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Metabolomics Applications of the BioCyc Databases and Pathway Tools Software. Peter D. Karp ecocyc.org SRI International biocyc.org metacyc.org. Overview. Overview of MetaCyc family of Pathway/Genome Databases ( PGDBs )

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slide1

Metabolomics Applications of the BioCyc Databases and Pathway Tools Software

  • Peter D. Karp ecocyc.org
  • SRI International biocyc.org
  • metacyc.org
overview
Overview
  • Overview of MetaCyc family of Pathway/Genome Databases (PGDBs)
    • BioCyc collection: EcoCyc, MetaCyc, HumanCyc, etc
    • CuratedPGDBs for Arabidopsis, Yeast, Mouse, Fly, etc
  • Overview of Pathway Tools software
  • Automatic generation of metabolic-flux models
metacyc family of pathway genome databases
MetaCyc Family ofPathway/Genome Databases
  • 6,000+ databases from many institutions
  • All domains of life with microbial emphasis
  • Genomes plus predicted metabolic pathways
  • DBs derived from MetaCyc via computational pathway prediction
  • Common schema
  • Common controlled vocabularies
  • Managed using Pathway Tools software

MetaCyc Family

6,000+

BioCyc.org

3,500

Archives of Toxicology 85:1015 2011

curated databases within the metacyc family
Curated Databases Within the MetaCyc Family

http://biocyc.org/otherpgdbs.shtml

pathway tools software
Pathway Tools Software
  • Comprehensive systems biology software environment
  • Create and maintain an organism database integrating genome, pathway, regulatory information
    • Computational inference tools
    • Interactive editing tools
  • Query and visualize that database
  • Generate steady-state metabolic flux models
    • Flux-balance analysis
  • Interpret omics datasets
  • Comparative analysis tools
  • Licensed by 5,000+ groups
motivations management of metabolic pathway data
Motivations: Management ofMetabolic Pathway Data
  • Organize growing corpus of data on metabolic pathways
    • Experimentally elucidated pathways in the biomedical literature
    • Computationally predicted pathways derived from genome data
  • Provide software tools for querying and comprehending this complex information space
  • Multiorganism view: MetaCyc
    • Unique, experimentally elucidated pathways across all organisms
    • Reference database for computational pathway prediction
  • Organism-specific view:
    • Organism-specific Pathway/Genome Databases
    • Detailed qualitative models of metabolic networks
    • Combine computational predictions with experimentally determined pathways
model organism databases organism specific databases
Model Organism Databases /Organism Specific Databases
  • DBs that describe the genome and other information about an organism
  • Every sequenced organism with an active experimental community requires a MOD
    • Integrate genome data with information about the biochemical and genetic network of the organism
    • Integrate literature-based information with computational predictions
  • Accurate metabolic modeling requires a curation effort
rationale for mods
Rationale for MODs
  • Each “complete” genome is incomplete in several respects:
    • 40%-60% of genes have no assigned function
    • Roughly 7% of those assigned functions are incorrect
    • Many assigned functions are non-specific
  • Need continuous updating of annotations with respect to new experimental data and computational predictions
    • Gene positions, sequence, gene functions, regulatory sites, pathways
  • MODs are platforms for global analyses of an organism
    • Interpret omics data in a pathway context
    • In silico prediction of essential genes
    • Characterize systems properties of metabolic and genetic networks
pathway genome database
Pathway/Genome Database

Pathways

Reactions

Compounds

Sequence Features

Proteins

RNAs

Regulation

Operons

Promoters

DNA Binding Sites

Regulatory Interactions

Genes

Chromosomes

Plasmids

CELL

biocyc collection of 3 000 pathway genome databases
BioCyc Collection of 3,000 Pathway/Genome Databases
  • Pathway/Genome Database (PGDB) – combines information about
    • Pathways, reactions, substrates
    • Enzymes, transporters
    • Genes, replicons
    • Transcription factors/sites, promoters, operons
  • Tier 1: Literature-Derived PGDBs
    • MetaCyc, HumanCyc, YeastCyc
    • EcoCyc -- Escherichia coli K-12
    • AraCyc – Arabidopsis thaliana
  • Tier 2: Computationally-derived DBs, Some Curation -- 34 PGDBs
    • Bacillus subtilis, Mycobacterium tuberculosis
  • Tier 3: Computationally-derived DBs, No Curation -- ~3,000 PGDBs
obtaining a pgdb for organism of interest
Obtaining a PGDB for Organism of Interest
  • Find existing PGDB in BioCyc
  • Find existing PGDB from larger MetaCyc family of PGDBs
    • http://biocyc.org/otherpgdbs.shtml
  • Download from PGDB registry
    • http://biocyc.org/registry.html
  • Create your own PGDB
pathway tools software pgdbs created outside sri
4,000+ licensees: 250 groups applying software to 1,700 organisms

Saccharomycescerevisiae, SGD project, Stanford University

135 pathways / 565 publications – BioCyc.org

FungiCyc, Broad Institute

Candida albicans, CGD project, Stanford University

dictyBase, Northwestern University

Mouse, MGD, Jackson Laboratory -- BioCyc.org

Drosophila, FlyBase, Harvard University -- BioCyc.org

Under development:

C. elegans, WormBase

Arabidopsis thaliana,TAIR, Carnegie Institution of Washington

288 pathways / 2282 publications – BioCyc.org

PlantCyc: Poplar, Cassava, Corn, Grape, Soy,Carnegie Institution

Six Solanaceae species, Cornell University

GrameneDB: Rice, Sorghum, Maize, Cold Spring Harbor Laboratory

Medicagotruncatula, Samuel Roberts Noble Foundation

ChlamyCyc, GoFORSYS

Pathway Tools Software: PGDBs Created Outside SRI
pathway tools software pgdbs created outside sri1
M. Bibb, John Innes Centre,Streptomycescoelicolor

F. Brinkman, Simon Fraser Univ, Pseudomonas aeruginosa

Genoscope,Acinetobacter

R.J.S. Baerends, University of Groningen, Lactococcuslactis IL1403, Lactococcuslactis MG1363, Streptococcus pneumoniae TIGR4, Bacillus subtilis 168, Bacillus cereus ATCC14579

Matthew Berriman, Sanger Centre, Trypanosomabrucei, Leishmania major

Sergio Encarnacion, UNAM, Sinorhizobiummeliloti

Mark van derGiezen, University of London, Entamoebahistolytica, Giardiaintestinalis

Pathway Tools Software: PGDBs Created Outside SRI
pathway tools software pgdbs created outside sri2
Pathway Tools Software: PGDBs Created Outside SRI
  • Large scale users:
    • C. Medigue, Genoscope, 500+ PGDBs
    • J. Zucker, Broad Inst, 94 PGDBs
    • G. Sutton, J. Craig Venter Institute, 80+ PGDBs
    • G. Burger, U Montreal, 60+ PGDBs
    • E. Uberbacher, ORNL 33 Bioenergy-related organisms
    • Bart Weimer, UC Davis, Lactococcuslactis, Brevibacterium linens, Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus johnsonii, Listeriamonocytogenes
  • Partial listing of outside PGDBs at http://biocyc.org/otherpgdbs.shtml
ecocyc project ecocyc org
EcoCyc Project – EcoCyc.org
  • E.coli Encyclopedia
    • Review-level Model-Organism Database for E. coli
    • Derived from 25,000 publications
  • “Multi-dimensional annotation of the E. coli K-12 genome”
    • Gene product summaries and literature citations
    • Evidence codes
    • Gene Ontology terms
    • Protein features (active sites, metal ion binding sites)
    • Multimeric complexes
    • Metabolic pathways
    • Regulation of gene expression and of protein activity
    • Gene essentiality data
    • Growth under alternative nutrient conditions

Karp, Gunsalus, Collado-Vides, Paulsen

Nuc. Acids Res. 41:D605 2013

ecocyc e coli dataset pathway genome navigator
EcoCyc = E.coli Dataset + Pathway/Genome Navigator

Pathways: 312

Reactions:

Metabolic: 1600

Transport: 370

Compounds: 2,400

EcoCyc v17.0

Citations: 24,000

Monomers: 4389

Complexes: 976

RNAs: 301

Regulation:

Operons: 4,500

Trans Factors: 222

Promoters: 3,770

TF Binding Sites: 2,700

Reg Interactions: 5,900

Genes: 4,499

URL: EcoCyc.org

perspective 1 ecocyc as online encyclopedia
Perspective 1:EcoCyc as Online Encyclopedia
  • All gene products for which experimental literature exists are curated with a minireview summary
    • 3,730 gene products contain summaries
    • Summaries cover function, interactions, mutant phenotypes, crystal structures, regulation, and more
  • Additional summaries and other data found in pages for genes, operons, pathways
  • Quick Search
perspective 2 ecocyc as queryable database
Perspective 2: EcoCyc as Queryable Database
  • High-fidelity knowledge representation amenable to structured queries
  • 333 database fields capture object properties and relationships
  • Each molecular species defined as a DB object
    • Genes, proteins, small molecules
  • Each molecular interaction defined as a DB object
    • Metabolic and transport reactions, regulation
  • Extensive search tools
    • Object-specific search Search Menu
    • Advanced search Search -> Advanced
paradigm 3 ecocyc as predictive metabolic model
Paradigm 3: EcoCyc as Predictive Metabolic Model
  • A steady-state quantitative model of E. coli metabolism can be generated from EcoCyc
  • Predicts phenotypes of E. coli knock-outs, and growth/no-growth of E. coli on different nutrients
  • Model is updated on each EcoCyc release
  • Serves as a quality check on the EcoCyc data
ecocyc accelerates science
EcoCyc Accelerates Science
  • Experimentalists
    • E. coli experimentalists
    • Experimentalists working with other microbes
    • Analysis of expression data
  • Computational biologists
    • Biological research using computational methods
    • Genome annotation
    • Study properties of E. coli metabolic and regulatory networks
  • Bioinformaticists
    • Training and validation of new bioinformatics algorithms – predict operons, promoters, protein functional linkages, protein-protein interactions,
  • Metabolic engineers
    • “Design of organisms for the production of organic acids, amino acids, ethanol, hydrogen, and solvents “
  • Educators
    • Microbiology and metabolism education
recent developments in ecocyc
Recent Developments in EcoCyc
  • EcoCyc contains six knock-out datasets for E. coli containing 13,000 growth observations
recent developments in ecocyc growth observation data
Recent Developments in EcoCyc –Growth-Observation Data
  • EcoCyc contains 1831 growth observations under 522 conditions for E. coli
  • Substantial number of discrepancies
    • 45 cases remain where growth status is unclear
metacyc meta bolic en cyc lopedia
MetaCyc: Metabolic Encyclopedia
  • Describes experimentally determined metabolic pathways, reactions, enzymes, and compounds
  • Literature-based DB with extensive references and commentary
  • MetaCycvsBioCyc: Experimentally elucidated pathways
  • Jointly developed by
    • P. Karp, R. Caspi, C. Fulcher, SRI International
    • L. Mueller, A. Pujar, Boyce Thompson Institute
    • S. Rhee, P. Zhang, Carnegie Institution

Nucleic Acids Research2012 Database Issue

metacyc data version 18 0
MetaCyc Data -- Version 18.0

“A Systematic Comparison of the MetaCyc and KEGG Pathway Databases

BMC Bioinformatics 2013 14(1):112

comparison with kegg
Comparison with KEGG
  • KEGG vsMetaCyc: Reference pathway collections
    • KEGG maps are not pathways Nuc Acids Res 34:3687 2006
      • KEGG maps contain multiple biological pathways
      • KEGG maps are composites of pathways in many organisms -- do not identify what specific pathways elucidated in what organisms
      • Two genes chosen at random from a BioCyc pathway are more likely to be related according to genome context methods than from a KEGG pathway
    • KEGG has few literature citations, few comments, less enzyme detail
  • KEGG vs organism-specific PGDBs
    • KEGG does not curate or customize pathway networks for each organism
    • Highly curatedPGDBs now exist for important organisms such as E. coli, yeast, mouse, Arabidopsis
pathway tools software1
Pathway Tools Software

+

PathoLogic

MetaCyc

Annotated

Genome

Pathway/Genome

Navigator

Pathway/Genome

Database

MetaFlux

Pathway/Genome

Editors

Briefings in Bioinformatics 11:40-79 2010

pathway tools enables multi use metabolic databases
Pathway Tools Enables Multi-Use Metabolic Databases

Metabolic Model

Encyclopedia

Queryable Database

Zoomable Metabolic Map

Omics Data

Analysis

pathway tools software pathologic
Pathway Tools Software: PathoLogic
  • Computational creation of new Pathway/Genome Databases
  • Transforms genome into Pathway Tools schema and layers inferred information above the genome
  • Predicts operons
  • Predicts metabolic network
  • Predicts which genes code for missing enzymes in metabolic pathways
  • Infers transport reactions from transporter names
pathway tools software pathway genome editors
Pathway Tools Software:Pathway/Genome Editors
  • Interactively update PGDBs with graphical editors
  • Support geographically distributed teams of curators with object database system
  • Gene and protein editor
  • Reaction editor
  • Compound editor
  • Pathway editor
  • Operon editor
  • Publication editor
pathway tools software pathway genome navigator
Pathway Tools Software:Pathway/Genome Navigator
  • Querying and visualization of:
    • Pathways
    • Reactions
    • Metabolites
    • Genes/Proteins/RNA
    • Regulatory interactions
    • Chromosomes
  • Modes of operation:
    • Web mode
    • Desktop mode
    • Most functionality shared
pathway tools software metaflux
Pathway Tools Software: MetaFlux
  • Speeds development of genome-scale metabolic flux models
  • Steady-state quantitative flux-models generated directly from PGDBs
  • Computed reaction fluxes can be painted onto metabolic overview diagram
  • Multiple gap filler accelerates model development by suggesting model completions:
    • Reactions to add from MetaCyc
    • Additional nutrients and secreted compounds
pathway tools schema ontology
Pathway Tools Schema / Ontology
  • 1064 classes
    • Datatype classes such as:
      • Pathways, Reactions, Compounds, Macromolecules, Proteins, Replicons, DNA-Segments (Genes, Operons, Promoters)
    • Taxonomies for Pathways, Reactions, Compounds
    • Cell Component Ontology
    • Evidence Ontology
  • 308 attributes and relationships
  • Span genome, metabolism, regulatory information
    • Meta-data: Creator, Creation-Date
    • Comment, Citations, Common-Name, Synonyms
    • Attributes: Molecular-Weight, DNA-Footprint-Size
    • Relationships: Catalyzes, Component-Of, Product
pathway prediction
Pathway Prediction
  • Pathway prediction is useful because
    • Pathways organize the metabolic network into mentally tractable units
    • Pathways guide us to search for missing enzymes
    • Pathway inference fills in holes in the metabolic network
    • Pathways can be used for analysis of high-throughput data
      • Visualization, enrichment analysis
  • Pathway prediction is hard because
    • Reactome inference is imperfect
    • Some reactions present in multiple pathways
    • Pathway variants share many reactions in common
    • Increasing size of MetaCyc
reactome inference
Reactome Inference
  • For each protein in the organism, infer reaction(s) it catalyzes
  • Protein functions can be specified in three ways:
    • Enzyme names (protein functions) (uncontrolled vocabulary)
    • EC numbers
    • Gene Ontology terms
  • Detect conflicts among this information
    • Example:
      • Yersiniapseudotuberculosis PB1
      • 2-succinyl-5-enolpyruvyl-6-hydroxy-3-cyclohexene-1-carboxylate synthase / EC 4.1.1.71
enzyme name matching
Enzyme Name Matching
  • Extraneous information found in gene product names
  • Putative carbamatekinase, alpha subunit
  • Carbamatekinase (abcD)
  • Carbamatekinase (3.2.1.4)
  • Monoamine oxidase B
  • bifunctionalproline dehydrogenase/pyrroline-5-carboxylate dehydrogenase
inference of metabolic pathways
Inference of Metabolic Pathways
  • For each pathway in MetaCyc consider
    • What fraction of its reactions are present in the just-inferred reactome of the organism?
    • Are enzymes present for reactions unique to the pathway?
    • Are enzymes present for designated “key reactions” within MetaCyc pathways?
      • Calvin cycle / ribulosebisphosphatecarboxylase
    • Is a given pathway outside its designated taxonomic range?
      • Calvin cycle: green plants, green algae, etc

Standards in Genomic Sciences 5:424-429 2011

evaluation of pathway inference
Evaluation of Pathway Inference
  • Define gold-standard pathway prediction set
    • E. coli, Yeast, Arabidopsis, Synechococcus, Mouse
    • Positive and negative pathways
  • PathoLogic achieved 91% accuracy

BMC Bioinformatics 11:15 2010

comparison with kegg1
Comparison with KEGG
  • KEGG vsMetaCyc: Reference pathway collections
    • KEGG maps are not pathways Nuc Acids Res 34:3687 2006
      • KEGG maps contain multiple biological pathways
      • KEGG maps are composites of pathways in many organisms -- do not identify what specific pathways elucidated in what organisms
    • KEGG modules are incomplete
    • KEGG has few literature citations, few comments, less enzyme detail
  • KEGG vs organism-specific PGDBs
    • KEGG does not curate or customize pathway networks for each organism
    • Highly curatedPGDBs now exist for important organisms such as E. coli, yeast, mouse, Arabidopsis
  • KEGG algorithms
    • Not published; accuracy unknown
pathway analysis of metagenomes
Pathway Analysis of Metagenomes
  • Bin the metagenome data and create separate PGDBs for each organism
    • Hallam lab
  • Compute list of all pathways present in the metagenome
analysis of high throughput datasets
Analysis of High Throughput Datasets
  • Genome-scale visualizations of cellular networks
  • Generated automatically from PGDB
  • Magnify, interrogate
  • Omics viewers paint omics data onto overview diagrams
    • Different perspectives on same dataset
    • Use animation for multiple time points or conditions
cellular overview diagram
Cellular Overview Diagram
  • Combines metabolic map and transporters
  • Automatically generated, organism-specific
  • Zoomable, queryable
regulatory overview
Regulatory Overview
  • Show regulatory relationships among gene groups
the atom mapping problem
The Atom-Mapping Problem
  • Definition: An atom-mapping is a bijection from reactant atoms to product atoms that specifies the terminus of each reaction atom
  • MetaCyc v17.5 contains 10,300 atom mappings
applications of atom mapping
Applications of Atom Mapping
  • Speed visual comprehension of reactions and pathways
applications of atom mapping1
Applications of Atom Mapping
  • Improve evaluation of computer-generated metabolic pathways
    • Do any feedstock atoms reach target compound?
    • What fraction of feedstock atoms reach target compound?
  • Facilitate design and interpretation of isotope-labeling experiments
atom mapping our approach
Atom Mapping: Our Approach
  • Weighted Minimal Bond-Edit Distance
    • Edit distance weighted by bond type and atom species
    • Computed using MILP for 9,390 MetaCyc reactions
    • Average time per reaction:
      • 73% are solved in less than 1 second
      • 96% are solved in less than 60 seconds
    • 96% of reactions had 1 or 2 solutions (with symmetries removed) – different bonds made/broken
  • Solution times are a function of the solver
    • SCIP vs CPLEX

J ChemInf Model. 2012 52:2970-82.

accuracy of our atom mappings
Accuracy of Our Atom Mappings
  • Use KEGG RPAIR as a gold standard
    • Caveats: Not clear which RPAIRs are curated; accuracy of RPAIR unclear
  • We implemented software to
    • Import KEGG and RPAIR into a Pathway Tools PGDB
    • Map atoms in KEGG reactions to corresponding atoms in MetaCyc reactions
  • 2,446 atom mappings from KEGG RPAIR could be compared to MetaCyc mappings
    • 25 disagreements:
    • 1 reaction: experimental evidence our mapping is correct
    • 2 reactions: similar to preceding
    • 22 reactions – KEGG is correct
routesearch software metabolism metabolic route search
RouteSearch Software --- Metabolism->Metabolic Route Search
  • User specifies feedstock compound and target compound
  • Software computes minimal-cost paths from feedstock to target based on reactions from
    • Current PGDB, plus, optionally
    • MetaCyc
  • Optimality criteria: minimize
    • Number of reactions
    • Number of lost atoms based on atom mappings
    • Number of reactions foreign to the organism
  • User interface guides exploration of solution pathways

Latendresse et al., Bioinformatics 2014

results sample metabolic engineering problems
Results: Sample Metabolic Engineering Problems
  • Five engineered pathways obtained from literature
    • 2-oxoisovalerate 3-methylbutanol (3-methylbutanol biosynthesis)
    • pyruvateisopropanol (isopropanol biosynthesis)
    • pyruvaten-butanol (pyruvate fermentation to butanol II)
    • 3-phospho-D-glycerate n-butanol (1-butanol autotrophic biosynthesis)
    • 3-dehydroquinate  vanillin (vanillin biosynthesis)
  • Given feedstock and target compounds, our system found the literature pathway in all five cases
pyruvate isopropanol
PyruvateIsopropanol
  • Two highest-ranked pwys shown
  • Best corresponds to pwy from literature
  • Search engine can continue to generate alternatives
metabolomics applications
Metabolomics Applications
  • MetaCyc contains extensive multiorganism metabolite database
  • Organism-specific metabolite databases in each PGDB
    • Genome+pathway context for interpreting metabolomics data
  • Monoisotopic mass searches
  • Paint metabolomics data onto pathway maps
  • Group transformations
  • Enrichment analysis
object groups
Object Groups
  • Collect and save lists of metabolites, genes, pathways, …
  • Share groups with colleagues
object groups1
Object Groups
  • Create manually, from files, from query results
  • Explore gene list interactively
  • Combine (union, intersection, subtraction)
object group transformations
Object Group Transformations
  • Transform metabolite group into group of metabolic pathways, then into gene group
  • Create group containing transcriptional regulator; transform to all genes it regulates
  • Transform gene group into group of regulators of those genes
  • Transform gene group into list of TF binding sites controlling those genes; into list of sequences
  • Create group of nucleotide positions; transform to closest genes; paint to cellular overview
object groups enrichment analysis
Object Groups: Enrichment Analysis

“My experiments yielded a set of genes/metabolites. What do they have in common?”

  • Given a group of genes:
    • What GO terms are statistically over-represented in that set?
    • What metabolic pathways are over-represented?
    • What transcriptional regulators are over-represented?
  • Given a set of metabolites:
    • What metabolic pathways are statistically over-represented in that set?
marriage of systems biology and model organism databases
Marriage of Systems Biology and Model-Organism Databases
  • Systems biology
    • Qualitative system-level analysis
    • Quantitative system-level modeling
  • Hypothesis: Strong synergies between MODs and SB
  • Curation is critical to SB and to MODs
    • Biological models undergo long periods of updating and refinement
    • Common curation effort for MOD and systems-biology model
  • MOD provides data needed for SB construction and validation
  • SB identifies errors and omissions in MOD, directs curation
  • Methodologies from MODs can benefit systems-biology models
    • Evidence codes
    • Mini-review summaries
    • Literature citations
flux balance analysis
Flux-Balance Analysis
  • Steady state, constraint-based quantitative models of metabolism
  • Starting information for organism of interest:

Nutrients

Secretions

Metabolic Reaction List

A

A

B

C

D

D

X

Biomass

flux balance analysis1
Flux Balance Analysis
  • Define system of linear equations encoding fluxes on each metabolite M
    • R1 + R2 = R3 + R4 + R5
  • Boundary reactions:
    • Exchange fluxes for nutrients and secretions
    • Biomass reaction L-arginine … + GTP … + …  biomass
  • Submit to linear optimization package
    • Optimize biomass production
    • Optimize ATP production
    • Optimize production of desired end product

R1

R3

M

R4

R2

R5

example
Example

Biomass: ATP:alanine 4:1

40

alanine

100

100

glucose

glucose

2 pyruvate

160

ATP

160

O2

O2

pathway prediction1
Pathway Prediction
  • Pathway prediction is useful because
    • Pathways organize the metabolic network into mentally tractable units
    • Pathways guide us to search for missing enzymes
    • Pathway inference fills in holes in the metabolic network
    • Pathways can be used for analysis of high-throughput data
      • Visualization, enrichment analysis
  • Pathway prediction is hard because
    • Reactome inference is imperfect
    • Some reactions present in multiple pathways
    • Pathway variants share many reactions in common
    • Increasing size of MetaCyc
fba results
FBA Results
  • FBA predicts steady-state reaction fluxes for the metabolic network
  • Remove reactions from model to predict knock-out phenotypes
  • Supply alternative nutrient sets to predict growth phenotypes
  • Predict growth rates, nutrient uptake rates
approach generate fba models from pathway genome databases
Approach: Generate FBA Models from Pathway/Genome Databases
  • Store and update metabolic model within PGDB
    • All query and visualization tools applicable to FBA model
    • FBA model is tightly coupled to genome and regulatory information
  • MetaFlux generates linear programming problem from PGDB reactions
  • Submit to constraint solver for model execution/solving
  • Tools to accelerate model refinement:
    • Reaction balance checking
    • Dead-end metabolite analysis
    • Visualize reaction flux using cellular overview
    • Multiple gap filling

MetaFlux: Latendresse et al, Bioinformatics 2012 28:388-96

metaflux fba model execution
MetaFlux FBA Model Execution
  • MetaFlux creates .lp file and executes SCIP solver
    • Konrad-Zuse-ZentrumfürInformationstechnik Berlin
  • Interpret SCIP output
    • Determine if SCIP found a solution
    • Map fluxes to PGDB reactions
  • Display resulting fluxes on the Cellular Overview
model debugging via dead end metabolite finder
Model Debugging ViaDead End Metabolite Finder
  • A small molecule C is a dead-end if:
    • C is produced only by metabolic reactions in Compartment, and no transporter acts on C in Compartment OR
    • C is consumed only by metabolic reactions in Compartment, and no transporter acts on C in Compartment
dead end metabolite analysis of ecocyc
Dead-End Metabolite Analysis of EcoCyc
  • (2R,4S)-2-methyl-2,3,3,4-tetrahydroxytetrahydrofuran
  • 3-hydroxypropionate
  • 4-methyl-5-(beta-hydroxyethyl)thiazole
  • 5,6-dimethylbenzimidazole
  • aminoacetaldehyde
  • cis-vaccenate
  • cobinamide
  • ethanolamine
  • methanol
  • oxamate
  • S-adenosyl-4-methylthio-2-oxobutanoate
  • S-methyl-5-thio-D-ribose
  • S2-
  • tetrahydromonapterin
  • urate
  • urea
  • 148 dead-end metabolites total
  • 16 dead-end metabolites in EcoCyc pathways:
model debugging via multiple gap filling
Model Debugging via Multiple Gap Filling
  • Most FBA models are not initially solvable because of incomplete or incorrect information
  • MetaFlux uses meta-optimization to postulate alterations to a model to render it solvable
  • Each alteration has an associated cost; minimize cost of alterations
  • Formulate as MILP and submit to SCIP
multiple gap filling of fba models
Multiple Gap Filling of FBA Models
  • Reaction gap filling (Kumar et al, BMC Bioinf 2007 8:212):
    • Reverse directionality of selected reactions
    • Add a minimal number of reactions from MetaCyc to the model to enable a solution
    • Reaction cost is a function of reaction taxonomic range
  • Metabolite gap filling: Postulate additional nutrients and secretions
  • Partial solutions: Identify maximal subset of biomass components for which model can yield positive production rates
milp objective function for gap filling
MILP Objective Function for Gap Filling

ΣwbBi + ΣwrRa+ΣwtRb +ΣwmRc +ΣwsSk +ΣwnNp

Where

  • Wb > 0, wr, wt, wm, ws, wn < 0 are weights for biomass, reactions (2), secretions, and nutrients
  • Bi, Ra, Rb, Rc, Sk, Np are binary variables

i

a

b

c

k

p

results fba model of human metabolism
Results – FBA Model of Human Metabolism
  • 46 biomass compounds
  • 13 nutrients
  • 2 secretions
  • 207 reactions carry non-zero flux
metaflux gap filler suggestions
MetaFlux Gap Filler Suggestions
  • Addition of 8 new reactions from MetaCyc; 4 supported by literature research
  • Reversal of 4 reactions confirmed by literature searches
  • Enzyme curated into wrong compartment
  • FBA analysis identified an amino-acid biosynthetic pathway that should not have been present in HumanCyc
  • Further issues identified by dead-end metabolite analysis and reachability analysis
other capabilities
Other Capabilities
  • Display and editing of protein features
  • Blast sequences against PGDBs
  • Retrieve nucleotide and amino acid sequences
  • Define Web links from PGDB objects to other web sites
  • Active community of contributors
    • JavaCyc, PerlCyc
    • SBML and BioPAX export tools
pathway tools implementation details
Pathway Tools Implementation Details
  • Platforms:
    • Macintosh, PC/Linux, and PC/Windows platforms
  • Same binary can run as desktop app or Web server
  • PGDBs can be stored in files, MySQL, Oracle
  • Production-quality software
    • Two regular releases per year
    • Extensive quality assurance
    • Extensive documentation
    • Auto-patch
    • Automatic DB-upgrade
accesing pgdb data
Accesing PGDB Data
  • Export to Genbank, SBML, BioPAX
  • Export to tab-delimited files
  • Export to attribute-value files
  • Attribute-value files can be imported into SRI’s BioWarehouse
    • Relational database system for bioinformatics database integration
  • APIs
    • Web services -- http://biocyc.org/web-services.shtml
    • Lisp
    • PerlCyc
    • JavaCyc
summary
Summary
  • Pathway/Genome Databases
    • MetaCyc non-redundant DB of literature-derived pathways
    • MetaCyc family of ~4,000 PGDBs
  • Pathway Tools software
    • Extract pathways from genomes
    • Distributed curation tools for PGDB development
    • Query, visualization, WWW publishing
    • Omics data analysis
    • Quantitative metabolic models
biocyc and pathway tools availability
BioCyc and Pathway Tools Availability
  • BioCyc.org Web site and database files freely available to all
  • Pathway Tools freely available to non-profits
    • Macintosh, PC/Windows, PC/Linux
acknowledgements
SRI

Suzanne Paley, Ron Caspi, Mario Latendresse, Ingrid Keseler, Carol Fulcher, Tim Holland, Markus Krummenacker, TomerAltman, Richard Billington, PallaviKaipa, DeepikaBrito

EcoCyc Collaborators

Julio Collado-Vides, Robert Gunsalus, Ian Paulsen

MetaCyc Collaborators

Sue Rhee, Peifen Zhang, Kate Dreher

Lukas Mueller, HartmutFoerster

Funding sources:

NIH National Institute of General Medical Sciences

Department of Energy

Acknowledgements

http://www.ai.sri.com/pkarp/talks/

BioCyc webinars:

biocyc.org/webinar.shtml

learn more
Learn More
  • Pathway Tools Tutorial
    • April 25-27
  • http://bioinformatics.ai.sri.com/ptools/tutorial/