Analysis of Regulatory and Interaction Networks from Clusters of Co-expressed Genes - Clustering Challenges in Biological Network - page 54

characterizing the mechanism of action of anti-inflammatory drugs. We conclude
by identifying two possible challenges in network biology, namely, the nature of
the interactions and the potentially limited information content of the temporal
gene expression experiments, and discuss expected implications.
3.1. Identification of Intervention Targets: Regulatory and Interaction
Networks
At any given time, a cell will only express a small fraction of the thousands of
genes in the organism's genome. Expressed genes reflect the structure and func-
tional capacities of the cell as well as the ability of the cell to respond to external
stimuli. In a complex organism, external stimuli to a great extent take the form of
chemical messages whose purpose is to coordinate the function of the complex so-
ciety of cells [1]. The transcription of genes is tightly regulated by DNA-binding
proteins (transcription factors, TF) that attach to the promoter region and regu-
late the expression of the corresponding genes. If the state of the cell is defined
by the genes that are expressed within it, and if the expression is the result of
the coordinated action of a group of transcription factors, then losing even a sin-
gle transcription factor can, and will have, a profound effect on the state of the
cell [2]. Targeting expression by controlling the regulatory process through the
corresponding transcription factors is emerging as a viable alternative for the iden-
tification of drug targets [3, 4] and the control of disease conditions [5]. Although
it is realized that gene expression is regulated at multiple levels (transcription-
ally, translationally and post-translationally) an effective and common means of
regulating it occurs at the transcription level [2].
In recent years, significant efforts have been made experimentally and com-
putationally to identify transcription factors, their target genes and the interac-
tion mechanisms that regulate gene expression [6, 7]. An important technique for
elucidating binding interactions is chromatin immunoprecipitation (ChiP) exper-
iments [8]. Computational methods are currently emerging to provide TF pre-
dictions where experimental data is not available [9]. However, physical bind-
ing of a TF is a necessary but not sufficient condition for transcription initiation
or regulation. Due to various complex post-translational modifications as well
as interactions among multiple TFs, the measured expression level of regulatory
genes does not reflect the actual activity of the TFs themselves. Therefore, reg-
ulator transcription levels are generally not appropriate measures of transcription
factor activity (TFA). Recently, methods combining TF-gene connectivity data
and gene expression measurements have emerged as a method to quantify these
regulatory interactions. Prominent examples are the decomposition-based meth-