Analysis of Regulatory and Interaction Networks from Clusters of Co-expressed Genes - Clustering Challenges in Biological Network - page 69

an interesting question. CDX and PAX-4 are not known to be expressed in the
liver, a fact which is supported by the expression levels found in our microar-
ray. However, biological processes associated with these two transcription factors
seem to correlate well with the clinical observations of corticosteroid administra-
tion. PAX-4 is normally associated with the differentiation of beta islet cells in
the pancreas [67] and consequently whose expression levels in the pancreas ought
to change in response to possible steroid induced diabetes due to the lowered sen-
sitivity of the overall organism to insulin. CDX on the other hand has also been
characterized as a regulator of cancer cell proliferation and is often up-regulated
in rapidly proliferating cells [68]. Given the presence of these transcription factors
the question arises as to whether a consistent set of genes is relevant over multiple
tissues and that the different expression profiles of these relevant genes are con-
trolled via the presence or absence of different transcription factors, or that these
transcription factor binding sites represent the presence of a currently unknown
transcription factor that binds to a very similar recognition sequence as CDX and
PAX-4.
3.3. Analysis of Interaction Networks
3.3.1. Expression Data
Male Sprague-Dawley rats were subjected to a cutaneous 3rd degree burn injury
consisting of a full skin thickness scald burn of the dorsum, calculated to be 20%
of the rat's total body surface area [69]. Liver samples were obtained at 5 time
points (0, 1, 4, 8, and 24h post burn). RNA extracted from the extracted livers was
isolated and subsequently hybridized to a U34A GeneChip that had 8,799 probes
represented on each chip. The control for this experiment was obtained at time
0, which was prior to the injury. It has been previously shown that time had no
significant effect upon the response of rats to the sham treatment [70].
3.3.2. Interaction Network Construction and Analysis
In vivo estimation of protein interactions is undoubtedly the best way of de-
termining biological interactions within the cell [71]. However, such an ap-
proach is rather impractical in exploratory analyses. Therefore, in silico meth-
ods have been proposed that, effectively, mine the available literature for estab-
lished relationships between proteins exemplified by software tools such as In-
genuity Pathways Analysis (http://www.ingenuity.com/) , GeneGo Systems Re-
construction (http://www.genego.com/) and Ariadne Genomics PathwayAssist
(http://www.ariadnegenomics.com/) are already available and able to construct