All students will be required to take five core graduate courses. The core courses are:
- Machine Learning
- Computational Genomics
- Computational Structural Biology
- Cellular and Systems Modeling
- Laboratory Methods for Computational Biologists
It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics and from statistical algorithmics. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate.
Dramatic advances in experimental technology and computational analysis are fundamentally transforming the basic nature and goal of biological research. The emergence of new frontiers in biology, such as evolutionary genomics and systems biology is demanding new methodologies that can confront quantitative issues of substantial computational and mathematical sophistication. In this course we will discuss classical approaches and latest methodological advances in the context of the following biological problems: 1) Computational genomics, focusing on gene finding, motifs detection and sequence evolution.2) Analysis of high throughput biological data, such as gene expression data, focusing on issues ranging from data acquisition to pattern recognition and classification. 3) Molecular and regulatory evolution, focusing on phylogenetic inference and regulatory network evolution, and 4) Systems biology, concerning how to combine sequence, expression and other biological data sources to infer the structure and function of different systems in the cell. From the computational side this course focuses on modern machine learning methodologies for computational problems in molecular biology and genetics, including probabilistic modeling, inference and learning algorithms, pattern recognition, data integration, time series analysis, active learning, etc.
This core course was offered jointly by Pitt and CMU for the first time in the 2005-2006 academic year. It is taught in the Fall semester. Topics covered include:
- applying computational and statistical methods to the analysis of DNA and protein structures
- representing protein, DNA and RNA structure
- homology modeling and protein structure prediction
- theoretical description of basic interactions, along with computational methods to estimate them
- statistical mechanical theory of molecules
- molecular dynamics and other sampling methods
- modeling protein flexibility, from side chains to loops to slow modes
- reaction paths and basics of path sampling
- protein-protein and protein-small molecule docking
- supramolecular assembly
- introduction to Quantitative Structure Activity Relationship (QSAR) in drug design
This core course was offered jointly by Pitt and CMU for the first time in the 2006-2007 academic year.
A graduate-level introduction into mathematical modeling and analysis of biological systems on the cellular and other levels. This condensed and broad course conveys the unity of the modeling methodology in biology. It spans a range of perspectives derived from the different disciplines from which this new area of research originated: biology, mathematics, engineering, and computer science. The systems covered include quantitative physiology, quantitative cell biology, biological networks, dynamic systems, cell mechanics, and systems modeling of critical illness. The quantitative physiology topics to be covered include hemodynamics, musculoskeletal systems, endocrinology, neuroendocrinology, gastrointestinal/renal, transport phenomena, and pathophysiological conditions. Quantitative cell biology topics surveyed are mathematical models of the cytoskeleton dynamics, intracellular transport, cell locomotion, spatially-distributed models of cell signaling, approaches to whole-cell modeling, and role of modeling in cell-biological research. Models of cellular mechanics will also be addressed. Mathematics of dynamic systems is presented in application to enzyme reactions, bistability in cellular signaling, programmed cell death, and the mechanisms behind the circadian and cell-division rhythms. Biological network theory is presented as it applies to metabolism, protein interactions, regulation of gene expression, and reverse engineering of the biological systems. Theoretical aspects of application of systems modeling to clinical research are also presented on an example of quantitative systems approach to inflammation, sepsis, and trauma. In addition, the course will survey computational methods and models that are broadly useful across the various system types examined. These will include random walk models, master equations, and continuous and discrete models of chemistry within the cell. Finally, the course will include a presentation of general discrete and continuous models broadly useful in cell and systems modeling as well as computational methods for optimization and parameter tuning on such models. Across the entire range of topics, the universality of the systems modeling methodology and its role in biomedical research are emphasized.
Computational biologists frequently focus on analyzing and modeling large amounts of biological data, often from high-throughput assays or diverse sources. It is therefore critical that students training in computational biology be familiar with the paradigms and methods of experimentation and measurement that lead to the production of these data. This one-semester laboratory course gives students a deeper appreciation of the principles and challenges of biological experimentation. Students learn a range of topics, including experimental design, structural biology, next generation sequencing, genomics, proteomics, bioimaging, and high-content screening. Class sessions are primarily devoted to designing and performing experiments in the lab using the above techniques. Students are required to keep a detailed laboratory notebook of their experiments and summarize their resulting data in written abstracts and oral presentations given in class-hosted lab meetings. With an emphasis on the basics of experimentation and broad views of multiple cutting-edge and high-throughput techniques, this course is appropriate for students who have never taken a traditional undergraduate biology lab course, as well as those who have and are looking for introductory training in more advanced approaches. Grading: Letter grade based on class participation, laboratory notebooks, experimental design assignments, and written and oral presentations.
Grading: Letter grade based on class participation, quizzes, and lab reports.