Computational Biology students take 21 credits of required coursework, which provides a broad foundation in biomedicine, covers current topics/seminars in computational biology, and goes in more depth on practical training in foundations of computational biology.
Computational Biology PhD students choose one 3-credit emphasis course that provides a deeper study in a specific area related to their dissertation research:
Students are required to take 3 additional credits of elective coursework from broad areas of biology, computer science, biology, and other related fields. Additional elective coursework can be taken if desired. Some example elective courses are listed below:
This course explores conflict of interest, responsible authorship, policies for handling misconduct, policies regarding the use of human and animal subjects, and data management. It fulfills the NIH requirement that training programs provide instruction in the responsible conduct of research.
BIMS 6000 is a fully immersive, 12-week comprehensive survey of cell and molecular biology, genetics and biochemistry, and the integration and practical reinforcement of these core areas. Didactic sessions are combined with small group interactive sessions, problem solving exercises, workshops, and hands-on analyses of data sets that are designed to teach students to think and communicate science, learn about broad experimental approaches, read, integrate and manage scientific literature, develop evaluative and analytical skills, identify important problems and ask good questions. This course is designed to equip students with core concepts and fundamental skill sets needed for biomedical research.
The course will cover human genetics and genomics, including the human/mammalian genome variation, determination of genomic variation on phenotype and disease risk, mapping and characterizing genetic variants on phenotype, determining the putative impact of genetic variants on gene expression (transcriptomics, epigenomics), the promise and implications of genome science on precision medicine and the ethical, legal & social implications.
Students will learn the basic concepts, technology, and processes that guide the practical use of common statistical methods. The course introduces descriptive and inferential statistics and applications to real-world data. Students will reinforce learning with problem sets, a publicly sharable R portfolio, and a final project to achieve practical competence in the use of statistical software and interpretation of results.
Provides students with a quantitative framework for identifying and addressing important biological questions at the molecular, cell, and tissue levels. Focuses on the interplay between methods and logic, with an emphasis on the themes that emerge repeatedly in quantitative experiments.
In this course students will gain working knowledge of constructing mathematical and computational models of biological processes at many levels of organizational scale from genome to whole-tissue. Students will rotate through several modules where they will hear lectures, read literature, and participate in discussions focused on the various modeling techniques. Prerequisites: 1. BME 6101/6102 (or equivalent); 2. One of the following courses in cellular and/or molecular biology: BME 2104, BME 7806.
A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. Papers will be drawn from recent and seminal publications in computational biology.
A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. Papers will be drawn from recent and seminal publications in computational biology.
A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. Students will select papers from recent and seminal papers in computational biology, and will lead paper discussions.
Students will learn both theoretical and practical foundations of computational methods for analysis of experimental data from various biological data types. The course will cover algorithms, statistical and computational methods, and application areas in computational biology, and will include both classical methods as well as recent advances. The course will also provide hands-on experience analyzing large-scale biological data. Prior coursework/experience in linear algebra, UNIX, and R and Python programming required.
Students will continue study in more advanced areas of computational biology, covering more advanced models, algorithms, and computational methods as applied to a variety of biological data types. Students will study theory and practice of machine learning methods commonly used in biology and implement and apply these models in various areas of biology.
Students will study theory and application of advanced algorithms, computational methods, and application areas for experimental data from genome and epigenome sequencing experiments. The course will include genomic data of various types, including chromatin accessibility, DNA binding proteins, RNA sequencing, and DNA methylation. Students will use R, Python, and shell scripting to complete assignments and a final project, and will finish the course with the ability to analyze genomics datasets. Students are expected to be familiar with basic computational methods used in genomics.
This course will cover fundamental topics in statistical genetics with a focus on concepts and methods critical to developing a concrete understanding of statistical genetics as it applies to public health genomics. Major topics covered in this course will include modes of genetic inheritance, heritability analysis, linkage and association mapping in human and mammalian genomes, integrative analysis leveraging molecular ‘omics data, and genetic risk prediction modeling.
This is a graduate-level machine learning course. Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers introductory topics about the theory and practical algorithms for machine learning from a variety of perspectives. Topics include supervised learning, unsupervised learning and learning theory. Prerequisite: Calculus, Basic linear algebra, Basic Probability and Basic Algorithm. Statistics is recommended. Students should already have good programming skills.