Course Schedule

Spring 2018

Core Courses

Description: 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.

Instructor: Benson; Credits 4; Tues/Thurs 3:30 pm – 6:15 pm

Description: Project course for first year Bioinformatics graduate students. Open-ended problems will involve bioinformatics as a key element, typically requiring the use of large data sets and computational analysis to make predictions about molecular function, molecular interactions, regulation, etc. Projects will be proposed by the Bioinformatics program faculty and selected by student in teams of three. The end result will be a set of predictions, some of which can be validated retrospectively using data available through online sources and some of which will require experimental validation. During the last 2 months of the academic year, teams will design feasible validation experiments in consultation with the experimental faculty.  1st year Bioinformatics PhD students only.

Instructor: Benson; Credits: 2; Thurs 11:00 am-12:15 pm

Description: The course focuses on mathematical models for exploring the organization, dynamics, and evolution of biochemical and genetic networks. Topics include: introductions to metabolic and genetic networks, deterministic and stochastic kinetics of biochemical pathways; genome-scale models of metabolic reaction fluxes; models of regulatory networks; modular architecture of biological networks.

Prereq: CAS MA 226 & CAS MA 242. ENG EK 102 can be used in lieu of the CAS MA 242 pre-req. Familiarity with differential equations and linear algebra at equivalent levels and the consent of instructor can be used in lieu of both pre-reqs.

Instructors: Segre & Korolev; Credits: 4; Mon/Wed 10:10 am – 11:55 am

Description: Bioinformatics is an interdisciplinary field devoted to managing and analyzing largescale biological data, such as the DNA sequence of the human genome, and has become an essential tool in interpreting and translating biological knowledge for use in a clinical setting. This course introduces graduate and upper–level undergraduate students to the principles of bioinformatic analysis applied to translational studies. Application topics will include gene expression analysis, biomarker development, and Genome Wide Association Studies (GWAS). Bioinformatics methods including microarray analysis, short read sequence analysis, biological pathways and geneset enrichment analysis, and Quantitative Trait Loci (QTL) will be covered. Lectures and assignments will be designed around reproducing the results of preselected studies from the literature that exemplify the topics. The primary focus will be using existing software tools and published data to perform analyses, but most tasks will require some programming.

Prereq: a strong background in molecular biology.  Students must bring laptop to each class.

Instructor: Labadorf; Credits: 4; LEC/LAB: Wed/Fri 2:30 pm – 4:15 pm

Description: This course will address the ethical, legal and scientific aspects of the new genetics. Students in bioinformatics will discuss the questions raised from another view that they normally would not see. As part of the new technologies, individuals, families and society as a whole will have to make decisions that will affect everyone.  We will analyze cases, question the legal system’s role in regulating this field, discuss options for now and analyze cases. Gene therapy, DNA forensics, new reproductive techniques and cloning are only a few of the topics that will be addressed.

Instructor: Garland ; Credits: 4; Date/time Mon 4:30 pm – 6:15 pm 

Description: Three laboratory rotations are required during a Bioinformatics Ph.D. student’s first year. Rotations typically last for a minimum of nine weeks. It is expected that the student will participate in the lab full time except for time spent on courses. One rotation must be experimental, one computational, and the third can be either. Stduents who participate in the Summer Wet-Lab Experience prior entering the program receive credit toward one of the required rotations.

Instructor: Tullius; Credits: 1 per rotation (3 total); Day: To be arranged

Description: BF821 is a graduate seminar covering current topics in bioinformatics. This is achieved through the critical reading, presentation, and discussion of recent literature. Additionally, the course is intended to give students the opportunity to practice and improve their scientific presentation abilities. As such, peer feedback on presentations is an integral aspect of the course. Students will present twice during the semester so that they may improve upon their presentation skills based on peer comments.

Instructor: Korolev; Credits: 2; Fri 10:10 am – 11:55 am

ENG BE 700-K3: Methods and Logic in Quantitative Biology

Biology is in the midst of a transformation into a fully quantitative, theory-rich science. For example, the advent of genomic techniques has presented the opportunity to study genetic processes on a genomic scale and to achieve quantitative understanding, not just of individual molecular mechanisms but also of their interactions and regulation at the systems level. This graduate course is based on original path-breaking papers in diverse areas of quantitative biology at the intersection of theory and experiment. Through close reading and discussion of these papers, students of diverse backgrounds (biology, engineering, physics, computational sciences) will learn essential ideas and how to communicate in a common language of modern/quantitative biology. This course should be considered essential education for students interested in pursuing research in systems biology, synthetic biology, and biophysics. Although it is a graduate course, we encourage advanced undergraduates to enroll, too.

Instructor: Khalil; Credits: Var.; Tues/Thurs 1:30 pm – 3:15 pm

ENG BE 700-K8: Systems Biology of Human Disease

This course will train students to apply or develop computational network, modeling, and machine learning concepts to probe into the systems biology of disease. 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 cause diseases. The course also aims to teach students to work in teams and develop the skills to plan and coordinate a scientific project. The course will cover computational frameworks, such as biological networks (including metabolic, regulatory, and signal transduction networks), micro array analysis, proteomic analysis, next generation sequencing, imaging, machine learning, probabilistic inference, genetics, pathway analysis, network and graph theory, and other technologies to medical diseases initially focusing on clincal 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 to physics or computer science can attend the class with some prerequisites.

Instructor: Kasif; Credits: 4; Tues/Thurs 9:00 am – 10:45 am

ENG BF 527-A1: Applications in Bioinformatics

The field of bioinformatics is concerned with the management and analysis of large biological datasets (such as the human genome) for the purpose of improving our understanding of complex living systems. This course introduces graduate students and upper-level undergraduate students to the core problems in bioinformatics, along with the databases and tools that have been developed to study them. Computer labs emphasize the acquisition of practical bioinformatics skills for use in students research. Familiarity with basic molecular biology is a prerequisite; no prior programming knowledge is assumed. Specific topics will include the analysis of biological sequences, structures, and networks.

Instructor: Leyfer; Credits: 4; LEC/LAB: Mon/Wed 2:30 pm – 4:15 pm

CAS BI/CH 528: Biochemistry Laboratory I, II

Emphasizes protein, carbohydrate, nucleic acid, and lipid chemistry. Development and use of modern instrumentation and techniques. Four hours lab, one hour discussion.

Prereq: CAS CH 204 ; CAS CH 212 ; CAS CH 214; or CASCH282.

Instructor: Tolan; Credits: 2; LEC: Fri 10:10 am – 11:00 am OR Fri 11:15 am – 12:05 pm OR Wed 12:20 – 1:10; LAB: Mon 2:30 pm – 4:20 pm OR Mon 5:30 pm – 9:30 pm OR Wed 2:30 pm – 6:30 pm OR Thursday 5:30 pm – 9:30 pm OR Friday 12:20 pm – 4:20 pm OR Fri 5:30 pm – 9:30 pm

CAS BI 553-A1: Molecular Biology II (CM)

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, mouse transgenics systems, signal transduction, chromatin structure, and cell cycle.

Prereq: CAS BI 552; (CAS BI/CH 421/422 recommended.)

Instructor: Naya; Credits: 4; LEC: Tues/Thurs 9:00 am – 10:45 am

 GRS BI 611-A1: Microbiome: Our Intimate Relationship with Microorganisms

The microbial community – referred to as “microbiome” – that colonizes the human body plays an important role in our health. Topics include (1) the human microbiome; and (2) fundamental aspects of the interactions between animals and the microorganisms that reside with them. Three hours lecture; one hour discussion.

Prereq: CASBI203 (or equivalent) & CASBI206 (or equivalent) or consent of instructor.

Instructor: Frydman; Credits: 4; LEC: Tues/Thurs 3:30 pm – 4:45 pm, DIS: Thurs 5:00 pm – 5:50 pm

GRS BI 735-A1: 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.

Instructor: Bradham; Credits: 4; LEC: Tues/Thurs 2:00 pm – 3:15 pm, DIS: Wed 2:30 pm – 4:15 pm

CAS BB 522-A1: 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 and analysis, reporter gene assays, protein-protein interactions, and culturing and yeast molecular biology.

Prereq: CAS BI 552.

Instructor: Gilmore; Credits: 4; Tues/Thurs 1:00 pm – 4:45 pm

CAS CH 525-A1: Physical Biochemistry

Introduction to physical chemical principles with topics in biochemistry, solution and solid phase chemistry of biomolecules as studied by equilibrium, hydrodynamics, and spectroscopic/quantum mechanical methods.

Instructor: Elliott; Credits: 4; Mon/Wed/Fri 10:10 am – 11:00 am

CAS CS 506: Computational Tools for Data Science

Covers practical skills in working with data and introduces a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. Emphasizes hands-on application of methods via programming.

Prereq: CAS CS 108 or CAS CS 111; CAS CS 132 or CAS MA 242 or CAS MA 442. CASCS 112 is recommended.

Instructor: TBA; Credits: 4; LEC: Tues0/Thurs 3:30 pm – 4:55 pm

CAS CS 542-A1: Machine Learning

Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Prereq: CAS CS 112; or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: Chin; Credits: 4; LEC: Tues/Thurs 5:00 pm – 6:15 pm; LAB: (A2) Fri 9:05 – 9:55; (A3) Fri 10:10 am – 11:00 am OR (A4) 11:15 am – 12:05 am

CAS MA 555-A1: Numerical Analysis I

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.

Prereq: CAS MA225 OR CAS MA230

Instructor: Fried; Credits: 4; Mon/Wed/Fri 3:45 pm – 4:25 pm

CAS MA 565-A1: 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.

Prereq: CAS MA226 OR CAS MA231

Instructor: Isaacson; Credits: 4; Tues/Thurs 11:00 am – 12:15 pm

CAS MA 581-A1: Probability

Basic probability, conditional probability, independence. Discrete and continuous random variables, mean and variance, functions of random variables, moment generating function. Jointly distributed random variables, conditional distributions, independent random variables. Methods of transformations, law of large numbers, central limit theorem.

Prereq: CAS MA225 OR CAS MA230.

Instructor: Weiner; Credits: 4; LEC: (A1) Mon/Wed/Fri 10:10 am – 11:00 am; DIS: A2 Mon 3:35 pm – 4:25 OR (A3) Mon 4:40 pm – 5:30 pm OR (A4) Tues 11:15 am – 12:05 pm OR (A5) Tues 3:35 pm – 4:25 pm

CAS MA 582-A1: 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.

Prereq: CAS MA581

Instructor: Weiner; Credits: 4; LEC: (A1) Mon/Wed/Fri 11:15 am – 12:05 pm; DIS: A2 Tues 3:35 pm – 4:25 OR (A3) Tues 5:00 pm – 5:50 pm OR (A4) Mon 1:25 am – 2:15 pm OR (A5) Mon 12:20 pm – 1:10 pm

CAS MA 583-A1: Introduction 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.

Prereq: CAS MA581 or consent of instructor.

Instructor: Spiliopoulos; Credits: 4; LEC: (A1) Tues/Thurs 9:30 am – 10:45 pm; DIS: A2 Tues 12:30 pm – 1:20 OR (A3) Tues 2:00 pm – 2:50 pm OR (A4) Wed 3:35 am – 4:25 pm OR (A5) Wed 4:40 pm – 5:30 pm

GRS MA 684-A1: 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. This course cannot be taken for credit in addition to the course with the same title that was previously numbered CAS MA 684.

Grad Prereq: one year of statistics

Instructor: Harbaugh; Credits: 4; LEC: Tues/Thurs 3:30 pm – 4:45 pm

GRS MA 882-A1: Seminar: Statistics

Advanced seminar in topics in statistics of current research interest.

Grad Prereq: GRS MA 782.

Instructor: TBA; Credits: 4; Tues/Thurs 9:30 am – 10:45 am

SPH BS 831-A1: Genomics Data Mining and Statistics

In this course, students will be presented with the methods for the analysis of gene expression data measured through microarrays. The course will start with a review of the basic biology of gene expression and an overview of microarray technology. The course will then describe the statistical techniques used to compare gene expression across different conditions and it will progress to describe the analysis of more complex experiments designed to identify genes with similar functions and to build models for molecular classification. The statistical techniques described in this course will include general methods for comparing population means, clustering, classification, simple graphical models and Bayesian networks. Methods for computational and biological validation will be discussed.

Instructor: Monti; Credits: 2; Fri 2:00 pm – 4:50 pm; Only meets Mar 16 – May 4

SPH BS 849-A1: Bayesian Modeling for Biomedical Research and Public Health

Bayesian methods have enjoyed a growing popularity in science and technology and have become the methods of analysis in many areas of public health and biomedical research including genetics and genomics, disease surveillance, disease mapping. Competent biostatisticians nowadays are expected to have knowledge in Bayesian modeling and Markov Chain Monte Carlo methods to be effective collaborators in interdisciplinary research groups. This course will introduce Bayesian statistical reasoning through graphical modeling and describe Markov Chain Monte Carlo methods for Bayesian inference. The course will cover Bayesian methods for estimation of odds and risk in observational studies; methods for multivariable linear, loglinear and logistic regression; hierarchical models; latent class modeling including hidden Markov models and model-based clustering. These topics will be taught using real examples from genetics, genomics, and observational studies, class discussion and critical reading. Students will be asked to analyze real data sets in their homeworks and a final project.

Instructor: Sebastiani; Credits: 2; Fri 2:00 pm – 4:45 pm; Only meets Jan 19 – Mar 2

SPH BS 860-A1: 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.

Grad Prereq: SPH BS858 or consent of instructor (dupuis@bu.edu).

Instructor: Dupuis; Credits: 4; Tues 6:00 pm – 8:50 pm

GRS MB 722-A1: Advanced Biochemistry

An advanced treatment of the underlying theories, principles, mechanisms, and chemistry of current biochemical investigation. Selected topics may include enzyme mechanics, protein structure and folding, bioinformatics, signal transduction, nucleic-acid protein interactions, techniques in proteomics, and genetic disease mechanisms.

Prereq: (CAS BI/CH 421 & 422) or (GRS BI/CH 621 & 622) or CAS CH 273 or GRS MB 721.

Instructor: Tolan; Credits: 4; LEC Tues/Thurs 12:30 pm – 1:45 pm; DIS Wed 4:30 pm – 6:15 pm

GMS PA 600: Introduction to Pathology and Pathophysiology of Disease

Lectures and interactive auto-tutorial case studies presenting the basic morphologic and functional changes associated with cell injury and death, inflammation, response to microorganisms, atherosclerosis, cancer, and organ system pathology.

Grad Prereq: GMS FC701, FC702, FC703 or GMS BI751 & PH730

Instructor: Slack; Credits: 4; Tues/Thurs 1:00 pm – 2:50 pm

Fall 2017

Core Courses

Description: The algorithmic and machine learning foundations of computational biology, combining theory with practice are covered. Principles of algorithm design and core methods in computational biology, and an introduction of important problems in computational biology. Hands on experience analyzing large-scale biological data sets.

Prereq: Fundamentals of programming and algorithm design (EK 127 or equivalent), basic molecular biology (BE 209 or equivalent), statistics and probability (BE 200 or equivalent), or consent of instructor.

Instructor: Galagan; Credits: 4; Tues/Thurs, 1:30 pm – 3:15 pm and Fri, 10:10 am – 11:55 am

Description: Introduction to statistical methods relevant to research in the computational sciences. Core topics include probability theory, estimation theory, hypothesis testing, linear models, GLMs, and experimental design. Emphasis on developing a firm conceptual understanding of the statistical paradigm through data analyses.  Required for entering Bioinformatics Ph.D. students.

Prereq: MA 225 & MA 242 or their equivalents.

Instructor: Sussman; Credits: 4; Tues/Thurs, 9:30 am – 10:45 am; DIS Section: (A2) Mon, 1:25-2:15 pm or (A3) 2:30-3:20 pm

Project course for first year Bioinformatics Ph.D. students. Open-ended problems will involve bioinformatics as a key element, typically requiring the use of large data sets and computational analysis to make predictions about molecular function, molecular interactions, regulation, etc. Projects will be proposed by the Bioinformatics program faculty and selected by student in teams of three. The end result will be a set of predictions, some of which can be validated retrospectively using data available through online sources and some of which will require experimental validation. During the last 2 months of the academic year, teams will design feasible validation experiments in consultation with the experimental faculty.

Instructor: Benson; Credits: 2; Wed, 12:20 pm – 2:05 pm

This course is for PhD 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. PhD students must complete one 9-week rotation in their first semester of matriculation and two in their second semester.

Instructor: Tullius; Credit: 1; ARR

Required for entering Bioinformatics Ph.D. students. The course will consist of a series of presentations by Bioinformatics faculty that focus on research projects being investigated in their laboratories. Emphasis is placed on the description of collaborative projects involving experimental and computational approaches to Bioinformatics research problems.

Instructor: TBA; Credits: 1; Mon, 2:30 pm – 4:15 pm 

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.

Instructor: Segrè; Credits: 2; Wed, 10:10 am – 11:50 am

Synthesis, structure, and function of biologically important macromolecules (DNA, RNA, and proteins). Regulation and control of the synthesis of RNA and proteins. Introduction to molecular biology of eukaryotes. Discussion of molecular biological techniques, including genetics and recombinant DNA techniques. Three hours lecture, one hour discussion.

Instructor: Loechler; Credits: 4; LEC Sections: (B1): Tues 6:00 – 7:45/Thurs 6:00 pm – 6:50 pm; DISC Sections: (D1) Thurs 7:05 – 7:55 pm

This course enrolls students who intend to pursue careers in medicine, dental medicine and/or medical research (either academic or industrial) – in particular, students in the BF for Translational Medicine track of the Bioinformatics MS program. After commencing briefly with general introductory material (published reviews and other relevant back-ground information), students will proceed to examine, discuss and evaluate recent papers that directly illustrate the use of bioinformatics either in pre-clinical or clinical research settings. Papers will be drawn from high-impact journals such as Nature, Science, PNAS, Cell, and Science Translational Medicine. Students will take turns presenting the papers to the class and provide a critical review of each, both orally and in writing. They will also complete a term paper in the form of a research proposal directed to the goal of using bioinformatics to advance a medical procedure – either diagnostic or therapeutic. Brief guest presentations by researchers in BUSM laboratories will be arranged as appropriate.

Prereq: consent of the instructor and completion of advising form. Email Katie Steiling (steiling@bu.edu) to enroll.

Instructor: Steiling; Credits: 2; Day/Time: Tues, 3:30 pm – 5:15 pm 

ENG BF 527: Applications in Bioinformatics

The field of bioinformatics is concerned with the management and analysis of large biological datasets (such as the human genome) for the purpose of improving our understanding of complex living systems. This course introduces graduate students and upper-level undergraduate students to the core problems in bioinformatics, along with the databases and tools that have been developed to study them. Computer labs emphasize the acquisition of practical bioinformatics skills for use in students research. Familiarity with basic molecular biology is a prerequisite; no prior programming knowledge is assumed. Specific topics will include the analysis of biological sequences, structures, and networks.

Instructor: Leyfer; Credits: 4; Tues/Thurs 1:30 pm – 3:15 pm

ENG BE 564: Biophysics of Large Molecules

The course considers the fundamental concepts of physical and mathematical description of polyatomic molecules and macromolecules on the basis of quantum and statistical mechanics. Special emphasis is given to molecular spectroscopy, the interaction of polyatomic molecules with electromagnetic radiation (visual light, ultraviolet and infrared radiation). Physics of macromolecules (or polymers) is treated in detail. Numerous biomedical applications of the fundamental concepts are considered including photosyntheses, molecular mechanism of vision, DNA damage under UV irradiation, structure of major biological molecules (proteins and nucleic acids).

Prereq: ENG EK 424

Instructor: Frank-Kamenetskii; Credits: 4; Tues/Thurs 1:30 pm – 3:15 pm.

ENG BE 700-A6: Methods & Logic in Quantitative Biology

Description: Biology is in the midst of a transformation into a fully quantitative, theory-rich science. For example, the advent of genomic techniques has presented the opportunity to study genetic processes on a genomic scale and to achieve quantitative understanding, not just of individual molecular mechanisms but also of their interactions and regulation at the systems level. The main focus of this course is the close reading of published papers illustrating the principles, achievements, and difficulties that lie at the interface of theory and experiment in biology. Two (or three) important papers, read in advance by all students, will be considered each week; the emphasis will be on discussion with students as opposed to formal lectures. Topics include: cooperativity, robust adaptation, gene regulation & genetic circuits, kinetic proofreading, pattern formation, sequence analysis, clustering, phylogenetics, analysis of fluctuations, maximum likelihood methods, and single-molecule approaches.

Prereq: Graduate standing or consent of instructor.

Instructor: Khalil; Credits: 4; Mon/Wed 10:10 am – 11:50 am.

CAS BI 527: Biochemistry Laboratory I

Emphasizes the purification and characterization of proteins and DNA. Development and use of modern instrumentation and techniques. Same as CH 527 and laboratory portion of CAS BI/CH 421. Four hours lab, one hour discussion.

Prereq: CAS CH 204 & CAS CH 212 & CAS CH 214 or CAS CH 282

Instructor: Tolan; Credits: 4; Multiple meetings.  See University class schedule for complete list.

CAS BI 560: Systems Biology

Examines critical components of systems biology, including design principles of biological systems (e.g., feedback, synergy, cooperativity), and the generation and analysis of large-scale datasets (e.g., protein- protein interaction, mRNA expression).

Prereq: CAS BI 552 or consent of the instructor.

Instructor: Siggers; Credits: 4; LEC: Mon/Wed/Fri 11:15 am – 12:05 pm, DIS: Wed 12:20 pm – 1:10 pm

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.

Prereq: CAS BI 206 and CAS BI 203; CAS BI 552 is recommended.

Instructor: McCall; Credits: 4; LEC: Tues/Thurs 9:30 am – 11:45 am, DIS Section: (B1) Wed 10:100 am – 11:00 am or (B2) Wed 1:25 pm – 2:15 pm

CAS BI 610: Developmental Biology

Comtemporary aspects of development, drawing from current literature. Emphasis on the use of experimental approaches to address topics such as polarity in the egg, body axis specification, embryonic patterning and organogenesis. Three hours lecture, one hour discussion.

Instructor: Bradham; Credits: 4; LEC: Tues/Thurs 2:00 – 3:15 pm, DIS Section: (B1) Wed 2:30 – 3:20 pm or (B2) 3:35 – 4:25 pm.

GRS BI 753: Advanced Molecular Biology

In-depth analysis of current topics in molecular biology regarding the flow of information in the nucleus of eukaryotic cells. Focus on primary literature. Includes genomic flexibility, signal transduction to the nucleus, chromatin structure, gene expression, cell cycle checkpoints, health-related topics.

Prereq: CAS BI 552 or consent of instructor.

Instructor: Hansen; Credits: 4; LEC: Tues/Thurs 3:30 pm – 4:45 pm, DIS: Wed 1:25 pm – 2:15 pm

GRS BI 755: Cellular and Systems Neuroscience

Survey course in neurobiology. Topics covered include cell biology of the neuron, development of the nervous system, synaptic plasticity, learning and behavior, and network modeling. Three hours lecture, one hour discussion.

Instructor: Chen; Credits: 4; Tues/Thurs 3:30 pm – 6:15 pm

GRS CH 751: Advanced Topics in Physical Chemistry

Current topics of research in physical chemistry. The course content varies with instructor.

Instructor: Ling; Credits: 4; Tues/Thurs 11:00 am – 12:15 pm

CAS CS 506: Computational Tools for Data Science

Covers practical skills in working with data and introduces a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. Emphasizes hands-on application of methods via programming.

Prereq: CAS CS 108 or CAS CS 111; CAS CS 132 or CAS MA 242 or CAS MA 442. CASCS 112 is recommended.

Instructor: Crovella; Credits: 4; Tues/Thurs 11:00 am – 12:15 pm

CAS CS 542: Machine Learning

Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Prereq: CAS CS 112 or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: Terzi; Credits: 4; Tues/Thurs 3:30 pm – 4:45 pm; LAB Section: (A2) Wed 9:05 am – 9:55 am, (A3) Wed 12:20 pm – 1:10 pm or (A4) Wed 1:25 pm – 2:15 pm.

CAS CS 565: Algorithmic Data Mining

Introduction to data mining concepts and techniques. Topics include association and correlation discovery, classification and clustering of large datasets, outlier detection. Emphasis on the algorithmic aspects as well as the application of mining in real-world problems.

Prereq: CASCS 112 & CAS CS 330 and familiarity with linear algebra, probability, and statistics.

Instructor: Terzi; Credits: 4; Mon/Wed 2:30 pm – 3:45 pm; LAB Section: (A2) Fri 9:05 am – 9:55 am, (A3) Fri 3:35 pm – 4:25 pm or (A4) Fri 1:25 pm – 2:15 pm.

SPH BS 858: Statistical Genetics I

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 analyses with related and unrelated individuals.

Prereq: SPH BS 723, BS 730 or equivalent as determined by instructor.

Instructor: Lunetta; Credits: 4; Thurs 2:00 pm – 4:50 pm

CAS MA 575: Linear Models

Post-introductory course in linear models, with focus on both principles and practice. Simple and multiple linear regression, weighted and generalized least squares, polynomials and factors, transformations, regression diagnostics, variable selection, and a selection from topics on extensions of linear models.

Prereq: CAS MA 214 & CAS MA 242 & CAS MA 581 or consent of instructor.

Instructor: Zhang; Credits: 4; Tues/Thurs 9:30 am – 10:45 am; DIS Section: (A2) Mon 9:05 – 9:55, (A3) Mon 1:25 – 2:15, (A4) Mon 2:30 – 3:20, (A5) Mon 3:35 – 4:25

CAS MA 579: Numerical Methods for Biological Sciences

Introduction to the use of numerical methods for studying mathematical models of biological systems. Emphasis on the development of these methods; understanding their accuracy, performance, and stability; and their application to the study of biological systems.

Prereq: CASMA226 OR CASMA231 or equivalent, and elementary knowledge of linear algebra.

Instructor: Isaacson; Credits: 4; Tues/Thurs 11:00 am – 12:15 pm

CAS MA 581: Probability

Basic probability, conditional probability, independence. Discrete and continuous random variables, mean and variance, functions of random variables, moment generating function. Jointly distributed random variables, conditional distributions, independent random variables. Methods of transformations, law of large numbers, central limit theorem.

Prereq: CAS MA 225 OR CAS MA 230 or consent of instructor

Instructor: Taqqu; Credits: 4; LEC: Mon/Wed/Fri 11:15 am – 12:05 pm & DIS Section: (A2) Mon 9:05 am – 9:55 am, (A3) Mon 1:25 pm – 2:15 pm, (A4) Mon 2:30 pm -3:20 pm, (A5) Tues 9:30 am – 10:20 am or (A6) Tues 5:00 pm – 5:50 pm

GRS MA 614: Statistical Methods II

Second course in statistics, embodying basic statistical methods used in educational and social science research. Reviews all basic concepts covered in a first statistics course (e.g., CAS MA 613) and presents, in detail, more advanced topics such as analysis of variance, covariance, experimental design, correlation, regression, and selected nonparametric techniques. A problem-solving course; students carry out analysis of data taken from educational and other social science sources. This course cannot be taken for credit in addition to the course entitled “Statistical Methods II” that was previously numbered CAS MA 614.

Prereq: Graduate standing in education or in the social sciences.

Instructor: Heeren; Credits: 4; Mon 2:30 pm – 5:15 pm

GRS MA 881: Seminar: Statistics

Advanced seminar in topics in statistics of current research interest.

Prereq: GRS MA782

Instructor: TBA; Credits: 4; Days/Times: arranged

GRS MB 721: Graduate Level Biochemistry

Introductory biochemistry course that in one semester covers the major principles of biochemistry; proteins, nucleic acids, carbohydrates, lipids, and metabolism. Emphasis on how knowledge was derived and the theoretical principles governing biochemistry.

Phd Students Only.

Instructor: Kornberg; Credits: 4; Tues/Thurs 12:30 pm – 1:45 pm & Wed 4:30 pm – 6:15 pm