Prediction of a Gene Regulatory Network in Cancer Cells

Abstract

The invention of high throughput technology like microarrays has enabled us to better understand how different cellular components interact. Thus created great interest in the field of Gene Regulatory Network(GRN) in particular. The interplay of interactions between DNA, RNA and proteins leads to genetic regulatory networks (GRN) and in turn controls the gene regulation. Directly or indirectly in a cell such molecules either interact in a positive or in repressive manner therefore it is hard to obtain the accurate computational models through which the final state of a cell can be predicted with certain accuracy. A variety of models and methods have been developed to address different aspects of GRN. Using the Time series data and applying it to these models researchers generate meaningful results i.e. how genes interact with one another. However results found are not of much accuracy due to presence of intrinsic noise of the expression measurements. In order to produce more accurate GRNs using one of the many models available, a new technique is proposed here.