Computational Biology

Networks and Pathways

Lecture Slides Week 11

Data is Interconnected

What is a Graph

Complexity

A network is a collection of interactionsA network is a collection of interactions

Pathways are a subset of networksPathways are a subset of networks

All pathways are networks of interactionsAll pathways are networks of interactions

not all networks are pathwaysnot all networks are pathways

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

A network is a collection of interactionsA network is a collection of interactions

Pathways are a subset of networksPathways are a subset of networksAll pathways are networks of interactions, however not All pathways are networks of interactions, however not

all networks are pathways!all networks are pathways!

Pathway is a biological network that corresponds to Pathway is a biological network that corresponds to a specific physiological process or phenotypea specific physiological process or phenotype

Biological pathways

Biological components interacting with each other Biological components interacting with each other over time to bring about a single biological effectover time to bring about a single biological effect

Pathways can be broken down sub-pathways Pathways can be broken down sub-pathways

Some common pathways: Some common pathways: signal transductionsignal transductionmetabolic pathways, gene regulatory pathwaysmetabolic pathways, gene regulatory pathways

Entities in one pathway can be found in othersEntities in one pathway can be found in others

3 types of interactions that can be mapped into pathways 3 types of interactions that can be mapped into pathways

protein (enzyme) – metabolite (ligand)protein (enzyme) – metabolite (ligand) metabolic pathwaysmetabolic pathways

protein – proteinprotein – proteincell signaling pathways, protein complexescell signaling pathways, protein complexes

protein – geneprotein – genegenetic networksgenetic networks

KEGG KEGG http://www.genome.jp/kegg/http://www.genome.jp/kegg/BioCyc BioCyc http://www.biocyc.org/http://www.biocyc.org/Reactome http://www.reactome.org/Reactome http://www.reactome.org/GenMAPP http://www.genmapp.org/GenMAPP http://www.genmapp.org/BioCarta http://www.biocarta.com/BioCarta http://www.biocarta.com/TransPATH http://www.biobase-TransPATH http://www.biobase-

international.com/pages/index.php?international.com/pages/index.php?id=transpathdatabasesid=transpathdatabases

Pathguide Pathguide – the pathway resource list – the pathway resource list http://www.pathguide.org/http://www.pathguide.org/

Available resources

Network Topology (PPI)

Network analysis and visualization tools

Databases for analysis

Text mining algorithms (e.g., natural language processing (NLP)) technologies

Expert human curation

Ingenuity Pathway Analysishttp://www.ingenuity.com/products/pathways_analysis.html

PathwayStudiohttp://www.ariadnegenomics.com/products/pathway-studio/

PathwayArchitect http://www.selectscience.net

Cytoscapehttp://www.cytoscape.org/

Biological Networkshttp://biologicalnetworks.net/

GeneGOhttp://www.genego.com/

Nanduri etal (unpublished)

GO term enrichment

Nanduri etal (unpublished)

Nanduri etal (unpublished)

Nanduri etal (unpublished)

Nanduri etal (unpublished)

End Theory I

5 min mindmapping

10 min break

Practice I

Cytoscape

Download and install cytoscape

Add the reactome app

Initialize the reactome app

Inspect some metabolic pathways

End Practice I

15 min break

Theory II

Pathways vs. networksGene 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 interactionsDegrees of connectivity follows the power lawHubs 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 curationSystem 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 networksLike 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)

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

Inferring pathways and networks

Experimental methodsMicroarray co-expressionQuantitative trait locus mapping (QTL)Isotope-coded affinity tagging (ICAT)Yeast two-hybrid assayGreen florescent protein tagging (GFP tagging)

Computational methodsDatabase-driven protein-protein interactionsExpression clustering techniquesLiterature-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 modelingVirtual 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)

End Theory II

5 min mindmapping

10 min break

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

Practice

Perform the steps as described here:

http://wiki.cytoscape.org/GettingStarted