Genome-Wide Scale-Free Network Inference for Candida albicans - Altwasser, Robert; Linde, Joerg; Buyko, Ekaterina; Hahn, Udo; Guthke, Reinhard (Frontiers in Microbiology, 2012)
Discovery of essential genes in pathogenic organisms is an important step in the development of new medication. Despite a growing number of genome data available, still little is known about C. albicans, the major fungal pathogen. Most of the human population carries C. albicans as commensal, but it can cause systemic infection that may lead to the death of the host if the immune system is deteriorated. In many organisms central nodes in the interaction network (hubs) play a crucial role for information and energy transport. Indeed, knock-outs of such hubs often leads to lethal phenotypes making them interesting drug targets. To identify these central genes via topological analysis, we inferred gene regulatory networks that are of sparse and scale-free. We collected information from various sources of information as prior knowledge to complement the limited expression data available. We utilise a linear regression algorithm to infer genome-wide gene regulatory interaction networks. To evaluate the predictive power of our approach, we used an automated text-mining system that scanned full-text research papers for known interactions. With the help of the compendium of known interactions, we also optimise the influence of the prior knowledge we obtained and the sparseness of the model to achieve best results. We compare the results of our approach with those of other state-of-the-art network inference methods and show that we outperform these methods. Finally we identified a number of hubs in the genome of the fungus and investigate their biological relevance.