About the course

Schedule

List of papers

Computational Systems Biology
Advanced Technologies in Bioscience 2008-2009
Chalmers Graduate School in Bioscience

Instructor: T. M. Murali

Dates: August 18-22, 2008
Location: MVF 31, Math building, Chalmers

What is Computational Systems Biology?

Cells, tissues, organs and organisms are systems of components whose interactions have been defined, refined, and optimised over hundreds of millions of years of evolution. Computational systems biology is a field that aims at a system-level understanding of biological systems by analysing biological data using computational techniques. Systems biology aims to answer the following key questions by integrating experimental and computational approaches:

1. What are the basic structures and properties of the biological networks in a living cell?
2. How does a biological system behave over time under various conditions?
3. How does a biological system maintain its robustness and stability?
4. How can we modify or construct biological systems to achieve desired properties?

Answers to these questions require breakthroughs in the fields of biology, chemistry, computer science, engineering, mathematics and other fields. The explosive progress of genome sequencing projects and the massive amounts of data that high-throughput experiments in DNA microarrays, proteomics, and metabolomics yield drive advances in this field. Sophisticated computational ideas process these data sources in an effort to systematically analyse and unravel the complex biological phenomena that take place in a cell.

In this course, we will emphasise a data-driven approach to computational systems biology, focussing on large-scale properties of molecular interaction networks. We will learn techniques from machine learning, data mining, and graph theory and apply them to solve specific biological questions.

Schedule

Each day of the course will consist of two hours of lectures (10am-12pm) and two hours of exercises (1pm-3pm). Exceptions are the first day, when we will have three hours of lectures; the fourth day, when we have guest lectures by Jasmin Fischer and Andrew Phillips from 1pm-3pm; and the fifth day, when the two-hour lecture on host-pathogen protein interaction networks is also a tutorial at ICSB 2008 from 4:15pm-6:15pm.

Date	Topic and papers	Lecture
Mon, Aug 18, 2008	Introduction to Computational Systems Biology	Lecture
Mon, Aug 18, 2008	Clustering gene expression data	Lecture
Mon, Aug 18, 2008	Application to find cancer gene modules	Lecture and Exercises
Tue, Aug 19, 2008	Biclustering gene expression data	Lecture
Tue, Aug 19, 2008	Application of biclustering to data integration in S. cerevisiae	Lecture
Wed, Aug 20, 2008	Response networks	Lecture
Wed, Aug 20, 2008	Network legos, building blocks of cellular wiring diagrams	Lecture
Thu, Aug 21, 2008	Gene function prediction	Lecture
Fri, Aug 22, 2008	Host-pathogen protein interaction networks Tutorial at ICSB 2008	Lecture

Papers to be covered

Reading assignments: Papers highlighted with a blue background and with a specific date to their top left are the ones we will discuss in class. Please try to read them before the class. The lists below include other relevant papers. On almost each one of these topics, the literature is too vast to be completely included in these lists.

Introduction to Computational Systems Biology

These articles provide very good introductions to the subject of (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.
Aug 18, 2008
• Building with a scaffold: emerging strategies for high- to low-level cellular modeling. Ideker T, Lauffenburger D. Trends Biotechnol. 2003 Jun;21(6):255-62. PMID: 12788545
• 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., Proc. Natl. Acad. Sci. USA 1998, volume 95, pp 14863--14868. This paper introduced hierarchical clustering to gene expression analysis.
  Aug 18, 2008
  • How does gene expression clustering work?, Patrik D'haeseleer, Nature Biotechnology, December 2005, Volume 23 No 12, pp 1499--1501, doi:10.1038/nbt1205-1499
  • Cluster Analysis for Gene Expression Data: A Survey, Daxin Jiang, Chun Tang, and Aidong Zhang, IEEE Transactions of Knowledge and Data Engineering, November 2004, Vol. 16, No. 11, pp. 1370--1386. Search on Google Scholar. Read Sections 1 and 2.1.1--2.1.4. The paper as a whole provides good coverage of different clustering techniques.
  • 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 of Basic Clustering Algorithms.
  Aug 18, 2008
  • A module map showing conditional activity of expression modules in cancer, Eran Segal, Nir Friedman, Daphne Koller and Aviv Regev, Nature Genetics 36, 1090--1098, 2004, doi:10.1038/ng1434.
  • 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.
  Aug 19, 2008
  • Discovering Statistically Significant Biclusters in Gene Expression Data, Sharan, Tanay, and Shamir, Bioinformatics, Vol. 18, 2002, Pages S136-S144, Proceedings of ISMB 2002. You can skip the lemmas and proofs in this paper, if you like.
  • 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.
• Applications of biclustering algorithms
  Aug 19, 2008
  • Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Tanay A, Sharan R, Kupiec M, Shamir R. Proc Natl Acad Sci U S A. 2004 Mar 2;101(9):2981-6. Epub 2004 Feb 18

Functional Annotation

• Constructing Functional Linkage Networks
  • 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.
• Functional annotation algorithms
  Aug 21 2008
  • Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genet. (2000) 25: 25-29.
    The Gene Ontology website
  Aug 21 2008
  • Whole-genome annotation by using evidence integration in functional-linkage networks. Karaoz U, Murali TM, Letovsky S, Zheng Y, Ding C, Cantor CR, Kasif S. Proc Natl Acad Sci U S A. 2004 Mar 2;101(9):2888-93. Epub 2004 Feb 23. PMID: 14981259
  • A critical assessment of Mus musculus gene function prediction using integrated genomic evidence , to appear in Genome Biology, 2008. All figures, short descriptions of algorithms, long descriptions of algorithms, supplementary web page.
  • Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M. Bioinformatics. 2005 Jun 1;21 Suppl 1:i302-i310.
  • Computational prediction of cancer-gene function, Pingzhao Hu, Gary Bader, Dennis A. Wigle and Andrew Emili, Nat Rev Cancer. 2008 Jan;7(1):23-34. Epub 2006 Dec 14.
  • 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.

Comparative Systems Biology

• Cross-species comparison of networks
  • Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Kelley BP, Sharan R, Karp RM, Sittler T, Root DE, Stockwell BR, Ideker T. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11394-9. Epub 2003 Sep 22.
  • Conserved patterns of protein interaction in multiple species. Sharan R, Suthram S, Kelley RM, Kuhn T, McCuine S, Uetz P, Sittler T, Karp RM, Ideker T. Proc Natl Acad Sci U S A. 2005 Feb 8;102(6):1974-9. Epub 2005 Feb 1.
  • Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. Sharan R, Ideker T, Kelley B, Shamir R, Karp RM. J Comput Biol. 2005 Jul-Aug;12(6):835-46.
  • Systematic identification of functional orthologs based on protein network comparison. Genome Res. 2006 Jan 27 Bandyopadhyay S, Sharan R, Ideker T.
• Cross-species and cross-condition comparison of gene expression data
  • Iterative signature algorithm for the analysis of large-scale gene expression data. Bergmann S, Ihmels J, Barkai N. Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.
  • Defining transcription modules using large-scale gene expression data. Ihmels J, Bergmann S, Barkai N. Bioinformatics. 2004 Sep 1;20(13):1993-2003. Epub 2004 Mar 25.
  • Similarities and differences in genome-wide expression data of six organisms. Bergmann, S. and Ihmels, J. and Barkai, N. 2004. PLoS Biol, 2(1):E9.
  • Coexpression analysis of human genes across many microarray data sets. Lee, H.K. and Hsu, A.K. and Sajdak, J. and Qin, J. and Pavlidis, P. 2004. Genome Res, 14(6):1085-94.
  • Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Rhodes, D.R. and Yu, J. and Shanker, K. and Deshpande, N. and Varambally, R. and Ghosh, D. and Barrette, T. and Pandey, A. and Chinnaiyan, A.M. 2004. Proc Natl Acad Sci U S A, 101(25):9309-14.

Structure of Molecular Interaction Networks

• Structure and Evolution
  • Network biology: understanding the cell's functional organization AL Barabasi, ZN Oltvai, Nature Reviews Genetics, 2004.
  • Biological context networks: a mosaic view of the interactome, Rachlin et al., Molecular Systems Biology 2: 66, 2008, doi:10.1038/msb4100103
  • Subnets of scale-free networks are not scale-free: Sampling properties of networks Michael P. H. Stumpf, Carsten Wiuf, and Robert M. May PNAS 2005 102: 4221-4224; published online before print as 10.1073/pnas.0501179102
  • Effect of sampling on topology predictions of protein-protein interaction networks. Han JD, Dupuy D, Bertin N, Cusick ME, Vidal M. Nat Biotechnol. 2005 Jul;23(7):839-44.
  • Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network. Zhang LV, King OD, Wong SL, Goldberg DS, Tong AH, Lesage G, Andrews B, Bussey H, Boone C, Roth FP. J Biol. 2005;4(2):6. Epub 2005 Jun 1. PMID: 15982408.
• Comparative analysis
  • Modeling cellular machinery through biological network comparison, Sharan R and Ideker T., Nat Biotechnol. 2006 Apr;24(4):427-33. PMID 16601728.
  • Transcriptional Regulation of Protein Complexes within and across Species. K. Tan, T. Shlomi, H. Feizi, T. Ideker, and R. Sharan. Proc. Natl. Acad. Sci. USA., 104 (4), 1283-1288, 2008
• Response networks
  Aug 20, 2008
  • Identification of functional modules using network topology and high-throughput data, Igor Ulitsky and Ron Shamir, BMC Systems Biology, Vol. 1, No. 1. (2007).
  • Network-Based Analysis of Affected Biological Processes in Type 2 Diabetes Models, Manway Liu, Arthur Liberzon, Sek W Kong, Weil R Lai, Peter J Park, Isaac S Kohane, Simon Kasif, PLoS Genetics, Vol. 3, No. 6. (1 June 2007), e96.
  • Tools enabling the elucidation of molecular pathways active in human disease: Application to Hepatitis C Virus infection, David J Reiss, Iliana Avila-Campillo, Vesteinn Thorsson, Benno Schwikowski, Timothy Galitski, BMC Bioinformatics, Vol. 6, No. 1. (20 June 2005).
• Building blocks
  Aug 20, 2008
  • Network Legos: Building Blocks of Cellular Wiring Diagrams, T. M. Murali and Corban G. Rivera, Journal of Computational Biology, special issue on RECOMB 2007, to appear, 2008.

Transcriptional Regulatory Networks

• Reconstructing transcriptional regulatory modules and networks
  • Systematic determination of genetic network architecture. Saeed Tavazoie et al. Nature Genetics, 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
  • Unraveling transcription regulatory networks by protein-DNA and protein-protein interaction mapping. Albertha J.M. Walhout Genome Res. 2006 Dec;16(12):1445-54. Epub 2006 Oct 19.
  • Transcriptional regulatory code of a eukaryotic genome. Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J, Jennings EG, Zeitlinger J, Pokholok DK, Kellis M, Rolfe PA, Takusagawa KT, Lander ES, Gifford DK, Fraenkel E, Young RA. Nature. 2004 Sep 2;431(7004):99-104. PMID: 15343339
  • 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 Zoltan N. Oltvai and Albert-Laszlo Barabasi. 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.

Data Integration

• Data Integration to Predict Molecular Interactions
  • Combining biological networks to predict genetic interactions. Wong SL, Zhang LV, Tong AH, Li Z, Goldberg DS, King OD, Lesage G, Vidal M, Andrews B, Bussey H, Boone C, Roth FP. Proc Natl Acad Sci U S A. 2004 Nov 2;101(44):15682-7. Epub 2004 Oct 20.
  • A mixture of feature experts approach for protein-protein interaction prediction, Yanjun Qi, Judith Klein-Seetharaman, and Ziv Bar-Joseph, BMC Bioinformatics 2007, 8(Suppl 10):S6 doi:10.1186/1471-2105-8-S10-S6
  • Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Qi Y, Bar-Joseph Z, Klein-Seetharaman J. Proteins. 2008 Jan 31;
  • Predicting protein complex membership using probabilistic network reliability. Asthana S, King OD, Gibbons FD, Roth FP. Genome Res. 2004 Jun;14(6):1170-5. Epub 2004 May 12. PMID: 15140827
  • Predicting co-complexed protein pairs using genomic and proteomic data integration. Zhang LV, Wong SL, King OD, Roth FP. BMC Bioinformatics. 2004 Apr 16;5:38. PMID: 15090078

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
• 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.
    • Still stratus not altocumulus: further evidence against the date/party hub distinction. Batada NN, Reguly T, Breitkreutz A, Boucher L, Breitkreutz BJ, Hurst LD, Tyers M. PLoS Biol. 2007 Jun;5(6):e154. To understand this paper, there are three preceding papers (references 1, 2, and 3 in this paper) that you must read.
    • Lethality and centrality in protein networks. Jeong H, Mason SP, Barabasi AL, Oltvai ZN. Nature. 2001 May 3;411(6833):41-2
• Host-pathogen protein interaction networks
  • Generation and analysis
    Aug 22, 2008
    • Herpesviral Protein Networks and Their Interaction with the Human Proteome, Peter Uetz, Yu-An Dong, Christine Zeretzke, Christine Atzler, Armin Baiker, Bonnie Berger, Seesandra V Rajagopala, Maria Roupelieva, Dietlind Rose, Even Fossum, Jurgen Haas, Science, Vol. 311, No. 5758. (13 January 2006), pp. 239-242.
    Aug 22, 2008
    • The Landscape of Human Proteins Interacting with Viruses and Other Pathogens, Matthew D. Dyer, T. M. Murali, and Bruno W. Sobral, PLoS Pathogens, volume 4, number 2, pp. e32, 2008.
    • Epstein-Barr virus and virus human protein interaction maps, Michael A A Calderwood, Kavitha Venkatesan, Li Xing, Michael R R Chase, Alexei Vazquez, Amy M M Holthaus, Alexandra E E Ewence, Ning Li, Tomoko Hirozane-Kishikawa, David E E Hill, Marc Vidal, Elliott Kieff, Eric Johannsen, Proc Natl Acad Sci U S A (19 April 2007).
  • Prediction
    Aug 22, 2008
    • Computational Prediction of Host-Pathogen Protein-Protein Interactions, Matthew D. Dyer, T. M. Murali and Bruno W. Sobral, Bioinformatics volume 23, number 13, pp. i159-i166, issue on Proceedings of the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2007.
    Aug 22, 2008
    • Host pathogen protein interactions predicted by comparative modeling, FP Davis, DT Barkan, N Eswar, JH McKerrow, A Sali, Protein Sci, Vol. 16, No. 12. (December 2007), pp. 2585-2596.
  • Host dependency factors
    • RNA interference screen for human genes associated with West Nile virus infection, Manoj N Krishnan, Aylwin Ng, Bindu Sukumaran, Felicia D Gilfoy, Pradeep D Uchil, Hameeda Sultana, Abraham L Brass, Rachel Adametz, Melody Tsui, Feng Qian, Ruth R Montgomery, Sima Lev, Peter W Mason, Raymond A Koski, Stephen J Elledge, Ramnik J Xavier, Herve Agaisse, Erol Fikrig, Nature (06 August 2008)
    • Drosophila RNAi screen identifies host genes important for influenza virus replication, Linhui Hao, Akira Sakurai, Tokiko Watanabe, Ericka Sorensen, Chairul A Nidom, Michael A Newton, Paul Ahlquist, Yoshihiro Kawaoka, Nature (09 July 2008)
    • Identification of Host Proteins Required for HIV Infection Through a Functional Genomic Screen, Abraham L Brass, Derek M Dykxhoorn, Yair Benita, Nan Yan, Alan Engelman, Ramnik J Xavier, Judy Lieberman, Stephen J Elledge, Science (10 January 2008), 1152725.
• 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
  • A network-based method for target selection in metabolic networks, R. Guimerà, M. Sales-Pardo and L.A.N. Amaral, Bioinformatics 2007 23(13):1616-1622; doi:10.1093/bioinformatics/btm150.
  • 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.