Cancer gene silencing network analysis using cellular automata

Abstract

Identification of cancer pathways is the central goal in the cancer gene expression data analysis. A cellular automaton is a dynamic system with cells, which are uniform, interconnected and discrete in nature. Cellular automata are well-known methods to predict network traffics in cellular spaces. Therefore, to predict cancer pathways involved, we propose a 2-dimensional cellular automata approach over a chosen cancer gene network. Focusing on the case study, we highlight the potential impact of spatial organization in cellular spaces for the evolution and engineering of gene silencing on cancer gene expression profiles. The gene regulatory network involved in gene silencing breast cancer cell line, analysed with a predefined ranking value, has been simulated using cellular automata to obtain proper insight view of selecting biomarker genes for breast cancer. The predicted biomarker genes have been analysed with other contemporary databases, like KEGG and biologically tested for gene enrichment analysis for their significances. This approach is a novel one in the sense of projecting oncology in cellular spaces over ranking values for predicting significant biomarkers in cancer.