University of California Berkeley 2006

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[[Berkeley2006-NandMain | Logic Computation in Cellular Networks]]<br>
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[[Berkeley2006-NandMain | Logic Computation in Bacterial Networks]]<br>
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[[Berkeley2006-NetworksMain | Extending to Cellular Networks]] <br>
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[[Berkeley2006-NetworksMain | Trained Learning in a Bacterial Network]] <br>

Revision as of 04:26, 28 October 2006

Addressable Conjugation in Bacterial Networks

Berkeley2006School.jpg
Networks of interacting cells provide the basis for neural learning. We have developed the process of addressable conjugation for communication within a network of E. coli bacteria. Here, bacteria send messages to one another via conjugation of plasmid DNAs, but the message is only meaningful to cells with a matching address sequence. In this way, the Watson Crick base-pairing of addressing sequences replaces the spatial connectivity present in neural systems. To construct this system, we have adapted natural conjugation systems as the communication device. Information contained in the transferred plasmids is only accessable by "unlocking" the message using RNA based 'keys'. The resulting addressable conjugation process is being adapted to construct a network of NAND logic gates in bacterial cultures. Ultimately, this will allow us to develop networks of bacteria capable of trained learning.

Our Team


High School

Matt Fleming
Kaitlin A. Davis

Undergrads

Bryan Hernandez
Jennifer Lu
Samantha Liang
Daniel Kluesing
Will Bosworth

Postdocs

John E. Dueber
J. Christopher Anderson

Faculty Advisors

Adam P. Arkin
Jay D. Keasling

The specific goals of our project were to:

Berkeley2006IgemRocks.jpgConstruct high-performance riboregulator pairs
Berkeley2006IgemRocks.jpgHarness the process of bacterial conjugation
Berkeley2006IgemRocks.jpgDemonstrate transmission of a coded message
Berkeley2006IgemRocks.jpgDevelop promoter tools to tune gene expression


To learn more about our system, follow the series of descriptions below:

High-performance Riboregulators


Harnessing Bacterial Conjugation


Tuning Constitutive Expression Levels


Logic Computation in Bacterial Networks


Trained Learning in a Bacterial Network



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