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Cited by 365Gene Regulatory Network AnalysisMicrobial Metabolic Engineering and BioproductionCRISPR and Genetic Engineering

Next-generation synthetic gene networks

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Lu, Timothy K, Khalil, Ahmad S, Collins, James J·2009·Nature Biotechnology
DOI 10.1038/nbt.1591PMC PMC2796205
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Abstract

Synthetic biology is focused on the rational construction of biological systems based on engineering principles. During the field's first decade of development, significant progress has been made in designing biological parts and assembling them into genetic circuits to achieve basic functionalities. These circuits have been used to construct proof-of-principle systems with promising results in industrial and medical applications. However, advances in synthetic biology have been limited by a lack of interoperable parts, techniques for dynamically probing biological systems and frameworks for the reliable construction and operation of complex, higher-order networks. As these challenges are addressed, synthetic biologists will be able to construct useful next-generation synthetic gene networks with real-world applications in medicine, biotechnology, bioremediation and bioenergy.

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  • Generative AI is projected to build and use next-generation synthetic gene networks for various AI applications in health care, including medical diagnostics, drug discovery, and medical research.

    “From a broader perspective, generative AI is projected to build and use next-generation synthetic gene networks for various AI applications in health care, including medical diagnostics, drug discovery, and medical research [73].”

    From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [73]

    The Abstract and Conclusions project medical applications for next-generation synthetic gene networks, and the Whole-Cell Biosensors and Response Systems section discusses potential medical sensing and imaging uses; page numbers are unavailable in our copy. The paper does not project that generative AI will build or use these networks, nor does it identify drug discovery as an AI application. (LLM, conf=0.88)

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Authors
Lu, T. K., Khalil, A. S., & Collins, J. J.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Boston University (US)
Corresponding author
Lu, Timothy K
ORCID
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2009-12
Volume
27
Issue
12
Pages
1139-1150
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
DOI
10.1038/nbt.1591
PMC
PMC2796205
OpenAlex
W2057993765
Cited by
365
References
5
Citations by year
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FWCI
15.45
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Howard Hughes Medical Institute
  • National Institutes of Health (DP1OD003644)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
Keywords
synthetic gene networks, synthetic gene circuits, synthetic biology, biological parts, interoperability, higher-order networks, bioremediation, bioenergy, biotechnology applications, medical applications
MeSH terms
DNA, Forecasting, Genetic Engineering, Molecular Biology, Signal Transduction, Gene Expression

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