Methods and resources for pathway analysis PABIO590B Week 2

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 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

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

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

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)

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)

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

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)

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 Review of pathway databases and tools Representing pathways and networks Methods of inferring pathways and networks Pathway and cellular simulations

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

BioCyc database 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

MetaCyc database Non-redundant reference database for metabolic pathways, reactions, enzymes and compounds Curation through experimental verification and manual literature review >1200 pathways from species (mostly plants and microorganisms)

Glycolysis pathway in MetaCyc

KEGG PATHWAY database 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)

Glycolysis pathway in KEGG

Global Pathway Map

BioCarta database 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

Glycolysis pathway in BioCarta

BioModels database 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

Glycolysis pathways in BioModels

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

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) BioPax

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 software data-sharing Tiered, upward-compatible architecture (two, upward-compatible levels, third planned) Primary intended use is for quantitative model simulations

SBML

BioPax Like SBML, XML-based pathway representation Tiered structure –Level 1: Metabolic pathway information –Level 2: Level 1 + Molecular interaction, post- translational modification Intended to be a lingua franca for pathway databases

BioPax XML representation

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 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 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 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

Spatial dimension Adapted from Kelly, H., via Neal, Yngve 2006 VHS, UW MEBI 591http:// 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

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

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)

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 Exercise