| Literature DB >> 31068573 |
Nathan Hillson1, Mark Caddick2, Yizhi Cai3, Jose A Carrasco4, Matthew Wook Chang5, Natalie C Curach6, David J Bell7, Rosalind Le Feuvre3, Douglas C Friedman8, Xiongfei Fu9, Nicholas D Gold10, Markus J Herrgård11, Maciej B Holowko12,13,14, James R Johnson2, Richard A Johnson15, Jay D Keasling1, Richard I Kitney7, Akihiko Kondo16, Chenli Liu9, Vincent J J Martin10, Filippo Menolascina17, Chiaki Ogino16, Nicola J Patron4, Marilene Pavan18, Chueh Loo Poh5, Isak S Pretorius19, Susan J Rosser17, Nigel S Scrutton3, Marko Storch7, Hille Tekotte17, Evelyn Travnik11, Claudia E Vickers12,13, Wen Shan Yew5, Yingjin Yuan20, Huimin Zhao21, Paul S Freemont22.
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Year: 2019 PMID: 31068573 PMCID: PMC6506534 DOI: 10.1038/s41467-019-10079-2
Source DB: PubMed Journal: Nat Commun ISSN: 2041-1723 Impact factor: 14.919
Fig. 1The Design-Build-Test-Learn (DBTL) biological engineering cycle. In simple terms the DBTL framework aims to fulfill particular design criteria for a synthetic biology application, which might for example be the production of a specific product at an optimal titer or the detection of a specific clinical biomarker using an engineered gut microbiome. The cycle begins with D (Design), which defines the desired target function/specifications and involves the computational design of genetic parts, circuits, regulatory and metabolic pathways to whole genomes; B (Build) involves the physical assembly of those designed genetic components; T (Test) involves the prototyping and testing of the assembled genetic designs in living cells (also called "chasses") at different scales, which also includes comprehensive analytical measurements (‘‘omics’’) of specific cellular components. This can also include testing components in cell-free extract systems; L (Learn) is the application of modeling and computational learning tools, which uses the data obtained in T to inform the design process. Iterations of the DBTL cycle results in genetic designs that aim to fulfill the design specifications established in D. In the figure the DBTL cycle is depicted around an imagined biofactory or biorefinery where many products will be produced using more sustainable and circular economic processes forming the future infrastructure for a global bioeconomy. (Credit: Christopher Johnson, DOE Agile BioFoundry, Golden, CO, USA)
Fig. 2Map of the Global Biofoundry Alliance. Map of the world showing the geographical locations of founding members of the Global Biofoundry Alliance with each biofoundry numbered. (1) DOE Agile BioFoundry (pending) located across Emeryville, CA, Richland, WA, Golden, CO, Lemont, IL, Los Alamos, NM, Oak Ridge, TN, and Idaho Falls, ID sites; (2) Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB), University of Illinois at Urbana-Champaign; (3) Concordia Genome Foundry, Concordia University Montreal; (4) DAMP lab, Boston University; (5) Edinburgh Genome Foundry, University of Edinburgh; (6) Earlham Institute, Norwich Research Park; (7) London DNA Foundry, Imperial College London; (8) SYNBIOCHEM, University of Manchester; (9) GeneMill University of Liverpool; (10) Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark; (11) Frontier Science Center for Synthetic Biology (MOE), Tianjin University; (12) Graduate School of Science, Technology and Innovation, Kobe University; (13) Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; (14) NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore; (15) Australian Foundry for Advanced Biomanufacturing (AusFAB), University of Queensland and (16) Australian Genome Foundry, Macquarie University. The Plotly.Py library was used to create the graphic and is open source under MIT license (https://github.com/plotly/plotly.py/blob/master/LICENSE.txt)