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Neural network computation by in vitro transcriptional circuits

Kim, Jongmin and Hopfield, John J. and Winfree, Erik (2004) Neural network computation by in vitro transcriptional circuits. Advances in Neural Information Processing Systems, 17 . pp. 681-688. ISSN 1049-5258.

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The structural similarity of neural networks and genetic regulatory networks to digital circuits, and hence to each other, was noted from the very beginning of their study [1, 2]. In this work, we propose a simple biochemical system whose architecture mimics that of genetic regulation and whose components allow for in vitro implementation of arbitrary circuits. We use only two enzymes in addition to DNA and RNA molecules: RNA polymerase (RNAP) and ribonuclease (RNase). We develop a rate equation for in vitro transcriptional networks, and derive a correspondence with general neural network rate equations [3]. As proof-of-principle demonstrations, an associative memory task and a feedforward network computation are shown by simulation. A difference between the neural network and biochemical models is also highlighted: global coupling of rate equations through enzyme saturation can lead to global feedback regulation, thus allowing a simple network without explicit mutual inhibition to perform the winner-take-all computation. Thus, the full complexity of the cell is not necessary for biochemical computation: a wide range of functional behaviors can be achieved with a small set of biochemical components.

Item Type:Article
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URLURL TypeDescription
Kim, Jongmin0000-0002-2713-1006
Winfree, Erik0000-0002-5899-7523
Additional Information:© 2004 MIT Press. We thank Michael Elowitz, Paul Rothemund, Casimir Wierzynski, Dan Stick and David Zhang for valuable discussions, and ONR and NSF for funding.
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Office of Naval Research (ONR)UNSPECIFIED
Record Number:CaltechAUTHORS:20111024-075732549
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:27363
Deposited By: Tony Diaz
Deposited On:24 Oct 2011 21:50
Last Modified:11 Nov 2020 20:43

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