Bioinformatics Programs and Classes at Boulder
Introduction
Although Boulder offers many bioinformatics-related programs and courses,
the relevant information is dispersed across many departments and can be
difficult to verify.
The intent of this page is to gather all relevant details in one location.
I have made every effort to confirm that the material presented here is up-to-date,
correct, and complete, but please contact
me if anything needs to change.
Classes
Disclaimer: I have not actually taken any of these classes, and the
recommendations below are based on my reading of the catalog (and web
pages, where applicable).
At the undergraduate level, I highlight a set of
courses that I think would be really necessary for understanding what existing
tools do and why they do them, in addition to courses that would provide
a sufficiently wide introduction to biology to see what the interesting questions are.
At the graduate level, I highlight a set of courses that would be useful
for getting to a stage where you would be able to develop new tools yourself.
Obviously, these are just guidelines: for instance, if you're primarily
interested in the mathematics and algorithms you could get by with less biology
(though you still need to know about the system you're modeling); conversely,
if you're primarily interested in analyzing a particular biological phenomenon
you can probably get by with less math.
Not all techniques are applicable to
all problems: for example, formal language theory turns out to be useful for
predicting RNA structure; combinatorics is useful for dealing with frequencies
of motifs; and differential equations are useful for modeling metabolic pathways.
Similarly, if you primarily plan to study plants, it would be a good idea to
take some botany.
Directly relevant classes not in 2002 catalog (some from past years)
Possibly useful classes: undergraduate
-
APPM 2710 (3). Java I Training and Mathematical Algorithms.
- APPM 2750 (4). Java 2 Training and Mathematical Algorithms.
- APPM 3170 (3). Discrete Applied Mathematics.
- APPM 3570 (3). Applied Probability.
- APPM 4520 (3). Introduction to Mathematical Statistics.
- APPM 4560 (3). Markov Processes, Queues, and Monte Carlo Simulations.
- APPM 4570 (3). Statistical Methods.
- APPM 4580 (3). Statistical Methods for Data Analysis.
- CHEM 3311 (4). Organic Chemistry 1.
- CHEM 3331 (4). Organic Chemistry 2.
- CHEM 4711 (3). General Biochemistry 1.
- CHEM 4731 (3). General Biochemistry 2.
- CHEN 2800 (3). Biophysics of Extreme Environments.
- CHEN 3010 (3). Applied Data Analysis.
- CHEN 3700 (3). Bioenergetics: Structure and Function.
- CHEN 4710 (3). Molecular Basis of Biological Behavior.
- CHEN 4800 (3). Bioprocess Engineering.
- CSCI 2270 (4). Computer Science 2: Data Structures.
- CSCI 3104 (4). Algorithms.
- CSCI 3256 (3). Numerical Computation.
- CSCI 3287 (3). Database and Information Systems.
- CSCI 3308 (3). Software Engineering Methods and Tools.
- CSCI 4448 (3). Object-Oriented Analysis and Design.
- EPOB 2060 (4). Cellular and Integrative Physiology.
- EPOB 2070 (4). Genetics: Molecules to Populations.
- EPOB 2080 (4). Evolutionary Biology.
- EPOB 3400 (4). Microbiology.
- EPOB 3520 (4). Plant Systematics.
- EPOB 4290 (3). Molecular Sytematics and Evolution.
- EPOB 4410 (4). Biometry.
- MATH 2400 (4). Analytic Geometry and Calculus 3.
- MATH 2510 (3). Introduction to Statistics.
- MATH 3000 (3). Introduction to Abstract Mathematics.
- MATH 3130 (3). Introduction to Linear Algebra.
- MATH 3170 (3). Combinatorics 1.
- MATH 4120 (3). Introduction to Operations Research.
- MATH 4510 (3). Introduction to Probability Theory.
- MATH 4520 (3). Introduction to Mathematical Statistics.
- MATH 4540 (3). Introduction to Time Series.
- MCDB 2150 (3). Principles of Genetics.
- MCDB 3120 (3). Cell Biology.
- MCDB 3500 (3). Molecular Biology.
- MCDB 4350 (3). Microbial Diversity and the Biosphere.
- MCDB 4410 (3). Human Molecular Genetics.
- MCDB 4426 (3). Cell Signaling and Developmental Regulation.
- MCDB 4471 (3). Mechanisms of Gene Regulation in Eukaryotes.
- MCDB 4970 (3). Seminar on Physical Methods in Biology.
- PSYC 4122 (3). Quantitative Genetics.
Possibly useful classes: graduate
- APPM/MATH 5520 (3). Introduction to Mathematical Statistics.
- APPM/MATH 5560 (3). Markov Processes, Queues, and Monte Carlo Simulations.
- APPM/MATH 5570 (3). Statistical Methods.
- APPM/MATH 5580 (3). Statistical Methods for Data Analysis.
- APPM/MATH 6520 (3). Mathematical Statistics.
- APPM/MATH 6550 (3). Introduction to Stochastic Processes.
- CHEM 5561 (3). Methods of Molecular Biophysics.
- CHEM 5711 (3). General Biochemistry 1.
- CHEM 5731 (3). General Biochemistry 2.
- CHEM 5771 (5). Advanced General Biochemistry 1.
- CHEM 5781 (5). Advanced General Biochemistry 2.
- CHEM 5801 (3). Advanced Signal Transduction and Cell Cycle Regulation.
- CHEM 5811 (3). Advanced Methods in Protein Sequencing and Analysis.
- CHEN 5710 (3). Molecular Basis of Biological Behavior.
- CHEN 5740 (3). Analytical Methods in Chemical Engineering.
- CHEN 5750 (3). Numerical Methods in Chemical Engineering.
- CHEN 5800 (3). Bioprocess Engineering.
- CSCI 7212 (3). Topics in Symbolic Artificial Intelligence.
- CSCI 5444 (3). Introduction to Theory of Computation.
- CSCI 5454 (3). Design and Analysis of Algorithms.
- CSCI 5714 (3). Formal Languages.
- CSCI 5646 (3). Numerical Linear Algebra.
- CSCI 6676 (3). Numerical Methods for Unconstrained Optimization.
- CSCI 6686 (3). Numerical Methods for Constrained Optimization.
- CSCI 5817 (3). Database Systems.
- CSCI 5917 (3). Database Practicum.
- CSCI 6448 (3). Object-Oriented Analysis and Design.
- CSCI 6838 (3). User Interface Design.
- EPOB 5290 (3). Molecular Systematics and Evolution.
- EPOB 5410 (4). Biometry.
- MATH 5150 (3). Linear Algebra.
- MCDB 5210 (3). Cell Structure and Function (Lecture and Discussion).
- MCDB 5220 (3). Molecular Genetics (Methods and Logic).
- MCDB 5230 (3). Gene Expression (Lecture and Discussion).
- MCDB 5339 (1). Cellular Adhesion, Cytoskeletal Organization, and Intercellular Signaling.
- MCDB 5350 (3). Microbial Diversity and the Biosphere.
- MCDB 5970 (3). Seminar on Physical Methods in Biology.
- PSYC 5102 (3). Behavioral Genetics.
- PSYC 5122 (3). Quantitative Genetics.
- PSYC 5232 (3). Molecular Genetics and Behavior.
- PSYC 5242 (3). Biometrical Methods in Behavioral Genetics.
Undergraduate Programs
At the undergraduate level, the best option is to get a degree in
either one of the lab sciences or in Math or CS. If going the lab science
route, Applied Math has a minor with Probability and Statistics emphasis
that would be extremely valuable. You should definitely take at least
one class in statistics, one lab class in biology, one lab class in
chemistry, and one class on algorithms.
Dual Masters in APPM/MCDB
Starting this year, Applied Math and MCDB are offering a 3-year, dual
MA/MS program. Applicants must meet the admission requirements for both
APPM and MCDB, and will take 21 credits in each department (including
a thesis submitted to MCDB).
Application details and requirements can be found
here.
For further information,
contact James Meiss.
PhD Programs
Although CU Boulder has no specific Bioinformatics degree program, it
is possible to conduct bioinformatics research in a range of different
departments (see FAQ). Right now, the best options
are probably to pursue a degree in MCDB or Biochemistry and to take math
and programming classes on the side, or to puruse a degree in Applied
Math (the Statistics track would provide an excellent background to most
of the techniques needed for developing new methods). However, several
other departments might provide a better match if you're interested in
specific research questions in those fields.
Page last updated 8/22/02.
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