4.8 Article

Synthetic recombinase-based state machines in living cells

期刊

SCIENCE
卷 353, 期 6297, 页码 363-+

出版社

AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.aad8559

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资金

  1. Ford Foundation Pre-doctoral Fellowships Program and Molecular Biophysics Training Grant [NIH/NIGMS T32 GM008313]
  2. NSF Alan T. Waterman Award [1249349]
  3. Center for Microbiome Informatics and Therapeutics
  4. NIH [DP2 OD008435, P50 GM098792]
  5. Office of Naval Research [N00014-13-1-0424, N0001411110725]
  6. NSF [MCB-1350625]
  7. NSF Expeditions in Computing Program Award [1522074]
  8. Defense Advanced Research Projects Agency [HR0011-15-C-0091]
  9. Direct For Computer & Info Scie & Enginr
  10. Division of Computing and Communication Foundations [1521925] Funding Source: National Science Foundation
  11. Division of Computing and Communication Foundations
  12. Direct For Computer & Info Scie & Enginr [1522074] Funding Source: National Science Foundation
  13. Div Of Molecular and Cellular Bioscience
  14. Direct For Biological Sciences [1350625] Funding Source: National Science Foundation

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State machines underlie the sophisticated functionality behind human-made and natural computing systems that perform order-dependent information processing. We developed a recombinase-based framework for building state machines in living cells by leveraging chemically controlled DNA excision and inversion operations to encode states in DNA sequences. This strategy enables convenient readout of states (by sequencing and/or polymerase chain reaction) as well as complex regulation of gene expression. We validated our framework by engineering state machines in Escherichia coli that used one, two, or three chemical inputs to control up to 16 DNA states. These state machines were capable of recording the temporal order of all inputs and performing multi-input, multi-output control of gene expression. We also developed a computational tool for the automated design of gene regulation programs using recombinase-based state machines. Our scalable framework should enable new strategies for recording and studying how combinational and temporal events regulate complex cell functions and for programming sophisticated cell behaviors.

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