All students will be required to take five core graduate courses. The core courses are:
CMU 10-701: Machine Learning
02-710/MSCBIO/CMPBIO 2070 Computational Genomics
MSCBIO/CMPBIO 2030 – Introduction to Computational Structural Biology
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
CMU 02-730/MSCBIO/CMPBIO 2040 Cellular & Systems Modeling
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.
CMU 02-760/MSCBIO/CMPBIO 2050 Laboratory Methods for Computational Biologists or CMU 02-761/02-762 Laboratory Methods for Automated Biology I and II
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 at the interface of biological experimentation and computation required to analyze the resulting data. 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 experiments and analyzing the resulting data. and performing experiments in the lab using the above techniques. Students are required to 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, experimental design assignments, and written and oral presentations.
As an alternative to 02-760, 02-761 and 02-762 can be taken in sequence to fulfill both the lab methods requirement and a specialization elective.
In the 02-761 and 02-762 sequence, students will receive the same content as 02-760 and they will also will learn about the essential technical and biological laboratory skills used to design and execute automated biological experiments. Students will learn the principles, experimental paradigms, and techniques for automating biological experimentation with the goal of enabling complete automation of biological experimentation (AI driven robotic experimentation). Students will learn the design concepts for automated experiments, engineering elements enabling hardware for preparing samples and doing automated data collection, and software for controlling that hardware. Instruments used will include liquid handling robots, plate readers, and automated microscopes. All experiments will be performed in the CMU Automation Lab which is the first Automation Lab designed specifically for training students in biological laboratory automation.