Computational Biology are new discipline born from the simultaneous progress of information technologies and bio-technologies and their application to the study of biological phenomena. They leverage on the increasing growth of available biomolecular information and bio-medical-molecular knowledge, which they significantly contribute to enhance and help to apply also in the clinic.
The course aims to illustrate how computer science principles, technologies, methods and instruments can be profitably used for the computational analysis, information content increment and interpretation of biological data produced by genome sequencing, gene expression measurements and proteomics. It will be highlighted as the application to biological data of the engineering themes of data bases, information theory, data and text mining and others can contribute to increasing biomedical knowledge and improving health care.
Design, execution and interpretation of multivariable experiments that produce large data sets; quantitative reasoning, models and simulations. Examples will be discussed to demonstrate “how” cell- level functions arise and “why” mechanistic knowledge allows us to predict cellular behaviors leading to disease states and drug responses.
An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome and protein including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools.