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Computational Life Science Biology Master's
Master of Science (M.S.)
Program type:
Major
Format:
On Campus
Est. time to complete:
2-3 years
Credit Hours:
30
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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Request More InfoWhy 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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