Program type:


On Campus
Est. time to complete:

2-3 years
Credit Hours:

Research, strong professor-student mentoring, high-quality instruction and professional community service are the foundation of our Biology master’s degree with a concentration in Computational Life Science.
The biology program provides students the option of selecting a research track leading to the Master of Science (MS) or Doctor of Philosophy (PhD) with a major in biology, or a non-research track leading to the Master of Arts (MA) with a major in biology. Students interested in obtaining both a master's degree and certification to teach life sciences at the secondary level may select the non-research Master of Science with a major in biology (Teaching in the Life Sciences). Students pursuing a research degree have the opportunity to conduct research leading to a thesis or dissertation in a variety of specializations, including aquatic biology, aquatic toxicology, ecology, forensic biology, genetics, limnology, microbiology, neurobiology, physiology and plant biology.

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Why Earn a Computational Life Science Biology Master's?

We offer opportunities to conduct research in:

  • Aquatic biology
  • Aquatic toxicology
  • Cell and molecular biology
  • Ecology
  • Environmental science
  • Forensic biology
  • Genetics
  • Neurobiology
  • Physiology
  • Plant sciences

Our faculty members include internationally renowned researchers who have earned recognition from the National Science Foundation, the American Association for the Advancement of Science and the American Society for Microbiology, among others.

Marketable Skills
  • Critical evaluation of literature
  • The ability to follow protocols and methodologies
  • Collection and analysis of complex data
  • Understanding of research regulations
  • Working knowledge of biological sub-disciplines

Computational Life Science Biology Master's Highlights

The Life Sciences Complex, which has Gold-level Leadership in Energy and Environmental Design certification for sustainability, includes an aquatics laboratory and four climate-controlled rooftop research greenhouses.
The Center for Network Neuroscience focuses on in vitro preparations, especially monolayer cultures of mammalian central nervous system cells, and emphasizes research on pattern generation, storage and recognition.
The BioDiscovery Institute (BDI) operates through a pipeline linking sustainable plant production platforms, metabolic engineering and the development of new materials. The institute touts a multidisciplinary team of researchers committed to collaborating on large research projects with an emphasis on application of findings and solutions to meet market issues and needs.
The Center for Watershed and Reservoir Assessment and Management conducts research on techniques and best management practices for assessing and managing watersheds and reservoirs, addressing current and emerging problems and issues.
The Advanced Environmental Research Institute (AERI) houses multidisciplinary research teams that conduct science-based environmental research that provides an understanding of how human actions impact the environment, and then use that knowledge to suggest scientific, engineering, policy and/or educational solutions.
Qualified students are supported through competitive teaching assistantships or research assistantships funded by research grants to faculty members.

Computational Life Science Biology Master's Courses You Could Take

Biocomputing (3 hrs)
Introduction to computational problems inspired by the life sciences and overview of available tools. Methods to compute sequence alignments, regulatory motifs, phylogenetic trees and restriction maps.
Biostatistics I (3 hrs)
Introduction to statistical methods, experimental design, data presentation and hypothesis testing in biological research. Statistical inference includes tests for normality, skewness, kurtosis, and two-sample data sets for goodness of fit, contingency, means, medians and non-parametric methods
Biostatistics II (3 hrs)
Continuation of Biostatistics I. Statistical methods and experimental designs in biological research. Coverage of parametric and non-parametric correlation, multi-sample inference tests (ANOVA) including one-way, block, nested and factorial designs; multiple range (comparison) analyses; simple linear, non-linear and multiple regressions; ANCOVA. Introduces multiple variable approaches including discriminate, factor and cluster analysis.
Computational Epidemiology (3 hrs)
Application of computational methods to problems in the fields of public health. Design and implementation of disease outbreak models.
Advanced Genetics (3 hrs)
Genetic structure and inheritance in viruses, bacteria and higher organisms, including gene biochemistry, gene expression, population genetics, cytogenetics and organelle genetics.
Advanced Topics in Computational Life Science (3 hrs)
Current research topics related to computational life sciences such as bioinformatics, computational epidemiology and population models.

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