ANALYZING AND MODELING LARGE BIOLOGICAL NETWORKS: INFERRING SIGNAL TRANSDUCTION PATHWAYS by ă GURKAN BEBEK Submitted in partial fulllment of the requirements for the Degree of Doctor of Philosophy Electrical Engineering And Computer Science Department CASE WESTERN RESERVE UNIVERSITY January 2007 UMI Number: 3226720 UMI Microform 3226720 Copyright 2006 by ProQuest Information and Learning Company All rights reserved This microform edition is protected against unauthorized copying under Title 17, United States Code ProQuest Information and Learning Company 300 North Zeeb Road P.O Box 1346 Ann Arbor, MI 48106-1346 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the dissertation of Gürkan Bebek candidate for the Ph.D degree * Jiong Yang (signed) _ (chair of the committee) S Cenk Sahinalp Tekin Ozsoyoglu Mark Adams Jing Li January 12, 2007 (date) _ *We also certify that written approval has been obtained for any proprietary material contained therein to Gamze Contents List of Tables iii List of Figures iv Introduction 1.1 1.1.1 Graph Theoretic Definitions 1.1.2 Signal Transduction Pathways 1.1.3 Protein-Protein Interactions 10 1.1.4 1.2 Background Discovery of Protein-Protein Interactions 11 Contributions 15 Evolutionary Models of Proteome Networks 2.1 18 Biological Networks 24 2.1.1 The Evolution of Protein-Protein Interactions 26 2.1.2 Random Network Models 28 2.1.3 Properties of Networks 32 2.2 Proteome Growth Model 35 2.3 Analysis of the Proteome Growth Model 36 2.3.1 Properties of the pure duplication model 37 2.3.2 On the degree distribution of the proteome growth model 41 i 2.4 Discussion Enhanced Duplication Model 44 48 3.1 Sequence Similarity Distribution in the Yeast Proteome 51 3.2 Enhanced Model Based on Sequence Similarity 56 3.3 Discussion 62 Discovering Signaling Pathways: PathFinder 4.1 65 PathFinder 71 4.1.1 Preliminary 73 Methods 75 4.2.1 Mapping Proteins to Functional Annotations 76 4.2.2 Mining Association Rules from Known Pathways 80 4.2.3 Constructing a Weighted Protein-Protein Interaction Network 87 4.2.4 Searching for Pathway Segments 89 4.3 Experiments on the Yeast Proteome Network 91 4.4 Discussion 102 4.2 Conclusions and Reflections 105 Bibliography 115 ii List of Tables 3.1 The average clustering coefficients of the DIP Protein-Protein Interaction Network, Proteome Growth Model, and the Enhanced Model 60 4.1 Binary Table Example 81 4.2 PathFinder Search Results 97 iii List of Figures 2-1 ℓ − hop 34 2-2 Percentage of singletons in the pure duplication model 40 2-3 Average degree of non-singleton nodes in pure duplication model 42 3-1 Degree distribution of the Yeast and the proteome growth model interaction networks 49 3-2 ℓ-hop degree distribution comparison of the Yeast and Proteome Growth Model 50 3-3 Distribution of pairwise sequence similarity of yeast proteins 54 3-4 Aggregate distribution of pairwise sequence similarity of yeast proteins 55 3-5 Enhanced Model Based on Sequence Similarity 57 3-6 Degree distribution of the proteome sequence similarity networks 59 3-7 Degree distribution of the interaction networks 59 3-8 ℓ-hop degree distribution of the yeast, proteome growth model and the sequence similarity enhanced model 61 4-1 MAP Kinase Pathways 74 4-2 PathFinder 77 4-3 Two interacting proteins and their linked annotation terms 79 4-4 Association Rule Mining Parameters 93 iv 4-5 PathFinder Ste7-Dig2 Simple Path Results 94 4-6 PathFinder Ste7-Dig2 Signaling Pathway Segment Results 96 4-7 The Pheromone Response Signaling Pathway 98 4-8 The High Osmolarity Signaling Pathway 101 v Acknowledgements It is with great pleasure that I would like to thank those who have helped me in my Ph.D studies I would like to acknowledge Dr S Cenk Sahinalp for recruiting me as a grad¸ uate student, and for his guidance throughout my education After his move to Vancouver, Canada, he offered me his continued help in finishing this program both financially and academically I have learned a great amount of skills from him, and I will still be a supporter of Dr Sahinalp after my graduation ¸ I am very grateful to Dr Jiong Yang, for accepting to take over my advisory duties and helping me accelerate my studies I appreciate his financial support during my last years and his guidance throughout my studies since he moved to Case His guidance on finding interesting problems and accurate approaches should be mentioned here I also would like to thank him for being my dissertation committee chair ă I would like to give my gratitude to Prof Meral Ozsoyo˘ lu and Prof Tekin g ă Ozsoyo lu, for their help and guidance during this last five years It has always g been an inspiration to see their academic achievements I especially would like to ă mention Prof Tekin Ozsoyo lus support and priceless advice during my last year g of study ă I would like to thank Prof Tekin Ozsoyo lu, Dr Mark Adams, and Dr Jing g Li for being on my dissertation committee I deeply appreciate their input to this dissertation and my research Soon after I met my wife, I was privileged to be introduced to the Wise, whom I am eternally indebted to, as I have gained so much from them I always feel welcome among them, and I am happy to make them proud by finishing this degree Here, I would like to mention Mrs Marilyn Wise for her support in every aspect of my life and sharing her spiritual enlightenment with me I appreciate her being vi Today, what is known about cells, the smallest entities of life, is still small Although the universe expands every single second, what is inside a living cell does not Still, much is unknown to completely understand its dynamics All in all, in this thesis, we have explored methodologies and developed models to further discover and explain life 114 Bibliography Abbott A (2002) Betting on tomorrow’s chips Nature 415: 112–114 Abello J, Buchsbaum AL, Westbrook J (1998) A Functional Approach to External Graph Algorithms In: European Symposium on Algorithms, pp 332–343 Agrawal R, Imielinski T, Swami AN (1993) Mining Association Rules between Sets of Items in Large Databases In: Buneman P, Jajodia S, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp 207–216, Washington, D.C Agrawal R, Srikant R (1994) Fast Algorithms for Mining Association Rules in Large Databases In: VLDB 1994: Proceedings of the 20th International Conference on Very Large Data Bases, pp 487–499, San Francisco, CA, USA: Morgan Kaufmann Publishers Inc Aiello W, Chung F (2001) Random Evolution in Massive Graphs In: FOCS 2001: Proceedings of the 42nd IEEE symposium on Foundations of Computer Science, p 510, Washington, DC, USA: IEEE Computer Society Aiello W, Chung F, Lu L (2000) A random graph model for massive graphs In: STOC 2000: Proceedings of the thirty-second annual ACM symposium on Theory of computing, pp 171–180, New York, NY, USA: ACM Press 115 Albert R, Jeong H, Barabasi AL (1999) Internet: Diameter of the World-Wide Web Nature 401: 130–131 Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks Nature 406: 378 Alberts B, et al (2002) Molecular Biology of the Cell [Book and CD-ROM] Garland Science Alon N, Yuster R, Zwick U (1994) Color-Coding Electronic Colloquium on Computational Complexity (ECCC) Aloy P, Russell RB (2002) Potential artefacts in protein-interaction networks FEBS Lett 530: 253–254 Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al (2000) Gene ontology: tool for the unification of biology The Gene Ontology Consortium Nat Genet 25: 25–29 Bader GD, Betel D, Hogue CW (2003) BIND: the Biomolecular Interaction Network Database Nucleic Acids Res 31: 248–250, URL http://bind.ca Bader GD, Hogue CW (2002) Analyzing yeast protein-protein interaction data obtained from different sources Nat Biotech 20: 991–997 Ball CA, Awad IAB, Demeter J, Gollub J, Hebert JM, et al (2005) The Stanford Microarray Database accommodates additional microarray platforms and data formats Nucleic Acids Research 33: D580+ Barabasi AL, Albert R (1999) Emergence of Scaling in Random Networks Science 286: 509–512 Bateson W (1909) Mendel‘s Principles of Heredity Genetics Heritage Pr 116 Bebek G, Berenbrink P, Cooper C, Friedetzky T, Nadeau J, et al (2006a) The degree distribution of General Duplication Models Theoretical Computer Science (to appear) Bebek G, Berenbrink P, Cooper C, Friedetzky T, Nadeau J, et al (2006b) Improved Duplication Models for Proteome Network Evolution Lecture Notes in Bioinformatics (in press) Bebek G, Yang J (2006) PathFinder: Mining Signal Transduction Pathway Segments from Protein Protein Interaction Networks Tech rep., Case Western Reserve Universiy Berger N, Bollobas B, Borgs C, Chayes J, O R (2003) Degree distribution of the FKP network model In: Proc ICALP, LNCS 2719, pp 725–738 Bhan A, Galas DJ, Dewey GT (2002) A duplication growth model of gene expression networks Bioinformatics 18: 1486–1493 Bollobas B (2001) Random Graphs Cambridge University Press Bollobas B, Borgs C, Chayes J, Riordan O (2003) Directed scale-free graphs In: SODA 2003: Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms, pp 132–139, Philadelphia, PA, USA: Society for Industrial and Applied Mathematics Bollob´ s B, Riordan O, Spencer J, Tusan´ dy G (2001) The degree sequence of a a a scale-free random graph process Random Structures and Algorithms 18: 279– 290 Bornholdt S, Ebel H (2001) World-Wide Web scaling exponent from Simon‘s 1955 model Physical Review E 64: 035104 117 Broder A, Kumar R, Maghoul F, Raghavan P, Rajagopalan S, et al (2000) Graph structure in the Web Computer Networks 33: 309–320 Brown KR, Jurisica I (2005) Online predicted human interaction database Bioinformatics 21: 2076–2082, URL http://ophid.utoronto.ca Campagne F, Neves S, Chang CW, Skrabanek L, Ram PT, et al (2004) Quantitative information management for the biochemical computation of cellular networks Sci STKE 2004 Cherry JM, Ball C, Weng S, Juvik G, Schmidt R, et al (1997) Genetic and physical maps of Saccharomyces cerevisiae Nature 387: 67–73 Choi C, Crass T, Kel A, Kel-Margoulis O, Krull M, et al (2004) Consistent remodeling of signaling pathways and its implementation in the TRANSPATH database Genome Inform Ser 15: 244–254 Chung F, Lu L, Dewey TG, Galas DJ (2003) Duplication models for biological networks J Comput Biol 10: 677–687 Cook SJ, McCormick F (1993) Inhibition by cAMP of Ras-dependent activation of Raf Science 262: 1069–1072 Cooper C, Frieze A (2003) A general model of web graphs Random Structures and Algorithms 22: 311–335 Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to Algorithms, Second Edition The MIT Press Cornell M, Paton NW, Oliver SG (2004) A critical and integrated view of the yeast interactome Comp Funct Genomics 5: 382–402 Dayhoff M, Schwartz R, Orcutt B (1978) A model of evolutionary change in proteins Atlas of Protein Sequence and Structure 5(3): 345–352 118 Deane CM, Salwinski L, Xenarios I, Eisenberg D (2002) Protein interactions: two methods for assessment of the reliability of high throughput observations Mol Cell Proteomics 1: 349–356 DeRisi JL, Iyer VR (1999) Genomics and array technology Curr Opin Oncol 11: 76–79 Eddy SR (2004) Where did the BLOSUM62 alignment score matrix come from? Nat Biotechnol 22: 1035–1036 Edwards AM, Kus B, Jansen R, Greenbaum D, Greenblatt J, et al (2002) Bridging structural biology and genomics: assessing protein interaction data with known complexes Trends Genet 18: 529536 Erdă s P, R nyi A (1959) On random graphs Publicationes Mathematicae Debrecen o e 6: 290–297 Faloutsos M, Faloutsos P, Faloutsos C (1999) On Power-law Relationships of the Internet Topology In: SIGCOMM, pp 251–262 Ferrer I Cancho R, Sol RV (2001) The small world of human language Proc R Soc Lond B Biol Sci 268: 2261–2265 Fields S (2005) High-throughput two-hybrid analysis The promise and the peril FEBS J 272: 5391–5399 Fields S, Song O (1989) A novel genetic system to detect protein-protein interactions Nature 340: 245–246 Fischer I (2002) Similarity-preserving Metrics for Amino-acid Sequences In: 22nd GIF Meeting on Challenges in Genomic Research: Neurodegenerative Diseases, Stem Cells, Bioethics 119 Force A, Lynch M, Pickett BF, Amores A, Yan YL, et al (1999) Preservation of Duplicate Genes by Complementary, Degenerative Mutations Genetics 151: 1531– 1545 Francke C, Siezen RJ, Teusink B (2005) Reconstructing the metabolic network of a bacterium from its genome Trends in Microbiology 13: 550–558 Gavin AC, Bsche M, Krause R, Grandi P, Marzioch M, et al (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes Nature 415: 141–147 Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, et al (2003) A protein interaction map of Drosophila melanogaster Science 302: 1727–1736 Goffeau A, Barrell BG, Bussey H, Davis RW, Dujon B, et al (1996) Life with 6000 genes Science 274 Gough NR, Adler EM, Ray LB (2004) Focus Issue: Cell Signaling–Making New Connections Sci STKE 2004: 12 Graeber TG, Eisenberg D (2001) Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles Nat Genet 29: 295–300 Grigoriev A (2003) On the number of protein-protein interactions in the yeast proteome Nucleic Acids Res 31: 4157–4161 Han JDJ, Dupuy D, Bertin N, Cusick ME, Vidal M (2005) Effect of sampling on topology predictions of protein-protein interaction networks Nature Biotechnology 23: 839–844 Hartuv E, Shamir R (2000) A clustering algorithm based on graph connectivity Information Processing Letters 76: 175–181 120 Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks Proc Natl Acad Sci U S A 89: 10915–10919 Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, et al (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry Nature 415: 180–183 Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, et al (2001) Integrated genomic and proteomic analyses of a systematically perturbed metabolic network Science 292: 929–934 Ispolatov I, Krapivsky PL, Yuryev A (2005) Duplication-divergence model of protein interaction network Phys Rev E Stat Nonlin Soft Matter Phys 71 Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, et al (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome Proc Natl Acad Sci U S A 98: 4569–4574 Ito T, Ota K, Kubota H, Yamaguchi Y, Chiba T, et al (2002) Roles for the Twohybrid System in Exploration of the Yeast Protein Interactome Mol Cell Proteomics 1: 561–566 Itzkovitz S, Alon U (2005) Subgraphs and network motifs in geometric networks Phys Rev E Stat Nonlin Soft Matter Phys 71 Jansen R, Greenbaum D, Gerstein M (2002) Relating whole-genome expression data with protein-protein interactions Genome Res 12: 37–46 Jeong H, Mason SP, Barabasi AL, Oltvai ZN (2001) Lethality and centrality in protein networks Nature 411: 41–42 Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL (2000) The large-scale organization of metabolic networks Nature 407: 651 121 Jin F, Hazbun T, Michaud GA, Salcius M, Predki PF, et al (2006) A poolingdeconvolution strategy for biological network elucidation Nature Methods 3: 183–189 Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes Nucl Acids Res 28: 27–30 Kauffman SA (1969) Metabolic stability and epigenesis in randomly constructed genetic nets J Theor Biol 22: 437–467 Kephart JO, White SR (1991) Directed-graph epidemiological models of computer viruses In: Proceedings., 1991 IEEE Computer Society Symposium on Research in Security and Privacy, pp 343–359 Kleinberg J, Kumar R, Raphavan P, Rajagopalan S, Tomkins A (1999) The Web as a graph: Measurements, models and methods In: Proceedings of COCOON, pp 1–17, Tokyo, Japan Kumar R, Raghavan P, Rajagopalan S, Sivakumar D, Tomkins A, et al (2000) Stochastic models for the Web graph In: FOCS 2000: Proceedings of the 41st Annual Symposium on Foundations of Computer Science, p 57, Washington, DC, USA: IEEE Computer Society Li S, Armstrong CM, Bertin N, Ge H, Milstein S, et al (2004) A map of the interactome network of the metazoan C elegans Science 303: 540–543 Li WH, Gu Z, Wang H, Nekrutenko A (2001) Evolutionary analyses of the human genome Nature 409: 847–849 Liolios K, Tavernarakis N, Hugenholtz P, Kyrpides NC (2006) The Genomes On Line Database (GOLD) v.2: a monitor of genome projects worldwide Nucleic Acids Research 34: D332–334 122 Liu Y, Zhao H (2004) A computational approach for ordering signal transduction pathway components from genomics and proteomics Data BMC Bioinformatics Madhani HD, Styles CA, Fink GR (1997) MAP kinases with distinct inhibitory functions impart signaling specificity during yeast differentiation Cell 91: 673– 684 Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, et al (1999) Detecting Protein Function and Protein-Protein Interactions from Genome Sequences Science 285: 751–753 May RM (1973) Stability and Complexity in Model Ecosystems (MPB-6) Princeton University Press Mewes HW, Heumann K, Kaps A, Mayer K, Pfeiffer F, et al (1999) MIPS: a database for genomes and protein sequences Nucleic Acids Res 27: 44–48, URL http://mips.gsf.de Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S, et al (2004) Superfamilies of evolved and designed networks Science 303: 1538–1542 Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, et al (2002) Network motifs: simple building blocks of complex networks Science 298: 824–827 Nadeau JH, Sankoff D (1997) Comparable Rates of Gene Loss and Functional Divergence After Genome Duplications Early in Vertebrate Evolution Genetics 147: 1259–1266 Neves SR, Iyengar R (2002) Modeling of signaling networks Bioessays 24: 1110– 1117 123 Newman MEJ, Strogatz SH, Watts DJ (2001) Random graphs with arbitrary degree distributions and their applications Physical Review E 64: 026118 Nielsen H, Engelbrecht J, von Heijne G, Brunak S (1996) Defining a similarity threshold for a functional protein sequence pattern: the signal peptide cleavage site Proteins 26: 165–177 Ohno S (1970) Evolution by gene duplication Springer-Verlag Pastor-Satorras R, Smith E, Sol RV (2003) Evolving protein interaction networks through gene duplication J Theor Biol 222: 199–210 Pearson WR, Lipman DJ (1988) Improved tools for biological sequence comparison Proc Natl Acad Sci U S A 85: 2444–2448 Penrose M (2003) Random Geometric Graphs (Oxford Studies in Probability) Oxford University Press, USA Przulj N, Corneil DG, Jurisica I (2004) Modeling interactome: scale-free or geometric? Bioinformatics 20: 3508+ Reboul J, Vaglio P, Rual JF, Lamesch P, Martinez M, et al (2003) C elegans ORFeome version 1.1: experimental verification of the genome annotation and resource for proteome-scale protein expression Nat Genet 34: 35–41 Redner S (1998) How Popular is Your Paper? An Empirical Study of the Citation Distribution Eur Phys Jour B 4: 131–134 Remm M, Storm CE, Sonnhammer EL (2001) Automatic clustering of orthologs and in-paralogs from pairwise species comparisons J Mol Biol 314: 1041–1052, URL http://inparanoid.cgb.ki.se 124 Rigaut G, Shevchenko A, Rutz B, Wilm M, Mann M, et al (1999) A generic protein purification method for protein complex characterization and proteome exploration Nat Biotechnol 17: 1030–1032 Roberto J Bayardo J (1998) Efficiently mining long patterns from databases In: SIGMOD 1998: Proceedings of the 1998 ACM SIGMOD international conference on Management of data, pp 85–93, New York, NY, USA: ACM Press Rubin GM, Yandell MD, Wortman JR, Gabor Miklos GL, Nelson CR, et al (2000) Comparative genomics of the eukaryotes Science 287: 2204–2215 Ruepp A, Zollner A, Maier D, Albermann K, Hani J, et al (2004) The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes Nucleic Acids Res 32: 5539–5545 Sachs K, Perez O, Pe‘er D, Lauffenburger DA, Nolan GP (2005) Causal ProteinSignaling Networks Derived from Multiparameter Single-Cell Data Science 308: 523–529 Scott J, Ideker T, Karp RM, Sharan R (2006) Efficient algorithms for detecting signaling pathways in protein interaction networks J Comput Biol 13: 133–144 Seoighe C, Wolfe KH (1999a) Updated map of duplicated regions in the yeast genome Gene 238: 253–261 Seoighe C, Wolfe KH (1999b) Yeast genome evolution in the post-genome era Curr Opin Microbiol 2: 548–554 Sharan R, Suthram S, Kelley RM, Kuhn T, McCuine S, et al (2005) Conserved patterns of protein interaction in multiple species Proc Natl Acad Sci U S A 102: 1974–1979 125 Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli Nat Genet 31: 64–68 Shlomi T, Segal D, Ruppin E, Sharan R (2006) QPath: a method for querying pathways in a protein-protein interaction network BMC Bioinformatics 7: 199 Simon H (1955) On a class of skew distribution functions Biometrika 42: 425–440 Smith MR, Degudicibus SJ, Stacey DW (1986) Requirement for c-ras proteins during viral oncogene transformation Nature 320: 540–543 Sprinzak E, Sattath S, Margalit H (2003) How reliable are experimental proteinprotein interaction data? J Mol Biol 327: 919–923 Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, et al (2006) BioGRID: a general repository for interaction datasets Nucleic Acids Research 34: D535, URL http://www.thebiogrid.org Steffen M, Petti A, Aach J, D‘haeseleer P, Church G (2002) Automated modelling of signal transduction networks BMC Bioinformatics Suthram S, Sittler T, Ideker T (2005) The Plasmodium protein network diverges from those of other eukaryotes Nature 438: 108–112 Tucker CL, Gera JF, Uetz P (2001) Towards an understanding of complex protein networks Trends in Cell Biology 11: 102–106 Uetz P, Finley RL (2005) From protein networks to biological systems FEBS Lett 579: 1821–1827 Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, et al (2000) A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae Nature 403: 623–627 126 van Noort V, Snel B, Huynen M (2004) The yeast coexpression network has a smallworld, scale-free architecture and can be explained by a simple model EMBO Reports 5: 280–284 Vazquez A, Flammini A, Maritan A, Vespignani A (2003) Modeling of protein interaction networks Complexus 1: 38 Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial analysis of gene expression Science 270: 484–487 Vincens P, Tarroux P (1988) Two-dimensional electrophoresis computerized processing Int J Biochem 20: 499–509 von Mering C, Krause R, Snel B, Cornell M, Oliver SG, et al (2002) Comparative assessment of large-scale data sets of protein-protein interactions Nature 417: 399–403 Wagner A (2001) The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes Mol Biol Evol 18: 1283–1292 Walhout AJ, Boulton SJ, Vidal M (2000a) Yeast two-hybrid systems and protein interaction mapping projects for yeast and worm Yeast 17: 88–94 Walhout AJ, Sordella R, Lu X, Hartley JL, Temple GF, et al (2000b) Protein Interaction Mapping in C elegans Using Proteins Involved in Vulval Development Science 287: 116–122 Warmka J, Hanneman J, Lee J, Amin D, Ota I (2001) Ptc1, a type 2C Ser/Thr phosphatase, inactivates the HOG pathway by dephosphorylating the mitogenactivated protein kinase Hog1 Mol Cell Biol 21: 51–60 Watts DJ, Strogatz SH (1998) Collective dynamics of ’small-world’ networks Nature 393: 440–442 127 Wolfe KH, Shields DC (1997) Molecular evidence for an ancient duplication of the entire yeast genome Nature 387: 708–713 Wu J, Dent P, Jelinek T, Wolfman A, Weber MJ, et al (1993) Inhibition of the EGFactivated MAP kinase signaling pathway by adenosine 3’,5’-monophosphate Science 262: 1065–1069 Xenarios I, Salwnski L, Duan XJ, Higney P, Kim SM, et al (2002) DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions Nucleic Acids Res 30: 303–305, URL http://dip.doe-mbi.ucla.edu Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, et al (2002) MINT: a Molecular INTeraction database FEBS Lett 513: 135– 140, URL http://cbm.bio.uniroma2.it/mint/index.html Zien A, Kă ffner R, Zimmer R, Lengauer T (2000) Analysis of Gene Expression u Data with Pathway Scores In: Altman R, et al., editors, ISMB00, pp 407–417, La Jolla, CA: AAAI 128 ... Gă rkan Bebek, Ph D u August 2006 vii Analyzing and Modeling Large Biological Networks: Inferring Signal Transduction Pathways Abstract by Gă rkan Bebek u Large scale two-hybrid screens have generated... is the minimum 1.1.2 Signal Transduction Pathways The molecular components involved in cellular signaling form signal transduction pathways A signal transduction pathway (signaling pathway) in... functional networks In most of the one-cell organisms, the variety of signal transduction pathways influences the number of ways the cell can react and respond to its environment Discovering signal transduction