Course Schedule: Spring 2010
ENG BE 500: Systems Biology of Human Disease
Prerequisites: Senior or Graduate Student Standing in BME or Bioinformatics.
For other disciplines such as medicine, biology, chemistry, physics, engineering or computer science, permission of instructor required. This course will train students to apply or develop new computational network and machine learning concepts to probe into the systems biology of disease. The course will cover computational frameworks such as biological networks (including metabolic, regulatory and signal transduction networks), microarray analysis, proteomic analysis, next generation sequencing, imaging, machine learning, genetics, pathway analysis and other technologies to medical diseases initially focusing on clinical problems such as cancer, diabetes, inflammation and aging. The course is aimed at seniors and graduate students in biomedical engineering or bioinformatics. However, students from other disciplines ranging from medicine and biology to physics or computer science can attend the class with some prerequisites. There are no exams and grading is based on bi-weekly homework, reading research papers, class presentations and a team project.
The aim of this course is to cover general concepts in biological computing that provide the foundation of thinking computationally about anomalous behavior in biological systems that causes disease. The course also aims to teach students to work in teams and develop the skills to plan and coordinate a scientific project. During the course we will have guest lectures from scientists working in local biotechnologies or hospitals.
Goals: The main aim is to prepare students to apply and develop new concepts in integrative and systems biology of human disease. This involves developing a familiarity with current high-throughput biology technologies, probing the complex systems biology of disease using these biotechnologies, storing, querying and manipulating massive amount of data, performing analysis of clinically relevant integrative data, producing models of systems across scales, capturing anomalous behavior in biological networks and making and validating predictions made by these network models.
Prof. Kasif, Mon. Weds. 2-4 pm
ENG BE 560: Biomolecular Architecture
Provides an introduction to the molecular building blocks and the structure of three major components of the living cells: the nucleic acids, the phospho-lipids membrane, and the proteins. The nucleic acids, DNA and RNA, linear information storing structure as well as their three-dimensional structure are covered in relationship to their function. This includes an introduction to information and coding theory. The analysis tools used in pattern identification representation and functional association are introduced and used to discuss the patterns characteristic of DNA and protein structure and biochemical function. The problems and current approaches to predicting protein structure including those using homology, energy minimization, and modeling are introduced. The future implications of our expanding biomolecular knowledge and of rational drug design are also discussed. Tues. Thurs. 2-4; Location and Instructor TBD
ENG BE 565: Molecular Biotechnology
Covers the basic properties of biological macromolecules and assemblies including proteins, nucleic acids, and membranes. Among the topics covered are the forces that govern biological structures, how proteins act as catalysts, how membranes act to store energy, and how nucleic acids and proteins are synthesized in cells. Methods for manipulating the living cells to change their properties and to produce specific proteins of nucleic acids are detailed. Tues. Thurs. 12-2; Professor Smith
ENG BE 768: Biological Database Analysis
Describes relational data models and database management systems. Teaches the theories and techniques of constructing relational databases with emphasis on those aspects needed for various biological data, including sequences, structures, genetic linkages and maps, and signal pathways. Introduces relational database query language SQL. Summarizes currently existing biological databases and the Web-based programming tools for their access. Object-oriented modeling is introduced primarily as a design aid for dealing with the particular complexities of biological information in standard RDB design. Emphasis will be on those problems associated with dealing with data whose nomenclature and interrelationships are undergoing rapid change. Professor Benson. Tues. Thurs. 4-6. Location TBD.
ENG BF 541: Internship in Bioinformatics
This course allows M.S. and Ph.D. students in bioinformatics to take part in an industrial internship. Students will be required to present a report on their training and/or make a presentation and poster as a part of participating in the University’s Science Day program (annual in March). Variable credits. Professor Mohr.
ENG BF 752: Law and Ethics of Biological Sciences
Course Description to follow.
ENG BF 778: Physical Chemistry for Systems Biology
This course introduces students to quantitative modeling in bioinformatics and systems biology. We begin with basic principles of statistical thermodynamics, chemical kinetics, with selected applications in biomolecular systems. Next we describe molecular driving forces in biology, and computation with biomolecular structures. Finally we discuss quantitative models of biomolecular networks, and design principles of biological circuits. Professor Xia. Mon. 1-4. Location TBD.
ENG BF 810: PhD Laboratory Rotation System
This course is for Ph.D. students to take part in a laboratory rotation system. Students will become familiar with research activity in Bioinformatics labs. These rotations will help students identify the laboratory in which they will perform their Dissertation research. Post-Bachelor Ph.D. students must complete one 9-week rotation in their first semester of matriculation and two in their second semester. Ph.D. standing, 1 credit per rotation. Professors Mohr and Segre.
ENG BF 821: Bioinformatics Graduate Seminar
In this course the students present advanced papers in Computational Biology and Bioinformatics. The papers are chosen to cover recent breakthroughs in genomics, computational biology, high-throughput biology, analysis methods, computational modeling, databases, theory and bioinformatics. 2 credits. Professor Xia. Weds. 1-3; Location TBD.
ENG BF 900: Pre-candidacy Research in Bioinformatics
For Ph.D. students prior to candidacy. Participation in a research project under the direction of two faculty advisors. Requires the development of a brief document outlining the proposed research leading to either a Ph.D. prospectus (for Ph.D. students). Variable credits. Professor DeLisi.
ENG BF 901: Post-candidacy Thesis/Research in Bioinformatics
For Ph.D. students post-candidacy. Participation in a research project under the direction of two faculty advisors. Variable credits.
CAS BI/CH 528: Biochemistry Laboratory, I and II
Emphasizes protein, carbohydrate, nucleic acid, and lipid chemistry. Development and use of modern instrumentation and techniques. Four hours lab, one hour discussion. Same as CAS CH528 and laboratory portion of CAS BI/CH422. Required for BMB students enrolled concurrently in GMS BI 556. Four hours lab, one hour discussion. Tues. 1:30-2:30. Professor Kyte. Location TBD.
ENG BE 566: DNA Structure and Function
Prereq: CH 102, PY 212, EK 424. Physical structure and properties of DNA. The physical principles of the major experimental methods to study DNA are explained, among them: X-ray analysis, NMR, optical methods (absorption, circular dichroism, fluorescence), centrifugation, gel electrophoresis, chemical and enzymatic probing. Different theoretical models of DNA are presented, among them the melting (helix-coil) model, the polyelectrolyte model, the elastic-rod model, and the topological model. Theoretical approaches to treat the models, (e.g., the Monte Carlo method) are covered. Special emphasis is placed on DNA topology and DNA unusual structures and their biological significance. Major structural features of RNA are considered in parallel with DNA. The main principles of DNA-protein interaction are presented. the role of DNA and RNA structure in most fundamental biological proceses, replication, transcription, recombination, reparation, and translation is considered. MW 10am-12pm in SAR 104. Sample Syllabus.
CAS BI 553: Molecular Biology II
Continuation of CAS BI 552 with emphasis on eukaryotes. General areas of focus include genome organization, mechanisms of gene regulation, and cell signaling. Topics including genomics, chromatin structure, cell cycle, mouse transgenics systems, signal transduction, and apoptosis. Professor Hansen. Tues. Thurs. 9:30-11, discussion Mon. 1-2 or 5-6. Location TBD.
CAS BI 572: Advanced Genetics
An in-depth study of eukaryotic genetics, ranging from the history and basic principles to current topics and modern experimental approaches. Genetics of Drosophila, C. elegans, mice, and humans are explored in detail, including readings from primary literature. Three hours lecture, one hour discussion. Professor McCall. Tues. Thurs. 12:30-2. Location TBD.
GRS BI 735: Advanced Cell Biology
Current understanding of essential topics and important problems in modern cell biology, with emphasis on recent experimental findings, research strategies and approaches, and new techniques for investigating how cells work. Three hours lecture, one hour discussion. Professor Bradham. Tues. Thurs. 11-12:30.
CAS BB 522: Molecular Biology Laboratory
Introduction to techniques of molecular biology research, including analysis of DNA, RNA, and protein molecules by techniques such as restriction enzyme digestions, PCR, subcloning, DNA sequencing, sequence analysis, reporter gene assays, protein-protein interactions, and culturing, transformation, and selection of yeast and bacteria. Professor Gilmore. Tues. Thurs. 1-5.
CAS CH 525: Physical Biochemistry
Cannot be taken as advanced course for chemistry majors or in addition to CAS CH351/352. Introduction to physical chemical principles with topics in biochemistry, solution and solid phase chemistry of biomolecules. Professor Allen. MWF 10-11.
CAS CH 724: The Design of Biochemical Systems
This course will present a new perspective on the chemical parts and processes of living organisms. Enabled by the “omics revolution” [the analysis of biological systems in their totality, starting with the determination of complete genome sequences (genomes), assessment of the all the kinds and amounts of expressed proteins in cells (proteomes), etc.], researchers nowadays can hope to uncover every detail of living systems at the chemical level. Such a catalog by itself affords little or no understanding of “how life works.” That requires an analysis akin to reverse-engineering a complex machine, and molecular life scientists have made substantial progress in that direction. This course will summarize the current status of that research program by viewing the work of evolution as a kind of (forward) engineering. We will examine the functions that characterize living organisms and recast them in terms of design specifications that could be presented to an engineer. Then we will review the details of some of the major biochemical component parts that satisfy those criteria with a view to understand-ing how and why Nature selected them in preference to plausible alternative designs. Finally we will examine the varying degrees of success that chemists have had in creating substitutes that can perform part or all of the functions of the natural systems, and survey the currently “hot” field of synthetic biology that builds on the chemical foundations and uses genetic engineering to create novel organisms capable of heretofore unknown behaviors. The course will also endeavor to formulate generalizations that can help systematize and understand the welter of biochemical details and empower students to approach totally new systems with the right questions. Professor Mohr. MW, 2-3:30 pm. Syllabus.
CAS MA 555: Numerical Analysis
Numerical solutions of equations, iterative methods, analysis of sequences. Theory of interpolation and functional approximation, divided differences. Numerical differentiation and integration. Polynomial theory. Ordinary differential equations. Professor Fried. MWF 3-4.
CAS MA 565: Mathematical Models in the Life Sciences
An introduction to mathematical modeling, using applications in the biological sciences. Mathematics includes linear difference and differential equations, and an introduction to nonlinear phenomena and qualitative methods. An elementary knowledge of differential equations and linear algebra is assumed. Professor Osan. Tues. Thurs. 12:30-2.
CAS MA 582: Mathematical Statistics
Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test. Professor Ginovyan. MWF 11-12am.
CAS MA 583: Intro to Stochastic Processes
Basic concepts and techniques of stochastic process as they are most often used to construct models for a variety of problems of practical interest. Topics include Markov chains, Poisson process, birth and death processes, queuing theory, renewal processes, and reliability. Professor Eden. MWF 10-11am.
CAS MA 684: Applied Multiple Regression and Multivariable Methods
Application of multivariate data analytic techniques. Multiple regression and correlation, confounding and interaction, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, MANOVA, discriminant analysis, regression with longitudinal data, repeated measures, ANOVA. Professor Heeren. Mon 3-6pm.
GRS MB 722: Advanced Biochemistry
Professor Tolan. Tue, Thurs 12:30-2; Wed. 2:30-6pm.
SPH BS 703: Biostatistics
This is the more advanced MPH biostatistics core course. This course is recommended for students concentrating in biostatistics or epidemiology, and for students with previous exposure to statistical methods or an interest in public health research methods. Topics include confidence intervals and hypothesis testing; sample size and power considerations; analysis of variance and multiple comparisons; correlation and regression; multiple regression and statistical control of confounding; logistic regression; and survival analysis. This course gives students the skills to perform, present, and interpret basic statistical analyses, using the R statistical computing package. For the more advanced topics, the focus is on interpretative skills and critically reading the literature. This course satisfies the core biostatistics requirement for MPH students. Biostatistics concentrators should take this course, though the course does not count towards the 16 required concentration credits. Students who take BS703 cannot take BS701 for degree credit. Professor Heeren. Tues. 6-8:45pm (Jan 12-May 4)
SPH BS 858: Statistical Genetics I
Grad Prereq: SPH BS723 or equivalent as determined by instructor (dupuis@bu.edu or klunetta@bu.edu). This course covers a variety of statistical applications to human genetic data, including collection and data management of genetic and family history information, and statistical techniques used to identify genes contributing to disease and quantitative traits in humans. Specific topics include basic population genetics, linkage analysis and genetic association analysis with related and unrelated individuals. Josée Dupuis amd Kathryn Lunetta, 4 cr. 1st sem. Thu 2:30-5:00
SPH BS 850: Advanced Statistical Methodology for the Computational Biosciences
This course will discuss in depth advanced statistical computing methods used in scientific, especially biomedical, applications: generation of random numbers, optimization methods, numerical integration and advanced computational tools such as the EM algorithm, importance sampling, Gibbs sampler, Metropolis Hastings, auxiliary variable methods, data augmentation, reversible jump MCMC, and population-based Monte Carlo. The second half of the course will involve detailed discussions of statistical models and methods for problems in genomics and computational biology, including dynamic programming, hidden Markov models, multiple sequence alignment, phylogenetic tree reconstruction, gene regulatory network discovery and analysis of genome tiling array data. Computer programming exercises would apply the methods discussed in class, primarily using the software R and BUGS/WinBUGS. During the course, students will form small groups to select a topic of interest, on which they will carry out a course project implementing statistical computing methods appropriate for the application. Professor Gupta. Mon 2:30-5pm; Jan 11-May 3.
SPH BS 860: Statistical Genetics II
This course covers current topics in statistical genetics, with emphasis on how statistical techniques can be used with various types of genetics data for mapping genes responsible/contributing to complex human diseases. Topics such as genetics map functions, gene mapping in experimental organisms, advanced linkage analysis methods, statistical approaches for the analysis of genome-wide high density SNP scans in unrelated and family samples will be discussed. Professor Dupuis. Tues. 6-8:50pm; Jan 12-May 4.
ENG EC 534: Discrete Stochastic Models
Markov chains, Chapman-Kolmogorov equation. Classification of states, limiting probabilities, Poisson process and its generalization, continuous-time Markov chains, queuing theory, reliability theory. Professor Levitin. Tue, Thurs 12-2pm.
ENG EC 730: Information-Theoretical Design of Algorithms
Recently developed information-theoretical approach to the analysis and design of computer algorithms. Previous knowledge of information theory or the theory of algorithms is not required, though desirable. Main topics include the complexity of algorithms; P, E, NP, and NP-hard problems; basic concepts of information theory, optimal coding; information-theoretical approach to sorting, order statistics, binary search, decision trees, hashing, minimization of Boolean functions, test, and similar problems; and design of efficient computer algorithms. Professor Levitin. Tue, Thurs 5-7pm.