Prediction of gene regulatory networks using differential expression of cDNA microarray data

Several often thousands functional genes control the growth, genetics, and behavior of living organisms by regulating different gene expressions. The genes in a normal cell control the process of cell growth, differentiation, reproduction, and apoptosis via multiple steps of interactive regulation mechanism. The mechanism of gene regulation is a very important process in human beings. If there is something wrong in the gene regulation mechanism, it may cause some diseases. It is very difficult to identify the regulatory relations among genes in human genome. Traditional biological research methods consume huge amount of time and man strength. In recent years, with the rapid development of technologies, cDNA can be used to analyze the changes of gene expressions in different cells in a high throughput manner. In this paper, we propose a novel bioinformatics approach to predict the regulatory network of genes based on differential expressions of cDNA microarray databases for tumor and normal tissues. The differences in regulatory networks of genes for tumor and normal tissues reveal the information of finding possible cancer-related genes. The predicted cancerous genes can then be provided to biologists for further verification through biological experiments.