Quantitative and Computational Biology

Academic Year: 
2019-20
Address: 
Carl Icahn Laboratory, Lewis-Sigler Institute for Integrative Genomics
Phone: 
609-258-9407
Website: 
Department of Quantitative and Computational Biology
Program offerings: 
Ph.D.
Director of Graduate Studies
Joshua Akey
Graduate Program Administrator
Jennifer Brick

Academic department detail

Overview

The Program in Quantitative and Computational Biology (QCB) is intended to facilitate graduate education at Princeton at the interface of biology, the more quantitative sciences, and computation. Administered from The Lewis-Sigler Institute for Integrative Genomics, QCB is a collaboration in multidisciplinary graduate education among faculty in the Institute and the Departments of Chemistry, Chemical and Biological Engineering, Computer Science, Ecology and Evolutionary Biology, Molecular Biology, and Physics. The program covers the fields of genomics, computational biology, systems biology, biophysics, quantitative genetics, molecular evolution, and microbial interactions.

Program Highlights

An Outstanding Tradition:  Chartered in 1746, Princeton University has long been considered among the world’s most outstanding institutions of higher education, with particular strength in mathematics and the quantitative sciences. Building upon the legacies of greats such as Compton, Feynman, and Einstein, Princeton established the Lewis-Sigler Institute of Integrative Genomics in 1999 to carry this tradition of quantitative science into the realm of biology.

World Class Research: The Lewis-Sigler Institute and the QCB program focus on attacking problems of great fundamental significance using a mixture of theory and experimentation. To maximize the chances of paradigm shifting advances, there is an emphasis on studying fundamental processes in biology, such as transcription and metabolism, in tractable model organisms including bacteria, yeasts, worms, and fruit flies.

World Class Faculty: The research efforts are led by the QCB program’s 40+ faculty, who include a Nobel Laureate, 8 members of the National Academy of Sciences, 4 Howard Hughes Investigators, and over a dozen early career faculty who have received major national research awards (e.g., NSF CAREER or NIH Innovator).

Personalized Education: A hallmark of any Princeton education is personal attention. The QCB program is no exception. Lab sizes are generally modest, typically 6 – 16 researchers, and all students have extensive direct contact with their faculty mentors. Many students choose to work at the interface of two different labs, enabling them to build close intellectual relationships with multiple principal investigators.

Stimulating Environment: The physical heart of the QCB program is the Carl Icahn Laboratory, an architectural landmark located adjacent to physics, biology, chemistry, neuroscience, and mathematics on Princeton’s main campus. Students have access to a wealth of resources, both intellectual and tangible, such as world-leading capabilities in DNA sequencing, mass spectrometry, and microscopy. They also benefit from the friendly atmosphere of the program, which includes tea and cookies every afternoon. When not busy doing science, students can partake in an active campus social scene and world class arts and theater events on campus.

Courses

Permanent Courses

Courses listed below are graduate-level courses that have been approved by the program’s faculty as well as the Curriculum Subcommittee of the Faculty Committee on the Graduate School as permanent course offerings. Permanent courses may be offered by the department or program on an ongoing basis, depending on curricular needs, scheduling requirements, and student interest. Not listed below are undergraduate courses and one-time-only graduate courses, which may be found for a specific term through the Registrar’s website. Also not listed are graduate-level independent reading and research courses, which may be approved by the Graduate School for individual students.

CHM 541 Chemical Biology II (also
QCB 541
) A chemically and quantitatively rigorous treatment of metabolism and protein synthesis, with a focus on modern advances and techniques. Topics include metabolic pathways and their regulation; metabolite and flux measurement; mathematical modeling of metabolism; amino acid, peptide and protein chemistry; protein engineering and selected applications thereof.
COS 551 Introduction to Genomics and Computational Molecular Biology (also
MOL 551
/
QCB 551
) Introduction to basic computational and genomic methods for analysis of biological systems. Topics include: sequence similarity and alignment, phylogenic inference, gene recognition, gene expression analysis, transcriptional networks, structure prediction, functional genomics and proteomics. This is primarily a graduate-level course; interested undergraduates will require permission from instructors.
MAT 586 Computational Methods in Cryo-Electron Microscopy (also
APC 511
/
MOL 511
/
QCB 513
) This course focuses on computational methods in cryo-EM, including three-dimensional ab-initio modelling, structure refinement, resolving structural variability of heterogeneous populations, particle picking, model validation, and resolution determination. Special emphasis is given to methods that play a significant role in many other data science applications. These comprise of key elements of statistical inference, image processing, and linear and non-linear dimensionality reduction. The software packages RELION and ASPIRE are routinely used for class demonstration on both simulated and publicly available experimental datasets.
QCB 501 Topics in Ethics in Science 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.
QCB 505 Topics in Biophysics and Quantitative Biology (also
PHY 555
) Analysis of recent work on quantitative, theoretically grounded approaches to the phenomena of life. Topics rotate from year to year, spanning all levels of biological organization, including (as examples) initial events in photosynthesis, early embryonic development, evolution of protein families, coding and computation in the brain, collective behavior in animal groups. Assumes knowledge of relevant physics and applicable mathematics at advanced undergraduate level, with tutorials on more advanced topics. Combination of lectures with student discussion of recent and classic papers.
QCB 508 Foundations of Applied Statistics and Data Science (with Applications in Biology) This course establishes a foundation in applied statistics and data science for those interested in pursuing data-driven research. The course may involve examples from any area of science, but it places a special emphasis on modern biological problems and data sets. Topics may include data wrangling, exploration and visualization, statistical programming, likelihood based inference, Bayesian inference, bootstrap, EM algorithm, regularization, statistical modeling, principal components analysis, multiple hypothesis testing, and causality. The statistical programming language R is extensively used to explore methods and analyze data.
QCB 511 Modeling Tools for Cell and Developmental Biology (also
CBE 511
) Using a number of real biological systems, course demonstrates how mathematical models of complex natural systems 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. For QCB program students, quantitatively inclined molecular biology students, and physics, chemistry and engineering students interested in quantitative biology. An extension of MOL 510.
QCB 515 Method and Logic in Quantitative Biology (also
PHY 570
/
EEB 517
/
CHM 517
/
MOL 515
) 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; emphasis will be on student discussion, not formal lectures. Topics include: cooperativity, robust adaptation, kinetic proofreading, sequence analysis, clustering, phylogenetics, analysis of fluctuations, maximum likelihood methods.
QCB 590 Extramural Research Internship in Quantitative and Computational Biology A summer term, full-time research internship at a host institution to perform scholarly research directly relevant to a student's thesis work. Research objectives are determined by the student's advisor in consultation with the outside host and QCB Director of Graduate Studies. An initial project proposal, monthly progress reports, and a final report are required. Enrollment is limited to QCB students who have successfully passed their general exams.