Identification of essential and functionally moduled genes through the microarray assay

TL;DR

This paper presents an in silico method using gene transcription networks derived from microarray data to identify essential genes and functional modules, aiding drug target discovery.

Contribution

The study introduces a novel graph-based approach to detect essential gene clusters and functional modules from microarray data, highlighting self-organized identification of critical gene groups.

Findings

Identification of two meaningful link fractions, pm and ps, related to network properties.

Discovery of a gene cluster with high essentiality and functional similarity at pm.

Demonstration that essential genes tend to have higher connectivity in the network.

Abstract

Identification of essential genes is one of the ultimate goals of drug designs. Here we introduce an {\it in silico} method to select essential genes through the microarray assay. We construct a graph of genes, called the gene transcription network, based on the Pearson correlation coefficient of the microarray expression level. Links are connected between genes following the order of the pair-wise correlation coefficients. We find that there exist two meaningful fractions of links connected, $p_m$ and $p_s$, where the number of clusters becomes maximum and the connectivity distribution follows a power law, respectively. Interestingly, one of clusters at $p_m$ contains a high density of essential genes having almost the same functionality. Thus the deletion of all genes belonging to that cluster can lead to lethal inviable mutant efficiently. Such an essential cluster can be identified in a self-organized way. Once we measure the connectivity of each gene at $p_s$. Then using the property that the essential genes are likely to have more connectivity, we can identify the essential cluster by finding the one having the largest mean connectivity per gene at $p_m$.