Multi-membership gene regulation in pathway based microarray analysis

We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency.We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.Pathway based microarray data analysis is an attempt to integrate microarray data analysis with biochemical pathway knowledge [1]. Rather than concentrating on the often subtle change occurring in the expression of individual genes, gene expression analysis is facilitated to identify coordinated changes occurring in the expression of sets of genes, forming biochemical pathways [2]. The ultimate goal of this approach is to decipher the functional state of a cell at the level of the underlying biochemistry.Biochemical pathway data is readily accessible in various public databases, such as KEGG [3], Reactome [4], SABIO-RK [5], EcoCyc [6] and others, while tools developed for visualisation of genes' behaviour, based on microarray data, include Eu.Gene [2], GenMapp [7], Cytoscape [8], Pathfinder [9], GeneNet [10] and GScope [11]. These software tools are based on superimposing a single microarray dataset on a biochemical pathway database, in order to visualise the expression of each individual gene per pathway and thus establish the state of individual pathways.However, genes in a biochemical pathway often show quite variable