoHsiang,
Tang Yinjie J.
Publication year - 2016
Publication title -
chembioeng reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.089
H-Index - 22
ISSN - 2196-9744
DOI - 10.1002/cben.201500024
Subject(s) - synthetic biology , metabolic engineering , standardization , chassis , computer science , biochemical engineering , production (economics) , metabolic flux analysis , bioproduction , systems biology , data science , artificial intelligence , biology , computational biology , microbiology and biotechnology , engineering , biochemistry , macroeconomics , structural engineering , metabolism , economics , enzyme , endocrinology , operating system
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.