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Cellular Computing$
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Martyn Amos

Print publication date: 2004

Print ISBN-13: 9780195155396

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780195155396.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 26 October 2021

Cellular Computation and Communication Using Engineered Genetic Regulatory Networks

Cellular Computation and Communication Using Engineered Genetic Regulatory Networks

(p.120) 7 Cellular Computation and Communication Using Engineered Genetic Regulatory Networks
Cellular Computing

Ron Weiss

Thomas F. ,Jr., Knight

Oxford University Press

In this chapter we demonstrate the feasibility of digital computation in cells by building several operational in vivo digital logic circuits, each composed of three gates that have been optimized by genetic process engineering. We have built and characterized an initial cellular gate library with biochemical gates that implement the NOT, IMPLIES, andANDlogic functions in E. coli cells. The logic gates perform computation using DNA-binding proteins, small molecules that interact with these proteins, and segments of DNA that regulate the expression of the proteins. We also demonstrate engineered intercellular communications with programmed enzymatic activity and chemical diffusions to carry messages, using DNA from the Vibrio fischeri lux operon. The programmed communications is essential for obtaining coordinated behavior from cell aggregates. This chapter is structured as follows: the first section describes experimental measurements of the device physics of in vivo logic gates, as well as genetic process engineering to modify gates until they have the desired behavior. The second section presents experimental results of programmed intercellular communications, including time–response measurements and sensitivity to variations in message concentrations. Potentially the most important element of biocircuit design is matching gate characteristics. Experimental results in this section demonstrate that circuits with mismatched gates are likely to malfunction. In generating biology’s complex genetic regulatory networks, natural forces of selection have resulted in finely tuned interconnections between the different regulatory components. Nature has optimized and matched the kinetic characteristics of these elements so that they cooperatively achieve the desired regulatory behavior. In building de novo biocircuits, we frequently combine regulatory elements that do not interact in their wild-type settings. Therefore, naive coupling of these elements will likely produce systems that do not have the desired behavior. In genetic process engineering, the biocircuit designer first determines the behavioral characteristics of the regulatory components and then modifies the elements until the desired behavior is attained. Below, we show experimental results of using this process to convert a nonfunctional circuit with mismatched gates into a circuit that achieves the correct response.

Keywords:   Autoinducer, BioSPICE, Codon, Dimerization, Fatty acid, Genetic process engineering, Inverter, Kinetic characteristic, Mutagenesis, Operator

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