Application of Computer Simulation and Genetic Algorithms to Gene Interactive Rules for Early Detection and Prevention of Cancer

Through cellular signaling networks, genes regulate the expression of other genes, which eventually result in stable phenotype structures such as tumor or nontumor cells. Often, tumor and nontumor cellular networks contain some similar cancer-causing genes, but due to corresponding gene regulatory network (GRN) in tumor networks, they end up forming cancerous cells, whereas in nontumor networks, they do not. If basic gene regulatory function rules could be estimated, potential for cancer could be detected before it actually happens. If these regulations could be modified, there is a potential to alter them in a way that evolution of cancerous cells could be avoided. This paper builds on the previous work where GRNs for hepatocellular cancer were estimated from microarray data and were used to detect the potential for cancer before it is actually developed. It applies a genetic-algorithm-based mathematical approach to determine the optimum change to induce to the nature of network regulatory rules to prevent formation of cancerous tumors. The approach presented here is based on the utilization of probabilistic Boolean networks on two models of GRNs: one for tumor and one for nontumor producing structures.