The curriculum of the Designated Emphasis in Computational and
Genomic Biology consists of specified courses, which may be either
independent from or an integral part of the Associated Program’s
PhD requirements. One goal is to provide students with a broad education
in Computational and Genomic Biology.
Students are required to take one class from three of the following
five categories (1-5). Two of the three categories must be outside
of your Associated Program’s PhD requirements.
- Computer Science and Engineering
- Biostatistics, Mathematics and Statistics
- Biology
- Chemistry, Chemical Engineering and Physics
- Computational Biology
Prior coursework may be used to fulfill the requirements if the
coursework is found to be equivalent to those classes listed below.
The selection of courses will be maintained and regularly updated
by the Graduate Advising
Committee to follow the rapid pace of research in genomics and
the offering of new courses on campus. An initial list is given
below.
Course Offerings (See course descriptions below)
- 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.
- Biology
- 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
| Students may apply
to the Designated Emphasis in Computational and Genomic Biology
upon completion of the following requirements: For a full description
of these courses and all others offered by the University, consult
the UCB General Catalog.
This information can be accessed via the UCB
Online Catalog or a hard copy can be purchased through The
Bears Student Store by calling (510) 444-6251 or (800) 766-1546.
Please see the DE Proposal for
more information. |
Course Descriptions
Bioengineering 131/231: Introduction to Computational Biology
Topics include computational approaches and techniques to gene structure
and finding, sequence alignment using dynamic programming, protein
folding and structure prediction, protein drug interactions, genetic
and biochemical pathways and networks, and microarray analysis.
Various case studies in these areas are reviewed and web-based computational
biology tools will be used by students. Computational biology research
connections to biotechnology will be explored.
Bioengineering 142: Programming and Algorithm Design for Computational
Biology & Genomics Applications
This course will introduce students to structured software development
and select principles of computer science with applications in computational
biology and allied disciplines. The principle language used for
instruction will be Java with a course module on Perl. Examples
and tutorials will draw from problems in computational biology.
The course will require one significant programming project, preferably
biologically oriented.
Bioengineering 143/243: Computational Methods in Biology
Topics include thermodynamics, statistical mechanics, classical
mechanics, and quantum mechanics that connect most directly to modern
simulation methodology. Various case studies in the areas of classical
dynamical simulations, ab initio dynamics, and Monte Carlo techniques
will be covered. The areas of mathematical optimization and "non-algorithmic"
computation such as neural networks and Hidden Markov Models will
also be considered.
Bioengineering 144: Introduction to Protein Bioinformatics
This course will introduce students to the fundamentals of molecular
biology and to the bioinformatics tools and databases used for the
prediction of protein function and structure. It is designed to
impart both a theoretical understanding of popular computational
methods and practical hands-on experience with protein sequence
analysis methods applied to real data.
Mathematics 127: Mathematical and Computational Methods in Molecular
Biology
Introduction to mathematical and computational problems arising
in the context of molecular biology. Theory and applications of
combinatorics, probability, statistics, geometry, and topology to
problems ranging from sequence determination to structure analysis.
Mathematics 290: Hidden Markov Models in Comparative Genomics
Topics in foundations of mathematics, theory of numbers, numerical
calculations, analysis, geometry, topology, algebra, and their applications,
by means of lectures and informal conferences; work based largely
on original memoirs.
Molecular & Cell Biology 137: Computer Simulation in Biology
Modeling and computer simulation of dynamic biological processes
using special graphical interfaces requiring very little mathematical
or computer experience. First half is realistic models from current
literature to teach concepts and technique; second is workshop for
student-selected individual projects.
Molecular & Cell Biology C246/C146: Topics in Computational
Biology & Genomics
Instruction and discussion of topics in genomics and computational
biology. Working from evolutionary concepts, the course will cover
principles and application of molecular sequence comparison, genome
sequencing and functional annotation, and phylogenetic analysis.
Also listed as Plant Biology C246 and Molecular and Cell Biology
C246.
Plant & Microbial Biology / Molecular & Cell Biology C148: Microbial
Genomics and Genetics
Course emphasizes bacterial and archaeal genetics and comparative
genomics. Genetics and genomic methods used to dissect metabolic
and development processes in bacteria, archaea, and selected microbial
eukaryotes. Genetic mechanisms integrated with genomic information
to address integration and diversity of microbial processes. Introduction
to the use of computational tools for a comparative analysis of
microbial genomes and determining relationships among bacteria,
archaea, and microbial eukaryotes. Also listed as Molecular and
Cell Biology C148.
Public Health 240D: Biostatistical Methods: Applications of
Statistics to Genetics and Molecular Biology
This course surveys applications of probability and statistics to
genetics and molecular biology, from the early Mendelian experiments
to modern day genomic research. Biological questions to be considered
include, but are not limited to, modeling meiosis; genetic mapping;
nucleotide and protein sequence analysis; molecular evolution; computational
gene finding; protein structure prediction; DNA microarray experiments.
Related statistical topics include stochastic processes (Markov
processes, hidden Markov models, Markov chain Monte Carlo); experimental
design; likelihood analysis; multiple hypothesis testing; prediction;
model selection; resampling. In addition to discussing specific
statistical methods, the course will provide an introduction to
basic notions in genetics and molecular biology and to the main
software packages for the analysis of biological data, with emphasis
on the R language and environment. The course will also involve
the critical reading of articles related to statistical analyses
in the biological and medical sciences.
Statistics/Bioengineering 141: Statistics for Bioinformatics
Study of bioinformatics problems such as DNA pattern finding, gene
expression data analysis, molecular evolution models, and biomolecular
sequence database searching. Introduction of the necessary probability
and statistics: events, (conditional) probability, random variables,
estimation, testing, and linear regression. Also listed as Statistics
C141.
Statistics 246: Statistics in Genetics
Modelling meiosis, linkage mapping, pedigree analysis, genetic epidemiology.
Clone libraries, physical mapping of chromosomes. Radiation hybrid
mapping. DNA and protein sequence analysis, molecular evolution,
sequence alignment, database searching. Analysis of microarray expression
data.
