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PICASso Course Listing
Click here for complete Princeton course offerings |
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| FALL 2006 |
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COS 551/MOL 551
Introduction to Genomics and Computational Molecular Biology
No P/D/F
Professor(s): Mona Singh, Saeed Tavazoie
Description/Objectives:
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.
Other Requirements:
Course Not Open to Freshmen
Schedule/Classroom Assignment:
Class Number: 22116 - Seminar S01 : 3:00 pm - 4:20 pm T Th
Carl Icahn Laboratory (ICAHN) 101 Location Photo
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QCB 301
Experimental Project Laboratory in Quantitative and Computational Biology
(ST) No P/D/F
Enrollment by application or interview.
Departmental permission required.
Maximum Enrollment: 20
Professor(s): Coleen T. Murphy, Manuel Llinás, David Botstein
Description/Objectives:
An intensive double credit course focusing on state-of-the-art experimental design and practice in quantitative biology. Emphasis is placed on functional genomics using global genome-wide measurements (e.g. microarray gene expression, sequence, phenotype) to understand physiological and evolutionary processes. Begins with a short introduction to technology and principles, followed by the design and execution of independent projects done by pairs of students in collaboration, with the continuing guidance and advice of the teaching staff. Prerequisites: CHM 231-234 and CHM 235-236. Four three-hour laboratories.
Reading/Writing Assignments: Selected papers from the literature will be discussed at the beginning of lab. Students will be expected to write a proposal for their independent laboratory project in week 3, a progress report in week 6, a final paper due on Dean's Date, and present the results of their project at a student symposium during reading period.
Requirements/Grading:
Paper in lieu of Midterm Exam: 15%
Paper in lieu of Final Exam: 35%
Papers: 25%
Oral Presentation(s): 25%
Prerequisites and Restrictions: CHM/COS/MOL/PHY 231-234 and CHM/COS/MOL/PHY 235/6, or by permission of the instructor. Students planning to major in Molecular Biology are encouraged to take MOL 350 in the spring of their sophomore year.
Other Information: Students will spend 12 hours doing laboratory work each week. Students will also participate in scientific literature review sessions, held during lab periods, and will be expected to present to the class a relevant scientific paper and prepare a 2-3 page written review. This is a double-weighted laboratory course. Open to juniors and seniors only, or by permission of the instructors.
Schedule/Classroom Assignment:
Class Number: 22775 - Laboratory B01 : 1:30 pm - 4:20 pm M T W Th
Carl Icahn Laboratory (ICAHN) 003 Location Photo
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ORF 538
Analytical and Computational Methods of Financial Engineering
Professor(s): K. Ronnie Sircar
Description/Objectives:
An introduction to analytical and computational methods common to financial engineering problems. Aimed at PhD students and advanced masters students who have studied stochastic calculus, the course focuses on uses of partial differential equations: their appearance in pricing financial derivatives, their connection with Markov processes, their occurrence as Hamilton-Jacobi-Bellman equations in stochastic control problems, and analytical, asymptotic, and numerical techniques for their solution.
Sample Reading List:
M. Freidlin , Functional Integration and Partial Differential Equations
B. Oksendal , Stochastic Differential Equations
P. Wilmott, S. Howison, J. DeWynne , Mathematics of Financial Derivatives
Examination Type: Final
Other Requirements:
Course Open to Graduate Students Only.
Schedule/Classroom Assignment:
Class Number: 21550 - Class C01 : 1:30 pm - 2:50 pm T Th
Computer Science Building (COMPU) 102 Location Photo
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MOL 457
Computational Aspects of Molecular Biology
Professor(s): John D. Welsh
Description/Objectives:
A discussion of the field of Bioinformatics, the application of computing to research in Molecular Biology. Topics include: nucleic acid and protein sequence analysis, secondary structure prediction, microarray analysis, sequence homology, the protein folding problem, molecular computers, Perl programming, and the use of the genetic databases.
Sample Reading List:
Andreas D. Baxevanis, B. F. Francis Ouellette , Bioinformatics:A Practical Guide to the Analysis of
Genes & Proteins, 3rd edition
1 or 2 research papers per week
Reading/Writing Assignments: Problem sets to be done out of class.A 3-5 page review of a research paper.
Requirements/Grading:
Take Home Midterm Exam: 35%
Take Home Final Exam: 35%
Papers: 10%
Problem Set(s): 20%
Other Requirements:
Course Not Open to Freshmen
Prerequisites and Restrictions: One 300 level course in Molecular Biology, Chemistry or Biochemistry. No courses in the biological sciences or other departmental courses may be taken pass/D/fail by molecular biology concentrators.
Other Information: Problem sets to be done out of class.
Related Web Site
Schedule/Classroom Assignment:
Class Number: 22397 - Lecture L01 : 10:00 am - 10:50 am M W F
Schultz Laboratory (SCHUL) 107 Location Photo
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ORF 557
Stochastic Analysis Seminar
Professor(s): René A. Carmona
Description/Objectives:
Recent developments in the theory and applications of the analysis of random processes and random fields. Applications include financial engineering, transport by stochastic flows, and statistical imaging.
Other Requirements:
Course Not Open to Freshmen
Schedule/Classroom Assignment:
Class Number: 21515 - Seminar S01 : 12:30 pm - 1:20 pm T Th
Bendheim Center (BENDC) 103 Location Photo
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ORF 474
Special Topics in Operations Research and Financial Engineering: Monte Carlo Simulation
Professor(s): William A. Massey
Description/Objectives:
An introduction to the uses of simulation and direct computation in analyzing stochastic models and interpreting real phenomena. The course deals with generating discrete and continuous random variables, stochastic ordering, the statistical analysis of simulated data, variance reduction techniques, statistical validation techniques, nonstationary Markov chains and Markov chain Monte Carlo methods. Applications are drawn from problems in finance, manufacturing and communication networks.
Sample Reading List:
Sheldon M. Ross , Simulation (3rd Edition)
Requirements/Grading:
Take Home Midterm Exam: 25%
Take Home Final Exam: 25%
Oral Presentation(s): 10%
Problem Set(s): 40%
Other Requirements:
Course Not Open to Freshmen
Prerequisites and Restrictions: ORF 309.
Schedule/Classroom Assignment:
Class Number: 22720 - Class C01 : 1:30 pm - 2:50 pm T Th
Engineering Quad J-Wing (EQUAJ) J201 Location Photo
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ORF 526
CLOSED
Stochastic Modeling
Professor(s): Savas Dayanik
Description/Objectives:
Fundamental models of random phenomena in financial engineering and operations research: Poisson processes, Markov chains, Brownian motion, and diffusion processes.
Sample Reading List:
E. Cinlar , Introduction to Stochastic Processes
G. Lawler , Introduction to Stochastic Processes
Other Requirements:
Course Not Open to Freshmen
Related Web Site
Schedule/Classroom Assignment:
Class Number: 21513 - Lecture L01 : CLOSED 9:00 am - 10:20 am M W
Friend Center (FRIEN) 108 Location Photo
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MAT 351/APC 351
Mathematical Modeling
(QR)
Professor(s): Philip J. Holmes
Description/Objectives:
MATHEMATICAL NEUROSCIENCE - Combines modeling w/applied math methods incl. PDE, probability, stochastic ODE, dynamical systems, cells as elec. circuits, Hodgkin-Huxely equ. describing spikes in single neurons & bursting neurons (e.g., breathing, moving, other rhythms), propagation of action potentials, reaction-diffusion equas., Hopfield-Grossberg neural nets, leaky accumulator models, drift-diffusion modles, information theoretic approaches to analysis of neural spike trains.
Sample Reading List:
Wilson, H.R. , Decisions and Actions: Dynamical Foundations of Neuroscience
Riecke, F., Warland, D., de Ruyter van Stevenick, R., , Spikes: Exploring the Neural Code
Requirements/Grading:
Other Exam: 25%
Oral Presentation(s): 10%
Problem Set(s): 65%
Prerequisites and Restrictions: Course is directed toward upperclass undergrads and first-year grads with knowledge of linear algebra and differential equations.
Schedule/Classroom Assignment:
Class Number: 22109 - Class C01 : 1:30 pm - 2:50 pm M W
Fine Hall (FINEH) 322 Location Photo
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AST 303
Modeling and Observing the Universe: Research Methods in Astrophysics
Professor(s): David N. Spergel, Michael A. Strauss
Description/Objectives:
How do we model and observe the universe? We discuss the wide range of observation tools available to the modern astronomer: space-based gamma ray telescopes, globe-spanning radio interferometry, optical telescopes and particle detectors. We review basic statistics and introduce students to modern techniques used in analysis and interpretation of modern data sets containing millions of galaxies, quasars and stars, as well as the numerical techniques used by theoretical astrophysicists to model these data. The course is problem-set-based and aims to provide students with tools needed for independent work both at Princeton and beyond.
Sample Reading List:
Press, Teukolvsky, Vetterling and Flannery , Numerical Recipes in C
Hale Bradt , Astronomy Methods:Physical Approach to Astronomical Observat
Reading/Writing Assignments: Weekly Problem Sets
Requirements/Grading:
Take Home Final Exam: 30%
Problem Set(s): 70%
Prerequisites and Restrictions: PHY103/104 OR 105/106, and MATH 103/104, or consent of instructor.
Schedule/Classroom Assignment:
Class Number: 22268 - Lecture L01 : 3:00 pm - 4:20 pm T Th
Peyton Hall (PEYTH) 145 Location Photo
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MAE 541/APC 571
Applied Dynamical Systems
Professor(s): Clarence W. Rowley
Description/Objectives:
Phase-plane methods and single-degree-of-freedom nonlinear oscillators; invariant manifolds, local and global analysis, structural stability and bifurcation, center manifolds, and normal forms; averaging and perturbation methods, forced oscillations, homoclinic orbits, and chaos; and Melnikov's method, the Smale horseshoe, symbolic dynamics, and strange attractors.
Sample Reading List:
J. Guckenheimer & P. Holmes , Nonlinear Oscillations, Dynamical Systems & Bifurcations of
A.A. Andronov, E.A. Vitt, S.E. Khaiken , Theory of Oscillators
M.W. Hirsch, S. SImale adn R.L. Devaney , Dirrential Equations, Dynamical Systems & An Intro to Chaos
Other Requirements:
Course Not Open to Freshmen
Other Information: Biweekly homework Assignments. Final Project or Substantial Take-Home Exam
Schedule/Classroom Assignment:
Class Number: 20316 - Lecture L01 : 1:30 pm - 2:50 pm T Th
Fine Hall (FINEH) 110 Location Photo
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CHE 442
Design, Synthesis, and Optimization of Chemical Processes
na, npdf
Professor(s): Christodoulos A. Floudas
Description/Objectives:
Introduction to chemical process flow-sheeting; process simulation design, sizing and cost estimation of total processes; process economics; introduction to optimization, linear programming, integer programming, and nonlinear programming; heat integration methods, minimum utility cost, minimum number of units, network optimization.
Sample Reading List:
L.T. Biegler, I.E. Grossmann and A.W. Westerberg , Analysis, Synthesis and Design of Chemical Processing
C.A. Floudas , Nonlinear and Mixed Integer Optimization: Fundamentals
W.D. Seider, J.D. Seader and D.R. Lewin , Process Design Principles
Reading/Writing Assignments: Total outside work load approximately six hours per week over the whole term, mostly concerned with problem solving. A major design project, which is addressed by groups of 2-3 students, runs throughout the semester.
Requirements/Grading:
Final Exam: 30%
Design Project(s): 60%
Problem Set(s): 10%
Other Requirements:
Course for Juniors and Seniors Only.
This course is required for Concentrators.
Prerequisites and Restrictions: CHE 341 and CHE 441.
Other Information: This is the capstone course in the chemical engineering curriculum, offered every Fall for seniors in the department.
Schedule/Classroom Assignment:
Class Number: 21206 - Laboratory B01 : 7:30 pm - 9:20 pm W
Engineering Quad A-Wing (EQUAA) A224 Location Photo
Class Number: 21207 - Lecture L01 : 11:00 am - 11:50 am M W F
Engineering Quad A-Wing (EQUAA) A224 Location Photo
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ELE 573/CHE 573
Cellular and Biochemical Computing Systems
Professor(s): Ron Weiss
Description/Objectives:
A discussion of synthetic biology, computational issues in modeling cellular systems, and the engineering of biochemical computing systems. Topics include modeling of genetic regulatory networks using continuous and stochastic methods, construction of synthetic gene networks, metabolic networks, signal transduction pathways, cell-to-cell signaling, molecular and DNA computing, molecular self-assembly, directed molecular evolution, transcriptional and translational regulation, oscillation and circadian clocks, cell differentiation and pattern formation, chemotaxis, molecular switches and molecular electronics, theory of chemical computation.
Requirements/Grading:
Design Project(s): 40%
Oral Presentation(s): 30%
Precept Participation: 20%
Other (See Instructor): 10%
Schedule/Classroom Assignment:
Class Number: 20515 - Lecture L01 : 3:00 pm - 4:20 pm M W
Friend Center (FRIEN) 112 Location Photo
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ELE 382
Distributed Algorithms and Optimization Methods for Engineering Applications
Professor(s): Mung Chiang
Description/Objectives:
Introduces distributed algorithms to optimize networked systems in electronic, mechanical, or biochemical substrates, and other methodologies of optimization, both structures and numerical algorithms, for a variety of engineering applications. Applications will be selectively drawn from the following: computer networking, Internet protocols, communication systems, signal processing, circuit design, controlled dynamic systems, computational geometry, and financial engineering.
Requirements/Grading:
Final Exam: 50%
Take Home Midterm Exam: 25%
Problem Set(s): 25%
Prerequisites and Restrictions: Prerequisite: MATH202. NO previous exposure to optimization theory, algorithms, or any specific application areas are required.
Schedule/Classroom Assignment:
Class Number: 20750 - Lecture L01 : 3:00 pm - 4:20 pm M W
Friend Center (FRIEN) 109 Location Photo
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ELE 525
Random Processes in Information Systems
Professor(s): Hisashi Kobayashi
Description/Objectives:
This course presents the fundamentals of applied random processes needed by students in communications, computer engineering, controls, and signal processing. Probability, random variables (discrete ad continuous), random number generation for simulation, random processes, stationarity and ergodicity, spectral analysis, Gausian processes, Brownian motion and diffusion processes, Poisson processes and brith-and-death processes, Large deviation approximation, and efficient simulation techniques.
Sample Reading List:
G.R. Grimmet & D.R Stirzaker , Probability and Random Processes
A. Papoulis , Probability, Random Variables & Stochastic Processes
Other Requirements:
Course Not Open to Freshmen
Other Information: Students taking this course should have a prior course in applied probability at the undergraduate level.
Schedule/Classroom Assignment:
Class Number: 20488 - Lecture L01 : 9:00 am - 10:20 am T Th
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MOL 215/EEB 215
CLOSED
Quantitative Principles in Cell and Molecular Biology
(ST) na, npdf
Professor(s): Edward C. Cox, Philip G. Felton
Description/Objectives:
Central concepts and experiments in cellular, molecular, and developmental biology with an emphasis on underlying physical and engineering principles. Topics include important insights into the genetic code; energetics and cellular organization: communication, feeding, and signaling between cells; ideas about feedback loops and cellular organization; problems and solutions in development; the mammalian genome and computational biology; and the organization of large cellular systems, such as the nervous and immune systems. Satisfies the requirement for entrance into the Department and the requirement for entrance into medical school.
Sample Reading List:
Alberts et al. , Essential Cell Biology, 2nd Edition
Original papers from the literature
Reading/Writing Assignments: Approximately one text chapter each week and one or two original research papers chosen either as founding papers in an area, or because of an insight into how to think about a field or discipline. In the project laboratory students will amplify and sequence their DNA and use the sequence to study the organization of the human genome; build a simple circuit illustrating feed-back principles; and construct a simple microscope to study unanswered questions in cell biology.
Requirements/Grading:
Midterm Exam: 35%
Final Exam: 35%
Lab Reports: 30%
Prerequisites and Restrictions: AP Biology, Physics, Calculus.
Other Information: This course is motivated by the observation that molecular biology and other physical and engineering sciences increasingly interact, and that this can be reflected in how introductory courses are taught. MOL 215 will therefore cover the basic principles of cell and molecular biology as an introduction to the discipline, but will also emphasize wherever possible insights available from engineering, physics, chemistry, and computer science. Students should not take both 214 and 215.
Related Web Site
Reserved Seats:
Freshmen 13
Schedule/Classroom Assignment:
Class Number: 22375 - Laboratory B01 : 1:30 pm - 4:20 pm T
Thomas Laboratory (THOML) 008 Location Photo
Class Number: 22376 - Laboratory B02 : 1:30 pm - 4:20 pm W
Thomas Laboratory (THOML) 009 Location Photo
Class Number: 22377 - Laboratory B03 : CLOSED 1:30 pm - 4:20 pm Th
Thomas Laboratory (THOML) 008 Location Photo
Class Number: 22374 - Lecture L01 : CLOSED 11:00 am - 11:50 am M W F
Carl Icahn Laboratory (ICAHN) 101 Location Photo
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MOL 515/PHY 570
Method and Logic in Quantitative Biology
Professor(s): Ned S. Wingreen, David Botstein
Description/Objectives:
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.
Sample Reading List:
Alon, U. et al , Robustness in Bacterial Chemotaxis
Luria, S. E. and Delbruck, M. , Mutations of Bacteria from Virus Sensitivity to Virus...
Goldbeter, A. and Koshland D. E. Jr , An Amplified Sensitivity Arising from Covalent...
Hopfield, J. J. , Kinetic Proofreading: A New Mechanism for Reducing Errors...
Smith, T.F. and Waterman, M.S. , Identification of Common Molecular Subsequences
See https://blackboard.princeton.edu for complete list.
Reading/Writing Assignments: Assigned readings from literature or selected texts prior to class discussion.
Requirements/Grading:
Precept Participation: 75%
Other (See Instructor): 25%
Prerequisites and Restrictions: Prior knowledge of calculus and linear algebra.
Other Information: This course is intended for graduate students interested in quantitative/computational biology. Undergraduates may register only with prior approval from one of the instructors. Assigned readings from literature or selected texts prior to class discussion. Grade based on precept participation 75%, homework 25%. Course requires prior experience with differential equations and some linear algebra.
Related Web Site
Schedule/Classroom Assignment:
Class Number: 22804 - Seminar S01 : 3:00 pm - 6:00 pm F
Carl Icahn Laboratory (ICAHN) 280 Location Photo
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ORF 311
Optimization under Uncertainty
na, npdf
Professor(s): John M. Mulvey
Description/Objectives:
A survey of quantitative approaches for making optimal decisions under uncertainty, including decision trees, Monte Carlo simulation, and stochastic programs. Forecasting and planning systems are integrated with a focus on financial applications. Two 90-minute classes
Sample Reading List:
Winston and Albright , Practical Management Science
Fourer, Gay, & Kernighan , Ampl: A Modeling Language for Math Prog.
Handouts: , Multi-objective optimization, optimization under uncertainty
Birge and Louveaux , Introduction to Stochastic Programming
Reading/Writing Assignments: Students will be required to design and build stochastic programming models using Excel Add-ins and the AMPL modeling language. A series of case studies will be discussed in precepts.
Requirements/Grading:
Midterm Exam: 25%
Final Exam: 40%
Programming Assignment: 10%
Problem Set(s): 25%
Other Requirements:
Course for Juniors and Seniors Only.
Prerequisites and Restrictions: 307 or MAT 305, and 309.
Schedule/Classroom Assignment:
Class Number: 21507 - Lecture L01 : 3:00 pm - 4:20 pm T Th
Friend Center (FRIEN) 004 Location Photo
Precept P01 : TBA
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