Analysis of Regulatory and Interaction Networks from Clusters of Co-expressed Genes - Clustering Challenges in Biological Network - page 53

Chapter 3
Analysis of Regulatory and Interaction Networks from Clusters of
Co-expressed Genes
E. Yang
Biomedical Engineering Department
Rutgers University, Piscataway, NJ 08854, USA
A. Misra
Department of Environmental and Occupational Medicine
Rutgers University, Piscataway, NJ 08854, USA
T. J. Maguire
Biomedical Engineering Department
Rutgers University, Piscataway, NJ 08854, USA
I. P. Androulakis ∗
Biomedical Engineering Department
Rutgers University, Piscataway, NJ 08854, USA
Extracting biological insight from high-throughput genomic studies of human
diseases remains a major challenge, primarily due to our inability to recog-
nize, evaluate and rationalize the relevant biological processes recorded from
vast amounts of data.
We will discuss an integrated framework combining fine-grained clustering
of temporal gene expression data, selection of maximally informative clusters,
based of their ability to capture the underlying dynamic transcriptional response,
and the subsequent analysis of the resulting network of interactions among genes
in individual clusters. The latter are developed based on the identification of
common regulators among the genes in each cluster through mining literature
data. We characterize the structure of the networks in terms of fundamental graph
properties, and explore biologically the implications of the scale-free character
of the resulting graphs. We demonstrate the biological importance of the highly
connected hubs of the networks and show how these can be further exploited
as targets for potential therapies during the early onset of inflammation and for
∗ Corresponding Author
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