This course will introduce the basic theory, models and techniques for ordinary and partial differential equations. Emphasis will be placed on the connection with other disciplines of science and engineering. We will try to strike a balance between the theoretical (e.g. existence and uniqueness issues, qualitative properties) and the more practical issues such as analytical and numerical approximations.Instructor(s): Weinan E
An introduction to the phenomenology of nonlinear dynamic behavior with emphasis on models of actual physical, chemical, and biological systems, involving an interdisciplinary approach to ideas from mathematics, computing, and modeling. The common features of the development of chaotic behavior in both mathematical models and experimental studies are stressed, and the use of modern data-mining tools to analyze dynamic data will be explored.Instructor(s): Yannis G. Kevrekidis
This course provides a comprehensive introduction to basic principles of macromolecular structure, stability, and interactions. Major topics include protein structure; protein thermodynamics and folding; nucleic acid structure and stability; principles of intermolecular recognition; and principles and practice of ligand binding analysis. Special emphasis is placed on understanding, in macromolecular systems, the relationships between structure and stability; the molecular origins of cooperative effects; and the relationships between covalent and non-covalent properties.Instructor(s): Jannette Carey
The course will analyse in depth the chemical, physical, and mathematical principles underlying the intermolecular interactions of macromolecules. The background, interdisciplinary context, and current understanding of the topic will be developed from close, directed readings of classical and contemporary articles from the research literature.Instructor(s): Jannette Carey
The course provides an in depth treatment of protein chemistry and natural products biosynthesis. Topics include; amino acid, peptide and protein chemistry; protein engineering; the logic and enzymology of natural product biosynthesis with a focus on non-ribosomal peptide synthesis and polyketide synthesis.Instructor(s): Tom Muir, Mohammad R. Seyedsayamdost
This course surveys the most important algorithms and data structures in use on computers today. Particular emphasis is given to algorithms for sorting, searching, and string processing. Fundamental algorithms in a number of other areas are covered as well, including geometric algorithms, graph algorithms, and some numerical algorithms. The course will concentrate on developing implementations, understanding their performance characteristics, and estimating their potential effectiveness in applications.Instructor(s): Kevin Wayne
Introduction to theoretical machine learning, including mathematical models of machine learning, and the design and rigorous analysis of learning algorithms. Likely topics include: bounds on the number of random examples needed to learn; learning from non-random examples in the on-line learning model; how to boost the accuracy of a weak learning algorithm; support-vector machines; maximum-entropy modeling; portfolio selection; game theory. Regular problem sets and final project. Attendance expected at all lectures.Instructor(s): Robert E. Schapire
Introduces students to computational issues involved in analysis and display of large-scale biological data sets. Algorithms covered will include clustering and machine learning techniques for gene expression and proteomics data analysis, biological networks, joint learning from multiple data sources, and visualization issues for large-scale biological data sets. No prior knowledge of biology or bioinformatics is required; an introduction to bioinformatics and the nature of biological data will be provided. In depth knowledge of computer science is not required, but students should have some understanding of programming and computation.Instructor(s): Olga Troyanskaya
Introduction to basic computational methods used for problems arising in molecular biology. Topics include computational approaches to: sequence similarity and alignment, phylogenic inference, gene recognition, gene expression analysis, structure prediction, and whole- and cross-genome analysis.Instructor(s): Anastasija Baryshnikova, Michael Levine, Mona Singh
How and where did life evolve? This advanced seminar will discuss the evolution of the molecules that sustain life (DNA, RNA and proteins) at both the micro and macro evolutionary levels. We will explore the role of these molecules in the origin and continued evolution of life. Topics include the origin of eukaryotes and organelles, comparative genomics, population genetics, the microbiome, and human evolution. One three-hour seminar. Note that students new to either evolution or genetics will find 309 more appropriate.Instructor(s): Laura Landweber
An advanced foundation in ecology, focusing on the 50 fundamental papers, is given. Topics include dynamics and structure of populations, communities and ecosystems, and conservation biology. (This is a core course.)Instructor(s): Peter Andolfatto, Andrew P. Dobson et al.
Systematic reviews of recent literature in areas of ecology, evolution, and animal behavior. The general survey of literature is supplemented with detailed discussion of selected research papers of unusual importance and significance. (This is a core course.)Instructor(s): Henry Horn, Corina Tarnita
This seminar will survey the evolutionary history of modern humans and the genetic basis of variation in our species through reading and discussion of classic and contemporary primary literature. Topics include the evolutionary origins of modern human populations, signatures of natural selection in the human genome, and approaches for discovering genetic variants that affect disease susceptibility and variation in normal traits. Significant emphasis will be placed on very recent advances made possible by the human genome project.Instructor(s): Peter Andolfatto, Leonid Kruglyak
This course focuses on the molecules and molecular assemblies that underlie cellular structure and function. Topics include protein structure and folding; ligand binding and enzyme catalysis; membranes, ion channels, and translocation; intracellular trafficking; signal transduction and cell-cell communication; and cytoskeleton assembly, regulation, and function. A major goal of the course is to increase proficiency in parsing and critically discussing papers from the primary literature.Instructor(s): Ileana M. Cristea et al.
Advanced-level discussion of the genetics and molecular biology of prokaryotic organisms and their associated bacteriophages. Emphasis is placed on original research papers; extensive reading required. Topics include the genetic code, mutagenesis, mechanisms of DNA replication, recombination, repair and transposition, gene structure and function, and mechanisms of gene regulation, protein synthesis and export.Instructor(s): Thomas Silhavy
A continuation of MOL 504, with two modules, Cell Biology II and Development. Cell Biology II concerns how cells assemble into functional tissues, covering the molecular components and fundamental concepts in cell communication, adhesion, shape, division, and differentiation. Development covers the basics of developmental biology, focusing on important concepts and model systems. Primary literature is used to introduce seminal works and classic approaches, modern experimental techniques, and outstanding questions in the field. Students learn the basis of a good paper, to read critically, and to think beyond the reading.Instructor(s): Rebecca D. Burdine, Danelle Devenport, Gertrud M. Schupbach, Jean E. Schwarzbauer
Designed for students in the biological sciences, this course focuses on the application of mathematical methods to biological problems. Intended to provide a basic grounding in mathematical modeling and data analysis for students who might not have pursued further study in mathematics. Topics include differential equations, linear algebra, difference equations, and probability. Each topic will have a lecture component and computer laboratory component. Students will work extensively with the computing package Matlab. No previous computing experience necessary. Two 90-minute lectures, one laboratory.Instructor(s): Thomas Gregor, Ned Wingreen
This course presents the genetic tools and logical framework used in the study of eukaryotic organisms, from the major model organisms (yeast, Drosophila, C. elegans, mouse and zebrafish) to humans. Discussion and readings from the primary literature focuses on basic cell, developmental, and quantitative biology. Material emphasizes genetic approaches building from classical methods to modern molecular genetics and genomics. Topics include suppression, synthetic lethality, epistasis, mutagenesis, mosaics, transgenics, ES cells, knock-in technologies and cell-type specific gene expression.Instructor(s): Elizabeth Gavis, Alexander Ploss, Mark Rose
The organization of intracellular components contributes to cell functionality. This course will focus on how intracellular components are organized, mechanisms of reorganization during various processes, and how changes in this organization impact cell behaviors. The dynamics of forming complex multicellular tissues will also be examined. Topics include spatial-temporal changes during cell growth and division, cell motility, polarity, shape changes, and cell differentiation using examples from both prokaryotes and eukaryotes. Analysis of techniques used to study cell architecture will be covered.
Prerequisites: MOL 342, 345 and 348 or permission of instructor.Instructor(s): Jean Schwarzbauer, Zemer Gitai
We explore the molecular events leading to the onset and progression of human cancer. We review the central genetic and biochemical elements that make up the cell cycle, followed by a survey of the signal transduction pathways and checkpoints that regulate it. We discuss oncogenes, tumor suppressor and mutator genes that act in these pathways and review the role of viral oncogenes and their action on cells. We investigate the role of cancer stem cells and the interaction between tumor and the host environment. We explore specific clinical case studies in light of the molecular events underlying different cancers.Instructor(s): Yibin Kang
An introduction to modern statistical methods for genomic data. Topics include analysis methods for gene expression arrays, characterizing large-scale patterns of variation in genomic data, inferring regulatory networks, population genetic modeling of SNP data, and integrating multiple types of genomic data. Methods recently introduced in the literature will be studied with the goal of formulating the unifying statistical principles of high-dimensional biological data analysis.Instructor(s): John Storey
An in-depth discussion of the genetic analysis of microbial systems, emphasizing examples from both prokaryotes and lower eukaryotes. The first part of the course features article-based discussions of the rationale, uses and limitations of advanced methods of genetic analysis, including suppression, synthetic lethality, and unlinked non-complementation. The remainder of the course consists of student led seminars on topics of current interest with the secondary aim of achieving proficiency in the public presentation of scientific seminars.
Prerequisites: MOL 505 and MOL 506Instructor(s): Mark Rose, Zemer Gitai
Viruses are unique parasites of living cells and may be the most abundant, highest evolved life forms on the planet. The general strategies encoded by all known viral genomes are discussed using selected viruses as examples. The first half of the course covers the molecular biology (the tactics) inherent in these strategies. The second half introduces the biology of engagement of viruses with host defenses, what happens when viral infection leads to disease, vaccines and antiviral drugs, and the evolution of infectious agents and emergence of new viruses.Instructor(s): Lynn W. Enquist
Major methods of statistics as applied to the engineering and physical sciences. The central theme is the construction of empirical models, the design of experiments for elucidating models, and the applications of models for forecasting and decision making under uncertainty. Three lectures. Prerequisite: 245 or equivalent.
An introduction to probability and its applications. Random variables, expectation, independence. Poisson processes, Markov chains, and Brownian motion. Stochastic models of queues, population dynamics, and reliability.Instructor(s): Ramon van Handel
The course presents a broader view of biological physics. While the course starts with an overview of the fundamentals of biological physics, using Frauenfelder's text book as a guide, the course will move on to higher levels of biological systems, using evolution and ecology as our primary organizing principles. The course will have a section on the Physics of Cancer at the end.Instructor(s): Robert H. Austin
This class is aimed at Juniors pursuing the QCB Certificate. In this course, we will discuss the independent research projects that the juniors are performing in order to provide guidance and feedback. We will emphasize critical thinking about experiments and large dataset analysis along with the ability to clearly communicate one's research. Students will present background research (journal club style presentations) and progress reports. Written work will consist of an NSF-style proposal and an NIH-style grant proposal/research paper.Instructor(s): Marcus Noyes
Discussion and evaluation of the role professional researchers play in dealing with the reporting of research, responsible authorship, human and animal studies, misconduct and fraud in science, intellectual property, and professional conduct in scientific relationships. Participants are expected to read the materials and cases prior to each meeting. Successful completion is based on regular attendance and active participation in discussion. This half-term course is designed to satisfy federal funding agencies' requirements for training in the ethical practice of scientists. Required for graduate students and post-docs.Instructor(s): Michael Kelly
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 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, kinetic proofreading, sequence analysis, clustering, phylogenetics, analysis of fluctuations, maximum likelihood methods.Instructor(s): Ned Wingreen
QCB Graduate Colloquium is a research colloquium that has been developed for QCB graduate students, held on Wednesdays during the fall and spring terms. In the fall, the colloquium will give students an opportunity to hear about the work our faculty are doing, and is intended to help students with their lab rotation decisions. Students will also get the chance to enhance their skills of orally presenting scientific material to an audience by presenting their lab rotation experiences to their peers.
Mathematical models of complex natural phenomena can organize large amounts of data, provide access to properties that are difficult or impossible to measure experimentally, and suggest new experimental tests of proposed regulatory mechanisms. Participants will demonstrate these ideas in the context of cell and developmental biology. Using a number of well-established experimental systems, such as dynamic instability of microtubules and circadian clocks, course introduces stochastic and deterministic models of reaction and diffusion processes and computational methods for their analysis.Instructor(s): Stanislav Shvartsman