Cell-free synthetic biology: Navigating the new frontiers of biomanufacturing and biological engineering

Lee, So Jeong; Kim, Dong-Myung

doi:10.1016/j.coisb.2023.100488

Cited by 2 publications

(1 citation statement)

References 57 publications

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“…The use cases discussed in various papers include: as a classifying text, generic search engine, generating ideas, helping with access to scientific knowledge, coding, patient care ( Clusmann et al, 2023 ), protein folding, proofreading, sequence analysis, summarizing knowledge, text mining of biomedical data, translation ( Clusmann et al, 2023 ), workflow optimization, foresight of future research directions ( Yan et al, 2023 ); collection of related synthetic biology data, and more ( Beardall et al, 2022 ; Clusmann et al, 2023 ; Tarasava, 2023 ). The promise of AI-enabled cell-free synbio systems ( Lee and Kim, 2023 ), which use molecular machinery extracted from cells, is particularly significant for automation and scale-up of biosensors among other things. It bears pointing out that the significant advances in cell-free synbio systems enabling the acceleration of biotechnology development, specifically its ability to enable rapid prototyping as well as the ability to conduct predictive modeling, pre-date generative AI by a decade ( Moore et al, 2018 ; Müller et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks

Undheim

2024

Front. Bioeng. Biotechnol.

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AI-enabled synthetic biology has tremendous potential but also significantly increases biorisks and brings about a new set of dual use concerns. The picture is complicated given the vast innovations envisioned to emerge by combining emerging technologies, as AI-enabled synthetic biology potentially scales up bioengineering into industrial biomanufacturing. However, the literature review indicates that goals such as maintaining a reasonable scope for innovation, or more ambitiously to foster a huge bioeconomy do not necessarily contrast with biosafety, but need to go hand in hand. This paper presents a literature review of the issues and describes emerging frameworks for policy and practice that transverse the options of command-and-control, stewardship, bottom-up, and laissez-faire governance. How to achieve early warning systems that enable prevention and mitigation of future AI-enabled biohazards from the lab, from deliberate misuse, or from the public realm, will constantly need to evolve, and adaptive, interactive approaches should emerge. Although biorisk is subject to an established governance regime, and scientists generally adhere to biosafety protocols, even experimental, but legitimate use by scientists could lead to unexpected developments. Recent advances in chatbots enabled by generative AI have revived fears that advanced biological insight can more easily get into the hands of malignant individuals or organizations. Given these sets of issues, society needs to rethink how AI-enabled synthetic biology should be governed. The suggested way to visualize the challenge at hand is whack-a-mole governance, although the emerging solutions are perhaps not so different either.

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