Early detection of cancer by regression analysis and computer simulation of gene regulatory rules

Cellular signaling and dynamic interaction among genes result in stable phenotype structures such as tumor or non tumor cells. Tumor and non-tumor cellular cells often contain some identical cancer causing genes but due to differences in their regulatory networks they evolve differently. As a result, if such regulatory networks are discovered one could predict whether a cell containing particular cancer genes will in fact end up to become a cancerous cell. This paper utilizes a mathematical approach to determine such regulatory rules for a set of cells containing cancer causing genes and uses computer simulation to predict whether in a long run a particular cell will evolve into a cancerous cell. The proposed process utilizes Probabilistic Boolean Networks (PBN) on two gene regulatory networks; one for tumor and one for non-tumor producing structures. The process uses a regression analysis to identify the regulatory networks and a computer simulation model to predict long term cancer potential.