Program Overview

Program in Quantitative and Computational Biology

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.

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.
• 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.
• 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.
• 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.

Program Offerings

Ph.D.

• Courses: The following core courses are required for all QCB students: QCB 515, QCB 537, QCB 538, and COS/QCB 551. Three elective courses must be taken from the lists below, including at least one from the quantitative course list and one from the biological course list.
  • 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, Design
    • COS 511 Theoretical Machine Learning
    • COS 513 Foundations of Probabilistic Modeling
    • COS 524/COS 424 Fundamentals of Machine Learning
    • COS 557 Artificial Intelligence for Precision Health
    • COS 597D Advanced Topics in Computer Science: Advanced Computational Genomics
    • COS 597F Advanced Topics in Computer Sci: Computational Biology of Single Cells
    • COS 597G Advanced Topics in Computer Sci: 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 550/MSE 560 Lessons from Biology to Engineer Tiny Devices
    • MAE 567/CBE 568 Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics
    • MAT 586/APC 511/MOL 511/QCB 513 Comp 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
    • ORF 570 Special Topics in Statistics and Operations Research: Transformers and Large Language Models
    • PHY 561/562 Biophysics
    • QCB 505/PHY 555 Topics in Biophysics and Quantitative Biology
    • QCB 508 Foundations of Statistical Genomics
  • Biological Courses (must take at least one):
    • CHM 403 Advanced Organic Chemistry
    • CHM/QCB 541 Chemical Biology II
    • EEB 504 Fundamental Concepts in Ecology, Evolution, and Behavior II
    • EEB 522 Colloquium on the Biology of Populations
    • 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 570 Biochemistry of Physiology and Disease
• Additional pre-generals requirements:
  • Research Colloquium: QCB Graduate Colloquium
  • Research 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 an 8-10 page written thesis proposal and a two-hour oral exam 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.
• Teaching: A student must teach a minimum of one full-time assignment or teach two part-time assignments. 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.
  • Dissertation and FPO: The dissertation and final public oral exam (FPO) are required for all Ph.D. students.

Faculty

• Director: Ned S. Wingreen
• Director of Graduate Studies: Ned S. Wingreen
• Executive Committee:
  • Britt Adamson, Molecular Biology
  • Joshua Akey, Integrative Genomics
  • 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
  • Michael A. Skinnider, Integrative Genomics
  • John D. Storey, Integrative Genomics
  • Olga G. Troyanskaya, Computer Science
  • 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
  • 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
  • Jerelle A. Joseph, Chemical and Biological Eng
  • Yibin Kang, Molecular Biology
  • Andrej Kosmrlj, Mechanical & Aerospace Eng
  • Fenna Krienen, Princeton Neuroscience Inst
  • 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

Permanent Courses

• 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.
• 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.
• COS 557 - Artificial Intelligence for Precision Health (also QCB 557): Introduces students to the promise and challenges of AI and machine learning methods, including large language models, in precision health.
• 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.
• 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.
• 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.
• 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.
• 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.
• QCB 570 - Biochemistry of Physiology and Disease (also MOL 570): This course explores the biochemical foundations of human physiology and how it is disturbed in disease.
• 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.