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Bioinformatics

Doctor of Philosophy in Bioinformatics

You may complete this pre-application to determine if you qualify.

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Faculty Coordinating Committee

Dr. Mark Borodovsky Chair
Dr. Leonid Bunimovich
Dr. Stephen Harvey Co-chair
Dr. Dick Lipton Co-chair
Dr. Allen Tannenbaum Co-chair
Dr. Loren Williams

Participating Schools

School of Chemistry and Biochemistry
School of Biology
School of Biomedical Engineering
College of Computing
School of Mathematics

1. Objective of the program

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The mission of the Georgia Tech Bioinformatics PhD Program is to educate and prepare graduate students to reach the forefront of leadership in the field of bioinformatics and computational biology; and to integrate research and education on the use of information technologies in biology and medicine. Thus, the program leading to a PhD in Bioinformatics is an interdisciplinary program spanning a variety of academic departments at Georgia Tech.

Bioinformatics is a multidisciplinary field in which physical sciences, life sciences, computer science, and engineering are merged to solve both fundamental and applied problems in biology and medicine. The outcomes of bioinformatics and computational biology particularly include

  1. new and global perspectives into the organization and function of biological systems (fundamental biology);
  2. new and novel targets for drug discovery and development; and
  3. genetic/proteomic profiling for pharmaco-genomics or personalized medicine.

Thus, Bioinformatics is emerging as a strategic discipline at the frontier between Biology, Biochemistry, Biomedicine, Bioengineering, Computer Science and Mathematics, impacting fundamental science, medicine, biotechnology, and society.

With its broad mission statement, this program at Georgia Tech has the following focus / strength areas:

  1. Development of software tools, algorithms, and databases for gene identification, protein structural prediction, clustering analysis, and data mining.
  2. Application of bioinformatics to disease diagnosis, classification, prognosis, and treatment.
  3. Application of bioinformatics to fundamental biology and systems biology.

There is an increasing demand for scientists with advanced training in Bioinformatics. Professionals in this area should have a thorough knowledge of Molecular Biology, Mathematics and Statistics as well as Computer Science and Engineering.

Back in 1997 the College of Science at Georgia Tech proposed and established a professional Master of Science in Bioinformatics degree program, the first of its kind in the United States. This interdisciplinary program consists of a unique combination of courses. The students are taught with the equal strength in several scientific disciplines and are prepared for further successful work in industry or academy. At present there are more than 40 students in the program, with twelve graduates already employed in Academy and Industry, particularly at SmithKlineGlaxo, Navartis, Johnson & Johnson, Informax, Los Alamos National Lab, the Vanderbilt University, Centers for Disease Control and Prevention, etc.

Since 1993, the School of Biology at Georgia Tech has implemented a PhD in Biology with concentration in Bioinformatics. This option will stay in place for those students who would like to pursue PhD in Biology.

The group of prospective applicants for the PhD program is expected to consist of students with a MS in Bioinformatics as well as holders of BS/BA and higher degrees in different disciplines. The applicants with Life science degrees are usually looking for the interdisciplinary education with the focus on Mathematics, Physics and Computer Science. The demand of this sort perfectly fits to what Georgia Tech can offer: high quality education in Mathematics, Physics and Computing along with advanced courses in Biology and Biochemistry.

2. Requirements and Prerequisites

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The successful applicants are expected to meet the following requirements.
GRE scores above the cut-off level established by the home unit.
GPA of at least 3.2 and/or top 5% in class standing
Taking subject GRE tests in Math and Biology is highly recommended.
Also the applicants are suggested to have one of the following qualifications:

  • MS in Bioinformatics
  • BS or MS in Bioengineering
    • programming skills i.e. in Perl or C languages (recommended sources "Beginning Perl for Bioinformatics" by James D. Tisdall and "Developing Bioinformatics Computer Skills" by Cynthia Gibas, Per Jambeck)
  • BS or MS in Biology,
    • programming skills i.e. in Perl or C languages (recommended sources "Beginning Perl for Bioinformatics" by James D. Tisdall and "Developing Bioinformatics Computer skills" by Cynthia Gibas, Per Jambeck)
    • three semesters of calculus, two semesters of physics
  • BS or MS in Computer Science
    • two semesters of Biological science or equivalent to knowledge of Molecular Biology at a level of "Genes VII" by Benjamin Lewin
    • three semesters of calculus
  • BS or MS in Physics, Chemistry, Biochemistry, Mathematics, Engineering
    • programming skills i.e. in Perl or C languages (recommended sources "Beginning Perl for Bioinformatics" by James D. Tisdall and "Developing Bioinformatics Computer Skills" by Cynthia Gibas, Per Jambeck)
    • two semesters of Biological science or equivalent to knowledge of Molecular Biology at a level of "Genes VII" by Benjamin Lewin

You may complete a pre-application to determine if you qualify.

3. Admission

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Successful candidates must be admitted by one of the participating units within the College of Engineering, College of Science or College of Computing as a "Home Unit". The candidate should apply directly to a Home Unit and designate the Bioinformatics Program as the degree program applied for. The admission decision is made jointly by the admission committee of the Bioinformatics program and the home unit admission committee. A copy of the original application is forwarded from the home unit to the Bioinformatics committee if the student indicated interest in the Bioinformatics program. If the Bioinformatics committee gives recommendation for the student admission (with or without Bioinformatics program financial support attached) the home unit makes the final decision based on the student’s credentials and the Bioinformatics committee recommendation. Once admitted the student should have all the rights a full-fledged PhD student within the home unit. If a student having entered the PhD program chooses a thesis advisor outside of his/her home unit, the student may request and complete a transfer (within a semester) to the school of the advisor with permission of the graduate committee of the new home unit.

4. Curriculum & Program Requirements

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The requirements for each student in the PhD program in Bioinformatics include the successful completion of a set of core courses in Biology, Biochemistry, Mathematics, Physics, Computer Science, etc, while the main emphasis of the program is on the successful completion of an original and independent research project. Each student must also complete a minor program of studies in accordance with Institute policies.

Admission to candidacy requires passing written and oral comprehensive examinations administered by the Bioinformatics PhD Graduate Committee along with the Graduate Committee of one of participating units (see the Comprehensive Exam section below). The PhD dissertation written on results of the individual research project should provide evidence that the PhD candidate is ready to start an independent research career. The PhD thesis should be defended publicly and approved by the thesis committee.

Credit hours requirements ( Each student regardless of his home unit is required to complete the following course work):

  1. 9 credit hours of Bioinformatics and Computational Bioscience (e.g. BIOL 7023 and BMED 6780, BMED 7027)
  2. 9 credit hours in Biology, Biochemistry or Biomedical Engineering (e.g. BIOL 6608, BIOL 7668, CHEM 6572)
  3. 9 credit hours of Mathematics and Computer Science (e.g. MATH 6266, MATH 6267, CS 6411)
  4. 9 credit hours of courses in an approved minor
  5. 24 research credit hours

Credit hours for courses in categories A, B, C could be completed by previous graduate studies (such as study in the Georgia Tech Master’s Degree in Bioinformatics program). Approval of transfer of credits from courses taken elsewhere is done by the Bioinformatics graduate committee.

Typically, 2/3 of credit hours in each category A, B, C, D should be at 6000 or higher level. Students can get appropriate credits for 4000 level courses from the list of recommended courses (see below), if the student’s thesis committee approves them and includes into a program of studies.

A student must maintain a GPA of 3.2 in his/her course work.

Participating Schools may have additional requirements and policies for students registered for the Bioinformatics PhD program in that School as the home unit. These further define the course of study, such as a requirement that courses in sections B or C must be taken in the home department, and/or specifics on affiliation of thesis committee members but do not constitute additional academic workload.

QUALIFYING EXAM
The student must successfully pass a qualifying exam within 24 months after entering the PhD program. The exam consists of written and oral parts. The written part has two sections, the bioinformatics section and an elective section chosen from written qualifying exam sections offered by one of the participating units (typically, but not necessarily, by the home unit). The oral examination is approximately one hour in length and focused on the proposal the student has to write as his tentative grant proposal on a Bioinformatics topic not directly related to his or her research though it could be a conceivable extension beyond the thesis research limits. The written exam in Bioinformatics and the oral exam are administered by a faculty committee consisting of:

  1. Two Bioinformatics Program faculty
  2. One faculty member from the Home Unit
  3. Thesis advisor as an observer, not as a participant (as a rule).

The committee is suggested by the advisor and approved jointly by the Chair of the Bioinformatics Graduate Committee and the Chair of a Home Unit Graduate Committee.

Students who wish to transfer to the Bioinformatics program after passing their qualifying exam in another PhD major can be admitted by the Bioinformatics Graduate committee without the requirement of passing the Bioinformatics qualifying exam. In this case the advisor (with co-advisor) and thesis committee may have to specify additional courses to be taken to satisfy the requirements of the program of study

Home Unit approval for degree petition, as well as approval by the Bioinformatics Graduate Committee, will be required.

THESIS
A student should choose a thesis advisor (from the Bioinformatics Program Faculty) and co-advisors within the first year of being in the PhD program. In the second year a student along with his advisor will have to assemble the thesis committee. The thesis committee should consist of a minimum of five faculty members. At least three members of the committee should be from Bioinformatics Program Faculty and at least two members of thesis committee should be from the home unit. Not later than in the middle of the third year a student has to present and defend a written PhD proposal.

RESEARCH PROGRESS
A student should meet with his/her thesis committee at least once a year to review the research progress.

PhD DISSERTATION
Within 5 years after entering the PhD program, the student is expected to complete the thesis research, and, typically, the student has to have the results of the research published in peer reviewed journals. Upon submitting a written thesis and public defense and approval by the committee, the student is awarded with PhD degree.

5. Administration

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The PhD program is administered by the Bioinformatics Graduate Committee formed at the Center of Bioinformatics and Computational Biology in co-ordination with Graduate Committees of the home units. The Bioinformatics Graduate Committee is selected by the Director of the Center of Bioinformatics and Computational Biology and the Faculty Co-ordinating committee from the Program Faculty at the Center. Program faculty may include those who are affiliated with Schools currently not listed in the Proposal as participating units. The list of participating units may increase in future if needed. The Director of the Center is appointed by the Provost in consultation with the Chairs and Deans of the participating units. The Chair of the Bioinformatics Graduate Committee is supposed to act for the Graduate Committee when it is not in session and will represent the Graduate Faculty in discussions of the PhD program with the participating units.

6. Program Faculty

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Membership in the program faculty requires participation in the program functions, particularly in admission and thesis committees and committees administering the PhD in the Bioinformatics qualifying exam. Program faculty are expected to support PhD students. Program faculty members have to contribute to development of new graduate courses and grant proposals supporting graduate training in Bioinformatics.

College of Sciences:
Mark Borodovsky Biology/Biomedical Engineering
Stephen Harvey Biology
Igor Zhulin Biology
Leonid Bunimovich Mathematics
Ron Shonkwiler Mathematics
Loren Williams Chemistry and Biochemistry
Nick Hud Chemistry and Biochemistry

College of Computing:
Dick Lipton
Shamkant Navathe
Ashwin Ram
Dana Randall
Bill Ribarsky
Chris Shaw

School of Biomedical Engineering:
Gang Bao
Allen Tannenbaum
May Dongmei Wang
Cheng Zhu

School of Electrical and Computer Engineering
Yucel Altunbasak
Tong Zhou

School of Industrial and System Engineering
Eva Lee
Kwok-Leung Tsui
Jeff Wu

7. Recommended courses for PhD in Bioinformatics

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Bioinformatics & Computational Biology courses:
BIOL 7023/BMED 7023 Bioinformatics (pending approval by BME)
CS 4710 Introduction to Computing in Bioinformatics
BIOL 4755 Mathematical Biology
BIOL 4803/8803 Introduction to Computational Genomics
BMED 7027/BIOL 8802 Topics in Bioinformatics and Systems Biology (pending approval by BME)

Biology & Biochemistry courses:
BIOL 4220 Bacterial and Viral Genetics
BIOL 4469 Molecular Biology
BIOL 4478 Biophysics
BIOL 6608 Prokaryotic Molecular Genetics
BIOL 6612 Advanced Bacterial Metabolism
BIOL 7010 Advanced Cell Biology
BIOL 7668 Eukaryotic Molecular Genetics
BIOL 7913 Advances in Microbiology
BIOL 7914 Advances in Bacteriology
BIOL 7963 Advances in Molecular Biology
BIOL 7964 Advances in Genetics

CHEM 6501 Biochemistry I
CHEM 6502 Biochemistry II
CHEM 6571 Enzymology
CHEM 6572 Macromolecular Structure
CHEM 6573 Molecular Biochemistry
CHEM 6581 Protein Crystallography
CHEM 6583 Drug Design and Discovery

Biomedical Engineering Courses
BMED 6779 Bioprocess Engineering
BMED 6780 Medical Image Processing
BMED 6788 Legal Issues in Biomedical Engineering

Mathematics & Computer Science courses
MATH 4221 Stochastic Processes I
MATH 4280 Intro to Information Theory
MATH 6014 Graph Theory
MATH 6266 Linear Statistical Models
MATH 6262 Statistical Estimation
MATH 6267 Multivariate Statistical Analysis
MATH 6705 Modeling and Dynamics
MATH 6761 Stochastic Processes I
MATH 6762 Stochastic Processes II
MATH 7016 Combinatorics
MATH 7018 Probabilistic Methods in Combinatorics

CS 4400 Introduction to Database Systems
CS 4500 Theory II
CS 4600 Introduction to Intelligent Systems
CS 4640 Machine Learning
CS 6230 High Performance Parallel Computing
CS 6300 Software Development Process
CS 6310 Software Architecture and Design
CS 6320 Software Requirements Analysis and Specifications
CS 6330 Software Generation, Testing and Maintenance
CS 6411 Object-Oriented Database Models and Systems
CS 6455 User Interface Design and Evaluation
CS 6480 Computer Visualization Techniques
CS 6485 Visualization Methods for Science and Engineering
CS 6505 Computability, Algorithms and Complexity
CS 6550 Design and Analysis of Algorithms
CS 6660 Intelligent Agents
CS 6705 Applications of Artificial Intelligence
CS 7001 Overview of Graduate Studies in Computing
CS 7450 Information Visualization
CS 7510 Graph Algorithms
CS 7610 Modeling and Design
CS 7645 Numerical Machine Learning

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