University Bulletin (2022-2023)

The quantitative biology specialization is available only to students enrolled and working toward the MS or 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 an MS or PhD degree with a specialization in quantitative biology (QB) should apply to one of the participating MS or PhD programs as described in the relevant section of this Bulletin. Enrolled graduate students who want to obtain the quantitative biology specialization should contact their 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.

Bruce Goode, Co-Chair, Liaison to Molecular and Cell Biology PhD Program
(Biology)

Jané Kondev, Co-Chair, Liaison to Physics PhD Program
(Physics)

Alexandre Bisson, Liaison to Biology PhD Program
(Biology)

Thomas Fai, Liaison to Mathematics PhD Progam
(Mathematics)

Paul Garrity, Liaison to Biology PhD Program
(Biology)

Paul Miller, Liaison to Neuroscience PhD Program
(Biology)

Avital Rodal, Liaison to Biology PhD Program
(Biology)

Stephen Van Hooser, Liaison to Biology PhD Program
(Biology)

Hannah Yevick, Liaison to Physics PhD Program
(Physics)

Students must complete all requirements for the degree of Master of Science or 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, BIOL 135b, and MATH 162a. No more than one of the computational methods courses may be counted toward the three-course quantitative biology specialization requirement.

QBIO 11a Nature's Nanotechnology
[ sn ]

Familiarity with high school math, physics, chemistry and biology is expected. Enrollment limited to QBReC Scholars. Formerly offered as FYS 11a.

Imagine a world occupied by machines whose size is 10,000 times smaller than the width of a human hair. Some of them produce fuel by harnessing solar energy, while others transport cargo on tracks only 10 atoms across, or assemble other machines following molecular blueprints. This is the bustling world inside a living cell, which we will explore using high school level math, physics and biology. Usually offered every year.
Jané Kondev (Physics)

QBIO 24b QBReC Lab

Prerequisite: QBIO 11a. Yields half-course credit. Formerly offered as EL 24b.

Students explore the living world through experimental and computational projects conducted in research labs. The emphasis is on interdisciplinary science where techniques from physics, chemistry and biology are used to develop a quantitative understanding of life at the molecular and cellular level. Usually offered every year.
Jané Kondev

QBIO 110a Numerical Modeling of Biological Systems
[ sn ]

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.
Staff

QBIO 120b Quantitative Biology Instrumentation Laboratory
[ sn ]

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.

Ben Rogers

BCHM 102a Quantitative Approaches to Biochemical Systems
[ dl sn ]

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.
Maria-Eirini Pandelia

BCHM 145a How to Decide: Bayesian Inference and Computational Statistics
[ sn ]

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

BIOL 107a Data Analysis and Statistics Workshop
[ dl qr sn ]

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

COSI 178a Computational Molecular Biology
[ dl sn ]

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.
Pengyu Hong

MATH 232a Numerical Methods for Scientific Computing
[ sn ]

Prerequisites: MATH 37a and MATH 122a, or permission of the instructor. A basic proficiency with a programming language such as Python or Matlab is required.

Studies numerical methods for linear algebra, ordinary and partial differential equations, and optimization. Equal emphasis will be placed on theory (stability, accuracy, and convergence) and practical problem-solving using a programming language such as Python. Usually offered every second year.

Thomas Fai

NBIO 136b Computational Neuroscience
[ dl sn ]

Prerequisites: MATH 10a or MATH 10b or MATH 15a and either NBIO 140b or PHYS 10b or PHYS 11b or COSI 11a.

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.
Paul Miller

PHYS 105a Biological Physics
[ sn ]

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.
Staff

QBIO 120b Quantitative Biology Instrumentation Laboratory
[ sn ]

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.

Ben Rogers