Course Descriptions
A new curriculum is in development. For additional course options, please review the nanocourses available through the Bioinformatics Core Facility
Biology for Non-Biologists Bootcamp
Course Director(s): Gaudenz Danuser, Ph.D., Luke Rice, Ph.D.
Credit hours: 2
Meeting Schedule: 1 week intensive course
It is anticipated that the Computational Biology track will attract mostly students with no formal undergraduate training in biology. However, to become effective computational biologists it is essential that the students of the track are inspired by important biological questions, that they can communicate with peers across all of biomedical sciences, and they can master the biological domain literature in their field of study. The students will acquire these skills in the semester-long core course (see next). To prepare non-biologists for the core course, we will offer a week-long bootcamp prior to the official start of graduate school. The bootcamp will introduce over 40 hours on 5 days the basic concepts and vocabulary of genetics, structural, molecular, cellular, and organismal biology. The course will be taught by faculty, who have experienced themselves a transition from mathematics/engineering/computer science into to the biomedical sciences.
Core Course
Fall semester, Year 1
Credit hours: 6 (2 each for genes, proteins, cells)
To obtain the fundamental knowledge necessary to practice modern biomedical sciences and to lay the foundation for future research collaborations, students will be required to complete a semester-long core course that introduces foundational concepts in designing experiments, interpreting data, and critical evaluation of primary research literature. The Core Course is comprised of three modules:
Genes -- Molecular genetics of model organisms; DNA replication, repair, and recombination; transcription; RNA catalysis, processing, and interference; translation; protein turnover; developmental biology; and genomics
Proteins -- The energetic basis of protein structure; stability; ligand binding and regulation; enzyme mechanics and kinetics; methods of purification; and analysis by spectroscopic methods.
Cells -- Cell structure; membrane biology; intracellular membrane and protein trafficking; energy conversion; signal transduction and second messengers; cytoskeleton; cell cycle; and introductory material in microbiology, immunology, and neurobiology.
Mathematical Foundations of Quantitative Biology – Part I
Fall 2nd quarter, Year 1
MFQB I: Modeling and predicting physical systems
Course Director(s): Kimberly Reynolds, Ph.D.
Credit hours: 2
Meeting Schedule: 2h lectures x 2 a week, 8 weeks
As scientists, we make careful but often indirect, incomplete, or noisy experimental measurements and use these to model the world around us. Mathematics gives us a language to quantitatively compare experiments, assess and model error, describe large datasets, abstract complex processes, and build predictive models. This course – the first in a two part series – focuses on the math behind physics-based modeling of biological systems. We will cover: series and combinatorics, differential equations, transform theory, and linear algebra. These ideas are integrated in a final section on multivariable calculus. Students will build comfort and familiarity with these techniques through both pencil-and-paper assignments and programming exercises in MATLAB. Students will practice abstracting and representing biological systems in mathematical terms, and develop familiarity with numerical (computational) strategies in differential equations and transform theory.
Mathematical Foundations of Quantitative Biology – II
Spring 3rd quarter, Year 1
MFQB II: Quantifying and representing information
Course Director(s): Kimberly Reynolds, Ph.D.
Credit hours : 2
Meeting Schedule: 2h lectures x 2 a week, 8 weeks
Much of modern computational biology is rooted in ideas from probability theory and information theory. In this second mathematics course, we discuss the quantification and representation of information. This course will cover concepts from probability theory, noise analysis, statistical hypothesis testing, information theory, graph theory and the foundations of computing. As in MFQB-I, Students will practice abstracting and representing biological systems in mathematical terms, and develop familiarity with numerical (computational) strategies. This course includes a final project in which students will apply concepts from the course to a biological problem of interest. MFQB I is a prerequisite (this can be waived with instructor consent).
Quantitative Biology
Spring 4th quarter, Year 1
Course Director(s): Milo Lin, Ph.D.
Credit hours : 1.5
Meeting Schedule: 1.5h lectures x 2 a week, 8 weeks
This course aims to provide the conceptual framework and mathematical techniques to predict the behavior of “complex” biological systems from first principles. Complexity will be defined in a hierarchical manner in terms of three orthogonal characteristics: linear/nonlinear, equilibrium/nonequilibrium, and static/time-varying. All categories of problems within the complexity hierarchy will be described intuitively and mathematically before being made manifest as a specific problem in biology. The goal is to foster a deep understanding of what makes different types of collective problems easy or hard and which techniques/properties are generally applicable for a given problem. The examples of biological systems will span length scales from proteins to populations, and operate on timescales from physiological to evolutionary time.
Software engineering for research computing – Part I
First two weeks of May, spring semester
Course Director(s): Gaudenz Danuser, Ph.D.
Credit hours : 3
Meeting Schedule: Intensive two week course
Computational approaches have played key roles in analyzing a myriad of biological data to elicit underlying principles and mechanisms. While we expect that all students on the Computational Biology track will bring solid programming skills from their undergraduate training, we offer a month-long intensive workshop where we study computer science with an emphasis on software engineering methods: algorithms, data structures, object-oriented programming, high-performance computing, software engineering process and techniques, and database design. Upon successful completion of this course, students will be equipped with the capability to develop efficient, functional, easy-to-use software and to design reusable and extendable software architecture that ensures reproducibility, stability, and robustness.
Software engineering for research computing – Part II
Second two weeks of May, summer semester
Course Director(s): Gaudenz Danuser, Ph.D.
Credit hours : 3
Meeting Schedule: Intensive two week course
Computational approaches have played key roles in analyzing a myriad of biological data to elicit underlying principles and mechanisms. While we expect that all students on the Computational Biology track will bring solid programming skills from their undergraduate training, we offer a month-long intensive workshop where we study computer science with an emphasis on software engineering methods: algorithms, data structures, object-oriented programming, high-performance computing, software engineering process and techniques, and database design. Upon successful completion of this course, students will be equipped with the capability to develop efficient, functional, easy-to-use software and to design reusable and extendable software architecture that ensures reproducibility, stability, and robustness.
Machine Learning
Fall semester, Year 2
Course Director(s): Yang Xie, Ph.D., Tao Wang, Ph.D., Bo Li, Ph.D., Jian Zhou, Ph.D.
Credit hours : 3
Meeting Schedule: 1.5h lecture, 1.5h problem session a week, 16 weeks
From the discovery of hidden subtypes in cancer patients to the identification of unknown patterns in imaging data, machine learning has rapidly advanced almost all fields of bioinformatics and biomedical engineering. In this course, we will cover the classic topics in traditional machine learning, including linear/logistic regression, classification/regression trees and random forest, Bayesian inference and Bayesian graphic network, and support vector machines. We will also introduce the basics concepts of neural network models and introduce the basics of deep learning methods.
Current Topics in Computational Biology
Year 2 forward
Course Director(s): Khuloud Jaqaman, Ph.D.
Credit hours : 1
Meeting Schedule: once a week (Mondays at 11)
Starting with the second year, all students of the Computational Biology track will participate in a journal club that covers landmark papers as well as the latest publications across a wide spectrum of the field. The journal club will be coordinated with invitations of speakers in the Computational Biology seminar series. Speakers will be encouraged to lead the journal club on one of their papers of choice, which can be their own work or a publication they deem as seminal to their current line of study. Students of the track will be allowed to invite two speakers of their own choosing per year, with assistance from the course director(s).
Statistical Analysis of Genetic Data (elective)
Course Director(s): Julia Kozlitina, Ph.D., Chao Xing, Ph.D.
Credit hours : 1
Meeting Schedule: 1.5 h lecture x 2 a week, for four weeks
Students have the option to take an additional course focused on genetic data analysis. This class covers the principles underlying classical genetic study designs. Then we describe how these principles apply to the design and analysis of modern genetic studies, involving high-throughput DNA sequencing data. By the end of this course, students will have acquired a basic understanding of the principles underlying genetic association and linkage studies, have the ability to perform simple analyses of genetic data, and be able to understand research studies in human genetics.
