Synthetic biology industry: data-driven design is creating new opportunities in biotechnology

被引:26
|
作者
Freemont, Paul S. [1 ,2 ,3 ,4 ]
机构
[1] Imperial Coll London, Dept Infect Dis, Sect Struct & Synthet Biol, Sir Alexander Fleming Bldg, London SW7 2AZ, England
[2] Imperial Coll Translat & Innovat Hub, UK Innovat & Knowledge Ctr Synthet Biol SynbiCITE, White City Campus,80 Wood Lane, London W12 0BZ, England
[3] Imperial Coll Translat & Innovat Hub, London BioFoundry, White City Campus,80 Wood Lane, London W12 0BZ, England
[4] Imperial Coll London, UK Dementia Res Inst, Care Res & Technol Ctr, Hammersmith Campus,Du Cane Rd, London W12 0NN, England
基金
英国工程与自然科学研究理事会; 英国生物技术与生命科学研究理事会; 英国科研创新办公室;
关键词
D O I
10.1042/ETLS20190040
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Synthetic biology is a rapidly emerging interdisciplinary research field that is primarily built upon foundational advances in molecular biology combined with engineering design. The field considers living systems as programmable at the genetic level and has been defined by the development of new platform technologies. This has spurned a rapid growth in start-up companies and the new synthetic biology industry is growing rapidly, with start-up companies receiving similar to$6.1B investment since 2015 and a global synthetic biology market value estimated to be $14B by 2026. Many of the new start-ups can be grouped within a multi-layer 'technology stack'. The 'stack' comprises a number of technology layers which together can be applied to a diversity of new biotechnology applications like consumer biotechnology products and living therapies. The 'stack' also enables new commercial opportunities and value chains similar to the software design and manufacturing revolution of the 20th century. However, the synthetic biology industry is at a crucial point, as it now requires recognisable commercial successes in order for the industry to expand and scale, in terms of investment and companies. However, such expansion may directly challenge the ethos of synthetic biology, in terms of open technology sharing and democratisation, which could unintentionally lead to multi-national corporations and technology monopolies similar to the existing biotechnology/biopharma industry.
引用
收藏
页码:651 / 657
页数:7
相关论文
共 50 条
  • [41] On the link between Education and Industry 4.0: a framework for a data-driven education design
    Spada, Irene
    Chiarello, Filippo
    Curreli, Alessandra
    Fantoni, Gualtiero
    PROCEEDINGS OF THE 2022 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON 2022), 2022, : 1670 - 1677
  • [42] Data-Driven Product Design and Axiomatic Design
    Yang, Bin
    Xiao, Ren-bin
    PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATICS AND COMPUTING (PIC), 2021, : 489 - 493
  • [43] Resourcing with data: Unpacking the process of creating data-driven value propositions
    Gunther, Wendy Arianne
    Mehrizi, Mohammad H. Rezazade
    Huysman, Marleen
    Deken, Fleur
    Feldberg, Frans
    JOURNAL OF STRATEGIC INFORMATION SYSTEMS, 2022, 31 (04):
  • [44] Data-driven Soft Sensors in the process industry
    Kadlec, Petr
    Gabrys, Bogdan
    Strandt, Sibylle
    COMPUTERS & CHEMICAL ENGINEERING, 2009, 33 (04) : 795 - 814
  • [45] Data-driven portfolio management for motion pictures industry: A new data-driven optimization methodology using a large language model as the expert
    Alipour-Vaezi, Mohammad
    Tsui, Kwok-Leung
    COMPUTERS & INDUSTRIAL ENGINEERING, 2024, 197
  • [46] Data-driven cardiovascular flow modelling: Examples and opportunities
    Arzani A.
    Dawson S.T.M.
    Journal of the Royal Society Interface, 2021, 18 (175)
  • [47] Data-driven synthetic wavefront generation for boundary layer data
    Utley, Jeffrey W.
    Buzzard, Gregery T.
    Bouman, Charles A.
    Kemnitz, Matthew R.
    UNCONVENTIONAL IMAGING, SENSING, AND ADAPTIVE OPTICS 2024, 2024, 13149
  • [48] Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization
    Gotz, David
    Borland, David
    IEEE COMPUTER GRAPHICS AND APPLICATIONS, 2016, 36 (03) : 90 - 96
  • [49] Data-Driven ICS Network Simulation for Synthetic Data Generation
    Kim, Minseo
    Jeon, Seungho
    Cho, Jake
    Gong, Seonghyeon
    ELECTRONICS, 2024, 13 (10)
  • [50] Data-driven design of molecular nanomagnets
    Duan, Yan
    Rosaleny, Lorena E.
    Coutinho, Joana T.
    Gimenez-Santamarina, Silvia
    Scheie, Allen
    Baldovi, Jose J.
    Cardona-Serra, Salvador
    Gaita-Arino, Alejandro
    NATURE COMMUNICATIONS, 2022, 13 (01)