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Engineered gene circuits

Author

Listed:
  • Jeff Hasty

    (University of California San Diego)

  • David McMillen

    (Boston University)

  • J. J. Collins

    (Boston University)

Abstract

A central focus of postgenomic research will be to understand how cellular phenomena arise from the connectivity of genes and proteins. This connectivity generates molecular network diagrams that resemble complex electrical circuits, and a systematic understanding will require the development of a mathematical framework for describing the circuitry. From an engineering perspective, the natural path towards such a framework is the construction and analysis of the underlying submodules that constitute the network. Recent experimental advances in both sequencing and genetic engineering have made this approach feasible through the design and implementation of synthetic gene networks amenable to mathematical modelling and quantitative analysis. These developments have signalled the emergence of a gene circuit discipline, which provides a framework for predicting and evaluating the dynamics of cellular processes. Synthetic gene networks will also lead to new logical forms of cellular control, which could have important applications in functional genomics, nanotechnology, and gene and cell therapy.

Suggested Citation

  • Jeff Hasty & David McMillen & J. J. Collins, 2002. "Engineered gene circuits," Nature, Nature, vol. 420(6912), pages 224-230, November.
    • Handle: RePEc:nat:nature:v:420:y:2002:i:6912:d:10.1038_nature01257
    DOI: 10.1038/nature01257
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    Citations

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    Cited by:

    1. Chih-Yuan Hsu & Bor-Sen Chen, 2016. "Systematic Design of a Metal Ion Biosensor: A Multi-Objective Optimization Approach," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-16, November.
    2. Tobias May & Lee Eccleston & Sabrina Herrmann & Hansjörg Hauser & Jorge Goncalves & Dagmar Wirth, 2008. "Bimodal and Hysteretic Expression in Mammalian Cells from a Synthetic Gene Circuit," PLOS ONE, Public Library of Science, vol. 3(6), pages 1-7, June.
    3. Javier Macia & Romilde Manzoni & Núria Conde & Arturo Urrios & Eulàlia de Nadal & Ricard Solé & Francesc Posas, 2016. "Implementation of Complex Biological Logic Circuits Using Spatially Distributed Multicellular Consortia," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-24, February.
    4. Brian Drawert & Andreas Hellander & Ben Bales & Debjani Banerjee & Giovanni Bellesia & Bernie J Daigle Jr. & Geoffrey Douglas & Mengyuan Gu & Anand Gupta & Stefan Hellander & Chris Horuk & Dibyendu Na, 2016. "Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-15, December.
    5. Avraham E Mayo & Yaakov Setty & Seagull Shavit & Alon Zaslaver & Uri Alon, 2006. "Plasticity of the cis-Regulatory Input Function of a Gene," PLOS Biology, Public Library of Science, vol. 4(4), pages 1-1, March.
    6. Kanakov, Oleg & Chen, Shangbin & Zaikin, Alexey, 2024. "Learning by selective plasmid loss for intracellular synthetic classifiers," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    7. Otero-Muras, Irene & Szederkényi, Gábor & Hangos, Katalin M. & Alonso, Antonio A., 2008. "Dynamic analysis and control of biochemical reaction networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(4), pages 999-1009.
    8. Luna Rizik & Loai Danial & Mouna Habib & Ron Weiss & Ramez Daniel, 2022. "Synthetic neuromorphic computing in living cells," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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