NSF Org:	IIS Division of Information & Intelligent Systems
Recipient:	VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY
Initial Amendment Date:	September 18, 2001
Latest Amendment Date:	September 18, 2001
Award Number:	0131899
Award Instrument:	Standard Grant
Program Manager:	Lawrence Brandt IIS  Division of Information & Intelligent Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	October 1, 2001
End Date:	September 30, 2003 (Estimated)
Total Intended Award Amount:	$100,000.00
Total Awarded Amount to Date:	$100,000.00
Funds Obligated to Date:	FY 2001 = $100,000.00
History of Investigator:	Allan Dickerman (Principal Investigator) dickerman@vt.edu Brett Tyler (Co-Principal Investigator)
Recipient Sponsored Research Office:	Virginia Polytechnic Institute and State University 300 TURNER ST NW BLACKSBURG VA  US  24060-3359 (540)231-5281
Sponsor Congressional District:	09
Primary Place of Performance:	VIRGINIA POLYTECH INST AND STATE UN 300 TURNER ST NW BLACKSBURG VA  US  24060-3359
Primary Place of Performance Congressional District:	09
Unique Entity Identifier (UEI):	QDE5UHE5XD16
Parent UEI:	X6KEFGLHSJX7
NSF Program(s):	DIGITAL GOVERNMENT
Primary Program Source:	app-0101
Program Reference Code(s):	1706, 1718, 9218, HPCC
Program Element Code(s):	170600
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

EIA-0131899
Dickerson, Allan
Virginia Polytechnic Institute and State University


SUMMARY

Cells routinely perform complex computational tasks that enable them to control the orchestration
of thousands of genes and communicate with other cells to manifest emergent properties such as growth
and differentiation.Understanding and engineering the algorithms underlying the complex computational
machinery of cells should have significant impact in science and technology,particularly biotechnology,
biocomputation and medicine.

Several notable recent reports demonstrate that it is possible to design and construct simple de
novo genetic circuits such as a switch and an oscillator in Escherichia coli .This work also revealed that
implementation of even the most simple circuits in vivo requires tedious optimization of often poorly-
understood protein-DNA interactions and mRNA and protein stabilities,among other parameters.We
propose to develop efficient,evolutionary design strategies for constructing functional de novo genetic
circuits.We will apply methods of molecular evolution,which have proven highly successful for
engineering proteins with improved or altered properties,to complex genetic systems involving multiple
repressors,operators,and promoters.We believe that evolution will prove to be generally applicable for
optimizing individual devices as well as complex genetic circuits,and our goal will be to demonstrate
how evolutionary searches are best performed in order to build libraries of devices and assemble them
into functional circuits.

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