Prediction of regulatory networks for non-model organisms

Abstract

Identification of gene regulatory networks is useful in understanding gene regulation in any organism. Some regulatory network information has already been determined experimentally and using statistical methods for model organisms, but much less has been identified for non-model organisms. The limited amount of data available for non-model organisms makes inference of regulatory networks difficult using the commonly used statistical methods. This thesis proposes a method to determine the regulatory links that can be mapped from a distant model to a non-model organism. Experiments are performed to map the regulatory network data of S. cerevisiae to A. thaliana and analyze the results. Mapping a regulatory network involves mapping the transcription factors and target genes from one genome to another. In the proposed method, different techniques for predicting transcription factors and target genes for the non-model organism using the available data for the model organism are compared and analyzed. The techniques that obtain the best results overall should be the ones chosen for these predictions. These predicted transcription factors and target genes are then integrated into predicted regulatory links for the non-model organism. A set of rules is then defined on the gene expression experiments to filter these predicted regulatory links that are well supported. Very limited available gene expression data of the non-model organism is used to filter the predicted regulatory links based on these rules to get rid of the high number of false positives. Finally, the filtered regulatory links are tested against a large dataset of gene expression experiments to illustrate that correctly predicted regulatory links are obtained. The links thrown out by filtration are also tested against the same gene expression dataset to illustrate the significance of this step to refine the results.