Designated Emphasis

Graduate training in computational biology is currently provided by the Designated Emphasis (DE) in Computational and Genomic Biology, a pseudo doctoral minor for existing graduate programs.

Designated Emphasis (DE) in Computational and Genomic Biology

The Designated Emphasis (DE) in Computational and Genomic Biology is a specialization applicable to Ph.D. students in select programs with research interests in computational biology, which we refer to as our Associated Programs. DE students receive a solid foundation in the different facets of genomic research and the ensuing competitive edge for the most desirable jobs in academia and industry, which increasingly require interdisciplinary training.

The requirements for completing the application are described below. Upon successful completion of the dissertation, the student’s final transcript will include the designation, “Ph.D. in [Home Program] with a Designated Emphasis in Computational and Genomic Biology.”

Students must apply to the DE several months in advance of the Qualifying Examination, which must include at least one member of the Group Faculty. The DE application review may take up to a month and the QE Application (due to Grad Div at least 3-weeks before the scheduled exam) must be signed by the DE Head Graduate Advisor (see PhD contacts). Please plan accordingly.


Associated Programs – To be admitted to the Designated Emphasis, students must be doctoral students in one of our Associated Programs.

Course Requirements – The curriculum of the Designated Emphasis consists of coursework which may be either independent from, or an integral part of, a student’s Associated Program. Our goal is to provide students with a broad education in Computational and Genomic Biology. Students are therefore required to take one class from three of the following five categories (three courses total), with at least two from outside of the student’s home degree program. The majority of the course requirements should be completed before the Qualifying Examination as the QE must include examination of knowledge within the area of Computational and Genomic Biology. The categories are:

  1. Computer Science and Engineering;
  2. Biostatistics, Mathematics, and Statistics;
  3. Biology;
  4. Chemistry, Chemical Engineering, and Physics;
  5. Computational Biology

See example course list below (list is not current, but shows relevant course topics).

Prior coursework may be used to fulfill the requirements if the coursework is found to be equivalent to those classes listed below. Students are required to earn letter grades (A-F) for coursework.

Seminars – In addition to the coursework requirement, students must attend regular seminar series, or equivalent, as designated by the Curriculum Committee.

Annual Retreat – Each year, the DE faculty and students convene for a two-day retreat, usually scheduled for the weekend preceding Thanksgiving. The retreat program includes student presentations, poster session, a student meeting and faculty meeting.

Qualifying Examination and Dissertation Requirements – Both the student’s Qualifying Examination committee and Dissertation committee must include at least one, but preferably two, Core faculty members from the Computational Biology Graduate Group. The faculty member(s) may either represent the home department or serve as an outside member. Satisfactory performance on the Qualifying Examination for the doctorate will be judged independently from the Designated Emphasis. The Qualifying Examination must include examination of knowledge within the area of Computational and Genomic Biology.

Normative Time – No additional time can be added to the normative time of your home department; however, due to the interdisciplinary nature of training and research in the Designated Emphasis, and depending on your background, completion of the DE could add one, possibly two, additional semesters to the student’s total program.

Application Procedure

Applications for admissions are reviewed on a rolling basis throughout the year by the DE Admissions Committee. Students should apply two semesters prior to the Qualifying Examination. Students are advised to notify both the graduate affairs officer (GSAO) of the home department and the DE program coordinator of their intent to apply as early as possible.

Hard copies of the following materials must be submitted to Kate Chase, Graduate Program Coordinator, to apply to the DE in Computational and Genomic Biology:

  1. DE CGB Petition (.pdf)
  2. Graduate Petition for Change of Major or Degree Goal (.pdf)
  3. Letter of recommendation from the student’s faculty advisor
  4. One page letter of intent summarizing the student’s background in computational and genomic biology, and outlining short- and long-term training and research goals in the field
  5. Most recent copies of all undergraduate and graduate transcripts
  6. The student’s curriculum vitae

Please note that review may take up to one month.

Course Requirements – example list (not current)

Computer Science and Engineering

  • Computer Science (COMPSCI) 170: Efficient Algorithms and Intractable Problems
  • Computer Science (COMPSCI) 186: Introduction to Database Systems
  • Electrical Engineering (EL ENG) 120: Signals and Systems
  • Electrical Engineering (EL ENG) 170A, 180A: Introduction to Modeling and Simulation
  • Electrical Engineering (EL ENG) 170B, 180B: Introduction to Modeling and Simulation
  • Electrical Engineering (EL ENG) 221A: Linear System Theory
  • Electrical Engineering (EL ENG) 222: Nonlinear Systems – Analysis, Stability and Control
  • Electrical Engineering (EL ENG) 223: Stochastic Systems: Estimation and Control
  • Electrical Engineering (EL ENG) 227A, B: Introduction to Convex Optimization
  • School of Information Management Science (SIMS) 255: Foundations of Software Design

Biostatistics, Mathematics, and Statistics

  • Mathematics (MATH) 110: Linear Algebra
  • Mathematics (MATH) 127: Mathematical and Computational Methods in Molecular Biology
  • Mathematics (MATH) 128A, B: Numerical Analysis
  • Mathematics (MATH) 228A, B: Numerical Solution of Differential Equations
  • Mathematics (MATH) 290: Hidden Markov Models in Comparative Genomics
  • Public Health (PB HLTH) 142A, B: Introduction to Probability and Statistics in Biology and Public Health
  • Public Health (PB HLTH) 143: Introduction to Statistical Methods in Computational and Genomic Biology
  • Public Health (PB HLTH) 240C: Biostatistical Methods: Computational Techniques
  • Public Health (PB HLTH) 240D: Biostatistical Methods: Applications of Statistics to Genetics and Molecular Biology
  • Public Health (PB HLTH) 243A: Multivariate Statistical Methods in Genomics
  • Public Health (PB HLTH) 244A, B: Stochastic Processes in Biology and Health
  • Public Health (PB HLTH) 248: Statistical/Computer Analysis Using SPLUS
  • Public Health (PB HLTH) 252B: Modeling the Dynamics of Infectious Disease Processes
  • Statistics (STAT) 101: Introduction to the Theory of Probability
  • Statistics (STAT) 102: Introduction to the Theory of Statistics
  • Statistics (STAT) 134: Concepts of Probability
  • Statistics (STAT) 135: Concepts of Statistics
  • Statistics (STAT) C141 / Bioengineering (BIO ENG) C141: Statistics for Bioinformatics
  • Statistics (STAT) 200A, B: Introduction to Probability and Statistics at an Advanced Level
  • Statistics (STAT) 210A, B: Theoretical Statistics
  • Statistics (STAT) 215A, B: Statistical Models: Theory and Application
  • Statistics (STAT) 232: Experimental Design
  • Statistics (STAT) 241A, B, C: Statistical Learning Theory
  • Statistics (STAT) 242A, B: Analysis of Multidimensional Data
  • Statistics (STAT) 243: Introduction to Statistical Computing
  • Statistics (STAT) 244: Statistical Computing
  • Statistics (STAT) 246: Statistical Genetics


  • Molecular and Cell Biology (MCELLBI) 110: General Biochemistry and Molecular Biology
  • Molecular and Cell Biology (MCELLBI) 111: Introduction to Structural Biology
  • Molecular and Cell Biology (MCELLBI) 140: General Genetics
  • Molecular and Cell Biology (MCELLBI) 142: Survey of General Genetics
  • Molecular and Cell Biology (MCELLBI) C148 / Plant and Microbial Biology (PLANTBI) C148: Microbial Genomics and Genetics
  • Molecular and Cell Biology (MCELLBI) 211: An Introduction to Structural Biology and Physical Biochemistry
  • Molecular and Cell Biology (MCELLBI) 240: Advanced Genetic Analysis

Chemistry, Chemical Engineering, and Physics

  • Chemistry (CHEM) 120B: Physical Chemistry
  • Chemistry (CHEM) 130A: Biophysical Chemistry
  • Chemistry (CHEM) 212: Bioorganic Chemistry
  • Chemistry (CHEM) 220B: Statistical Mechanics
  • Chemistry (CHEM) 223A: Chemical Kinetics
  • Chemistry (CHEM) C230: Protein Chemistry, Enzymology, and Bio-organic Chemistry
  • Chemistry (CHEM) 231A: Advanced Biophysical Chemistry
  • Chemical Engineering (CHM ENG) 142: Chemical Kinetics and Reaction Engineering
  • Chemical Engineering (CHM ENG) 150A, B: Transport Processes
  • Chemical Engineering (CHM ENG) 162: Dynamics and Control of Chemical Processes
  • Chemical Engineering (CHM ENG) 244: Kinetics and Reaction Engineering
  • Physics (PHYSICS) 205A, B: Advanced Dynamics

Computational Biology

  • Bioengineering (BIO ENG) 131: Introduction to Computational Biology
  • Bioengineering (BIO ENG) 142: Programming and Algorithm Design for Computational Biology & Genomics Applications
  • Bioengineering (BIO ENG) 143: Computational Methods in Biology
  • Bioengineering (BIO ENG) 144: Introduction to Protein Bioinformatics
  • Integrative Biology (IB) 206: Phylogenetics
  • Molecular and Cell Biology (MCELLBI) 137: Computer Simulation in Biology
  • Molecular and Cell Biology (MCELLBI) 247: Genome Project Laboratory
  • PLANTBI C246 (also MCELLBI C246, BIOENG C246): Topics in Computational Biology & Genomics