Michigan State University

What you'll study

This programme combines core quantitative training with applied coursework and research in modern computational biology. Typical core topics include mathematical modelling of biological systems, statistical inference for high‑throughput data, algorithms for sequence and structure analysis, machine learning for biological data, and numerical methods for differential equations. Students also take modules in genomics and transcriptomics, systems biology, population and evolutionary modelling, bioinformatics software development, and data visualisation.

Programme structure is flexible and commonly includes a mix of required core courses, elective specialisation courses, seminar attendance and a supervised research project or thesis. Students may choose a research‑oriented path that culminates in a master’s thesis or a professional coursework path with a practicum or capstone project. Seminars and journal clubs provide regular exposure to current research and opportunities to present work.

• Core modules: mathematical and statistical foundations, computational methods, bioinformatics algorithms.
• Electives: genomics, proteomics, structural bioinformatics, systems and network biology, machine learning, scientific computing.
• Research component: independent project or thesis under a faculty advisor; options for collaborative projects with life‑science or clinical laboratories.
• Professional skills: scientific communication, reproducible research, software development and project management.

Entry requirements

Applicants are expected to hold a bachelor’s degree from an accredited institution in mathematics, statistics, computer science, engineering, biology or a closely related discipline. Typical preparation includes undergraduate coursework in calculus, linear algebra, probability and statistics, programming (e.g. Python, R or C/C++), and at least one course in molecular or cellular biology for students coming from quantitative backgrounds.

• Academic transcript demonstrating strong performance in quantitative and relevant science courses.
• A personal statement describing research interests and relevant experience.
• Curriculum vitae or résumé highlighting technical skills, programming experience and research or project work.
• Two to three academic or professional letters of recommendation.
• Proof of English proficiency for applicants whose first language is not English (accepted tests and scores follow university policy).

The programme may consider prior research experience, internships or relevant employment in lieu of some formal prerequisites; applicants are encouraged to contact the programme for advice about specific preparation. Standardised tests (such as the GRE) are not universally required; consult the programme for current testing policies.

Career prospects

Graduates are prepared for a wide range of careers at the interface of computation and the life sciences. Common pathways include roles as bioinformaticians, computational biologists, data scientists and quantitative analysts in biotechnology and pharmaceutical companies, healthcare and clinical informatics groups, agricultural and environmental research organisations, and contract research organisations.

Other graduates transition to academic or government research labs, pursue PhD study in computational biology or related fields, or work in software development and engineering roles that support scientific research. The programme’s combination of theoretical training and practical project experience also suits students aiming to join startups or interdisciplinary teams working on genomics, precision medicine, synthetic biology and systems modelling.

Why study at Michigan State University

Michigan State University offers this degree within an interdisciplinary environment that draws faculty from mathematics, statistics, computer science, and the life sciences, providing strong opportunities for cross‑disciplinary collaboration. Students benefit from access to well‑equipped computational resources, core genomics and proteomics facilities, and a range of active research groups working on problems from fundamental biological theory to translational biomedical applications.

MSU’s location and partnerships create chances for internships and collaborative projects with local biotech companies, medical centres and agricultural research organisations. The programme emphasises hands‑on training, reproducible workflows and professional development, helping graduates move seamlessly into research positions, industry roles or further graduate study.