Inference of Gene Regulatory Network with S-system and Artificial Bee Colony Algorithm

Gene Regulatory Network (GRN) mainly refers to the behavior of thousands of genes (inside the chromosome of cell) with other genes. Each gene has expression levels and interaction with other genes. Due to the invention of "DNA microarray technology" in biotechnology, we are able to find gene expressions from real genetic regulatory networks. Now it's the time to find interactions among genes from gene expressions. If we can find those values then we can predict our gene behavior much early. Thus it can be a revolutionary step towards medicine and diagnosis sector. If we reach better accuracy then it will also help us to develop tissue and organs. That means, for chronic disease or any other problem if one's heart cannot pump blood, then he can repair his heart by making a new heart developed from the muscle cells from any other organ of his body. Biological systems are very much complex in nature. And S-system is a recent and popular class to model biological systems. Hence, we are using S-system class for modeling Genetic Regulatory Networks contain a large number of genes and artificial bee colony is best suited for population based problems. Thus, we are proposing an inference algorithm of gene regulatory network on the framework of artificial bee colony algorithm and S-system class of ordinary differential equations (ODEs).