GBCB Course Requirements

For the purposes of this program and to insure that students have some breadth of exposure, four specialty tracks are defined:

  • Life sciences
  • Computer science
  • Statistics
  • Mathematics


A student will select one of the specialty tracks as his/her primary track, which will typically be consistent with the student’s undergraduate training. The other tracks will be denoted the secondary tracks for that student. In addition, a core curriculum that is common to all students has been defined. A general description of the credit hour requirements for students, showing the minimum credit hours from each requirement category, is given in the table below. Requirements will differ among the specialty tracks, with some tracks requiring more coursework, with correspondingly fewer credit hours of Research and Dissertation.


Curriculum Requirements for a Ph.D. in the Program

Requirement Description Credit Hrs
Core Curriculum GBCB 5874: Problem Solving in Genetics, Bioinformatics, and Computational Biology 3
  STS 5444 : Issues in Bioethics 3
  GBCB 5004 : Seminar in Genetics, Bioinformatics, and Computational Biology. Students in the program will be required to register for seminar every semester. Most semesters, students will register on a pass-fail basis, but during their course of study, at least 2 formal presentations must be given and graded on an A-F basis. Note that the dissertation defense seminar does not count as one of these two presentations 3
Primary Track At least 9 credit hrs from the primary track. For students whose primary track is the Life Science track, 3 of these credit hrs must be a genomics course (e.g., CSES/GBCB 5844 - Plant Genomics) 9
Secondary Tracks At least 12 credit hrs must be taken, with the courses coming from at least two of the three secondary tracks. For students whose primary track is not in Life Sciences, at least 6 of these credit hrs must be in the life sciences track. Students in the Statistics primary track must take CSES/GBCB 5844 (Plant Genomics) or equivalent 12
General Electives Choice of at least 6 credit hrs of electives 6
Research and Dissertation Sufficient to accumulate 90 total credit hrs 54
  Total required credit hours 90

Course Descriptions

Catalog descriptions of each of the courses that constitute the core curriculum are provided below.

Title Description
GBCB 5874: Problem Solving in Genetics, Bioinformatics and Computational Biology This course provides an essential experience in bioinformatics research for graduate students. The course is built around multidisciplinary teams of computer science, life science, and statistics researchers engaged in a bioinformatics project directed by one or more faculty members. Each student joins a team and provides his or her expertise in a collaboration to accomplish a specified research goal. he student must report on the scientific findings, as well as his or her impressions of the experience. In turn, the other research team members provide feedback on the student’s performance for purposes of evaluation and improvement. The student is exposed to the scientific method as it applies in the life sciences and bioinformatics; the nature of contemporary bioinformatics tools and their integration; strategies for publishing results in bioinformatics; and opportunities in research careers in bioinformatics
STS 5444: Issues in Bioethics Identification and analysis of ethical issues arising in basic and applied biological, medical, environmental, ecological, and energy studies. (3H, 3C)
GBCB 5004: Seminar in Genetics, Bioinformatics, and Computational Biology Review and discussion of current topics and literature in genetics, bioinformatics, and computational biology by students, Virginia Tech faculty, and outside speakers. Students give formal presentations of research results or current literature. May be taken on pass-fail basis. (1H, 1C)


Some courses have been designed to be entry points for students into their secondary tracks. These courses are as follows:

Life Science Courses Description
PPWS/GBCB 5314: Biological Paradigms for Bioinformatics This course is an intensive introduction to the central paradigms of molecular cell biology for bioinformatics. Material from cell molecular biology, and genetics will be presented, and placed in a genomics context. The course prepares students in mathematical disciplines to interact in teams in the pursuit of bioinformatics research. Pre: Senior or graduate standing in mathematically-based disciplines such as computer science, statistics, mathematics or engineering. (3H, 3C) I
BCHM 5024: Computational Biochemistry for Bioinformatics Protein structure and function, protein characterization, enzyme kinetics, and analysis of metabolic control for students with a background in computer science, mathematics, statistics, or engineering. Pre: B.S or senior standing in computer science, mathematics, statistics, or engineering or permission of the Dept. of Biochemistry. (3H, 3C) II
Computer Science Courses Description
CS/GBCB 5045-5046: Computation for the Life Sciences I and II Fundamentals of computer science, including specific programming languages; program design, implementation, and testing; programming language syntax and semantics; abstraction and object-oriented programming; data structures; algorithms and algorithm analysis; software engineering; databases; user interfaces; distributed and parallel computing; and computer networks. Background needed by graduate students pursuing the bioinformatics option in computer science, life sciences, or statistics but not having a computer science background. Not for CS major or minor credit; not for graduate credit in CSA program. Pre: Graduate standing with bioinformatics option or permission of the instructor. (3H,3C) 5045: I; 5046: II
Mathematics Courses Description
MATH/GBCB 5415, 5416: Continuous Models in Biological Applications Introduction to mathematical techniques for modeling and simulation, parameter identification and analysis of biological systems. Emphasis on both theoretical and practical issues and methods of computation, with concrete applications. Suitable for students from the mathematical and life sciences who have a basic foundation in multivariate calculus and ordinary differential equations. (3H,3C) 5415: I; 5416: II
Statistics Courses Description
STAT 5615-5616: Statistics in Research 5615: Concepts in statistical inference, including base probability, estimation, and test of hypothesis, point and interval estimation and inferences; categorical data analysis; simple linear regression; and one-way analysis of variance. 5616: Multiple linear regression; multi-way classification analysis of variance; randomized block designs; nested designs; and analysis of convariance. (3H, 3C) 5615: I, III; 5616: II, IV


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