Quantitative and Computational Biology Academic Year 2023 – 2024 Jump To: Jump To: General Information 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. Department for program: Quantitative and Computational Biology Director of Graduate Studies: Ned Wingreen Graduate Program Administrator: Jennifer Giraldi Overview The Program in Quantitative and Computational Biology (QCB) is intended to facilitate graduate education at Princeton at the interface of biology and 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, Computer Science, Ecology and Evolutionary Biology, Molecular Biology, and Physics. The program covers the fields of genomics, computational biology, systems biology, evolutionary and population genomics, statistical genetics, and metabolomics and proteomics. 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 Turing, von Neumann, Tukey, 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, computation, and experimentation. World Class Faculty: The research efforts are led by the QCB program’s 50+ faculty, who include a Nobel Laureate, members of the National Academy of Sciences, Howard Hughes Investigators, and numerous faculty who have received major national research awards (e.g., NIH Pioneer, NIH Innovator, Packard, NSF PECASE, NSF CAREER, etc.). 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 biology, chemistry, physics, 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. Apply Application deadline December 1, 11:59 p.m. Eastern Standard Time (This deadline is for applications for enrollment beginning in fall 2024) Program length 5 years Fee $75 GRE General Test - not accepted Program Offerings Ph.D. Program Offering: Ph.D. Courses Five courses, QCB515, QCB535, QCB537, QCB538, and COS/QCB551, are required for all students, as is a Responsible Conduct in Research (RCR) course. Two elective courses must be taken from the list below, including at least one from the quantitative course list. Courses not on the approved lists may be taken as electives with approval from the DGS. Note: The full course of study must be reviewed and approved by the Director of Graduate Studies (DGS). Quantitative Courses (must take at least one) APC 524/MAE 506/AST 506 Software Engineering for Scientific Computing CBE 517 Soft Matter Mechanics: Fundamentals & Applications CHM 503/CBE 524/MSE 514 Introduction to Statistical Mechanics CHM 515 Biophysical Chemistry I CHM 516 Biophysical Chemistry II CHM 542 Principles of Macromolecular Structure: Protein Folding, Structure, and Design COS 511 Theoretical Machine Learning COS 524/COS 424 Fundamentals of Machine Learning COS 597D Advanced Topics in Computer Science: Advanced Computational Genomics COS 597F Advanced Topics in Computer Science: Computational Biology of Single Cells COS 597G Advanced Topics in Computer Science: Understanding Large Language Models COS 597O Advanced Topics in Computer Science: Deep Generative Models: Methods, Applications & Societal Considerations ELE 535 Machine Learning and Pattern Recognition MAE 567/CBE 568 Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics MAE 550/MSE 560 Lessons from Biology for Engineering Tiny Devices MAT 586/APC 511/MOL 511/QCB 513 Computational Methods in Cryo-Electron Microscopy MOL 518 Quantitative Methods in Cell and Molecular Biology MSE 504/CHM 560/PHY 512/CBE 520 Monte Carlo and Molecular Dynamics Simulation in Statistical Physics & Materials Science NEU 437/537 Computational Neuroscience NEU 501 Cellular and Circuits Neuroscience NEU 560 Statistical Modeling and Analysis of Neural Data ORF 524 Statistical Theory and Methods PHY 561/2 Biophysics QCB 505/PHY555 Topics in Biophysics and Quantitative Biology QCB 508 Foundations of Statistical Genomics Biological Courses CHM 403 Advanced Organic Chemistry CHM/QCB 541 Chemical Biology II EEB 504 Fundamental Concepts in Ecology, Evolution, and Behavior II EEB 507 Recent Research in Population Biology MAE 566 Biomechanics and Biomaterials: From Cells to Organisms MAE 567/CBE 568 Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics MOL 504 Cellular Biochemistry MOL 506 Cell Biology and Development MOL 518 Quantitative Methods in Cell and Molecular Biology MOL 521 Systems Microbiology and Immunology MOL 523 Molecular Basis of Cancer MOL 559 Viruses: Strategy & Tactics QCB 490 Molecular Mechanisms of Longevity QCB 535 Biological Networks Across Scales: Open Problems and Research Methods of Systems Biology Selected undergraduate courses of interest (Note: these do not count towards course requirements) APC 350 Introduction in Differential Equations COS 226 Algorithms and Data Structures COS 343 Algorithms for Computational Biology EEB 324 Theoretical Ecology MOL/QCB 485 Mathematical Models in Biology ORF/MAT 309/380 Probability and Stochastic Systems QCB 302 Research Topics in QCB QCB 311 Genomics Additional pre-generals requirements Research Colloquium: QCB Graduate Colloquium QCB Graduate Colloquium is a research colloquium that has been developed for QCB graduate students, held weekly on an afternoon during the fall and spring terms. First, second, and fourth year graduate students have the opportunity to present their research to peers. Rotations All students are required to complete a minimum of three research rotations during their first year of graduate study, with a maximum of four, to explore possible research advisers. General exam The general examination is usually taken in January of the second year, and consists of a 7 page written thesis proposal and a 2-hour oral session on the student’s thesis proposal. Qualifying for the M.A. The Master of Arts (M.A.) degree is normally an incidental degree on the way to a full Ph.D. and is earned after a student successfully passes the general examination. It may also be awarded to students who, for various reasons, leave the Ph.D. program, provided the student has completed all coursework, pre-generals requirements, and the written portion of the generals examination. Teaching A student must teach a minimum of one full-time assignment (6 AI hours) or teach two part-time assignments of 2 or more AI hours each. Students will typically teach in year 4 of the program. Post-Generals requirements Committee Meetings Research progress is overseen by a thesis committee selected by the student after passing the general exam. The committee consists of the thesis adviser(s) and two additional faculty members. At least one member must be QCB faculty. The thesis committee must be approved by the DGS. Annual thesis committee meetings are mandatory. Dissertation and FPO The dissertation and final public oral exam (FPO) are required for all Ph.D. students. All students must write and successfully defend their dissertation according to Graduate School rules and requirements. Faculty Director Ned S. Wingreen Director of Graduate Studies Ned S. Wingreen Executive Committee Brittany Adamson, Molecular Biology Joshua Akey, Integrative Genomics Julien F. Ayroles, Ecology & Evolutionary Biology William Bialek, Physics Michelle M. Chan, Molecular Biology Thomas Gregor, Physics Sarah D. Kocher, Ecology & Evolutionary Biology Michael S. Levine, Molecular Biology Coleen T. Murphy, Molecular Biology Yuri Pritykin, Computer Science Joshua D. Rabinowitz, Chemistry Joshua W. Shaevitz, Physics Stanislav Y. Shvartsman, Chemical and Biological Eng Mona Singh, Computer Science John D. Storey, Integrative Genomics Olga G. Troyanskaya, Computer Science Eric F. Wieschaus, Molecular Biology Ned S. Wingreen, Molecular Biology Martin Helmut Wühr, Molecular Biology Associated Faculty Mohamed S. Abou Donia, Molecular Biology Robert H. Austin, Physics Bonnie L. Bassler, Molecular Biology Clifford P. Brangwynne, Chemical and Biological Eng Mark P. Brynildsen, Chemical and Biological Eng Curtis G. Callan, Physics Daniel J. Cohen, Mechanical & Aerospace Eng Ileana M. Cristea, Molecular Biology Danelle Devenport, Molecular Biology Adji Bousso Dieng, Computer Science Tatiana Engel, Princeton Neuroscience Inst Jianqing Fan, Oper Res and Financial Eng Elizabeth R. Gavis, Molecular Biology Zemer Gitai, Molecular Biology Frederick M. Hughson, Molecular Biology Martin C. Jonikas, Molecular Biology Yibin Kang, Molecular Biology Andrej Kosmrlj, Mechanical & Aerospace Eng Andrew M. Leifer, Physics Simon A. Levin, Ecology & Evolutionary Biology Jonathan M. Levine, Ecology & Evolutionary Biology Lindy McBride, Ecology & Evolutionary Biology Tom Muir, Chemistry Mala Murthy, Princeton Neuroscience Inst Cameron A. Myhrvold, Molecular Biology Celeste M. Nelson, Chemical and Biological Eng Sabine Petry, Molecular Biology Catherine Jensen Peña, Princeton Neuroscience Inst Eszter Posfai, Molecular Biology Ben Raphael, Computer Science Mohammad R. Seyedsayamdost, Chemistry Corina E. Tarnita, Ecology & Evolutionary Biology Jared E. Toettcher, Molecular Biology Samuel S. Wang, Princeton Neuroscience Inst Haw Yang, Chemistry Ellen Zhong, Computer Science For a full list of faculty members and fellows please visit the department or program website. 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 (Half-Term) 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 Statistical Genomics 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 515 - Method and Logic in Quantitative Biology (also CHM 517/EEB 517/MOL 515/PHY 570) 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 570 - Biochemistry of Physiology and Disease This course explores the biochemical foundations of human physiology and how it is disturbed in disease. We discuss the roles of metabolic, the cardiovascular, and immune systems in various diseases, particularly cancer. Specific topics include: the functions of the major organ systems, and how we measure and model their activity; nutrition and the maintenance of metabolic homeostasis; the anti-tumor immune response; the origins, consequences, and major treatment paradigms of cancer; and the process of translating basic science into novel therapies. The class consists of lectures and student-led discussions of scientific papers. 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.
