Methods and resources for pathway analysis

PABIO590BWeek 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 networks Biochemical pathways

Curation Relatively easy: automated and manual

Difficult: mostly manual

Nodes Genes or gene products Any 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 low Relatively high

Pathway and network granularity

Level of detail

Eff

ort

to

cu

rate

General interaction

networks

Mathem

atical

simulation m

odels

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 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

http://www.biocyc.org

MetaCyc 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 MetaCyc

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

Global Pathway Map

BioCarta 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 BioCarta

BioModels 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 BioModels

Comparison of pathway databases

MetaCyc/

BioCyc

KEGG PATHWAYS

BioCarta BioModels

Curation Manual and automated

Automated Manual Manual

Size ~621+ pathways ~289 reference pathways

~355 pathways ~126 models

Nomenclature EC, GO EC, KO None GO

Organism coverage

~500 species Various Primarily 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

<myXMLData><someTag>Some data here</someTag><anotherTag>More stuff here</anotherTag><attributeTag data=“embedded in tag” />

</myXMLData>

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., http://www.fas.org/resource/05242004121456.pdf , via Neal, Yngve 2006 VHS, UW MEBI 591

Tem

po

ral

inte

rval

s

0.1 nm 10nm 1um 1mm 1cm 1m

pico

sec.

n

anos

ec.

m

icro

sec.

m

illis

ec.

sec

. m

in.

yr.

quantumm

echanics

molecular dynam

ics

cellular processes

systems physiology

organs and organisms

Simulation methods and techniques

Biological process Phenomena Computation scheme

Metabolism Enzymatic reaction Differential-algebraic equations, flux-based analysis

Signal transduction Binding Differential-algebraic equations, stochastic algorithms, diffusion-reaction

Gene expression Binding

Polymerization Degradation

Object-oriented modeling, differential-algebraic equations, stochastic algorithms, boolean networks

DNA replication BindingPolymerization

Object-oriented modeling, differential-algebraic equations

Membrane transport Osmotic pressureMembrane 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