2003/01/09 by K. Rho, Rho, K., H. Jeong +3
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #FOS: Biological sciences #FOS: Physical sciences #Quantitative Biology (q-bio) #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.stat-mech #q-bio
paper · pdf · doi:10.48550/arxiv.cond-mat/0301110
21 pages, 8 figures, 1 table, LaTeX
arxiv created 2003/01/09 · arxiv updated 2009/11/30
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, pm and ps, where the number of clusters becomes maximum and the connectivity distribution follows a power law, respectively. Interestingly, one of clusters at pm 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 ps. 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 pm.