Overview

Big data, in all its aspects including multi-scale modeling, is rapidly transforming life sciences research at all levels of scale from molecules to the ecosystems. This includes areas as diverse as protein chemistry, genomics, systems biology, medical informatics, bioengineering, ecology, and environmental sciences. As a result, there is an increasing need for researchers trained at the intersection of the life sciences and data sciences (mathematics, statistics and computer sciences), in industry and in government, as well as in academia. For example, a recent report from the Coalition of State Bioscience Institutes1 stated that there was exceptional demand for knowledge workers with “Advanced/specialization degrees, such as bioinformatics, biostatistics and computational biology”.

A key requirement for effective research and training at the intersection of life and data sciences is the ability of researchers to communicate across disciplinary boundaries. Thus a life scientist needs sufficient familiarity with the concepts and tools of computer science and statistics to communicate effectively with specialists in those areas.

The BLDS graduate minor provides graduate students in the life sciences with foundational training in quantitative sciences within a research context, and those in the quantitative sciences with foundational training in life sciences within a research context. Students will extend their ability to participate in cross-disciplinary collaborations, and provide a foundation for more advanced training in diverse areas of computational biology. The minor will strengthen the training of students in a wide variety of existing Ph.D. programs including Molecular and Cellular Biology, Biochemistry and Biophysics, Bioengineering, Botany and Plant Pathology, Comparative Health Sciences, Computer Science, Earth, Ocean and Atmospheric Sciences, Fisheries and Wildlife, Integrative Biology, Mathematics, Microbiology, Public Health, and Statistics. The curriculum of the minor is designed flexibly to facilitate integration with the curricula of diverse Ph.D. programs.

[1] Booz & Company. Demand for Talent: Current & Projected Workforce Trends in the Life Science Industry.  (Coalition of State Bioscience Institutes, 2014).

The minor will familiarize M.S. and Ph.D. graduate students in the life sciences with research concepts and methodologies in quantitative sciences, and those in the quantitative sciences with research concepts and methodologies in life sciences. The disciplinary learning goals of the minor are by nature foundational. Thus, for example, students with advanced expertise in life sciences will receive foundational training in computer science, statistics and mathematics. Students with advanced expertise in computer science will receive foundational training in life science, statistics and, if needed, mathematics. A capstone collaborative problem-solving course is required by all students.

Students enrolled in the minor will be advised by a member of the Curriculum Committee of the minor. Furthermore, one member of their graduate committee must be an approved Graduate Faculty member in this minor.

For more information contact Brett Tyler

Course Requirements

The minor is open to both M.S. and Ph.D. students. Ph.D. students must complete at least 18 credit hours for the minor and M.S. students must complete 15 credit hours.

Students must select courses from at least two disciplinary focal areas outside their undergraduate and graduate majors. For example a life sciences student might take courses in mathematics and computer science, while a statistics student might take courses in computer science and life sciences. In each focal area, Ph.D. students must take at least 5 units and M.S. students at least 3 units. Some courses span more than one focal area; these courses may not be counted towards two focal areas simultaneously.

Some courses that are electives in an M.S. or Ph.D. major may also be counted towards the BLDS minor, with approval of a student’s BLDS advisor. For example, a Ph.D. student in Molecular and Cellular Biology (MCB) may select “MCB 576 Introduction to Computing in the Life Sciences” as an elective for their MCB requirements, and also as computer science credit for the BLDS minor. Students may complete all the coursework in a single year (encouraged), or may choose spread the courses out of several years. With approval from the student’s advisor, courses taken prior to enrolling in the minor may be counted towards the course requirements of the minor.

Required by all students

BOT 599. Special Topics [Collaborative problem-solving in biological data science] (3)

This course is being taught for the first time in Spring 2017 (contact: Jeff Anderson)

Students who do not complete an ethics and professionalism class as part of their Ph.D. major must take MCB 557. Scientific Skills and Ethics (3) or an equivalent course.

Students are recommended to choose their courses from the following lists, depending on their prior preparation as an undergraduate. Equivalent or more advanced courses may be substituted after consultation with a student’s BLDS advisor. Some courses require pre-requisites. Some courses span more than one focal area; such courses can be counted towards one or other of those focal areas, but not both.

Life Sciences Focal Area

BB 585: Applied Bioinformatics (3) §
BI 592. Theoretical Ecology (4) †
BOT 599. Special Topics [Introduction to Genome Biology] (3) †
BOT/MCB 575: Comparative Genomics (4)
MB 668: Microbial Bioinformatics and Genome Evolution (4) †
MTH 527. Introduction to Mathematical Biology (3)
MTH 528. Stochastic Elements in Mathematical Biology (3)
VMB 631. Mathematical Modeling of Biological Systems (3) †
VMB 651. Selected Topics [Cancers Systems Biology] (3) †
VMB 670. Introduction to Systems Biology (2) †
Z 594. Community Ecology (5) †

Mathematics Focal Area

MTH 527. Introduction to Mathematical Biology (3)
MTH 528. Stochastic Elements in Mathematical Biology (3)
MTH 563. Probability I (3) §
   or  ST 521. Introduction to Mathematical Statistics I (4) ‡
MTH 564. Probability II (3) §
   or ST 522. Introduction to Mathematical Statistics II (4) ‡
VMB 631. Mathematical Modeling of Biological Systems (3) †

‡ The following sequences qualify for Mathematics Focal Area credit: MTH 563-MTH 564, MTH 564-ST 521, ST 521 – MTH 564. ST 521 – ST 522 does not qualify. Only one pair of courses can be claimed for credit. 

Statistics Focal Area

H 524. Introduction to Biostatistics (4) †
H 566. Data Mining in Public Health (3) §
H 580. Linear Regression and Analysis of Time to Event Data (4)
H 581. Generalized Linear Models and Categorical Data Analysis (4)
MCB 599. Special Topics [Data Programming in R I and II] (2) †
MTH 563. Probability (3) §‡
    or ST 521. Introduction to Mathematical Statistics I (4) #
MTH 564. Probability (3) §‡
    or ST 522. Introduction to Mathematical Statistics II (4) #
    or ST 511-513. Methods of Data Analysis (4,4,4) #
ST 591. Introduction to Quantitative Genomics (3) †
ST 592. Statistical Methods for Genomics Research (3) §
ST 599. Special Topics [Topics in Data Visualization] (3)

 # The following sequences qualify for Statistics Focal Area credit: ST 511-513, MTH 563-MTH 564, MTH 564-ST 521, ST 521-MTH 564, or ST 521-ST 522. Only one of these sequences can be claimed for Statistics focal area credit. 

Computer Science Focal Area

BB 585. Applied bioinformatics (3) §
CS 519 Selected Topics/Computer Sci [Algorithms for Computational Biology] (3) §
   or BB 599. Special Topics [Algorithms for Computational Biology] (3) §
CS 534. Machine Learning (4) †
CS 546. Networks in Computational Biology (3) §
ECE 560. Stochastic Signals and Systems (4)
ECE 564. Digital Signal Processing (4)
FW 599. Special topics [Machine Learning Topics in Species Distribution Modeling] (3)
MCB 599. Special Topics [Introduction to Linux and the command line] (2) †
MCB 599. Special Topics [Introduction to Python I and II] (2) §
MCB 599. Special Topics [Data Programming in R I and II] (2) §
MCB 599. Special Topics [Simulating Natural Systems] (1) §
MCB 576. Introduction to Computing in the Life Sciences] (3) §
VMB 670. Introduction to Systems Biology (2) †

† No prerequisites
§ Recommended prerequisites may be waived with instructor approval
Note: All of the 599 classes here represent classes that are in transition to becoming regular offerings.

For more information contact Brett Tyler