GGSB COMPUTATIONAL TRACK COURSEWORK
The Computational Track trains students to address fundamental biological questions while developing expertise in three core skill sets essential for computational genomics:
- Probabilistic modeling
- Statistical inference
- Computational algorithms and data structures
The curriculum also emphasizes communication skills, both in writing and speaking, recognizing that computational biologists must be able to clearly explain complex concepts, share their results effectively, and collaborate with colleagues from diverse scientific backgrounds.
Course Requirements Overview
To complete the Computational Track, students must take:
- 3 required core courses
- 3 core electives (chosen from an approved list)
- 1 additional elective (chosen from an approved list or by petition)
- 2 lab rotations
- AllStars
- Responsible, rigorous, and reproducible conduct of research: R3CR (Ethics)
1) Required Core Courses (3 courses) Students must complete all of the following:
- STAT 24400 – Statistical Theory and Methods I (Autumn)
- HGEN 48600 – Fundamentals of Computational Biology: Models and Inference (Winter)
- HGEN 48800 – Fundamentals of Computational Biology: Algorithms and Applications (Spring)
2) Core Electives (3 courses) Choose three from the following:
- HGEN 47000 – Human Genetics I (Autumn)
- MGCB 31400 – Genetic Analysis of Model Organisms (Autumn)
- HGEN 47500 – Genetic Mechanisms from Variation to Evolution (Autumn)
- ECEV 35600 – Principles of Population Genetics I (Winter)
- ECEV 31100 – Evolution of Biological Molecules (Winter)
- HGEN 47100 – Introductory Statistical Genetics (Winter)
- HGEN 47600 — Genetics and Beyond: Environmental and GxE Factors in Human Phenotypes (Winter)
- BCMB 32200 – Biophysics of Biomolecules (Spring)
- GENE 36420— Statistical Inference in Biology: Intuition from a Historical Perspective (Spring)
- GENE 45100 — Chemical Biology of Drug Actions and Metabolism (Spring)
-GENE 46100 — Deep Learning in Genomics (Spring)
- HGEN 46900 – Human Variation and Disease (Spring)
- HGEN 47800 – Quantitative Genetics for the 21st Century (Spring)
- HGEN 47300 – Genomics and Systems Biology (Spring)
- MGCB 32000 – Quantitative Analysis of Biological Dynamics (Spring)
3) Additional Electives (1 course) Choose one from the following list:
- STAT 34300 – Applied Linear Statistical Methods (Autumn)
- STAT 37790 – Topics in Statistical Machine Learning (Autumn)
- STAT 30900 – Mathematical Computation I: Matrix Computation (Autumn)
- ECEV 32000 – Introduction to Scientific Computing for Biologists (Winter)
- BIOS 20187 – Fundamentals of Genetics (Winter)
- STAT 24500 – Statistical Theory and Methods II (Winter)
- STAT 32950 – Multivariate Statistical Analysis: Applications and Techniques (Winter)
- ECEV 42900 – Theoretical Ecology (Winter)
- BIOS 20186 – Fundamentals of Cell and Molecular Biology (Spring)
- STAT 24610 – Pattern Recognition (Spring)
- STAT 30210 – Bayesian Analysis and Principles of Statistics (Spring)
- STAT 35500 – Statistical Genetics (Spring)
- STAT 37710 – Machine Learning (Spring)
Note: Students may petition the GGSB Student Affairs/Curriculum Committee for approval of an elective course not listed above.
4) Lab Rotations (2) Students complete two research rotations before joining a thesis lab:
- BSDG 40100 – Non-Thesis Research (Autumn, Winter, Spring, or Summer)
Students typically begin rotations in the Autumn Quarter while taking their first required courses, complete their second rotation in Winter or Spring, and match with a thesis lab by the Summer Quarter. This alignment allows students to transition smoothly into planning and initiating their dissertation research as they begin their second year.
5) Additional Required Courses
- GENE 31900 – Introduction to Research (“AllStars”) (Autumn)
- BSDG 55100 – Responsible, Rigorous, and Reproducible Conduct of Research (R3CR) (Winter)
For additional information please click here to view the Doctoral Training in Computational Genomics website.
There are several overlapping sets of policies governing PhD graduate students in the Biological Sciences Division, including those from university, divisional, and program-specific sources, as well as the Office of Research Safety.
- University of Chicago Policies
- Biological Sciences Division policies governing graduate students
- Office of Research Safety
HOW TO APPLY TO THE UCHICAGO BIOSCIENCES PROGRAM
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- GGSB Handbook - Coming Soon
- GGSB Course Descriptions – Empirical Track - Coming Soon
- GGSB Course Descriptions – Computational Track -Coming Soon