Microarray technology enables investigators to simultaneously measure the expression of thousands of genes and holds the promise to cast new light onto the regulatory mechanisms of the genome. A main avenue of experimental investigation, leveraging on this technology, is based on the temporal dissection of cellular mechanisms. Temporal experiments offer the possibility of observing these mechanisms in action and to break down the genome into sets of genes involved in the same processes. The overall goal of this project is to develop an unsupervised approach and an integrated software environment to automatically discover regulatory mechanisms from temporal microarray experiments. The hypothesis underpinning our approach is that complex interaction patterns can be identified through analysis of conditional rather than marginal gene expression profiles. This novel approach also provides principled guidance to experimental design and sampling strategies, and it naturally extends to a large class of statistical models, able to capture a wider range of dynamic behaviors and experimental designs. We plan to develop a comprehensive framework to design and analyze microarray data collected through temporal experiments. This framework will be used to specify and answer the critical design questions of sample size and sampling frequency determination. Using this framework, we will develop a new model-based approach and an iterative search algorithm, called Conditional Clustering, to identify different patterns of behavior determined by a set of genes through the analysis of the behavior of a gene given a set of other genes, rather than the behavior of each gene in isolation. We will implement this design and analysis framework in a computer program that will be distributed over the Internet.This project brings together researchers in artificial intelligence, theoretical statistics and experimental design with a long track record of methodological contributions to bioinformatics to develop a novel methodological approach to a critical question at the forefront of genomic research. ? ?

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG003354-02
Application #
7176152
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Good, Peter J
Project Start
2006-02-03
Project End
2009-01-31
Budget Start
2007-02-01
Budget End
2008-01-31
Support Year
2
Fiscal Year
2007
Total Cost
$305,165
Indirect Cost
Name
Children's Hospital Boston
Department
Type
DUNS #
076593722
City
Boston
State
MA
Country
United States
Zip Code
02115
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