1. Rationale. The Society for Industrial and Applied Mathematics (SIAM) has just published its. A. Background

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1 Computational Biology: a major within the ECAS Interdepartmental Studies B.S. Bioinformatics Area of Emphasis Biomathematics Area of Emphasis Participating Departments: Biology, Mathematics, and Statistics 1. Rationale A. Background Computational biology is at the center of the biology revolution that has accelerated since the sequencing of the human genome. The importance of computational biology to the Eberly College of Arts and Sciences is substantial as is evidenced by targeted hires in Biology, Mathematics, and Statistics in areas such as bioinformatics, biostatistics, genomics, and systems biology. Currently, West Virginia University has no programs or majors in computational biology at either the undergraduate or graduate level. The Departments of Biology, Mathematics, and Statistics recognized the need for a major in computational biology and the interdisciplinary nature of this program. Based on discussions with the ECAS Dean and the Associate Dean for Research and Graduate Studies, a planning committee was established consisting of the chairs and faculty representatives from Biology, Mathematics, and Statistics. Computational biology will attract two types of students: those primarily interested in the biological sciences who want enhanced coursework in mathematics and statistics, and those interested in the mathematical sciences who want to apply mathematics and statistics to the biological sciences. Thus two areas of emphasis (AOE) are being established: bioinformatics, for those primarily interested in the biological sciences; biomathematics, for those primarily interested in the mathematical sciences. Students in this major will be in strong demand by biotechnology or pharmaceutical companies and by various governmental agencies and research institutes. For example, the Bureau of Labor Statistics stated in their Career Guide to Industries: Much of the basic biological research done in recent years has resulted in new knowledge, including the successful identification of genes. Life and physical scientists will be needed to take this knowledge to the next stage, which is to understand how certain genes function so that gene therapies can be developed to treat diseases. Computer specialists such as systems analysts, biostatisticians, and computer support specialists also will be in demand as disciplines such as biology, chemistry, and electronics continue to converge and become more interdisciplinary, creating demand in rapidly emerging fields such as bioinformatics and nanotechnology. Further, the Bureau states: Biological scientists, for example, may be employed in biotechnology or pharmaceuticals, both growing areas. The Society for Industrial and Applied Mathematics (SIAM) has just published its

2 latest brochure on applied mathematics and computational science careers outside of academia and lists bioinformatics as an emerging field. They state: A career in bioinformatics (computational biology) can include a wide range of biological fields from genomics to neuroscience and anywhere in between. For example, mapping and understanding the human genome relies on the use of sophisticated mathematical and computational tools. Other research challenges include understanding how genes interact, how they are switched on or off, and how they differ from one individual to another. There is a great need for newer and better mathematical and computational tools to make research quicker and cheaper, resulting in the creation of new career opportunities in technology, medicine, and drug development and design. Finally, students with this major would be well prepared to enroll in medicine or in basic health sciences graduate degree programs, particularly students in the Bioinformatics AOE. B. Value Added Comparing Computational Biology (CB) to the Existing Biology B.S. The current WVU Biology degree programs, either alone or as a double major or major plus minor, cannot accommodate the increased requirements of the proposed Computational Biology major. To illustrate this, one can compare the most equivalent current and proposed degrees; the Biology B.S. and the Computational Biology Bioinformatics (CB-Bioinformatics) B.S. There is more than one way to select CB-Bioinformatics Math/Stat/Science electives to maximize Biology B.S. graduation requirements, but the result would be a shortfall not less than the following: CB-Bioinformatics requirements in addition to Biology requirements: Math264, Math466 (or 475), STAT312, STAT423, STAT443: 16 cr. Biology requirements in addition to CB-Bioinformatics requirements CHEM234, CHEM235, CHEM236, a Group III biology elective, PHYS112: 12 cr. A Biology undergraduate student could not reasonably complete the material we think is needed for the CB-Bioinformatics degree in fewer than five years. The CB-Biomathematics degree is even less similar to a Biology degree. Within the Computational Biology program, students will be able to choose from two Areas of Emphasis: Bioinformatics or Biomathematics. The Bioinformatics AOE will have a strong biology component and allow for the development of mathematical and statistical tools, while the Biomathematics AOE will focus on more advanced mathematics and statistics used in biological applications as well as selected biology coursework.

3 C. Projected Enrollments The steady state headcount of the Computational Biology program is estimated to be about 50 students. This takes attrition into account attrition, with estimates of 10 graduates per year once the program is well established. This program is not intended to be large. Rather, the goal is to attract high-quality students who want training in both the biological and mathematical sciences. Number of Majors: FIVE-YEAR PROJECTION OF PROGRAM SIZE First Second Third Fourth Fifth Year Year Year Year Year (2009) (2010) (2011) (2012) (2013) Headcount _ FTE majors _ Number of student credit hours generated by majors in the program (entire academic year): _ _ Number of degrees to be granted (annual total): D. Required Courses 1. Existing Courses Existing courses in biology, mathematics, and statistics will be minimally impacted and will require no additional sections as outlined in the attached departmental support letters. 2. New Courses The programmatic courses new to Biology are the Genomics (BIOL 420) and the Bioinformatics Basics (BIOL 364) courses. The Genomics course has been taught as a special topics course several times, but it is being submitted for approval to the Senate in conjunction with this major, even though this course should have wide appeal. Mathematics will include a new introductory mathematics course (MATH 264)

4 oriented towards biological problem solving, which will include components of MATH 251, MATH 261, and MATH 343. This will lead to either a Graph Methods (MATH 475) or a Systems Biology (MATH 466) course in mathematics (both new courses). STAT 215, along with the above-mentioned biology courses, will lead to a Bioinformatics Computing course (Stat 423), using the high-level statistical R environment, and the Computational Genomics course (Stat 443). The latter two courses are new and will be taught by Statistics. We now address the distinction between the Bioinformatics and Biomathematics Areas of Emphasis within the Computational Biology major. Comparing Bioinformatics to the Biomathematics Area of Emphasis The two Areas of Emphasis, Bioinformatics and Biomathematics, will present different options for students in the program and, as a result, these students will impact the participating departments in slightly different ways. Students within the Computational Biology program will tend to have strong mathematical skills as well as an interest in studying biological processes. In practice, this group of students either will be more inclined towards biology, and need mathematical and statistical tools as a support skill set, or they will have a strong mathematical background and be interested in biological applications. To better serve these two groups, two different areas of emphasis within the program provide different course tracks. In the Bioinformatics AOE, the program offers students the opportunity to develop a substantial background in Biology while also developing the basic mathematical and statistical tools needed to apply them to the study of biological processes. This will impact the Department of Biology most heavily and will add additional students in a number of biology courses, but not to the extent that new sections will be needed, as well as in the new courses outlined for Statistics and Mathematics. In the Biomathematics AOE, students will develop a strong background in Mathematics and Statistics and will then apply them to selected topics in Biology. For this AOE, coursework will focus on strong mathematics and statistics preparation that focuses on data mining, modeling, and analysis. Students in this program will take the new courses outlined as well as existing major courses in Mathematics and Statistics. The impact on the Mathematics and Statistics courses will be to increase the number of students, but not to the extent that new sections will be needed. Overall, the effect of these students will be to strengthen these existing course offerings. The complementary nature of the Bioinformatics track and the Biomathematics track, as proposed here, is supported by NSF and is a continuing source of funding by this agency. Therefore we feel that the implementation of these new curricula will support funding efforts by the recently hired ECAS educator faculty.

5 E. Interdepartmental Management The Computational Biology program, as a major in the Interdepartmental Studies B.S. program, involves the formal cooperation of the Departments of Biology, Mathematics, and Statistics. These departments not only have jointly developed the plan for this major, but they will also be responsible for its implementation and continuation. Advising will be concentrated in the Department of Biology for the Bioinformatics AOE and in the Department of Mathematics for the Biomathematics AOE. Certain elements of the Computational Biology major, e.g., the capstone, need to be coordinated among Biology, Mathematics, and Statistics. Thus, a Coordinating Committee, consisting of the chairs of the undergraduate curriculum committees from each department and one other representative from each department will meet as required to determine policies and to resolve programmatic and student issues. 2. Catalog Description Computational Biology Jeffrey D. Wells, Biology Chair Edgar J. Fuller, Mathematics Chair E. James Harner, Statistics Chair Degree Offered: Interdepartmental Studies Bachelor of Science Major: Computational Biology Nature of the Program The Computational Biology major is designed for students interested in the biological, mathematical, and statistical sciences who also have an interest in bioinformatics, genomics, and systems biology. The major has two tracks (Areas of Emphasis): Bioinformatics and Biomathematics. The Bioinformatics track has strong course requirements in biology with substantial components in both mathematics and statistics, whereas the biomathematics track has strong course requirements in mathematics and statistics with substantial components in biology. Students graduating in this major will be well prepared to enroll in graduate school in the biological, health, mathematical, or statistical sciences. They will also be in strong demand by biotechnology or pharmaceutical companies, and by

6 various governmental agencies and research institutes. Computational Biology would also be an excellent option for students who wish to enter Medicine. Students enrolled in this major will develop conceptual understanding of and strong skills in genomics, bioinformatics, and systems biology. The teaching of these biological areas will rely strongly on appropriate mathematical, probabilistic, and statistical methods. In addition, students will model these biological systems using statistical programming environments such as R, technical computing languages such as MATLAB, and symbolic systems such as Mathematica. Performance Requirements To maintain Computational Biology major status and to graduate, students must maintain at least a 2.0 overall GPA and a 2.25 cumulative GPA in all coursework in biology, mathematics, and statistics. Students must satisfy all the requirements for the GEC and the ECAS Bachelor of Science. Degree Requirements Bioinformatics Area of Emphasis: A minimum of 128 hours is required for graduation. The Core Curriculum requires the following courses (63 credit hours): BIOL 115, 117, 219, 221, 324, 364, 420; CHEM 115, 116, 233 MATH 155, 156, 264, 466 or 475; STAT 215, 312, 423, 443 Biology electives (at least 11 credit hours): BIOL 310, *311, 312, 313, 316, 348, 410, *411, *412, 413, *414, 424, *432, 439, 461, *464. These electives must include two courses with a laboratory (indicated by a * above). Capstone experience: 3 credit hours from BIOL 497. Students can petition the Coordinating Committee to take 3 additional research credit hours from BIOL 497, MATH 497, or STAT 497. The capstone course(s) should be taken during the student's senior year. A final research paper must be written for each of the 3-hour research courses. Math/Stat/Science Electives: at least 6 credit hours: MATH 222, 251, 261, 367,420, 421, 464, 465 STAT 313, 316, 331, 421, , 462 PHYS 111, 112; AEM 341, AGBI 410, BIOC 339

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