AbstractsBiology & Animal Science

Over the last decade, high throughput biological data, have been accumulating at rapidly increasing rates, providing the opportunity to gain insight into various fundamental biological processes. Such large-scale data have been explored using network representation and graph theory to study biological relationships. Meanwhile, a great amount of effort has also been dedicated to integrate diverse biological data types in order to build networks and apply computational analysis to distill meaningful information for specific biological problems. As a result, network-based analysis has become a powerful paradigm to model and study large-scale biological data. The goal of network-based analysis of human complex traits is to annotate or predict new relationships between biological entities, such as proteins, drugs and phenotypes. Furthermore, such analysis can facilitate the diagnosis and prognosis of common complex diseases. This thesis comprises three contributions. First, heterogeneous biological data are integrated and a novel tool has been developed to easily construct and navigate networks representing the large scale data. In addition the resulting networks can be analyzed using computational methods to solve specific biological problems. Second, an integrative network-based pathway analysis for genome-wide association studies (GWAS) has been proposed to take advantage of the large scale network to combine topological connectivity with signals from GWAS in order to detect enriched pathways. Third, an integrative strategy combines multiple quantitative profiles with a large scale network to assist the biomarker selection for ovarian cancer using two different computational methods: (A) an aggregate ranking to score the candidate proteins and (B) pathway analysis to find enriched sub-networks. These three contributions demonstrate a pipeline to model large heterogeneous biological data in terms of networks and conduct network-based analysis for understanding the molecular basis of human diseases.