Fuzzy logic-based gene regulatory network

DNA microarray technology enables a parallel analysis of the expression of genes in an organism. The wealth of spatio-temporal data generated by this technology allows researchers to potentially reverse engineer the genetic network. Fuzzy logic has been proposed as a method to analyze the relationships between genes. This method can identify interacting genes that fit a known fuzzy model of gene interaction by testing all combinations of gene expression profiles. However, this approach is slow and computationally complex. This paper introduces improvements made in terms of reducing computation time and generalizing the gene regulatory model to accommodate co-activators and co-repressors. Improvement in computation time is achieved by using clustering as a pre-processing method, thereby reducing the total number of gene combinations analyzed. This will allow the algorithm to run in a shorter amount of time with minimal effect on the results. The proposed technique will pave the way towards the creation of a generalized gene interaction model that can accommodate any combination of genes.