Gene expression in the cell cycle of human T lymphocytes: I. Predicted gene and protein networks

Abstract

The key genes involved in the cell cycle of human T lymphocytes were identified by iterative searches of gene-related databases, as derived also from DNA microarray experimentation, revealing and predicting interactions between those genes, assigning scores to each of the genes according to numbers of interaction for each gene weighted by significance of each interaction, and finally applying several types of clustering algorithms to genes basing on the assigned scores. All clustering algorithms applied, both hierarchical and K-means, invariably selected the same six "leader" genes involved in controlling the cell cycle of human T lymphocytes. Relations of the six genes to experimental data describing switching between stages of cell cycle of human T lymphocytes are discussed.

LeaderGene: A Fast Data-mining Tool for Molecular Genomics

“…The leader gene approach has been already described in detail [5]. The bioinformatics/data mining algorithm followed is summarized in Figure 1.…”

“…The leader gene theoretical approach has already been applied and validated on some human biological or pathological processes, such as human T lymphocytes cell cycle, human periodontitis and human kidney transplant tolerance and rejection [3][4][5][6]8]. In this paragraph, we briefly present main findings obtained with this algorithm, with a special focus on human T lymphocytes cell cycle.…”

“…Leader gene identification was tried for the first time on human T lymphocytes cell cycle [5]. This particular system was chosen because it is much known and was quantitatively characterized time ago [9][10][11][12].…”

“…Recently, we proposed a bioinformatics algorithm, based on the identification of genes involved in a given process and their scoring of importance via a cluster analysis, which allowed us to determine the most important genes, that we call "leader genes" [3][4][5][6]. The basis of the scoring system relies upon the calculation of interactions among genes, performed with software available in the web, such as STRING [7].…”

“…The basis of the scoring system relies upon the calculation of interactions among genes, performed with software available in the web, such as STRING [7]. The number of links for each gene is then weighted and the final weighted numbers of links are clustered, in order to make a hierarchical classification of genes [5]. In this way, it becomes possible to draw and to update maps of the major biological control systems, and to integrate them in a concise manner to discern common patterns of interactions between gene expression and their correlated coding of proteins.…”