A Graduate Program in Quantitative Biology
Last updated: July 18, 2019 at 3:28 PM
The quantitative biology specialization is available only to students enrolled and working toward the PhD degree in one of the six participating graduate programs: biochemistry and biophysics, chemistry, computer science, molecular and cell biology, neuroscience, and physics. Individuals who want to obtain a PhD degree with a specialization in quantitative biology should apply to one of the participating PhD programs as described in the relevant section of this Bulletin. Enrolled PhD students who want to obtain the quantitative biology specialization should contact their PhD program’s graduate program chair or quantitative biology liaison for further information. Students wishing to obtain the specialization are advised also to contact one of the quantitative biology co-chairs for information about participating in the noncurricular educational activities of the quantitative biology program.
Jeff Gelles, Co-Chair
Jané Kondev, Co-Chair, Liaison to Physics PhD Program
Irving Epstein, Liaison to Chemistry PhD Program
Bruce Goode, Liaison to Molecular and Cell Biology PhD Program
Pengyu Hong, Liaison to Computer Science PhD Program
Eve Marder, Liaison to Neuroscience PhD Program
Christopher Miller, Liaison to Biochemistry and Biophysics PhD Program
Students must complete all requirements for the degree of Doctor of Philosophy in the program in which they are enrolled. In addition, students must successfully complete three of the following four courses: 1) QBIO 120b, 2) BCHM 102a, 3) either PHYS 105a or BCHM 145a, and 4) an approved computational methods course. Other courses may be substituted only with the written approval of the co-chair. The approved computational methods courses are QBIO 110a, COSI 178a, NBIO 136b, BIOL 107a and BIOL 135b. No more than one of the computational methods courses may be counted toward the three-course quantitative biology specialization requirement.
Courses of Instruction
Numerical Modeling of Biological Systems
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Prerequisite: MATH 10a and b or equivalent.
Modern scientific computation applied to problems in molecular and cell biology. Covers techniques such as numerical integration of differential equations, molecular dynamics and Monte Carlo simulations. Applications range from enzymes and molecular motors to cells. Usually offered every second year.
Quantitative Biology Instrumentation Laboratory
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Focuses on optical and other instruments commonly used in biomedical laboratories to make quantitative measurements in vivo and in vitro. Students disassemble and reconfigure modular instruments in laboratory exercises that critically evaluate instrument reliability and usability and investigate the origins of noise and systematic error in measurements. Usually offered every year.
Quantitative Approaches to Biochemical Systems
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Prerequisite: BCHM 100a or equivalent and Math 10a and b or equivalent.
Introduces quantitative approaches to analyzing macromolecular structure and function. Emphasizes the use of basic thermodynamics and single-molecule and ensemble kinetics to elucidate biochemical reaction mechanisms. Also discusses the physical bases of spectroscopic and diffraction methods commonly used in the study of proteins and nucleic acids. Usually offered every year.
How to Decide: Bayesian Inference and Computational Statistics
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Prerequisites: Math 10a and b.
A calculus-based courses that teaches the theory and practice of modern statistical methods used by experimental scientists. Topics include Bayesian inference, maximum likelihood estimation, and computational resampling methods. The course consists of a mixture of small lectures and in-class computational exercises. Usually offered ever third year.
Jeff Gelles and Douglas Theobald
Data Analysis and Statistics Workshop
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The interpretation of data is key to making new discoveries, making optimal decisions, and designing experiments. Students will learn skills of data analysis and computer coding through hands-on, computer-based tutorials and exercises that include experimental data from the biological sciences. Knowledge of very basic statistics (mean, median) will be assumed. Usually offered every year.
Stephen Van Hooser
Computational Molecular Biology
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Open to advanced undergraduate students and graduate students.
Information and computing technologies are becoming indispensable to modern biological research due to significant advances of high-throughput experimental technologies in recent years. This course presents an overview of the systemic development and application of computing systems and computational algorithms/techniques to the analysis of biological data, such as sequences, gene expression, protein expression, and biological networks. Hands-on training will be provided. Usually offered every other year.
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Prerequisite: MATH 10a and either NBIO 140b or PHYS 10a or approved equivalents.
An introduction to concepts and methods in computer modeling and analysis of neural systems. Topics include single and multicompartmental models of neurons, information representation and processing by populations of neurons, synaptic plasticity and models of learning, working memory, decision making and neural oscillations. The course will be based on in-class computer tutorials, assuming no prior coding experience, with reading assignments and preparation as homework. Usually offered every second year.
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Physical forces in living matter are studied from the perspective offered by statistical mechanics, elasticity theory, and fluid dynamics. Quantitative models for biological structure and function are developed and used to discuss recent experiments in single-molecule biology. Usually offered every second year.