The MS program in computational life sciences introduces students to a burgeoning new field. Huge leaps in processing technologies have thrown open the doors for new research techniques and exciting opportunities for interdisciplinary collaborations, focusing heavily here on genomics data generation, analysis and interpretation.

Students are introduced to a suite of statistical tools and computational approaches that enable them to uncover correlations, glean new understanding, and help solve scientific problems.

Students examine many different types of data generated from a wide range of fields, including ecology, botany, evolutionary biology, neuroscience, molecular and cellular biology, and animal behavior. Students have the opportunity to investigate topics such as DNA, RNA, protein, imaging, conservation and even historical data from long-term ecological research sites.

Finally, students explore the ethical implications of collecting, analyzing and sharing the results of computational life sciences data.

The current degree requirements are below. Remember you are required to fulfill the requirements from the academic year you were admitted. Please refer to the handbook from your year of admission as needed.

30 credit hours including the required applied project course (BIO 593), or
30 credit hours including the required capstone course (BIO 597)

We highly recommend that students take a computing in life science restricted elective and a biology restricted elective in their first semester. 

Required Core (1 credit hour)

• BIO 511 Big Data in Context: Ethics, Policy, History and Philosophy (1)

OR

• BIO 610 Introduction to Responsible Conduct of Research (RCR) in Life Sciences (1)

Restricted Electives (18-20 credit hours)
Choose two computing in life sciences courses for six to seven credit hours:

• BIO 539 Computing for Research (3)

• BIO 591 Topic: Quantitative Methods in Conservation and Ecology (4)

• BIO 598 Topic: Genomics Research Experience (3)

• BIO 598 Topic: Medical Genetics and Genomics (3)

• BIO 598 Topic: Transcriptomics (3)

• BIO 591 Programming for Biologists (3)

Choose two statistics and mathematics courses for six to seven credit hours:

• BIO 514 Statistical Models for Biology (4)

• BIO 579 Topic: Data Analysis and Visualization in R (3)

• BIO 598 Topic: Data Analysis and Visualization in R II (3)

• BIO 598 Topic: General Linear Models for Biology (3)

Choose two biology courses for six credit hours:

• BIO 543 Molecular Genetics and Genomics (3)

• BIO 544 Discovering Biodiversity (3)

• BIO 598 Topic: Population and Community Ecology (3)

• EVO 601 Principles of Evolution (3)

• MCB 540 Functional Genomics (3)

Electives or Research (6-8 credit hours)

Please see course offerings sheet at the bottom of this page for electives options.

Culminating Experience (3 credit hours)

• BIO 593 Applied Project (3)

  • • An applied project is best when you will apply methods learned during the degree to some well-defined problem; for example, carrying out a certain suite of computational analyses to a dataset. (you must have Program Director approval well ahead of your final semester for this option)

• BIO 597 Capstone (3)

  • • A capstone is best for more open-ended writing tasks; for example, performing a literature review of computational software available for a particular type of analysis and writing a description/evaluation of this literature.