From 2006.igem.org

Revision as of 00:39, 31 January 2007 (view source) JCAnderson (Talk | contribs) (→Awarded:) ← Older edit		Revision as of 22:23, 4 April 2007 (view source) JCAnderson (Talk | contribs) (→Awarded:) Newer edit →
Line 1:		Line 1:
	[[Image:Berkeley200BestPartPic.jpg|650px]]<br>		[[Image:Berkeley200BestPartPic.jpg|650px]]<br>
	==Awarded:==		==Awarded:==
-	<span style='font-size:23.0pt'>
	'''1st place for Best Part'''<br>		'''1st place for Best Part'''<br>
	<br>		<br>
	'''3rd place for Best Measurement and Part Characterization'''<br>		'''3rd place for Best Measurement and Part Characterization'''<br>
	<br>		<br>
-	<br>	+
-	<br>	+	==Special Thanks:==
-	</span>	+	The Berkeley iGEM team very gratefully acknowledges the generous support of
		+	Microsoft, which helped make our team members' participation possible.
	<h1>Addressable Conjugation in Bacterial Networks</h1>		<h1>Addressable Conjugation in Bacterial Networks</h1>

---

Revision as of 22:23, 4 April 2007

Awarded:

1st place for Best Part

3rd place for Best Measurement and Part Characterization

Special Thanks:

The Berkeley iGEM team very gratefully acknowledges the generous support of Microsoft, which helped make our team members' participation possible.

Addressable Conjugation in Bacterial Networks

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: Construct high-performance riboregulator pairs Harness the process of bacterial conjugation Demonstrate transmission of a coded message Construct a bacterial learning network To learn more about our system, follow the series of descriptions below: High-performance Riboregulators Harnessing Bacterial Conjugation Controlling Message Sending and Receiving New Biobrick-Compatible Plasmids Logic Computation in Bacterial Networks Trained Learning in a Bacterial Network

---

To view our notebooks and protocols, visit our working website at [http://openwetware.org/wiki/IGEM:UC_Berkeley/2006 Berkeley 2006 iGEM]
See our parts list at [http://partsregistry.org/cgi/partsdb/pgroup.cgi?pgroup=iGEM2006partsregistry.org/cgi/partsdb/pgroup.cgi?pgroup=iGEM2006&group=Berkeley Registry 2006 Berkeley iGEM]
Download our Powerpoint presentation