Readings

LEC # TOPICS READINGS
Part 1: Using DNA Sequence to Explain Mechanism
1 Course Introduction  
2 Pairwise Alignment Durbin, R., S. R. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge, UK: Cambridge University Press, 1999. ISBN: 9780521629713.
3 Finding Regulatory Sequences in DNA: Motif Discovery Hughes, et al. Journal of Molecular Biology 296, no. 5 (2000): 1205-14.
4 Finding Regulatory Sequences in DNA: Motif Discovery (cont.)
Part 2: Observing the Mechanism of Transcriptional Regulation
5 Microarray Technology

Qun Pan, et al. “Revealing Global Regulatory Features of Mammalian Alternative Splicing Using a Quantitative Microarray Platform.” Molecular Cell 16 (December 22, 2004): 929–941.

Wen Zhang, et al. “The Functional Landscape of Mouse Gene Expression.” Journal of Biology (2004): 3:21.

Tong Ihn Lee, et al. “Transcriptional Regulatory Networks in Saccharomyces Cerevisiae.” Science (2002).

6 Expression Arrays, Normalization, and Error Models

Hwa Yang, Yee, et al., “Normalization for cDNA Microarray Data: A Robust Composite Method for Addressing Single and Multiple Slide Systematic Variation.” Nucleic Acid Research 30, no. 4 (2002).

Newton, et al. “On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data.” Journal of Computational Biology 8, no. 1 (2001).

7 Expression Profiles, Clustering, and Latent Processes

Eisen, et al. “Cluster Analysis and Display of Genome-wide Expression Patterns.” PNAS 95, no. 25 (1998).

Liao, et al. “Network Component Analysis: Reconstruction of Regulatory Signals in Biological Systems.” PNAS 100, no. 26 (2003).

Dueck and Frey. “Probabilistic Sparse Matrix Factorization.” Technical report, University of Toronto, 2004. ( PDF )

8 Computational Functional Genomics Introduction to SVD
9 Stem Cells and Transcriptional Regulation Dor, Y., J. Brown, O. Martinez, and D. A. Melton. “Adult Pancreatic Beta-cells are Formed by Self-duplication Rather Than Stem-cell Differentiation.” Nature 429, no. 6987 (2004): 41-6.
10

Part One: An Example of Clustering Expression Data

Part Two: Computational Functional Genomics (cont.)

Alizadeh, et. al. “Distinct Types of Diffuse Large B-cell Lymphoma Identified by Gene Expression Profiling.” Nature 403 (February 2000).

Ramalho-Santos, et. al. “Stemness: Transcriptional Profiling of Embryonic and Adult Stem Cells.” Science 298 (October 2002).

Ivanova, et. al. “A Stem Cell Molecular Signature.” Science 298 (October 2002).

  • Fortunel, et al. “Comment on” ‘Stemness’: Transcriptional Profiling of Embryonic and Adult Stem Cells," and “A Stem Cell Molecular Signature”." Science 302, no. 5644 (2003): 393.
  • Evsikov, et al. “Comment on” ‘Stemness’: Transcriptional Profiling of Embryonic and Adult Stem Cells," and “A Stem Cell Molecular Signature”." Science 302, no. 5644 (2003):393.
  • Ivanova, N. B., et al. “Response to Comments on” ‘Stemness’: Transcriptional Profiling of Embryonic and Adult Stem Cells," and “A Stem Cell Molecular Signature”." Science 302, no. 5644 (2003): 393.

Vogel. “‘Stemness’ Genes Still Elusive.” Science 302 (October 2003).

11 Project Group Meetings  
12 Project Group Initial Presentations  
13 Computational Discovery of Regulatory Networks

Heckerman, David. “A Tutorial on Learning with Bayesian Networks.” Microsoft Technical Report MSR-TR-95-06 (1996).

Hartemink, Alex. “Principled Computational Methods for the Validation and Discovery of Genetic Regulatory Networks.” MIT PhD Thesis (2001).

14 RNA Silencing Cooper, G. F., and E. Herskovits. “ A Bayesian Method for the Induction of Probabilistic Networks from Data .” KSL-91-02 (Knowledge Systems Laboratory, Stanford University), November 1993.
Part 3: Building Predictive Network Models of Transcriptional Regulation
15 Computational Functional Genomics (cont.)

Li, et al., “A Map of the Interactome Network of the Metazoan C. elegans.” Science 303 (January 2004).

Giot, et al. “A Protein Interaction Map of Drosophila melanogaster.” Science 302 (December 2003).

Gavin, et al. “Functional Organization of the Yeast Proteome by Systematic Analysis of Protein Complexes.” Nature 415 (January 2002).

Ho, et al. “Systematic Identification of Protein Complexes in Sacchormyces Cerevisiae by Mass Spectrometry.” Nature 415 (January 2002).

Tong, Lesage, et al. “Global Mapping of the Yeast Genetic Interaction Network.” Science 303 (February 2004).

Phizicky, Bastiaens, Zhu, Snyder, and Fields. “Protein Analysis on a Proteomic Scale.” Nature 422 (March 2003).

von Mering, et al. “Comparative Assessment of Large-scale Data Sets of Protein-protein Interactions.” Nature 417 (May 2002).

16 Human Regulatory Networks

Heckerman. “A Tutorial on Learning with Bayesian Networks.” Microsoft Technical Report MSR-TR-95-06, 1996.

Yeang, et al. “Physical Network Models.” Journal of Computational Biology 11, nos. 2-3 (2004): 243-263.

17 Protein Networks

Gavin, A. C., et al. “Functional Organization of the Yeast Proteome by Systematic Analysis of Protein Complexes.” Nature 415, no. 6868 (2002): 141-7.

Tong, A. H., et al. “Global Mapping of the Yeast Genetic Interaction Network.” Science 303, no. 5659 (2004): 808-13.

18 Causal Models  
19 Causal Bayesian Networks, Active Learning  
20 From Biological Data to Biological Insight  
21 Modeling Transcriptional Regulation

Arkin, Ross, and McAdams. “Stochastic Kinetic Analysis of Developmental Pathway Bifurcation in Phage Lambda-Infected Escherichia coli Cells.” Genetics 149 (August 1998): 1633–1648.

Gilman, Arkin. “Genetic ‘Code’: Representations and Dynamic Models of Genetic Components and Networks.” Annu Rev Genomics Hum Genet 3 (2002): 341–69.

McAdams, Arkin. “It’s A Noisy Business!: Gene Regulation at the Nanomolar Scale.” Trends in Genetics 15 (1999): 65-69.

———. “Simulation of Genetic Circuits.” Annu Rev Biophys Biomol Struct 27 (1998): 199–224.

Gillespie. “Exact Stochastic Simulation of Coupled Chemical Reactions.” Journal of Physical Chemistry 81, no. 25 (1977): 2340-2361.

22 Dynamics  

Course Info

Learning Resource Types

notes Lecture Notes
group_work Projects with Examples