About the course

Syllabus

Schedule

Aug 26 Introduction
Aug 28 Topics and Schedule
Sep 4 Basic clustering algorithms
Applications to yeast data
Sep 11 Biclustering gene expression data
Sep 18 Reconstructing transcriptional regulatory modules
Sep 25 Transcriptional Regulatory network structure
Oct 2 Developmental regulatory networks
Oct 9 Generating PPI data
Oct 16 Reliability and structure of PPI data
Oct 23 Functional annotation of proteins
Oct 30 Structure of metabolic networks
Nov 6 Reconstruction of metabolic networks
Nov 13 Modelling metabolic networks
Nov 20 Designer networks
Dec 4 Modelling metabolic networks
Tuesday, Dec 9 Wrap-up

Useful links

CS 6104: Advanced Topics Course on Computational Systems Biology

T. M. Murali

Fall 2003, Thursdays, 5-7:45, McBryde 307

One page description of the course (PostScript, PDF)

Papers to be covered

Introduction to Computational Systems Biology

• From molecular to modular cell biology, L H Hartwell, J J Hopfield, S Leibler & A W Murray, Nature 402, C47 - C52 (1999)
• A New Approach To Decoding Life: Systems Biology Trey Ideker, Timothy Galitski, Leroy Hood Annual Review of Genomics and Human Genetics Sep 2001, Vol. 2: 343-372. Local PDF copy.
• Computational systems biology, H. Kitano, Nature 420, 206 - 210 (2002).
• Systems Biology: A Brief Overview, H. Kitano, Science, 295, 1662-1664, 2002.
• Looking beyond the details: a rise in system-oriented approaches in genetics and molecular biology, H Kitano, Curr Genet. 2002 Apr;41(1):1-10, PMID: 12073094
• Overview of the Alliance for Cellular Signaling, Nature, 420, 703 - 706 (12 December 2002).
• Chemical & Engineering News systems biology article

Gene Expression Analysis

• Overviews/Surveys/Intros
  • Genomics, gene expression and DNA arrays, Lockhart, D.J. and Winzeler, E.A. 2000, Nature 405: 827-836.
  • Data analysis and integration: of steps and arrows. Michael Bittner, Paul Meltzer & Jeffrey Trent. Nature Genetics, volume 22 no. 3, pp 213 - 215. A perspective on a paper by Tavazoie et al, containing a commentary on clustering gene expression data. Local PDF copy.
  • Vector algebra in the analysis of genome-wide expression data Kuruvilla FG, Park PJ, Schreiber SL. Genome Biol. 2002;3(3):RESEARCH0011. Epub 2002 Feb 13. For life science students without a strong mathematical background, this paper is a good introduction to the natural way of representing gene expression data as vectors.
• Basic clustering algorithms: these papers present applications of hierarchical and k-means clustering and self-organising maps to DNA microarray data sets.
  • Cluster analysis and display of genome-wide expression patterns, Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D. (1998) Proc. Natl. Acad. Sci. USA 95, 14863-14868. This paper introduced hierarchical clustering to gene expression analysis.
  • The Transcriptional Program in the Response of Human Fibroblasts to Serum, Iyer et al., Science, Volume 283, Number 5398, Issue of 1 Jan 1999, pp. 83-87. Another early paper on the use of clustering algorithms on gene expression data.
  • Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Tamayo P. et al. Proc Natl Acad Sci U S A. 1999 Mar 16;96(6):2907-12. Uses self-organizing maps (SOMs) to cluster DNA microarray data.
• Applications to Yeast gene expression data.
  • Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles, Roberts, C.J. et al. Science 287: 873-880, 2000.
  • Functional discovery via a compendium of expression profiles. Hughes TR et al. Cell. 2000 Jul 7;102(1):109-26. (Local PDF copy)
• Biclustering algorithms
  • Biclustering of Expression Data. Cheng and Church, ISMB 2000: 93-103.
  • Discovering Statistically Significant Biclusters in Gene Expression Data, Sharan, Tanay, and Shamir, Bioinformatics, Vol. 18,, 2002, Pages S136-S144, Proceedings of ISMB 2002.
  • Plaid Models for Gene Expression Data, Lazzeroni and Owen, Statistica Sinica Vol. 12, No. 1, pp. 61-86. January, 2002.
    Art Owen's web page on plaid models.
  • Decomposing Gene Expression into Cellular Processes, Segal, Battle, and Koller, Pacific Symposium on Biocomputing, 8, 89-100, 2003.
  • Extracting Conserved Gene Expression Motifs from Gene Expression Data, T. M. Murali and Simon Kasif, the Pacific Symposium on Biocomputing, 8, 77-88, 2003.
    T. M. Murali's web page on xMotifs.
  • Analysis of Gene Expression Microarrays for Phenotype Classification, Andrea Califano, Gustavo Stolovitzky, Yuhai Tu, ISMB 2000.
    This paper discusses applying a pattern-discovery algorithm called SPLASH initially developed for finding patterns in strings to gene expression data. You will need to read the SPLASH paper to understand this paper.
  • Coupled two-way clustering analysis of gene microarray data, Getz, Levine, and Domany, PNAS, October 24, 2000, vol. 97, 12079-12084.

Transcriptional Regulatory Networks

• Reconstructing transcriptional regulatory modules and networks
  • Systematic determination of genetic network architecture. Saeed Tavazoie et al. Nature Genetic volume 22 no. 3 pp 281 - 285. Local PDF copy. Read the commentary on this paper.
  • Identifying regulatory networks by combinatorial analysis of promoter elements. Pilpel, Sudarsanam, and Church. Nat Genet. 2001 Oct;29(2):153-9. Local PDF copy.
  • Revealing Modular Organization in the Yeast Transcription Network Jan Ihmels, Gilgi Friedlander, Sven Bergmann, Ofer Sarig, Yaniv Ziv and Naama Barkai, Nat Genet. 2002 Aug;31(4):370-7. Local PDF copy.
  • Module Networks: identifying regulatory modules and their condition-specific regulators from gene expression data, Segal et al., Nat Genet. 2003 Jun;34(2):166-76. Local PDF copy.
  • Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling, Gardner et al., Science 301: 102-105, 2003.
• Structure of regulatory networks
  • Deciphering gene expression regulatory networks. Wyrick JJ, Young RA. Curr Opin Genet Dev. 2002 Apr;12(2):130-6. Local PDF copy. This paper is a survey.
  • Motifs, modules and games in bacteria. Wolf DM, Arkin AP. Curr Opin Microbiol. 2003 Apr;6(2):125-34. Local PDF copy. This paper is a survey.
  • Network motifs in the transcriptional regulation network of Escherichia coli. Shen-Orr SS, Milo R, Mangan S, Alon U. Nat Genet. 2002 May;31(1):64-8.
  • Network Motifs: Simple Building Blocks of Complex Networks R. Milo et al. Science Volume 298, Number 5594, Issue of 25 Oct 2002, pp. 824-827. Local PDF copy.
  • Transcriptional regulatory networks in Saccharomyces cerevisiae. Lee et al. Science. 2002 Oct 25;298(5594):799-804. Local PDF copy.
  • Life's Complexity Pyramid Zoltán N. Oltvai and Albert-László Barabási. Science 2002 298: 763-764. A prespective on the previous two papers. Local PDF copy.
  • Convergent evolution of gene circuits. Gavin C Conant & Andreas Wagner. Nature Genetic, volume 34 no. 3 pp 264-266. Local PDF copy
  • On schemes of combinatorial transcription logic. Buchler, N. E., Gerland, U., Hwa, T. (2003). Proc. Natl. Acad. Sci. U. S. A. 100: 5136-5141
• Developmental regulatory networks
  • Genomic Cis-Regulatory Logic: Experimental and Computational Analysis of a Sea Urchin Gene, Chiou-Hwa Yuh, Hamid Bolouri, Eric H. Davidson, Science 279, 1896 (1998). Local PDF copy.
  • A Genomic Regulatory Network for Development, Eric H. Davidson et al., Volume 295, Number 5560, Issue of 1 Mar 2002, pp. 1669-1678. Local PDF copy.
  • New computational approaches for analysis of cis-regulatory networks. Brown CT et al. Dev Biol. 2002 Jun 1;246(1):86-102. Local PDF copy.
  • Modeling DNA sequence-based cis-regulatory gene networks. Bolouri H, Davidson EH. Dev Biol. 2002 Jun 1;246(1):2-13. Local PDF copy. This paper is a survey.
  • Transcriptional regulatory cascades in development: Initial rates, not steady state, determine network kinetics. H. Bolouri and E. H. Davidson. PNAS, August 5, 2003; 100(16): 9371 - 9376. Local PDF copy.
  • Regulatory gene networks and the properties of the developmental process. Davidson, E. H., McClay, D. R., Hood, L. (2003). Proc. Natl. Acad. Sci. U. S. A. 100: 1475-1480. Local PDF copy.

Protein-Protein Interaction (PPI) Networks

• Surveys
  • Computational methods of analysis of protein-protein interactions. Lukasz Salwinski and David Eisenberg. Current Opinion in Structural Biology Volume 13, Issue 3 , June 2003, Pages 377-382. Local PDF copy.
• Generating PPI data
  • Biological experiments
    • A novel genetic system to detect protein-protein interactions Fields, S. and Song, O. 1989. . Nature 340: 245-246. This paper is not available on the web. Please see the Y2H section of the Developmental Biology website.
    • A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae Uetz, P. et al., Nature 403: 623-627, 2000.
    • A comprehensive two-hybrid analysis to explore the yeast protein interactome, Takashi Ito et al., PNAS, April 10, 2001, vol. 98, 4569-4574.
    • Deciphering protein complexes and protein interaction networks by tandem affinity purification and mass spectrometry: analytical perspective, A. Shevchenko, D. Schaft, A. Roguev, W. W. M. P. Pijnappel, A. F. Stewart, and A. Shevchenko, Mol. Cell. Proteomics, March 1, 2002; 1(3): 204 - 212.
    • Functional organization of the yeast proteome by systematic analysis of protein complexes, Gavin AC et al., Nature, 415 (6868): 141-147 JAN 10 2002
    • Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry Yuen Ho et al. Nature 415, 180 - 183 (10 January 2002).
  • Computational techniques and analyses
    • PreBIND and Textomy - mining the biomedical literature for protein-protein interactions using a support vector machine. Donaldson I, Martin J, De Bruijn B, Wolting C, Lay V, Tuekam B, Zhang S, Baskin B, Bader GD, Michalickova K, Pawson T, Hogue CW. Related Articles, Links BMC Bioinformatics. 2003 Mar 27;4(1):11.
    • Analysis of genomic and proteomic data using advanced literature mining. Hu Y, Hines LM, Weng H, Zuo D, Rivera M, Richardson A, LaBaer J. J Proteome Res. 2003 Jul-Aug;2(4):405-12. Local PDF copy.
    • A network of protein-protein interactions in yeast, Benno Schwikowski, Peter Uetz, and Stanley Fields, Nature Genetics, December 2000 Volume 18 Number 12 pp 1257 - 1261
  • Functional linkage graphs
    • Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. Matteo Pellegrini, Edward M. Marcotte, Michael J. Thompson, David Eisenberg, and Todd O. Yeates, PNAS, Vol. 96, Issue 8, 4285-4288, April 13, 1999. Local PDF copy.
    • Detecting Protein Function and Protein-Protein Interactions from Genome Sequences. Edward M. Marcotte, Matteo Pellegrini, Ho-Leung Ng, Danny W. Rice, Todd O. Yeates, David Eisenberg. Local PDF copy.
    • Predictome: a database of putative functional links between proteins, J. C. Mellor, I. Yanai, K. H. Clodfelter, J. Mintseris, and C. DeLisi, Nucleic Acids Res., January 1, 2002; 30(1): 306 - 309.
• Reliability and Structure of PPI data
  • Reliability of PPI data
    • Analyzing yeast protein-protein interaction data obtained from different sources. Bader GD, Hogue CW. Nat Biotechnol. 2002 Oct;20(10):991-7.
    • Comparative assessment of large-scale data sets of protein-protein interactions. von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P. Nature. 2002 May 23;417(6887):399-403.
    • Protein Interactions: Two Methods for Assessment of the Reliability of High Throughput Observations, C. M. Deane, L. Salwinski, I. Xenarios, and D. Eisenberg, Mol. Cell. Proteomics, May 1, 2002; 1(5): 349 - 356.
    • Assessing experimentally derived interactions in a small world, D. S. Goldberg and F. P. Roth, PNAS, April 15, 2003; 100(8): 4372 - 4376.
  • Structure of PPI networks
    • On the number of protein-protein interactions in the yeast proteome, A. Grigoriev, Nucleic Acids Res., July 15, 2003; 31(14): 4157 - 4161.
    • Lethality and centrality in protein networks. Jeong H, Mason SP, Barabasi AL, Oltvai ZN. Nature. 2001 May 3;411(6833):41-2
• Functional annotation of proteins using PPI networks and other data
  • A combined algorithm for genome-wide prediction of protein function Marcotte, E.M., Pellegrini, M., Thompson, M.J., Yeates, T.O., and Eisenberg, D. 1999. Nature 402: 83-86. Local PDF copy.
  • The identification of functional modules from the genomic association of genes, Berend Snel, Peer Bork, and Martijn A. Huynen, PNAS, April 30, 2002, vol. 99, 5890-5895.
  • Prediction of Protein Function Using Protein-Protein Interaction Data. Minghua Deng, Kui Zhang, Shipra Mehta, Ting Chen, Fengzhu Su IEEE Computer Society Bioinformatics Conference (CSB) 2002. 197-206. Web site. Local PDF copy.
  • An integrated probabilistic model for functional prediction of proteins. Minghua Deng, Ting Chen, Fengzhu Sun RECOMB 2003: 95-103. Local PDF copy.
  • Predicting protein function from protein/protein interaction data: a probabilistic approach. Letovsky S and Kasif S. Bioinformatics. 2003 Jul;19 Suppl 1:I197-I204. Local PDF copy.
  • Global protein function prediction from protein-protein interaction networks. Vazquez A, Flammini A, Maritan A, Vespignani A. Nat Biotechnol. 2003 Jun;21(6):697-700. Epub 2003 May 12. Local PDF copy.
  • A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Troyanskaya OG, Dolinski K, Owen AB, Altman RB, Botstein D. Proc Natl Acad Sci U S A. 2003 Jul 8;100(14):8348-53. Epub 2003 Jun 25. Local PDF copy.
• Databases
  • Predictome: a database of putative functional links between proteins, J. C. Mellor, I. Yanai, K. H. Clodfelter, J. Mintseris, and C. DeLisi, Nucleic Acids Res., January 1, 2002; 30(1): 306 - 309.
  • DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. I. Xenarios, L. Salwinski, X. J. Duan, P. Higney, S.-M. Kim, and D. Eisenberg, Nucleic Acids Res., January 1, 2002; 30(1): 303 - 305.
  • Describing Biological Protein Interactions in Terms of Protein States and State Transitions : THE LiveDIP DATABASE, X. J. Duan, I. Xenarios, and D. Eisenberg, Mol. Cell. Proteomics, February 1, 2002; 1(2): 104 - 116.
  • InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes, S.-K. Ng, Z. Zhang, S.-H. Tan, and K. Lin, Nucleic Acids Res., January 1, 2003; 31(1): 251 - 254
  • The Mammalian Protein-Protein Interaction Database and Its Viewing System That Is Linked to the Main FANTOM2 Viewer, H. Suzuki et al., Genome Res., June 1, 2003; 13(6): 1534 - 1541.

Metabolic Networks

• Structural properties of metabolic networks
  • Control Motifs for Intracellular Regulatory Networks. Christopher V. Rao and Adam P. Arkin. Annu. Rev. Biomed. Eng. 2001. 3:391-419. Local PDF copy.
  • The large-scale organization of metabolic networks Jeong et al., Nature. 2000 Oct 5;407(6804):651-4
  • Error and attack tolerance of complex networks, Jeong et al., Nature. 2000 Jul 27;406(6794):378-82
  • Hierarchical Organization of Modularity in Metabolic Networks, Ravasz et al., Science. 2002 Aug 30;297(5586):1551-5
  • Surfing the p53 network. Bert Vogelstein, David Lane and Arnold J. Levine. Nature 408, 307 - 310 (2000). This paper describes the deleterious effects of one of the key "hubs" in our cell, the p53 protein.
  • A Brief History of Generative Models for Power Law and Lognormal Distributions. M. Mitzenmacher. To appear in Internet Mathematics. Read this paper if you are interested in the mathematical background and history of the power law.
• Reconstruction of metabolic networks
  • Regulation of Gene Expression in Flux Balance Models of Metabolism. J. theor. Biol. (2001) 213, 73-88. Markus W. Covert, Christophe H. Schilling and Bernhard Palsson. Local PDF copy.
  • Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network, Jochen Förster et al., Genome Research Vol. 13, Issue 2, 244-253, February 2003.
  • Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L. Science. 2001 May 4;292(5518):929-34. Local PDF copy.
• Modelling metabolic networks
  • Computational studies of gene regulatory networks: in numero molecular biology. Hasty, J., McMillen, D., Isaacs, F. & Collins, J. J. Nature Rev. Genet. 2, 268-279 (2001). Local PDF copy.
  • Regulation of Gene Expression in Flux Balance Models of Metabolism. J. theor. Biol. (2001) 213, 73-88. Markus W. Covert, Christophe H. Schilling and Bernhard Palsson. Local PDF copy.
  • Analysis of optimality in natural and perturbed metabolic networks. Daniel Segrè, Dennis Vitkup, and George M. Church. PNAS, November 12, 2002, vol. 99, 15112-15117.
  • GENETIC "CODE": Representations and Dynamical Models of Genetic Components and Networks Alex Gilman and Adam P. Arkin. Annual Review of Genomics and Human Genetics Sep 2002, Vol. 3, pp. 341-369. Local PDF copy.
  • A model of excitation and adaptation in bacterial chemotaxis Peter A. Spiro, John S. Parkinson, and Hans G. Othmer Proc. Natl. Acad. Sci. USA Vol. 94, pp. 7263-7268, July 1997
• Other topics
  • The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models, Hucka M et al. Bioinformatics. 2003 Mar 1;19(4):524-31. PMID: 12611808

Designer Networks

Engineered Gene Circuits Hasty J, McMillen D, Collins JJ. Nature. 2002 Nov 14;420(6912):224-30. Local PDF copy.
• Construction of a genetic toggle switch in Escherichia coli. Gardner, T. S., Cantor, C. R. & Collins, J. J. Nature 403, 339-342 (2000). Local PDF copy.
• A synthetic oscillatory network of transcriptional regulators. Elowitz, M. B. & Leibler, S. Nature 403, 335-338 (2000). Local PDF copy.
• Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Tegner J, Yeung MK, Hasty J, Collins JJ. Proc Natl Acad Sci U S A. 2003 May 13;100(10):5944-9. Local PDF copy.
• Engineering stability in gene networks by autoregulation. Becskei, A. & Serrano, L. Nature 405, 590-593 (2000). Local PDF copy.
• Combinatorial synthesis of genetic networks. Guet, C., Elowitz, M., Hsing, W. & Leibler, S. Science 296, 1466-1470 (2002). Local PDF copy.

Data Sets For Course Projects

• DNA microarray data sets
  • Human cancer data sets: a good source is the data sets section of the Cancer Genomics research group at MIT's Whitehead Institute. The following links point to local copies of some of these data sets:
    • ALL-AML: Microarray data, class labels/phenotypes
    • B-cell lymphoma: Microarray data
    • Central Nervous System: Microarray data, class labels/phenotypes
    • Global Cancer Map: Microarray data, class labels/phenotypes
  • Yeast data sets: an excellent source is the Expression Junction section of the Saccharomyces Genome Database. I could not find any direct link to a site containing all the data sets, so you have to click on each individual web supplement to download the data. I have linked to some local copies below.
    • Stress data of Gasch et al.: Microarray data, class labels/phenotypes
    • alpha-factor Rosetta data set: Microarray data, class labels/phenotypes
    • Gene knock-out Rosetta data set: Microarray data, class labels/phenotypes
• Functional annotations: Gene Ontology (GO), KEGG, MIPS
  • Gene Ontology annotations for Saccharomyces cerevisiae
  • Gene Ontology annotations for Homo sapiens
  • KEGG pathway lists
  • MIPS functional categories
• Protein-protein interactions
  • GRID data set
  • GRID data set restricted to interactions reported in at least two publications
• Transcriptional regulatory networks: the files below are not regulatory networks per se but are binary (0-1) matrices that state whether than binding site for a particular transcription factor appears in the promotor region of a gene.