The biological complexity of the genotype-phenotype relation: from genes and proteins to phenotypes and diseases

To unveil the biological complexity at the basis of the genotype-phenotype relation it is fundamental to integrate knowledge that is to integrate the different omics describing the levels of biological complexity: genomics, proteomics, transcriptomics, metabolomics and interactomics. The situation gets more complicated when we move the focus to diseases and phenotypes. The identification of molecular mechanisms behind different phenotypes offers a way to understand the processes that lead to disease insurgence and progression. Another issue in computational biology is the prediction of specific phenotypic effect of gene and protein variants, to test the performance of computational methods towards experiment in vivo and in vitro. The main aim of this thesis is to study the relations among genes, variations, diseases and phenotypes with the approaches of computational biology, integrating information from different resources to make a step forward in the direction of unveiling the biological complexity. After a general introduction, we present the webservers eDGAR and PhenPath, collecting and analysing the gene-disease associations and the phenotypes-biological processes associations, respectively. We then assessed whether disease-related variations induce perturbations of the protein stability. To this aim, we developed a new predictor called INPS-3D. We test our predictors participating in international experiments on specific study cases. Thanks to the expertise acquired in the field, we also collaborate with the Sant’Orsola Genetic Medical Unit of the Department of Medicine and Surgery of the University of Bologna, building a series of models of protein structure of myosin 1F and its variants related to the thyroid cancer. Concluding, we tried to depict the biological complexity merging a large-scale approach with the analysis of specific study cases, providing webservers, tools and computation methods to help researchers in directing further experiments.