Electives

All students will be required to take three graduate elective courses (3 credits/9 units each): a life sciences elective, a specialization elective, and an open elective.
The specialization areas are:

Required Life Sciences Electives (3 credits/9 units)

Students with previous experience in graduate-level Life Sciences courses may convert this elective to an Open Elective with the approval of the Program Directors.

NMR in Biomedical Sciences

-

Biological Imaging and Fluorescence Spectroscopy

This course covers principles and applications of optical methods in the study of structure and function in biological systems. Topics to be covered include: absorption and fluorescence spectroscopy; interaction of light with biological molecules, cells, and systems; design of fluorescent probes and optical biosensor molecules; genetically expressible optical probes; photochemistry; optics and image formation; transmitted-light and fluorescence microscope systems; laser-based systems; scanning microscopes; electronic detectors and cameras: image processing; multi-mode imaging systems; microscopy of living cells; and the optical detection of membrane potential, molecular assembly, transcription, enzyme activity, and the action of molecular motors. This course is particularly aimed at students in science and engineering interested in gaining in-depth knowledge of modern light microscopy.

Advanced Genetics

This course considers selected current topics in genetics at an advanced level. The emphasis is on classroom discussion of research papers, supplemented with individual and group exercises. Topics change yearly. Recent topics have included genome imprinting in mammals, chromatin boundaries and long distance gene regulation, learning and memory in Drosophila,and the kinetochore complex in yeast. Must obtain a minimum grade of B in 03-330 to take this course.

Advanced Biochemistry

This is a special topics course in which selected topics in biochemistry will be analyzed in depth with emphasis on class discussion of papers from the recent research literature. Topics change yearly. Recent topics have included single molecule analysis of catalysis and conformational changes; intrinsically disordered proteins; cooperative interactions of aspartate transcarbamoylase; and the mechanism of ribosomal protein synthesis.

Advanced Cell Biology

This course covers fourteen topics in which significant recent advances or controversies have been reported. For each topic there is a background lecture by the instructor, student presentations of the relevant primary research articles and a general class discussion. Example topics are: extracellular matrix control of normal and cancer cell cycles, force generating mechanisms in trans-membrane protein translocation, signal transduction control of cell motility, and a molecular mechanism for membrane fusion.

The structure and expression of eukaryotic genes are discussed, focusing on model systems from a variety of organisms including yeast, flies, worms, mice, humans, and plants. Topics discussed include (1) genomics, proteomics, and functional proteomics and (2) control of gene expression at the level of transcription of mRNA from DNA, splicing of pre-mRNA, export of spliced mRNA from the nucleus to the cytoplasm, and translation of mRNA.

Advanced Developmental Biology

This course examines current topics in developmental biology at an advanced level. The course is team-taught by faculty from Carnegie Mellon University, the University of Pittsburgh Department of Biological Sciences, and the University of Pittsburgh Medical School. Each year several areas of current research are examined. Previous topics have included pattern formation, molecular signaling pathways, morphogen gradients, cell movements, and stem cells. Emphasis is on critical reading of original research papers and classroom discussion, with supporting lectures by faculty.

Physical Virology

Like all branches of physical science, physical virology encompasses a search for simplifying generalities. However, viruses display a kaleidoscopic diversity that imposes limits on any generalization and provides tremendous opportunity for discovery.

The course covers latest methods in biological physics as well as fundamentals in physics of DNA, protein self-assembly and membranes using viruses as a physical object. This course also provides introductory level biochemistry and molecular biology lectures so that students with any background can participate in the course. Being an interdisciplinary and up-to-date research field involving fundamental theory and numerous applications, the emerging field of physical virology is aimed to attract students from any of the natural science disciplines (physics, chemistry and biology).

Advanced Physiology

This course is an introduction to human physiology and includes units on all major organ systems. Particular emphasis is given to the musculoskeletal, cardiovascular, respiratory, digestive, excretory, and endocrine systems. Modules on molecular physiology tissue engineering and physiological modeling are also included. Due to the close interrelationship between structure and function in biological systems, each functional topic will be introduced through a brief exploration of anatomical structure. Basic physical laws and principles will be explored as they relate to physiologic function.

Mechanisms of Cellular Communication, Structure and Morphology

This course will survey and discuss current literature pertaining to advances in understanding how cells regulate complex behaviors such as migration, cell polarity, protein/membrane trafficking, cell and tissue morphology, and cell proliferation/survival and how lesions in these processes result in human disease

Neurophysiology

This course examines the electrical properties of nerve cells and the mechanisms by which nerve cells communicate. The following topics will be covered: electrical principles used by nerve cells, the basis of the resting potential, the function of voltage-dependent ionic channels, the mechanisms by which action potentials are generated, neurotransmitter receptor function, and the physiology of fast synaptic communication.

Advanced Topics in Cell Biology

-

Molecular Evolution

Sequencing technology is continually progressing, and genome sequences from different species and populations continue to become available in increasing numbers. Such data allows
questions about molecular function and evolution to be addressed in new and exciting ways. This course introduces students to the evolutionary analysis of DNA and amino acid sequences. Lectures on theory will be accompanied by practical instruction in the use of contemporary computational methods and software. Topics include: population genetics of selection and mutation, models of sequence evolution, phylogenetic models, analysis of multiple sequence alignments for rates and patterns of divergence, inference of natural selection, and coevolution between proteins. Emphasis is placed on quantitative modeling and the biological principles underlying observed patterns of molecular evolution. Interested students should have a basic grasp of molecular biology and calculus.

This is the first of three courses, which together constitute the common core of the first year of the molecular biophysics graduate program. Here the emphasis is on the structural foundations, especially that of proteins and nucleic acids. Fundamental results are covered together with experimental techniques (X-ray, diffraction, NMR, EM/cryoEM, AFM, CD/ORD, Raman and fluorescence spectroscopy), as well as structural systematics and informatics.

Course is concerned primarily with the structure and functions of proteins and nucleic acids. These are large polymers where structure and function are determined by the sequence of monomeric units. Topics will include the physical and chemical properties of the monomer units (amino acids/nucleotides); the determination of the linear sequence of these units; the size, shape and general properties of the biopolymers in aqueous systems; and the relation between structure and function, particularly in transport (hemoglobin) and in catalysis (enzymes).

 

Specialization: Bioimage Informatics (3 credits/9 units)

Bioimage Informatics draws upon advances in signal processing, optics, probe chemistry, molecular biology and machine learning to provide answers to biological questions from the growing numbers of biological images acquired in digital form. Microscopy is one of the oldest biological methods, and for centuries it has been paired with visual interpretation to learn about biological phenomena. With the advent of sensitive digital cameras and the dramatic increase in computer processing speeds over the past two decades, it has become increasingly common to collect large volumes of biological image data that create a need for sophisticated image processing and analysis. In addition, dramatic advances in machine learning during the same period set the stage for converting imaging from an observational to a computational discipline and allow the direct generation of biological knowledge from images.

Bioimage Informatics

-

Automation of  Biological Research

This course covers principles and applications of optical methods in the study of structure and function in biological systems. Topics to be covered include: absorption and fluorescence spectroscopy; interaction of light with biological molecules, cells, and systems; design of fluorescent probes and optical biosensor molecules; genetically expressible optical probes; photochemistry; optics and image formation; transmitted-light and fluorescence microscope systems; laser-based systems; scanning microscopes; electronic detectors and cameras: image processing; multi-mode imaging systems; microscopy of living cells; and the optical detection of membrane potential, molecular assembly, transcription, enzyme activity, and the action of molecular motors. This course is particularly aimed at students in science and engineering interested in gaining in-depth knowledge of modern light microscopy.

Computer Vision

This course introduces the fundamental techniques used in computer vision, that is, the analysis of patterns in visual images to reconstruct and understand the objects and scenes that generated them. Topics covered include image formation and representation, camera geometry and calibration, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, image segmentation, object recognition. The material is based on graduate-level texts augmented with research papers, as appropriate. Evaluation is based on homeworks and final project. The homeworks involve considerable Matlab programming exercises.

Medical Image Analysis

The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Student will develop practical experience through projects using the National Library of Medicine Insight Toolkit (ITK), a new software library developed by a consortium of institutions including CMU. In addition to image analysis, the course will describe the major medical imaging modalities and include interaction with practicing radiologists at UPMC.

Visual Learning and Recognition

A graduate course in Computer Vision with emphasis on representation and reasoning for large amounts of data (images, videos and associated tags, text, gps-locations etc) toward the ultimate goal of Image Understanding. We will be reading an eclectic mix of classic and recent papers on topics including: Theories of Perception, Mid-level Vision (Grouping, Segmentation, Poselets), Object and Scene Recognition, 3D Scene Understanding, Action Recognition, Contextual Reasoning, Image Parsing, Joint Language and Vision Models, etc. We will be covering a wide range of supervised, semi-supervised and unsupervised approaches for each of the topics above.

Specialization: Cellular and Systems Modeling (3 credits/9 units)

Cellular and Systems Modeling undertakes the ambitious task of studying the dynamics of biological and biomedical processes from a whole system point of view. The observed systems range over orders of magnitude, from tissue to cells to molecular assemblies! Engineering tools are used along with genome-scale information in mathematical and/or computational models that usually adopt a top-down approach. Modeling diseases, entire ‘virtual’ cells, or subcellular networks of interactions are among typical tasks. Major research topics include the modeling of complex signaling and regulatory networks, transport mechanisms, spatio-temporal evolution of microphysiological events, as well as establishing the links between the development of complex phenotypes and the seemingly unrelated molecular events.

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.

Computational Methods for Biological Modeling and Simulation

This course covers a variety of computational methods important for modeling and simulation of biological systems. It is intended for graduates and advanced undergraduates with either biological or computational backgrounds who are interested in developing computer models and simulations of biological systems. The course will emphasize practical algorithms and algorithm design methods drawn from various disciplines of computer science and applied mathematics that are useful in biological applications. The general topics covered will be models for optimization problems, simulation and sampling, and parameter tuning. Course work will include problems sets with significant programming components and independent or group final projects.

Machine Learning from Neural Cortical Circuits

-

Computational Models of Neural Systems

-

Statistical Methods for Neuroscience

-

Cellular Biomechanics

This course discusses how mechanical quantities and processes such as force, motion, and deformation influence cell behavior and function, with a focus on the connection between mechanics and biochemistry. Specific topics include: (1) the role of stresses in the cytoskeleton dynamics as related to cell growth, spreading, motility, and adhesion; (2) the generation of force and motion by moot molecules; (3) stretch-activated ion channels; (4) protein and DNA deformation; (5) mechanochemical coupling in signal transduction; (6) protein trafficking and secretion; and (7) the effects of mechanical forces on gene expression. Emphasis is placed on the biomechanics issues at the cellular and molecular levels; their clinical and engineering implications are elucidated.

Computational Models in Neuroscience

Course covers computational and mathematical neuroscience. Class will do modeling and analysis of complex dynamics of single neurons and large-scale networks.

Computational Neuroscience Methods

This course offers an introduction to modeling methods in neuroscience. Topics range from modeling the firing patterns of single neurons to using computational methods to understand neural coding. Some systems level modeling is also done.

Computational Cell Biology

This course introduces a number of modeling methods for biological systems. We will examine a number of problems from cell biology, immunology, population biology, physiology and molecular genetics. The main tools will be techniques from ordinary and partial differential equations. Discrete and delay-differential equations will also be used however the background for these will not be assumed. We will take models from current and classic papers in the field.

Systems Approach to Inflammation

This course is focused on particular topics of great biologic complexity in critical illness, where modeling has the potential to translate in improved patient care. Lectures are provided by basic (biological and mathematical sciences) and clinical faculty, in conjunction with members of industry and speakers from outside institutions. This information will be communicated within the framework of defined themes that describe the complexity of inflammation in acute and chronic illnesses.

Drug Discovery

Drug discovery is an interdisciplinary science that seeks to identify small molecular and/or biologic entities for therapeutic intervention and to understand integrated biological systems and processes at the functional and molecular levels. This course will discuss various topics that are relevant to current approaches and principles in drug discovery including drug origins, drug target identification, and validation, biochemical and cell-based screening approaches, proteomic approaches to drug discovery, computational chemistry and biology, and quantitative systems pharmacology, as well as other topics in preclinical and clinical drug development, personalized medicine, Chinese herbal medicines and intellectual property. The course will include case studies intended to aid students in a full understanding of the drug discovery process.

 

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.

Computational Medicine

Modern medical research increasingly relies on the analysis of large patient datasets to enhance our understanding of, and our ability to treat human diseases. This course will focus on the computational problems that arise from studies of human diseases and the translation of research to the bedside to improve human health. The topics to be covered include computational strategies for advancing personalized medicine, pharmacogenomics for predicting individual drug responses, metagenomics for learning the role of the microbiome in human health, mining electronic medical records to identify disease phenotypes, and case studies in complex human diseases such as cancer and asthma. We will discuss how machine learning and other computational methods are being used by clinicians. Class sessions will consist of lectures, discussions of papers from the literature, and guest presentations by clinicians and other domain experts. Students enrolled in 02-518 will be graded based on homeworks, paper summaries, and a course project. Students enrolled in 02-718 will be graded based on in-class presentations, written summaries of papers, and a course project.

Specialization: Computational Genomics (3 credits/9 units)

Computational Genomics entails efforts to digest the daunting quantity of genomic and proteomic data now available by systematic development and application of probability and statistics theories, information technologies and data mining techniques. Linguistics methods are viewed as promising tools towards elucidating sequence-structure-function relations, and complementing computational genomics studies. Computational genomics targets understanding gene/protein function, identifying and characterizing cellular regulatory networks and discerning the link between genes and diseases. Discovery and processing of this information is pivotal in the development of novel gene therapy strategies and tools.

Molecular Evolution

Sequencing technology is continually progressing, and genome sequences from different species and populations continue to become available in increasing numbers. Such data allows questions about molecular function and evolution to be addressed in new and exciting ways. This course introduces students to the evolutionary analysis of DNA and amino acid sequences. Lectures on theory will be accompanied by practical instruction in the use of contemporary computational methods and software. Topics include: population genetics of selection and mutation, models of sequence evolution, phylogenetic models, analysis of multiple sequence alignments for rates and patterns of divergence, inference of natural selection, and co-evolution between proteins. Emphasis is placed on quantitative modeling and the biological principles underlying observed patterns of molecular evolution. Interested students should have a basic grasp of molecular biology and calculus.

Computational Molecular Biology and Genomics

An advanced introduction to computational molecular biology, using an applied algorithms approach. The first part of the course will cover established algorithmic methods, including pairwise sequence alignment and dynamic programming, multiple sequence alignment, fast database search heuristics, hidden Markov models for molecular motifs and phylogeny reconstruction. The second part of the course will explore emerging computational problems driven by the newest genomic research. Course work includes four to six problem sets, one midterm and final exam. A project based on recent results from the genomics literature will be required of students taking 03-711.

Computational Methods for Biological Modeling and Simulation

This course covers a variety of computational methods important for modeling and simulation of biological systems. It is intended for graduates and advanced undergraduates with either biological or computational backgrounds who are interested in developing computer models and simulations of biological systems. The course will emphasize practical algorithms and algorithm design methods drawn from various disciplines of computer science and applied mathematics that are useful in biological applications. The general topics covered will be models for optimization problems, simulation and sampling, and parameter tuning. Course work will include problems sets with significant programming components and independent or group final projects.

Advanced Topics in Computational Genomics

Research in biology and medicine is undergoing a revolution due to the availability of high-throughput technology for probing various aspects of a cell at a genome-wide scale. The next-generation sequencing technology is allowing researchers to inexpensively generate a large volume of genome sequence data. In combination with various other high-throughput techniques for epigenome, transcriptome, and proteome, we have unprecedented opportunities to answer fundamental questions in cell biology and understand the disease processes with the goal of finding treatments in medicine. The challenge in this new genomic era is to develop computational methods for integrating different data types and extracting complex patterns accurately and efficiently from a large volume of data. This course will discuss computational issues arising from high-throughput techniques recently introduced in biology, and cover very recent developments in computational genomics and population genetics, including genome structural variant discovery, association mapping, epigenome analysis, cancer genomics, and transcriptome analysis. The course material will be drawn from very recent literature. Grading will be based on weekly write-ups for ciritiques of the papers to be discussed in the class, class participation, and a final project. It assumes a basic knowledge of machine learning and computational genomics.

Automation of Biological Research

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.

Statistical Foundations for Bioinformatics Data Mining

This course provides an intermediate-level understanding of statistical foundations to prepare students for the competent use of data analysis methods in common practice in bioinformatics. Statistical ideas covered include probability distributions, likelihood theory, Bayesian and frequentist concepts, estimation, hypothesis testing and significance testing, multiplicity adjustments, the EM and MCMC algorithms, random walks, Poisson processes and Markov chains. Application areas include biological swquence analysis and microarray analysis. Students will learn the R statistical language. The R packages Bioconductor and BRB array tools for microarray analysis will be studied.

Introductory High-Throughput Genomics Data Analysis I: Data Mining and Applications

-

Human Population Genetics

-

Molecular Evolution 

Sequencing technology is continually progressing, and genome sequences from different species and populations continue to become available in increasing numbers. Such data allows questions about molecular function and evolution to be addressed in new and exciting ways. This course introduces students to the evolutionary analysis of DNA and amino acid sequences. Lectures on theory will be accompanied by practical instruction in the use of contemporary computational methods and software. Topics include: population genetics of selection and mutation, models of sequence evolution, phylogenetic models, analysis of multiple sequence alignments for rates and patterns of divergence, inference of natural selection, and co-evolution between proteins. Emphasis is placed on quantitative modeling and the biological principles underlying observed patterns of molecular evolution. Interested students should have a basic grasp of molecular biology and calculus.

Computational Methods for Proteomics and Metabolomics

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.

Computational Medicine

Modern medical research increasingly relies on the analysis of large patient datasets to enhance our understanding of, and our ability to treat human diseases. This course will focus on the computational problems that arise from studies of human diseases and the translation of research to the bedside to improve human health. The topics to be covered include computational strategies for advancing personalized medicine, pharmacogenomics for predicting individual drug responses, metagenomics for learning the role of the microbiome in human health, mining electronic medical records to identify disease phenotypes, and case studies in complex human diseases such as cancer and asthma. We will discuss how machine learning and other computational methods are being used by clinicians. Class sessions will consist of lectures, discussions of papers from the literature, and guest presentations by clinicians and other domain experts. Students enrolled in 02-518 will be graded based on homeworks, paper summaries, and a course project. Students enrolled in 02-718 will be graded based on in-class presentations, written summaries of papers, and a course project. 

Genomics and Epigenetics of the Brain

This course will provide an introduction to genomics, epigenetics, and their application to problems in neuroscience. The rapid advances in genomic technology are in the process of revolutionizing how we conduct molecular biology research. These new techniques have given us an appreciation for the role that epigenetics modifications of the genome play in gene regulation, development, and inheritance. In this course, we will cover the biological basis of genomics and epigenetics, the basic computational tools to analyze genomic data, and the application of those tools to neuroscience. Through programming assignments and reading primary literature, the material will also serve to demonstrate important concepts in neuroscience, including the diversity of neural cell types, neural plasticity, the role that epigenetics plays in behavior, and how the brain is influenced by neurological and psychiatric disorders. Although the course focuses on neuroscience, the material is accessible and applicable to a wide range of topics in biology. 

Specialization: Computational Structural Biology (3 credits/9 units)

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.

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.

-

This is a seminar-style course on the current literature in computational structural biology.
This course develops the methods of statistical mechanics and uses them to calculate observable properties of systems in thermodynamic equilibrium. Topics treated include the principles of classical thermodynamics, canonical and grand canonical ensembles for classical and quantum mechanical systems, partition functions and statistical thermodynamics, fluctuations, ideal gases of quanta, atoms and polyatomic molecules, degeneracy of Fermi and Bose gases, chemical equilibrium, ideal paramagnetics and introduction to simple interacting systems. 3 hrs. lecture, 1 hr. recitation. Typical Texts: Reif, Statistical and Thermal Physics; Pathria, Statistical Mechanics.

This course deals with the elements of polymer science and engineering necessary for entry-level understanding of polymer technology. While the chemistry determines macromolecular microstructure, an understanding of polymer manufacture and processing requires the addition of physical chemistry and transport phenomena. The essential material covered in this class includes the elements of polymers thermodynamics, rheology, mechanical behavior and equipment design.

Basic quantum mechanics, with emphasis on the theory of chemical structure and dynamics.

Development of equilibrium statistical mechanics and thermodynamics. Applications to chemical systems. These applications include solutions, phase transitions (Ising model) and reaction theory.

This course covers the basic as well as certain selected topics pertaining to the physicochemical origins of architecture and motility of biological cells. It is aimed at graduate students pursuing degrees in various fields of biology (and also in mathematics, physics, chemistry, or engineering), who have taken university-level courses in mathematics, physics, and chemistry. This course material draws upon the variety of quantitaive disciplines but maintains a biological perspective. Physical properties and chemical kinetics that determine the structure and function of the cytoskeleton (the assembly of non-covalent polymers at the base of the cellular architecture) will be covered, as will the physicochemical mechanisms of motility driven by biological force-generating macromolecules. The final grade will be based on homework problems and on a closed-book exam. The didactic material will be presented from the perspective of a practical researcher, and the problem sets will emphasize developing a sense of what makes for a good research strategy.

This course consists of a series of lectures and tutorial sessions which focus on the general principles of pharmacology. Major topics are principles of pharmacokinetics (including drug absorption, distribution, and metabolism) and pharmacodynamics (quantitation of drug-receptor interactions).

-

This course examines molecular mechanisms of drug interactions with an emphasis on drugs that modulate cell signaling, cellular responses to drugs. The course will include student participation through presentations and discussion of relevant contemporary scientific literature. Topics include: cell cycle checkpoints and anti-cancer drugs, therapeutic control of ion channels, and blood glucose, anti-inflammatory agents and nuclear receptor signaling.

The main subject matter of this course will be a survey of group theory methods and their applications in various fields of physics. Selected topics involving analytic functions, operator algebra, and solutions of the differential and integral equations of physics will be addressed. Some numerical analysis and computational work will also be incorporated.

This is the first term of a 2-term course with emphasis on statistical mechanics. Discussion of microcanonical, canonical, and grand canonical ensembles, the passage to quantum mechanics, and the use of density matrix. The Gibbs approach to the second law. Fermi-Dirac and Bose-Einstein statistics, in both weak and strong degeneracy approximations. Transport phenomena including the fluctuation dissipation theorem and the master equation.

Computer modeling is playing an increasingly important role in chemical, biological and materials research. This course provides an overview of computational chemistry techniques including molecular mechanics, molecular dynamics, electronic structure theory and continuum medium approaches. Sufficient theoretical background is provided for students to understand the uses and limitations of each technique. An integral part of the course is hands on experience with state-of-the-art computational chemistry tools running on graphics workstations. 3 hrs. lec.

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.