Topics in Computational Biology
Course Approval Information:
As high-throughput methods for biological data generation become more prominent and the
amount and complexity of the data increase, computational methods have become essential to
biological research in this post-genome age. In turn, biological problems are motivating
innovations in computational sciences, such as computer science, information science,
mathematics, operations research and statistics. There is high demand for scientists who are
capable of bridging these disciplines. This course aims to create an environment that transcends
traditional departmental boundaries and facilitate communications between researchers from life
sciences and computational sciences. Through reading and discussion of literature, small
research and data analysis projects, students will be introduced to current problems (e.g.,
regulatory motif finding, microarray data analysis, biomedical literature mining, signal
transduction network modeling, cis-regulatory network discovery, etc.) in computational biology
and some of the methods for studying them. Students are required to give a presentation on
published research or on your present research. Each presentation will normally last 60 minutes
with additional time for questions and discussions. Each presenter must submit a written report
on your presentation. The report should contain 1) the background of the research, 2) the
motivation for the research, 3) the approach, 4) the results, and 5) criticisms and/or suggested
ways to improve the discussed methods. By mid-term, each student should propose a small
research project for the course. Teamwork is strongly encouraged. Grading will be based on the
project and on class participation.
Suggested paper list:
Liu et al. (2002) An Algorithm for Finding Protein-DNA Interaction Sites with Applications to
Chromatin Immunoprecipitation Microarray Experiments. Nat Biotech
Zhou et al. (2004) CisModule: De Novo discovery of cis-regulatory modules by hierarchical
mixture modeling. PNAS.
Frazer et al. (2003) Cross-Species Sequence Comparisons: A Review of Methods and Available
Resources. Genome Research. Vol 13, Issue 1, 1-12.
Ren et al. (2000) Genome-wide location and function of DNA-binding proteins. Science 290:
2306-2309.
Conlon et al. (2003) Motif regressor, PNAS 100: 3339.
Lee et al (2002) Transcriptional regulatory networks in S. cerevesiae. Science 298:799
Siepel A, Haussler D. (2004). Combining phylogenetic and hidden Markov models in
biosequence analysis. J. Comput Biol. 11(2-3): 413-28.
Wang and Stormo (2003) Combining phylogenetic data with co-regulated genes to identify
regulatory motifs. Bioinfo, 19:2369.
Boffelli et al. (2004) Comparative genomics at the vertebrate extremes. Nat Rev Genet. 2004
Jun;5(6):456-65.
Miller et al. (2004) Comparative genomics. Annu Rev Genomics Hum Genet. 5:15-56.
Tusher, V. G., Tibshirani, R., and Chu, G. (2001) Significance analysis of microarrays applied to
the ionizing radiation response. Proc. Natl. Acad. Sci. USA, 98: 5116-5121
Storey, J.D. and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl.
Acad. Sci. USA, 100:9440-9445.
Stuart et al (2003) A gene-coexpression network for global discovery of conserved genetic
modules. Science. 302(5643):249-255.
Friedman, N. (2004) Inferring cellular networks using probabilistic graphical models. Science.
vol 303, 799-805.
Pe'er D. (2005) Bayesian network analysis of signaling networks: a primer. Sci STKE. 281, pl4.
Raychaudhuri et al. (2002) Associating Genes with Gene Ontology Codes Using a Maximum
Entropy Analysis of Biomedical Literature. Genome Research. 12(1):203-214.
Koike et al. (2005) Automatic extraction of gene/protein biological functions from biomedical
text. Bioinformatics, 21(7): 1227 - 1236.
Westhof and Fritsch. (2000) RNA folding: beyond Watson-Crick pairs. Structure, 8, R55-R65.
Troyanskaya et al (2003) A Bayesian framework for combining heterogeneous data sources for
gene function prediction (in Saccharomyces cerevisiae). PNAS. 100(14):8348-8353.
Segal et al (2003) Module Networks: Identifying Regulatory Modules and their Condition
Specific Regulators from Gene Expression Data. Nature Genetics. 34(2): 166-76.
Ge et al. (2003) Integrating 'omic' information: a bridge between genomics and systems biology.
Trends in Genetics. 19(10):551-60.