Graduate Courses

@ University of Pittsburgh @ Carnegie Mellon University @ Duquesne University

University of Pittsburgh

A. School of Medicine

1. Interdisciplinary Biomedical Sciences

INTBP 2000, Foundations of Biomedical Science

Primary objectives of the course mechanisms controlling cell, tissue and organ function, and to develop an understanding of the experimental evidence supporting these concepts through an integrated presentation of material from biochemistry, cell biology, genetics, immunology, microbiology, neurobiology, pathology, pharmacology, and physiology. The development of critical thinking skills will be emphasized through an evaluation of experimental evidence and reading of the primary literature.

INTBP 2030, Introduction to Biocomputing

This course will provide students with the skills needed to prepare written and oral scientific presentations. Topics to be covered include web browsers, library database searches, use of bibliographic management software, proper citation usage, electronic journal access, use and manipulation of PDF files, Powerpoint presentations, molecular biology databases available on the web, construction and use of relational databases, BLAST searches, nucleic acid sequence analysis programs and molecular structure analysis programs.

2. Center for Biomedical Informatics (Medical Informatics Training Program)

BIOINF 2051, Bioinformatics I: Introduction to Bioinformatics

This course is designed to provide an understanding of some important topics in bioinformatics and computational biology. Students will be able to learn about problems involved in the generation and analysis of biological data such as DNA/protein sequences and protein structures. The course is intended to provide a fairly good understanding of the commonly used algorithms in the analysis of genomic data, and hands-on experiences with accessing and using relevant databases. Emphasis is on genomic aspects of computational biology with some overview of proteomics and structural aspects.

BIOINF 2052, Bioinformatics II: Introduction to Computational Structural Biology

This course is a general introduction to current theories and methods used in computational structural biology. Fundamental concepts of probability, statistics, statistical thermodynamics and polymer physics will be considered as well as a general description of our current knowledge of biomolecular structure and dynamics for modeling and simulations of biological interactions and function. The Protein Data Bank and software commonly used in computational structural biology will be used for modeling and simulations of structure and dynamics.

BIOINF 2053, Bioinformatics III: Computational Biology Laboratory

This course will comprise summer workshops offered through the Pittsburgh Supercomputing Center, and will provide hands-on experience within sub-areas of computational biology.

B. Department of Chemistry

CHEM 3430, Introduction to Modern Computational Chemistry

This course will introduce students to the important techniques in computational chemistry including electronic structure theory and Monte Carlo and molecular dynamics simulations. Systems to be considered willrange from isolated gas-phase molecules to solids and surfaces, to biomolecules in aqueous environments.  Students will acquire "hands-on" experience with state-of-the-art software packages including Gaussian98, MOLPRO, Jaguar, Materials Studio, and Tinker.

CHEM 3450, Molecular Modeling and Graphics

This course will introduce the student to computational methods to determine molecular structures and stabilities, Monte Carlo and molecular dynamics simulation methods, and the use of graphics for displaying structures, charge densities, and other properties. Use will be made of both microcomputers and the Cray C-90 at the Pittsburgh Supercomputing Center.

C. Department of Computer Science

CS 2110, Theory of Computation

This course deals with computability theory, automata theory and formal languages. Various models of computation will be examined, their relations to each other and their properties will be studied. Topics include models for computable functions and Church's thesis, models for recognizers and their relation to formal grammars, algorithmically solvable and unsolvable problems, and the complexity of computations.

CS 3350, Modeling and Simulation

The background, justification, definitions, and uses of modeling and simulation are detailed. Topics range from system conceptualization, purposeful representation, and modeling to implementation, validation, and processing of the simulation model. Primary attention is paid to dynamic discrete systems, although continuous systems are also covered. Examples are drawn from a variety of application fields. Methodological approaches are emphasized. Students are required to implement and test a substantial simulation model.

CS 3630, Interactive Computer Graphics

This course includes: description of types, characteristics, and purposes of interactive graphics devices/systems; selected topics on graphics-oriented hardware, software, data structures, and system configuration;important techniques such as scaling, translation, rotation, clipping, windowing, and hidden-line removal, together with applicable 2D and 3D mathematical methods and psychological considerations; applications work involving locally-available graphics equipment.

D. Department of Mathematics

MATH 3370, Mathematical Neuroscience

This course is an introduction to computational and mathematical neuroscience, i.e., study of biological nervous systems in terms of biophysical and mathematical models. We shall discuss methods that have been developed in modeling and analyzing complex dynamics of single neurons and large-scale networks. Emphasis will be made on how to characterize properties of the synaptic connectivity pattern in a given brain area, and how this, as well as intrinsic cellular properties and neuromodulators, determines the network behavior. Two main themes are (1) to understand the rhythmogenesis and the functions of synchronous neuronal firings and various oscillatory modes; and (2) to describe synaptic plasticity during development or learning. The course will be self-contained, and will duly take into account the backgrounds of those from neuroscience and mathematics alike.

MATH 3375, Computational Neuroscience

This course offers an introduction to modeling methods in neuroscience. It illustrates how models can extend and evaluate neuroscience concepts. Basic techniques of modeling biophysics, excitable membranes, small network and large scale net work systems will be introduced. The course begins with a consideration of mathematical models of excitable membranes, including the Hodgkin- Huxley model and simplifications such as the Morris-Lecar and FitzHugh-Nagumo models. It will provide hands-on laboratory experience in modeling membranes, neurons, and neural networks. The course explores the use of differential equations, numerical simulation and graphical techniques for modeling neural systems. The range of topics include simulations of electrical properties of membrane channels, single cells, neuronal networks and cognitive simulation. Students will be afforded laboratory experience in computer modeling, and they will develop computational neuroscience models in the course. Prerequisites for the course include basic knowledge of calculus, neuroscience, and some computer programming.

E. Department of Physics and Astronomy

PHYS 2274, Computational Physics

The course will illustrate how computation is used in some areas of physics and related disciplines, including statistical physics, quantum physics and chemistry, biophysics and nonlinear dynamics. The mathematical techniques surveyed will include ordinary differential equations, solution of linear systems of equations, the Monte Carlo method, the Fast-Fourier Transform, and finite-difference solution of partial differential equations. Applications to topics in classical mechanics (molecular dynamics, classical chaos), quantum mechanics (molecular electronic structure, time-dependent wavepacket propagation, path integrals for quantum statistical mechanics), statistical physics (Ising models) and biophysics (ion transport through protein channels, protein folding) will be presented. If time permits, applications to interdisciplinary topics such as neural network theory will be considered. Some introduction to supercomputer architectures and parallel computing will be provided.

Carnegie Mellon University

A. Department of Biological Sciences

03-871, Structural Biophysics

A graduate level introduction to the use of biophysical methods in studying the structure of biological macromolecules such as DNA and proteins and assemblies of these molecules including DNA-protein complexes, viruses and membranes. Lecture material will cover the study of macromolecules, both in vitro, and if appropriate, in vivo. Topics covered include X-ray crystallography, NMR, microscopy, molecular dynamics and spectroscopic methods.

B. Department of Chemistry

09-75, NMR Techniques, Instrumentation and Signal Processing

This course is intended for students of chemistry, biology and physics who are interested in deeper understanding of the instrumentation and signal processing in NMR spectroscopy and imaging. The introductory part deals with the basic ideas behind high resolution NMR in liquids. The second part of the course is devoted to the description and brief analysis of major components of the NMR instrument. The third and last part is devoted exclusively to the digital processing of the NMR signals by computers. The relations between the time domain and the frequency domain are thoroughly discussed and the principles of manipulation of spectra by a computer are given.

C. Department of Computer Science

45-963, Mining Data for Decision Making

Data mining (or Knowledge Discovery in Databases) involves a collection of techniques for extracting patterns and trends in large databases. This course is a hands-on introduction to the area with an emphasis on aspects useful to business managers. At the end of the course, students will better understand the need and appropriate place for data mining, the major techniques used in data mining, and the important pitfalls to watch out for.

Duquesne University

A. Department of Chemistry and Biochemistry

CHEM 534, Basic NMR Techniques,

Course will cover aspects of 1H, 2H, 13C, 31P, and 19F, diamagnetic and paramagnetic NMR, beginning with the basic experiment and proceeding through standard two-dimensional experiments. Considerable time will be spent on discussions of the interpretation of spectra including chemical shifts and spin-spin coupling. Hands on exercises will include sample preparation, pulse sequences, and instrument function.

CHEM 540, Applied Quantitative Methods of Computational Chemistry

Course will focus on the use of modern workstation and software to address computational problems in chemistry. Topics will include platform choice, operation systems, and system requirements. Additional topics will survey software for modeling the behavior of chemical systems with emphasis on hands on experiments. Problems addressed will include topics in condensed-phase matter, molecular dynamics, spectroscopy prediction, energy minimization, and biological systems.