Program Overview

The Computational Biology PhD program is a research-oriented program. Most of the time spent working on the degree will be spent doing independent research. The first 1-2 years also have required and elective coursework that provide foundational training in computational biology to prepare students for the research that will be the focus of the last 3-4 years of the program. Learn more about admissions requirements and faculty research areas.

Typical Timeline

Students typically matriculate in July and start rotations in the summer. In the Fall semester of the first year, they continue rotations, and take the Core Course (BIMS 6000) and Research Ethics (BIMS 7100). In the Spring semester of the first year, students affiliate with the Computational Biology PhD program and take the two CompBio modules (COBI 8101 and COBI 8102). This is also when they begin their thesis research with program faculty. In the Fall of year 2, students take the first Colloquium course (COBI 7001) and their emphasis course (*e.g.* COBI 8301 or COBI 8302). In the Spring of year 2, students continue the Colloquium series (COBI 7002), and then electives as needed. At this point, coursework is mostly complete. Students take the Colloquium course one final time in year 3. The remaining time is spent on research.

Required Coursework

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.

• BIMS 6000 → Core Course in Integrative Biosciences (10 credits)
• BIMS 7100 → Research Ethics (1 credits)
• COBI 7001 → Colloquium in Computational Biology I (2 credits)
• COBI 7002 → Colloquium in Computational Biology II (2 credits)
• COBI 7003 → Advanced Colloquium in Computational Biology (2 credits)
• COBI 8101 → Computational Biology I (2 credits)
• COBI 8102 → Computational Biology II (2 credits)

Emphasis requirement

Computational Biology PhD students choose one 3-credit emphasis course that provides a deeper study in a specific area related to their dissertation research:

• BME 8315 → Systems Bioengineering and Multi-Scale Models (3 credits)
• COBI 8301 → Computational Genomics (3 credits)
• COBI 8302 → Statistical Genetics (3 credits)

Elective coursework

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:

• BIMS 8075 → Recent Advances in Public Health Genomics (3 credits)
• BIMS 8380 → Practical Biomedical Statistics I (2 credits)
• BIMS 8382 → Practical Biomedical Statistics II (2 credits)
• BME 7370 → Quantitative Biological Reasoning (3 credits)
• CS 6316 → Machine Learning (3 credits)
• STAT 6021 → Linear Models for Data Science (3 credits)
• SYS 6081 → Data Mining (3 credits)

Research credits

Beyond these courses, students must earn at least 72 credits, including any other didactic course credits or research credits. Students typically fill the remainder of the required credits with research.

Course descriptions

• BIMS 6000 → Core Course in Integrative Biosciences (10 credits)

  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.

• BIMS 7100 → Research Ethics (1 credits)

  "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 8075 → Recent Advances in Public Health Genomics (3 credits)

  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.

• BIMS 8380 → Practical Biomedical Statistics I (2 credits)

  "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. "

• BIMS 8382 → Practical Biomedical Statistics II (2 credits)

  "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."

• BME 7370 → Quantitative Biological Reasoning (3 credits)

  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.

• BME 8315 → Systems Bioengineering and Multi-Scale Models (3 credits)

  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.

• COBI 7001 → Colloquium in Computational Biology I (2 credits)

  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.

• COBI 7002 → Colloquium in Computational Biology II (2 credits)

  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.

• COBI 7003 → Advanced Colloquium in Computational Biology (2 credits)

  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.

• COBI 8101 → Computational Biology I (2 credits)

  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.

• COBI 8102 → Computational Biology II (2 credits)

  "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."

• COBI 8301 → Computational Genomics (3 credits)

  "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."

• COBI 8302 → Statistical Genetics (3 credits)

  "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."

• CS 6316 → Machine Learning (3 credits)

  "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."

• STAT 6021 → Linear Models for Data Science (3 credits)

  "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."

• SYS 6081 → Data Mining (3 credits)

  "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."