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Genome-Scale
Modeling of Low Dose Irradiation Responses Using Microarray
Based Gene Networks
Matthew
Coleman
coleman16@llnl.gov
Lawrence Livermore National Laboratory
Why
This Project:
Cells
and tissues with similar radiation response phenotypes are
predicted to have common ionizing radiation (IR)-induced gene
expression profiles that are controlled by shared groups of
regulatory elements (also known as synergistic gene groups).
Microarrays make it possible to identify these responses.
Statistical methods are being developed to evaluate and characterize
changes in gene expression that will make it possible to define
cellular and molecular pathways that lead from early response
to the development of disease.
Our
overall objective is to utilize genome-scale expression microarray
data in conjunction with DNA sequence/pattern databases available
on the Web, to build a computer-based gene-network model for
identifying, grouping and predicting regulatory elements that
control differential aspects of the early cellular responses
to IR. This research project will:
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Group
genes identified by microarray experiments into IR-responsive
clusters based on their relative-transcript abundance
and their differential IR radiation responses at low (10cGy)
and high doses
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Identify regulatory elements (and their locations relative
to the open reading frame) that distinguish among separate
IR responsive gene clusters
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Compare IR responsive gene clusters identified “in
silico” to those IR responsive genes identified
on microarrays in many different laboratories.
To
accomplish this we have brought together a diverse group of
investigators to build a gene pathway model of the cellular
controls of radiation response. This project takes advantage
of stand alone software (CLUSFAVOR,
http://mbcr.bcm.tmc.edu/genepi/) to identify clusters and groups
of interesting IR responsive genes that are then used to search
relational database systems (http://www.llnl.gov/CASC/datafoundry/index.html,
SDM http://sdm.lbl.gov/sdmcenter/)
and to parse information from genomic databases such as GenBank,
KEGG and UniGene. Obtained sequence information is then used
for promoter analysis (ModelInspector,
http://genomatix.gsf.de/) to identify synergistic gene groups
that share conserved regulatory elements. This database is being
designed to allow data analysis across multiple platforms (i.e.,
cDNA as well as oligo-array data).
In the future, experimental low-dose radiation array data sets
from microarray experiments will be compared to the resulting
database of synergistic groups and their regulatory elements
to assess the ability and accuracy of our ability to predicted
the same IR responsive genes and pathways. The identification
and characterization of regulatory element profiles of IR-responsive
genes will provide valuable understanding of the genetic mechanisms
of IR-response and should provide powerful biological indicators
of genetic susceptibilities for tissue and genetic damage. The
resulting model is being developed as a foundation for a unique
predictor of new genes and for testing new hypotheses related
to exposure of IR based on coordinate gene/pathway interactions.
Such a model will link early molecular and cellular changes
to cancer processes.
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