Syllabus for STAT
315
Fundamentals of
Computational Biology
Professors Jun Liu and Wing Hung Wong
Departments of Statistics and Biostatistics
Course Description: A substantial core of
computational biology (or bioinformatics) methods has been developed during the
past two decades to meet the need of biological scientists for data storage,
data retrieval, and data analysis. A main problem that motivated early research
in computational biology is protein sequence analysis. Recently, because of the
dramatic increase in many types of biological data due to the human genome
project and other high-throughput projects, the scope of bioinformatics
research has been extended to embrace diverse topics such as micro-array
analysis, protein classification, regulatory motif analysis, RNA analysis,
structural and functional predictions, gene prediction, etc. This one-year
course is intended to provide coverage of these developments of bioinformatics
in the past thirty years with an emphasis on topics of recent interest. It is
widely recognized that research in this field is interdisciplinary in nature
and requires knowledge in computational algorithms, statistics, and molecular
biology. Students in this class are expected to spend a substantial amount of
time reading research articles/monographs ranging from statistics to biology.
Course Meetings: Every Friday from 1:00PM to 3:15PM at Science Center 110 (just changed from SC 209); each meeting consists of
two hourly lectures and a short break in between.
Course requirements: presentation
of readings and researches related to designated articles (students can work in
team). In approximately chronological order, the following topics will be
covered in the fall semester:
- Lecture 1 (9/22/00). Overview of
data resources: review of biological databases, data formats, and methods
for retrieving relevant data.
- Lecture 2 (9/29/00). Dynamic
programming-based method for sequence analysis.
- Lecture 3 (10/6/00). Essential
stochastic processes and probability theory.
- Lecture 4 (10/13/00). Hidden Markov
models (HMM) for biological sequences.
- Lecture 5 (10/20/00). Basics of
statistical estimation and computation; missing data formulation;
iterative algorithms. Multiple
alignment via HMM (PFAM etc).
- Lecture 6 (10/27/00).
Block-motif model and Gibbs sampling algorithms for multiple sequence
alignment.
- Lecture 7
(11/3/00). Statistical models for discovering gene regulatory motifs.
- Lecture 8 (11/10/00). Analysis of cDNA sequences
- Lecture 9 (11/17/00). BLAST Theory.
- Lecture 10 (12/1/00). Inference
in phylogenetic trees.
- Lectures 11-14 (12/8; 12/15; 1/5;
1/12). Student presentations.
Main References/Textbooks:
- Durbin, R. Eddy, S.,
Krogh, A. and Mitchison, G. (1998). Biological Sequence Analysis
--- Probabilistic models of proteins and nucleic acids. Cambridge
University Press.
- Waterman, M. (1995). Introduction
to Computational Biology. Chapman & Hall.
Recommended Readings:
- Baxevanis, A.D.,
Ouellette, B.F.F. (1998). Bioinformatics: A Practical Guide to the
Analysis of Genes and Proteins. Wiley-Interscience.
- Lodish et al. (2000). Molecular
Cell biology (4th Edition). W.H. Freeman & Co.
Preview for the next semester: the course in the next
quarter will emphasize on functional genomics. A partial list of potential
topics is:
- Micro-array analysis and statistical clustering methods
- Protein structure predictions (simulation and threading)
- Proteomics
- Gene regulation network
- Pharmacogenetics
- Final projects
