Determination and study of a dominant Genetic Network responsible for the growth of a fungus using the concepts of Bayesian Algorithm

The Bayesian belief network is a powerful knowledge representation and reasoning tool under conditions of uncertainty to analyze gene expression patterns. Nowadays, this is an important tool to construct mathematical models based on probability to identify any particular dominant Genetic Network of any organism under observation. The present study deals with analysis of a set of micro array data collected at a regular interval of time throughout the growth phase of a fungus Burkholderia pseudomalli. In the first phase of the study, emphasis was given to recover a set of most dominant genes among the set of all possible expressed genes found in the microarray experiment. These dominant genes are then used to find out a dominant Genetic Network by applying the Bayesian Algorithm. Thus, the most dominant genetic network for the growth and development of the fungus under consideration was obtained. The genetic network represents the set of responsible genes in the growth process and their inter relationships. The Microarray data set represents the external manifestation of internal genetic activity resulting into genetic network. Here, from the set of 5289 genes in 47 consecutive time instances, were taken for analysis. Out of them, 400 most pertinent genes for the growth process were determined using a new technique namely ‘Fidelity Matrix Process’. Genetic Network for these 400 genes has been constructed and studied using Bayesian Belief Network Technique. The present reduction method was found to be more efficient in terms of computation when compared contemporary studies done many scientists. The results of the present study may be extensively applied in reducing a huge number of genetic expression rate data without any complex computation, studying unknown biological processes and systems, treating complicated diseases and even designing drugs for some incorrigible syndromes.