1. Computational Biology Networks and Pathways Lecture Slides Week 11

2. Data is Interconnected

3. What is a Graph

4. Complexity

5. A network is a collection of interactions Pathways are a subset of networks All pathways are networks of interactions not all networks are pathways

6. Young et. al: Transcriptional Regulatory Networks in Saccharomyces cerevisiae; Science 2002

7. A network is a collection of interactions Pathways are a subset of networks All pathways are networks of interactions, however not all networks are pathways! Pathway is a biological network that corresponds to a specific physiological process or phenotype

8. Biological pathways Biological components interacting with each other over time to bring about a single biological effect Pathways can be broken down sub-pathways Some common pathways: signal transduction metabolic pathways, gene regulatory pathways Entities in one pathway can be found in others

9. 3 types of interactions that can be mapped into pathways protein (enzyme) – metabolite (ligand) metabolic pathways metabolic pathways protein – protein cell signaling pathways, protein complexes protein – gene genetic networks

10. KEGG http://www.genome.jp/kegg/ BioCyc http://www.biocyc.org/ Reactome http://www.reactome.org/ GenMAPP http://www.genmapp.org/ BioCarta http://www.biocarta.com/ TransPATH http://www.biobase- international.com/pages/index.php?id=transpathda tabases Pathguide – the pathway resource list http://www.pathguide.org/ http://www.pathguide.org/ Available resources

11. Network Topology (PPI)

12. Network analysis and visualization tools Databases for analysis Text mining algorithms (e.g., natural language processing (NLP)) technologies Expert human curation

13. Ingenuity Pathway Analysis http://www.ingenuity.com/products/pathways_analysis.html PathwayStudio http://www.ariadnegenomics.com/products/pathway-studio/ PathwayArchitect http://www.selectscience.net Cytoscape http://www.cytoscape.org/ Biological Networks http://biologicalnetworks.net/ GeneGO http://www.genego.com/

14. Nanduri etal (unpublished)

15. GO term enrichment Nanduri etal (unpublished)

19. End Theory I 5 min mindmapping 10 min break

20. Practice I

21. Cytoscape Download and install cytoscape Add the reactome app Initialize the reactome app Inspect some metabolic pathways

22. End Practice I 15 min break

23. Theory II

24. Pathways vs. networks Gene networks Clusters of genes (or gene products) with evidence of co- expression Connections usually represent degrees of co-expression In-depth knowledge of process is not necessary Networks are non-predictive Biochemical pathways Series of chained, chemical reactions Connections represent describable (and quantifiable) relations between molecules, proteins, lipids, etc. Enzymatic process is elucidated Changes via perturbation are predictable downstream

25. Pathways vs. networks Gene networksBiochemical pathways Curation Relatively easy: automated and manual Difficult: mostly manual Nodes Genes or gene productsAny general molecule Edges Levels of co- expression/influence or a qualitative relation Representation of possibly quantifiable mechanisms between compounds Fidelity Low – usually very little detail High – specific processes Predictive power Relatively lowRelatively high

26. Pathway and network granularity Level of detail Effort to curate General interaction networks Mathematical simulation models Probabilistic networks Qualitative networks Curated reaction pathways

27. Introduction to pathways and networks Examples of pathways and networks Review of pathway databases and tools Representing pathways and networks Methods of inferring pathways and networks Pathway and cellular simulations

28. Yeast gene interaction network Tong, et al., Science 303, 808 (2004)

29. Characteristics of the yeast gene network Some genes (e.g. regulatory factors) act as ‘hubs’ in a network and have many interactions Degrees of connectivity follows the power law Hubs may make interesting anti-cancer targets Clusters of genes with known function suggest function for hypothetical genes in same cluster Network characteristics can be used to predict protein- protein interactions Path between two genes tends to be short (average ~3.3 hops) Tong, et al., Science 303, 808 (2004)

30. E. coli metabolic pathway Karp, et al., Science 293, 2040 (2001) glycolysis

31. Pathways: E. coli metabolic map Encompasses >791 chemical compounds in >744 noted biochemical reactions Pathway was compiled via literature information extraction and extensive manual curation System allows for users to indicate evidence of pathway annotations Curation is done collaboratively with numerous experts outside of EcoCyc Karp, et al., Science 293, 2040 (2001)

32. Pathways in bioinformatics Most resources for pathways focus on metabolic pathways (signaling and regulatory gaining prominence) Pathways as a very specific subtype of networks Like networks, can be made in computable (symbolic) form Specificities in chemical reactions are more predictive Pathways can chain together, forming larger pathways Karp, et al., Science 293, 2040 (2001)

33. Pathway repositories BioCyc/MetaCyc Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY DB BioCarta BioModels database

34. BioCyc database http://www.biocyc.org http://www.biocyc.org Pathway/genome database (PGDB) for organisms with completely sequenced genomes 409 full genomes and pathways deposited Species-specific pathways are inferred form MetaCyc Query/navigation/pathway creation support through the Pathway Tools software suite

35. http://www.biocyc.org

36. MetaCyc database http://www.metacyc.org http://www.metacyc.org Non-redundant reference database for metabolic pathways, reactions, enzymes and compounds Curation through experimental verification and manual literature review >1200 pathways from 1600+ species (mostly plants and microorganisms)

37. http://www.metacyc.org

38. Glycolysis pathway in MetaCyc

39. KEGG PATHWAY database http://www.kegg.com http://www.kegg.com Consolidated set of databases that cover genomics (GENE), chemical compounds (LIGAND) and reaction networks (PATHWAY) Broad focus on metabolics, signal transduction, disease, etc. Species-specific views available (but networks are static across all organisms)

40. http://www.kegg.com

41. Glycolysis pathway in KEGG

42. Global Pathway Map

43. BioCarta database http://www.biocarta.com http://www.biocarta.com Corporate-owned, publicly-curated pathway database Series of interactive, “cartoon” pathway maps Predominantly human and mouse pathways Contains 120,000 gene entries and 355 pathways

44. http://www.biocarta.com

45. Glycolysis pathway in BioCarta

46. BioModels database http://www.biomodels.net http://www.biomodels.net Database for published, quantitative models of biochemical processes All models/pathways curated manually, compliant with MIRIAM Models can be output in SBML format for quantitative modeling 86 curated models, 40 models pending curation

47. http://www.biomodels.net

48. Glycolysis pathways in BioModels

49. Comparison of pathway databases MetaCyc/ BioCyc KEGG PATHWAYS BioCartaBioModels Curation Manual and automated AutomatedManual Size ~621+ pathways~289 reference pathways ~355 pathways~126 models Nomenclature EC, GOEC, KONoneGO Organism coverage ~500 speciesVariousPrimarily human and mouse ~475 species Visuals Species-specific custom Reference and species-specific Animated, cartoonish Non-standardized Primary usage PGDB, computational biology PGDB, pathway comparisons Human pathways, disease Simulations, modeling

50. Introduction to pathways and networks Examples of pathways and networks Review of pathway databases and tools Representing pathways and networks Methods of inferring pathways and networks Pathway and cellular simulations

51. Inferring pathways and networks Experimental methods Microarray co-expression Quantitative trait locus mapping (QTL) Isotope-coded affinity tagging (ICAT) Yeast two-hybrid assay Green florescent protein tagging (GFP tagging) Computational methods Database-driven protein-protein interactions Expression clustering techniques Literature-mining for specified interactions

52. Introduction to pathways and networks Examples of pathways and networks Review of pathway databases and tools Representing pathways and networks Methods of inferring pathways and networks Pathway and cellular simulations

53. Cellular simulations Study the effect perturbation has on a pathway (and thus the organism) Generally require extensive detail on the pathway or reactions of interest (flux equations, metabolite concentration, etc.) Cellular pathway simulations must manage both temporal and spatial complexity

54. Spatial dimension Adapted from Kelly, H., http://www.fas.org/resource/05242004121456.pdf, via Neal, Yngve 2006 VHS, UW MEBI 591http://www.fas.org/resource/05242004121456.pdf Temporal intervals 0.1 nm 10nm 1um 1mm1cm1m picosec. nanosec. microsec. millisec. sec. min. yr. quantum mechanics molecular dynamics cellular processes systems physiology organs and organisms

55. Simulation methods and techniques Biological processPhenomenaComputation scheme MetabolismEnzymatic reactionDifferential-algebraic equations, flux-based analysis Signal transductionBindingDifferential-algebraic equations, stochastic algorithms, diffusion- reaction Gene expressionBinding Polymerization Degradation Object-oriented modeling, differential-algebraic equations, stochastic algorithms, boolean networks DNA replicationBinding Polymerization Object-oriented modeling, differential-algebraic equations Membrane transportOsmotic pressure Membrane potential Differential-algebraic equations, electrophysiology Adapted from Tomita 2001

56. Research in simulation and modeling Virtual Cell (National Resource for Cell Analysis and Modeling) MCell (the Salk Institute) Gepasi (Virginia Tech) E-CELL (Institute for Advanced Biosciences, Keio University) Karyote/CellX (Indiana University)

57. End Theory II 5 min mindmapping 10 min break

58. Term Project Max 3000 words Focus on results and their discussion Make sure to incorporate all the little hints we gave Incorporate runtime for the new dataset as another performance measure

59. Practice Perform the steps as described here: http://wiki.cytoscape.org/GettingStarted