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The need for adaptability in detection, characterization, and attribution of biosecurity threats

Author

Listed:
  • William Mo

    (The Charles Stark Draper Laboratory, Inc.
    University of Colorado Boulder)

  • Christopher A. Vaiana

    (Inc.)

  • Chris J. Myers

    (University of Colorado Boulder)

Abstract

Modern biotechnology necessitates robust biosecurity protocols to address the risk of engineered biological threats. Current efforts focus on screening DNA and rejecting the synthesis of dangerous elements but face technical and logistical barriers. Screening should integrate into a broader strategy that addresses threats at multiple stages of development and deployment. The success of this approach hinges upon reliable detection, characterization, and attribution of engineered DNA. Recent advances notably aid the potential to both develop threats and analyze them. However, further work is needed to translate developments into biosecurity applications. This work reviews cutting-edge methods for DNA analysis and recommends avenues to improve biosecurity in an adaptable manner.

Suggested Citation

  • William Mo & Christopher A. Vaiana & Chris J. Myers, 2024. "The need for adaptability in detection, characterization, and attribution of biosecurity threats," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-55436-y
    DOI: 10.1038/s41467-024-55436-y
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    1. Johnny H. Hu & Shannon M. Miller & Maarten H. Geurts & Weixin Tang & Liwei Chen & Ning Sun & Christina M. Zeina & Xue Gao & Holly A. Rees & Zhi Lin & David R. Liu, 2018. "Evolved Cas9 variants with broad PAM compatibility and high DNA specificity," Nature, Nature, vol. 556(7699), pages 57-63, April.
    2. Joseph L. Watson & David Juergens & Nathaniel R. Bennett & Brian L. Trippe & Jason Yim & Helen E. Eisenach & Woody Ahern & Andrew J. Borst & Robert J. Ragotte & Lukas F. Milles & Basile I. M. Wicky & , 2023. "De novo design of protein structure and function with RFdiffusion," Nature, Nature, vol. 620(7976), pages 1089-1100, August.
    3. John B. Ingraham & Max Baranov & Zak Costello & Karl W. Barber & Wujie Wang & Ahmed Ismail & Vincent Frappier & Dana M. Lord & Christopher Ng-Thow-Hing & Erik R. Van Vlack & Shan Tie & Vincent Xue & S, 2023. "Illuminating protein space with a programmable generative model," Nature, Nature, vol. 623(7989), pages 1070-1078, November.
    4. Nicole N. Thadani & Sarah Gurev & Pascal Notin & Noor Youssef & Nathan J. Rollins & Daniel Ritter & Chris Sander & Yarin Gal & Debora S. Marks, 2023. "Learning from prepandemic data to forecast viral escape," Nature, Nature, vol. 622(7984), pages 818-825, October.
    5. Joseph H. Collins & Kevin W. Keating & Trent R. Jones & Shravani Balaji & Celeste B. Marsan & Marina Çomo & Zachary J. Newlon & Tom Mitchell & Bryan Bartley & Aaron Adler & Nicholas Roehner & Eric M. , 2021. "Engineered yeast genomes accurately assembled from pure and mixed samples," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    6. Alec A. K. Nielsen & Christopher A. Voigt, 2018. "Deep learning to predict the lab-of-origin of engineered DNA," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    7. Vladimir Gligorijević & P. Douglas Renfrew & Tomasz Kosciolek & Julia Koehler Leman & Daniel Berenberg & Tommi Vatanen & Chris Chandler & Bryn C. Taylor & Ian M. Fisk & Hera Vlamakis & Ramnik J. Xavie, 2021. "Structure-based protein function prediction using graph convolutional networks," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    8. Tijana Radivojević & Zak Costello & Kenneth Workman & Hector Garcia Martin, 2020. "A machine learning Automated Recommendation Tool for synthetic biology," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    9. Qi Wang & Bryce Kille & Tian Rui Liu & R. A. Leo Elworth & Todd J. Treangen, 2021. "PlasmidHawk improves lab of origin prediction of engineered plasmids using sequence alignment," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    10. Ethan C. Alley & Miles Turpin & Andrew Bo Liu & Taylor Kulp-McDowall & Jacob Swett & Rey Edison & Stephen E. Stetina & George M. Church & Kevin M. Esvelt, 2020. "A machine learning toolkit for genetic engineering attribution to facilitate biosecurity," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    Full references (including those not matched with items on IDEAS)

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