Computational Structural Biology aims at establishing biomolecular sequence-structure-function relations using fundamental principles of physical sciences in theoretical models and simulations of structure and dynamics. After the advances in complete genomes sequencing, it became evident that structural information is needed for understanding the origin and mechanisms of biological interactions, and designing/controlling function. Computational Structural Biology emerged as a tool for efficient identification of structure and dynamics in many applications. Major research topics include protein folding, protein dynamics with emphasis on large complexes and assemblies, protein-protein, protein-ligand and protein-DNA interactions and their functional implications. Drug design and protein engineering represent applications of note.
Specialization Electives (3 credits/9 units)
Biology has been revolutionized by automated methods for generating large amounts of data on diverse biological processes. This, in addition to the finding that many more components are involved in each process than had earlier been thought, has led to a transition from a reductionist paradigm of biological research involving detailed study of single molecules or events to a systems biology paradigm involving comprehensive, systematic studies combined with computational data analysis. Integration of data from many types of experiments will be required to construct detailed, predictive models of cell, tissue or organism behaviors, and the complexity of the systems suggests the need for these models to be constructed automatically. This will require iterative cycles of acquisition, analysis, modeling, and experimental design, since it is not feasible to do all possible biological experiments. This course will cover a range of automated biological research methods, especially high-throughput screening and next generation sequencing, and a range of relevant computational methods, especially model structure learning and active learning. It assumes a basic knowledge of machine learning. Class sessions will consist of a combination of lectures and discussions of important research papers. Grading will be based on class participation, homeworks and a final project.
Some of the most interesting and difficult challenges in computational biology and bioinformatics arise from the determination, manipulation, or exploitation of protein structures. This course will survey these challenges and present a variety of computational methods for addressing them. The course is appropriate for both students with backgrounds in computer science and those in the life sciences.
Proteomics and metabolomics are the large scale study of proteins and metabolites, respectively. In contrast to genomes, proteomes and metabolomes vary with time and the specific stress or conditions an organism is under. Applications of proteomics and metabolomics include determination of protein and metabolite functions (including in immunology and neurobiology) and discovery of biomarkers for disease. These applications require advanced computational methods to analyze experimental measurements, create models from them, and integrate with information from diverse sources. This course specifically covers computational mass spectrometry, structural proteomics, proteogenomics, metabolomics, genome mining and metagenomics.