Fall 2024

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

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.

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

Fundamentals of ML; regression analysis, classification methods, clustering techniques; dimensionality reduction methods, ensemble learning and deep learning technologies; applications in various biological contexts; project-based.

Introduction to the mathematical modeling and statistical analysis of genetic variation, including genetic drift, natural selection, mutation, migration, population structure, family studies and GWAS.

Introduction to physical and statistical methods, molecular simulations, machine learning and AI for structural bioinformatics studies of proteins, nucleic acids, molecular complexes and whole cells.

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

Student-led discussions of recent research in quantitative biology and presentation of their own research.

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

Discussion-based ethics course for graduate students, with topics including authorship, peer review, publication, research misconduct, discrimination in science, data privacy and algorithmic bias.

An introduction to scientific data analysis; fundamentals of data structures and algorithms; specialized algorithms used in the sciences; challenges posed by large scientific data sets.

Practical experience in computational molecular biology applications. Mathematical and statistical software packages relevant to genomic analysis. Retrieval and analysis of genomic data from databases.

Applications of the mathematical, statistical and computational sciences to data from molecular biology. Statistics for genomic sequence data: DNA sequence assembly, significance of alignment scores, hidden Markov models, genetic mapping, models of sequence evolution and microarray analysis.

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

Practical principles for the long-term development of effective teaching within science and engineering disciplines. Intended for teaching assistant training as required by the university.

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.