Computational Life Sciences

Do you have a curious mind and a passion for solving problems in new ways? You can make a powerful impact in today's fast-changing research landscape by using new technologies to mine life-changing answers from life science data.

Description

In the life sciences, employers need graduates who are skilled in understanding and interpreting data. In particular, students who have experience using new, computational approaches and computer programs to process data are in demand. By completing this concentration in computational life sciences, students learn how to identify and interpret data that is generated from a wide range of fields in the life sciences. These fields include, but are not limited to: ecology, botany, evolutionary biology, neuroscience, molecular and cellular biology, and animal behavior. Students learn about many types of data generated from sources such as DNA, RNA, protein, imaging, conservation and even from long-term ecological research sites.

Students are introduced to a suite of computational approaches that are used to analyze, visualize and interpret this data. Finally, students will delve into the ethical implications of collecting, analyzing and sharing the results of computational life sciences data.

Requirements

• Students must complete a minimum of 15 credit hours including 12 of upper division courses in the life sciences and at least 6 upper division hours taken with the College of Liberal Arts and Sciences.
•  The core consists of one computing course and one ethics course.
• A minimum of nine credit hours in elective courses complete the certificate.
• The computing course not used toward the core requirements may be used toward the elective credit hours.
• A grade of C (2.00 on a 4.00 scale) or higher is required for all courses used toward the concentration.

Required Courses -- 6 credit hours

BIO 312 / PHI 320: Bioethics (HU) OR
BIO 316 / HPS 330: History of Biology: Conflicts and Controversies (H) OR 
BIO 317 / HPS 323: History of Science II (HU & H) OR
BIO 318 / HPS 331: History of Medicine (HU & H) OR
BIO 416 / HPS 410: Biomedical Research Ethics (L) (3)

BIO 439: Computing for Research OR
BIO 440 / MBB 440: Functional Genomics (3)

Electives -- 9 credit hours

BIO 355 Introduction to Computational Molecular Biology (CS) (3) (Pre- or corequisite(s): BIO 340 or LSC 347 or MBB 347 with C or better)

BIO 411 Quantitative Methods in Conservation and Ecology (4) (Prerequisite(s): BIO 320 or 322 with C or better; BIO 415 or STP 226 or STP 231 with C or better; MAT 210, 251, 265, or 270 with C or better)

BIO 415 Biometry (CS) (4) (Prerequisite(s):  MAT 210, 251, 265 or 270 with C or better, or a 200 level statistics course (STP 226, or 231) with C or better)

BIO 439 Computing for Research (3) (Prerequisite(s) with C or better: BIO 181; BIO 182 OR BIO 281; BIO 282; Credit is allowed for BIO 439 or BIO 539 or EVO 539 or MCB 539 or BIO 498 (Computing Rsrch) or BIO 598 (Computing Rsrch) or EVO 598 (Computing Rsrch) or MCB 598 (Computing Rsrch))

BIO 440 Functional Genomics (3) (Prerequisite(s): BIO 340 or LSC 347 or MBB 347 with C or better; Credit is allowed for only BIO 440 or CBS 540 or MBB 440 or MCB 540)

BIO 494 Data Analysis in Neuroscience (3)
BIO 494 Data Analysis in Neuroscience (3)
BIO 494 Data Analysis and Visualization in R (3)
BIO 494 Computational Genomic Analysis (3)
BIO 498 Programming for biologists (3)
BME 494 Systems Biology of Disease (3)
BMI 311 Modeling Biomedical Knowledge (3)
BMI 312 Modeling Biomedical Data (3)
BMI 330 Topics in Translational Bioinformatics (3)
DAT 301 Exploring Data in R and Python (4)
GIS 469 / SOC 469 Multivariate Statistics for Social Sciences (3)
GIS 471 Spatial Statistics for Geography and Planning (3)
MAT 353 Mathematics and Cancer (3)
MAT 451 Mathematical Modeling (CS) (3)

If not used as the required computing course, students may include BIO 439 or BIO 440 as a certificate elective.