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A synthetic protein-level neural network in mammalian cells

Chen, Zibo and Linton, James M. and Zhu, Ronghui and Elowitz, Michael B. (2022) A synthetic protein-level neural network in mammalian cells. . (Unpublished)

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Artificial neural networks provide a powerful paradigm for information processing that has transformed diverse fields. Within living cells, genetically encoded synthetic molecular networks could, in principle, harness principles of neural computation to classify molecular signals. Here, we combine de novo designed protein heterodimers and engineered viral proteases to implement a synthetic protein circuit that performs winner-take-all neural network computation. This "perceptein" circuit includes modules that compute weighted sums of input protein concentrations through reversible binding interactions, and allow for self-activation and mutual inhibition of protein components using irreversible proteolytic cleavage reactions. Altogether, these interactions comprise a network of 310 chemical reactions stemming from 8 expressed protein species. The complete system achieves signal classification with tunable decision boundaries in mammalian cells. These results demonstrate how engineered protein-based networks can enable programmable signal classification in living cells.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper Information and code
Chen, Zibo0000-0003-2990-2895
Zhu, Ronghui0000-0001-8171-482X
Elowitz, Michael B.0000-0002-1221-0967
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. We thank L. Chong, X. J. Gao, and U. Alon for scientific input; J. Gregrowicz, R. Du, B. Emert, B. Gu, F. Horns, D. Li, A. Lu, K. Luo, Y. Takei, and S. Xia for critical feedback. This research was supported by the National Institute of Health grant R01 MH116508 and the Allen Discovery Center program under Award No. UWSC10142, a Paul G. Allen Frontiers Group advised program of the Paul G. Allen Family Foundation. M.B.E. is a Howard Hughes Medical Institute Investigator. Z.C. is supported by the Damon Runyon Cancer Research Foundation DRG-2388-20 and is a fellow of the Burroughs Wellcome Fund Career Awards at the Scientific Interface. Author contributions: Z.C. and M.B.E. conceived and designed the study; Z.C. performed mathematical modeling with help from R.Z. and M.B.E.; Z.C. and J.M.L. performed experiments; R.Z. constructed the reporter cell line; Z.C. and M.B.E. wrote the manuscript with input from all authors. Data and materials availability: Raw data and code used for simulation and data analysis can be downloaded from Competing Interest Statement. Z.C. and M.B.E. have filed a provisional patent application based on this work. This article is subject to HHMI's Open Access to Publications policy. HHMI lab heads have previously granted a nonexclusive CC BY 4.0 license to the public and a sublicensable license to HHMI in their research articles. Pursuant to those licenses, the author-accepted manuscript of this article can be made freely available under a CC BY 4.0 license immediately upon publication.
Funding AgencyGrant Number
NIHR01 MH116508
Paul G. Allen Frontiers GroupUWSC10142
Howard Hughes Medical Institute (HHMI)UNSPECIFIED
Damon Runyon Cancer Research FoundationDRG-2388-20
Burroughs Wellcome FundUNSPECIFIED
Record Number:CaltechAUTHORS:20220712-283791000
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Official Citation:A synthetic protein-level neural network in mammalian cells Zibo Chen, James M Linton, Ronghui Zhu, Michael Elowitz bioRxiv 2022.07.10.499405; doi:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115493
Deposited By: George Porter
Deposited On:13 Jul 2022 20:42
Last Modified:13 Jul 2022 20:42

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