Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.

Aspergillus flavus infections in Galleria mellonella : a pathogen-host model system for the study of emerging diseases

To study emerging diseases, I employed a model pathogen-host system involving infections of insect larvae with the opportunistic fungus Aspergillus flavus, providing insight into three mechanisms ofpathogen evolution namely de novo mutation, genome decay, and virulence factoracquisition In Chapter 2 as a foundational experiment, A. flavus was serially propagated through insects to study the evolution of an opportunistic pathogen during repeated exposure to a single host. While A. flavus displayed de novo phenotypic alterations, namely decreased saprobic capacity, analysis of genotypic variation in Chapter 3 signified a host-imposed bottleneck on the pathogen population, emphasizing the host's role in shaping pathogen population structure. Described in Chapter 4, the serial passage scheme enabled the isolation of an A. flavus cysteine/methionine auxotroph with characteristics reminiscent of an obligate insect pathogen, suggesting that lost biosynthetic capacity may restrict host range based on nutrient availability and provide selection pressure for further evolution. As outlined in Chapter 6, cysteine/methionine auxotrophy had the pleiotrophic effect of increasing virulence factor production, affording the slow-growing auxotroph with a modified pathogenic strategy such that virulence was not reduced. Moreover in Chapter 7, transformation with a virulence factor from a facultative insect pathogen failed to increase virulence, demonstrating the necessity of an appropriate genetic background for virulence factor acquisition to instigate pathogen evolution.