Spring 2025

Introduction to quantitative biology and its role in biology and relationship to chemistry, computer science, engineering, mathematics, medicine and physics, including the field's impact on industry.

The course will explore the science of modern drug discovery and development, its key challenges and pitfalls in the personal health and socioeconomic context.

An introductory course in ethics that will navigate through issues of eugenics, the origins of scientific data and, importantly, to the frontiers of future research.

Statistical methods in biological science and medicine, including populations and samples, random sampling, confidence intervals, paired samples and regression.

Introduction to general statistical theory with higher-level mathematics for biological data analysis. Basic programming in R. Discussion of techniques to describe, visualize and model different types of data.

A project-based course integrating biology, computer science, and statistics through the analysis of genomic datasets available publicly or collected and sequenced during the semester.

Al techniques including traditional machine learning and advanced deep learning methods for genomics, system biology, data integration, drug discovery and medical imaging; project-based.

Introduction to algorithms and statistics for genome analysis and their applications. Analysis of DNA sequencing, sequence comparison, genetic variation, gene expression, disease association and evolution.

Foundational skill-building for modeling and analysis of biological data. Hands-on instruction for developing and simulating models of dynamic biological systems.

Individual research and readings. Project approval by faculty adviser and QCB department required.

Experience in conducting research and writing an honors thesis or publishable manuscript under the supervision of a faculty adviser.

In-depth study of molecular biology techniques and experiments with an emphasis on those that are able to generate "big data." Intended for quantitative science students.

Computational methods for simulation and analysis of metabolic networks, cellular signaling pathways, and gene regulatory networks. Biochemical kinetics, differential equations, stability analysis, sensitivity analysis.

Experience and training in giving science research presentations. Students will discuss recent research in computational biology and bioinformatics.

Seminar for Computational Biology and Bioinformatics PhD students in which students present their own original research results.

Practical guidance on the writing of scientific articles and research proposals in the field of Quantitative and Computational Biology.

Applications of the mathematical, statistical and computational sciences to data from molecular biology. Algorithms for genomic sequence data: sequence and map assembly and alignment, RNA secondary structure, protein structure, gene-finding, and tree construction.

Research leading to a graduate degree. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Part-time or full-time, practical work experience in the student's field of study. The internship must be located at an off-campus facility. Students are supervised by faculty. May not be taken until the student has completed one semester of enrollment in a graduate program with a cumulative 3.0 GPA. The course is graded CR/NC.

Research leading to the doctorate. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

For doctoral students who have passed the qualifying exam. Credit on acceptance of dissertation.

For doctoral students who have passed the qualifying exam. Credit on acceptance of dissertation.

For doctoral students who have passed the qualifying exam. Credit on acceptance of dissertation.

For doctoral students who have passed the qualifying exam. Credit on acceptance of dissertation.

For doctoral students who have passed the qualifying exam. Credit on acceptance of dissertation.