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Methods and resources for pathway analysis PABIO 590 B Week 2 Methods and resources for pathway analysis PABIO 590 B Week 2

Pathways overview • • • Introduction to pathways and networks Examples of pathways and Pathways overview • • • 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

Pathways vs. networks Gene networks • Clusters of genes (or gene products) with evidence Pathways vs. networks Gene networks • Clusters of genes (or gene products) with evidence of coexpression • 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

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

n tio ac re ys ed wa at th ur pa C m si n tio ac re ys ed wa at th ur pa C m si tic ilis s ab rk ob wo Pr net Level of detail al s ic el at d m mo he n at io M ulat e iv at s lit rk ua o Q etw n n io ct ra te in rks al o er etw en n G Effort to curate Pathway and network granularity

 • • • Introduction to pathways and networks Examples of pathways and networks • • • 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

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

Characteristics of the yeast gene network • Some genes (e. g. regulatory factors) act 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 interactions • Path between two genes tends to be short (average ~3. 3 hops) Tong, et al. , Science 303, 808 (2004)

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

Pathways: E. coli metabolic map • Encompasses >791 chemical compounds in >744 noted biochemical 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 Eco. Cyc Karp, et al. , Science 293, 2040 (2001)

Pathways in bioinformatics • Most resources for pathways focus on metabolic pathways (signaling and 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)

 • • • Introduction to pathways and networks Examples of pathways and networks • • • 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

Pathway repositories • Bio. Cyc/Meta. Cyc • Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway repositories • Bio. Cyc/Meta. Cyc • Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY DB • Bio. Carta • Bio. Models database

Bio. Cyc database http: //www. biocyc. org • Pathway/genome database (PGDB) for organisms with Bio. Cyc database 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 Meta. Cyc • Query/navigation/pathway creation support through the Pathway Tools software suite

http: //www. biocyc. org http: //www. biocyc. org

Meta. Cyc database http: //www. metacyc. org • Non-redundant reference database for metabolic pathways, Meta. Cyc database 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)

http: //www. metacyc. org http: //www. metacyc. org

Glycolysis pathway in Meta. Cyc http: //www. metacyc. org Glycolysis pathway in Meta. Cyc http: //www. metacyc. org

KEGG PATHWAY database http: //www. kegg. com • Consolidated set of databases that cover KEGG PATHWAY database 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)

http: //www. kegg. com http: //www. kegg. com

Glycolysis pathway in KEGG http: //www. kegg. com Glycolysis pathway in KEGG http: //www. kegg. com

Global Pathway Map Global Pathway Map

Bio. Carta database http: //www. biocarta. com • Corporate-owned, publicly-curated pathway database • Series Bio. Carta database 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

http: //www. biocarta. com http: //www. biocarta. com

Glycolysis pathway in Bio. Carta http: //www. biocarta. com Glycolysis pathway in Bio. Carta http: //www. biocarta. com

Bio. Models database http: //www. biomodels. net • Database for published, quantitative models of Bio. Models database 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

http: //www. biomodels. net http: //www. biomodels. net

Glycolysis pathways in Bio. Models http: //www. biomodels. net Glycolysis pathways in Bio. Models http: //www. biomodels. net

Comparison of pathway databases Meta. Cyc/ Bio. Cyc Curation Manual and KEGG PATHWAYS Bio. Comparison of pathway databases Meta. Cyc/ Bio. Cyc Curation Manual and KEGG PATHWAYS Bio. Carta Bio. Models Automated Manual ~289 reference pathways ~355 pathways ~126 models EC, KO None GO Various Primarily human and mouse ~475 species Reference and species-specific Animated, cartoonish Non-standardized PGDB, pathway comparisons Human pathways, disease Simulations, modeling automated Size ~621+ pathways Nomenclature EC, GO Organism ~500 species coverage Visuals Species-specific custom Primary usage PGDB, computational biology

 • • • Introduction to pathways and networks Examples of pathways and networks • • • 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

Pathway formats • Extensible Markup Language (XML) • Systems Biology Markup Language (SBML) • Pathway formats • Extensible Markup Language (XML) • Systems Biology Markup Language (SBML) • Bio. Pax

Extensible Markup Language (XML) • Standard of representing information in a machine-readable way • Extensible Markup Language (XML) • Standard of representing information in a machine-readable way • Similar to HTML; tags can enclose or contain data Some data here More stuff here

Systems Biology Markup Language • XML-based language for representing biochemical reactions • Oriented towards Systems Biology Markup Language • XML-based language for representing biochemical reactions • Oriented towards software data-sharing • Tiered, upward-compatible architecture (two, upward-compatible levels, third planned) • Primary intended use is for quantitative model simulations

SBML SBML

Bio. Pax • Like SBML, XML-based pathway representation • Tiered structure – Level 1: Bio. Pax • Like SBML, XML-based pathway representation • Tiered structure – Level 1: Metabolic pathway information – Level 2: Level 1 + Molecular interaction, posttranslational modification • Intended to be a lingua franca for pathway databases

Bio. Pax XML representation Bio. Pax XML representation

 • • • Introduction to pathways and networks Examples of pathways and networks • • • 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

Inferring pathways and networks • Experimental methods – – – Microarray co-expression Quantitative trait 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

 • • • Introduction to pathways and networks Examples of pathways and networks • • • 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

Cellular simulations • Study the effect perturbation has on a pathway (and thus the 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

microsec. millisec. min. yr. ns d an io ys s m ph lo gy microsec. millisec. min. yr. ns d an io ys s m ph lo gy ce nanosec. is s ro es ss am yn d ar s ic picosec. n ga or em st rp la llu Temporal intervals ga or sy ce ul ec ol m m s tu ic an an qu ech m 0. 1 nm 10 nm 1 um 1 mm 1 cm 1 m Spatial dimension Adapted from Kelly, H. , http: //www. fas. org/resource/05242004121456. pdf , via Neal, Yngve 2006 VHS, UW MEBI 591

Simulation methods and techniques Biological process Phenomena Metabolism Enzymatic reaction Signal transduction Binding Computation Simulation methods and techniques Biological process Phenomena Metabolism Enzymatic reaction Signal transduction Binding Computation scheme Differential-algebraic equations, flux-based analysis Differential-algebraic equations, stochastic algorithms, diffusionreaction Gene expression Binding Polymerization Degradation Object-oriented modeling, differential-algebraic equations, stochastic algorithms, boolean networks DNA replication Binding Polymerization Object-oriented modeling, differential-algebraic equations Membrane transport Osmotic pressure Membrane potential Differential-algebraic equations, electrophysiology Adapted from Tomita 2001

Research in simulation and modeling • Virtual Cell (National Resource for Cell Analysis and 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/Cell. X (Indiana University)

Exercise Your task is to: • Identify the functions of proteins X, Y & Exercise Your task is to: • Identify the functions of proteins X, Y & Z • Identify the pathway(s) in which they are involved • Look for differences in pathways between databases • Examine the same pathway(s) in humans