Computational Biology

The first part of this course covers a variety of items raised by the contemporary biology and medicine. Topics in first 3 weeks will include: genetic and epigenetic code, organisation of the genome, hierarchic and non-hierarchic regulation of the life at cell, organ, organism and population level, genomics, proteomics, metabolomics, evolution, genealogy, omics databases, principles of systems biology, high-throughput molecular technologies and gene diagnostics. The next four weeks connect these basic aspects with quantitative, personalized and predictive approach of cancer, cardiovascular (e.g. hypertension and myocardial infarct), neurodegenerative (e.g. Alzheimer and multiple sclerosis) and metabolic diseases (e.g. diabetes and obesity), arthritis, asthma, AIDS, flu epidemics as well as vaccination, drug development and gene therapy. A separate chapter will cover the principles of stem (embryonic, tissue and induced pluripotent-) cell research.  This part of the course can serve as a preparation for a course on computational science in biomedicine, and to handle very large biological data sets.    The second part of the course introduces the statistical, algorithmic, knowledge representational, and information technological aspects of bioinformatics with particular emphases on medical genomics and personalized medicine. We discuss the characteristics of high-throughput data, the basics of Bayesian decision theory, the foundations of graphical models including Markov networks and Bayesian networks, modern conceptualization of causation, and corresponding intensive statistical methods. We overview multiple methods of gene expression data analysis including dimensionality reduction techniques, clustering, biclustering, Markov network learning, subgraph learning, module network learning, and gene prioritization. Finally we discuss methods in statistical genetics including linkage analysis, candidate gene and whole genome association, and the principles of adaptive study design. 

3 lectures and 1 exercise.

Part 1 Alberts B. et al, Molecular biology of the cell, Falus A. (ed.)  Immunogenomics and Human Disease, John Wiley & Sons, Ltd.  (2005), Falus A (Editor) Clinical Applications of Immunomics. Springer, 2008. Lodish H et al. http://bcs.whfreeman.com/lodish5e/default.asp?s=&n=&i=&v=&o=&ns=0&t=&uid=0&rau=0, Malcolm A et al Genomics, Proteomics and systems Biology, www.bio.davidson.edu/genomics ;  ; Part 2: Pierre Baldi, Soren Brunak, Sren Brunak: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) Further  recommended reading::D. Siegmund, B. Yakir, The statistics of gene mapping, Springer, 2007, Pierre Baldi DNA Microarrays and Gene Expression : From Experiments to Data Analysis and Modeling  

Requirements: In Part 1 the students are required to present solid knowledge in basic systems biology and to design a realistic research plan in biomedical science or pharmacogenomics. A ppt presentation is expected to perform. Critical element of the grading will be the convincing presentations.   The written exam and the presentations will illustrate students’ ability to form a convincing logical demonstration to the different audiences. The students will be also exposed to simulated conflict situations by receiving comments from the audience.  Grading will be based on the following criteria:  - Evaluation of the basic knowledge                                       40 points  - Clarity of the approach of the experimental problem                       20 points  - Presentation skills                                                      20 point  - Class participation & activity on the sessions                             20 points    In Part 2 the students are required to prepare a report of their data analysis and present their work in a live PowerPoint presentation.   Critical element of the grading will be the presentation and the quality of the analysis.   Grading will be based on the following criteria:  - Problem formulation, goals of the data analysis                        20 points  - Statistical aspects                                              20 points  - Computational aspects                                            20 points  - Interpretation, including presentation                           20 points  - Embedding and application                                    20 points