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.
This course will introduce students to the theory and practice of modeling biological systems from the molecular to the population level with an emphasis on intracellular processes. Topics covered include kinetic and equilibrium descriptions of biological processes, systematic approaches to model building and parameter estimation, analysis of biochemical circuits modeled as differential equations, modeling the effects of noise using stochastic methods. A range of biological models and applications will be considered including gene regulatory networks cell signaling, molecular motors, and developmental biology. Weekly recitations will introduce computational skills and provide students hands-on experience with methods and models presented in class. Course requirements include weekly homework assignments, a final project, and a take-home exam.
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.