1. Semantic Aggregation, Integration, and Inference of Pathway Data
(Pedantic Aggravation, Irritation, and Interference)
Co-Destructors:
Joanne Luciano, PhD
Jeremy Zucker
ISMB 2005 Tutorial
Detroit Michigan
June 25th 2005

2. Overview Time Out (15 minutes) Introduction (45 minutes)
Workshop Case Studies & Exercises (2 hrs 15 minutes)
Subdivide into groups of triads and dyads
Case Study I (45 minutes)
Case Study II (45 minutes)
Case Study III (45 minutes)
Lessons Learned (30 minutes)
Lessons Not Yet Learned (take home)

3. Introduction (45 minutes)
Semantic Aggregation, Integration and Inference of Pathway Data
Pathway Data (domain)
What is it?
What does it look like?
Why do we care? (motivation)
Definitions & Disclaimers
Strategies

4. So many pathway databases, so little time.
Pathway Data (domain)
What is it?
Pathway Databases
So many pathway databases, so little time.
Graphic from Mike Cary and Gary Bader

5. Different types of pathways (different strokes for different folks, it’s OK.)
Glycolysis
Protein-Protein
Apoptosis
Lac Operon
Also include gene regulation
lac operon
LacZ, the first gene of the lac operon, encodes the enzyme b-galactosidase which breaks down lactose to galactose and glucose. The initiation of transcription of lac operon is controlled by the following mechanisms participating lac repressor protein and CAP: (1) lactose addition increases the concentration of allolactose which binds to the repressor protein and removes it from the DNA, and (2) glucose addition decreases the concentration of cAMP; because cAMP no longer binds to CAP, this gene activator dissociates from the DNA, turning of the operon. LacZ, the first gene of the lac operon, encodes the enzyme b-galactosidase which breaks down lactose to galactose and glucose. The initiation of transcription of lac operon is controlled by the following mechanisms participating lac repressor protein and CAP: (1) lactose addition increases the concentration of allolactose which binds to the repressor protein and removes it from the DNA, and (2) glucose addition decreases the concentration of cAMP; because cAMP no longer binds to CAP, this gene activator dissociates from the DNA, turning of the operon.
Molecular
Interaction
Networks
Gene
Regulation
Metabolic
Pathways
Signaling
Pathways
The Main Categories

6. Different representations of the same pathways
<!ELEMENT reaction (substrate*,product*)>
<!ATTLIST reaction name %keggid.type; #REQUIRED>
<!ATTLIST reaction type %reaction-type.type; #REQUIRED>
<!ELEMENT substrate EMPTY>
<!ATTLIST substrate name %keggid.type; #REQUIRED>
<!ELEMENT product EMPTY>
<!ATTLIST product name %keggid.type; #REQUIRED>
starts at a-D-Glucose 1P
KEGG Reference Pathway GLYCOLYSIS

7. Different representations of the same pathways
reactions.dat
This file lists all chemical reactions in the PGDB.
Attributes:
UNIQUE-ID
TYPES
COMMON-NAME
ACTIVATORS
BASAL-TRANSCRIPTION-VALUE
DBLINKS
DELTAG0
DEPRESSORS
EC-LIST
EC-NUMBER
ENZYMATIC-REACTION
EQUILIBRIUM-CONSTANT
IN-PATHWAY
INHIBITORS
LEFT
MOVED-IN
MOVED-OUT
OFFICIAL-EC?
REACTANTS
REQUIREMENTS
RIGHT
SIGNAL
SPECIES
SPONTANEOUS?
STIMULATORS
SYNONYMS
starts at b-D-glucose6-phosphate
BioCYC Reference Pathway GLYCOLYSIS

8. Different representations of the same pathways
<reaction name="R_alpha_D_glucose_6_phosphate_D_fructose_6_phosphate" id="R_163457">
<listOfReactants>
<speciesReference species="R_30537_alpha_D_Glucose_6_phosphate" />
</listOfReactants>
<listOfProducts>
<speciesReference species="R_29512_D_Fructose_6_phosphate" />
</listOfProducts>
<listOfModifiers>
<modifierSpeciesReference species="R_163455_glucose_6_phosphate_isomerase_dimer_name_copied_from_complex_in_Homo_sapiens_" />
</listOfModifiers>
</reaction>
DatabaseObject [41245]
Event [8285]
Reaction [6598]
ConcreteReaction [4034]
GenericReaction [2564]
Reactome Pathway GLYCOLYSIS

9. Different representations of the same pathways
Does not compute.
Pretty,
but useless
Reactions clickable but...
Starts at Glucose (but it doesn’t matter)
BioCarta Reference Pathway GLYCOLYSIS

10. Pathway Data Why do we care?
Pathway Research has Broad Impact
Drug Discovery (pathway of target, safety)
Basic Science (identify pathways)
Disease Research (cancer pathways)
Environmental Research (microbial research)
Combine knowledge from multiple sources
Whole is greater than the sum of its parts
Biological knowledge is fragmented
Need database to manage resources

11. Definitions & Disclaimers
Aggregation
2 (or more) data sources, different data models, common link between (among) them.
Integration
2 (or more) data sources, same data model, semantic mapping and instance merging required.
Inference
1 (or more) data sources, one data model, creating new instances or new relationships.
(Evidence code type kind of “inference”)
Disclaimer
“Controlled” Vocabulary scope = this tutorial

12. Assembling Knowledge Aggregation, Integration, Inference
“When it comes to data cleaning, there’s no such thing as a free lunch.” Tim Berners-Lee
Some tasks are specific to a use case, some are common to more than one and there’s no escaping others.

13. Bridging Chemistry and Molecular Biology
Different Views have different semantics: Lenses
When there is a correspondence between objects, a semantic binding is possible
Example of semantic web technology being used to join (aggregate) two databases.
Uniprot:P49841
Apply Correspondence Rule: if ?target.xref.lsid == ?bpx:prot.xref.lsid then ?target.correspondsTo.?bpx:prot
Source: Eric Neumann
Haystack BioDASH Demo

14. Seamark Demonstration: Identification of new drug candidates
GO2Keyword.rdf
UniProt.rdf
GO.rdf
Keywords.rdf
Taxonomy.rdf
PubMed.xml
Citation
IntAct.rdf
Organism
Enzymes.rdf
OMIM.rdf
GO2OMIM.rdf
GO2Enzyme.rdf
MIM Id
KEGG.rdf
Keyword
GO2UniProt.rdf
Protein
Enzyme
ProbeSet.rdf
Gene
Probe
Pathway
Compound
1. Differentiate different forms of disease
2. Identify patients subgroups.
3. Identify top biomarkers
4. Identify function
5. Identify biological and chemical properties and disease associations of biomarker
6. Identify documents
7. Identify role in metabolic pathways
8. Identify compounds that interact
9. Identify and compare function in other organisms
10. Identify any prior art
Example of semantic web technology being used to join (aggregate) two databases.

15. SMBL integration using BioPAX
Use BioPAX to Address SBML’s data integration issues
Different data types, same representation
Same data, different representations
External references…
Synonyms…
Provenance…

16. A problem: same representation different semantics (SBML)
Protein-Protein Interaction
<reaction
id=“pyruvate_dehydrogenase_cplx”/>
<listOfReactants>
<speciesRef species=“PdhA”/>
<speciesRef species=“PdhB”/>
</listOfReactants>
<listOfProducts>
<speciesRef species=“Pyruvate_dehydrogenase_E1”/>
</listOfProducts>
</reaction>
Biochemical Reaction
<reaction
id=“pyruvate_dehydrogenase_rxn”/>
<listOfReactants>
<speciesRef species=“NADP+”/>
<speciesRef species=“CoA”/>
<speciesRef species=“pyruvate”/>
</listOfReactants>
<listOfProducts>
<speciesRef species=“NADPH”/>
<speciesRef species=“acetyl-CoA”/>
<speciesRef species=“CO2”/>
</listOfProducts>
<listOfModifers>
<modifierSpeciesRef species=“pyruvate_dehydrogenase_E1”/>
</listOfModifiers>
</reaction>
Example of semantic mapping:Protein-protein interaction and Biochemical reaction are two different types or interactions but they are represented as one type (reaction) in SMBL.

17. SBML annotated with BioPAX
<sbml xmlns:bp=“
xmlns:owl="
xmlns:rdf="
<listOfSpecies>
<species id=“PdhA” metaid=“PdhA”>
<annotation>
<bp:protein rdf:ID=“#PdhA”/>
</annotation>
</species>
<species id=“NADP+” metaid=“NADP+”>
<bp:smallMolecule rdf:ID=“#NADP+”/>
</listOfSpecies>
<listOfReactions>
<reaction id=“pyruvate_dehydrogenase_cplx”>
<bp:complexAssembly rdf:ID=“#pyruvate_dehydrogenase_cplx”/>
</reaction>
</listOfReactions>
species is protein
protein is PdhA
species is small molecule
small molecule is NADP+

18. BioPAX: External References
<species id=“pyruvate” metaid=“pyruvate”>
<annotation
xmlns:bp=“
<bp:smallMolecule rdf:ID=“#pyruvate”>
<bp:Xref>
<bp:unificationXref rdf:ID=“#unificationXref119">
<bp:DB>LIGAND</bp:DB>
<bp:ID>c00022</bp:ID>
</bp:unificationXref>
</bp:Xref>
</bp:smallMolecule>
</annotation>
</species>
Example of BioPAX Example of integration
We can use the annotation field of SBML, which is typically free format text, to carry the BioPAX ontology as a controlled vocabulary in order to achieve aggregation. Here we point to the BioPAX namspace as the source of the controlled vocabulary that will define the terms Xref, smallMolecule, unificationXref and ID. The metadid and rdfid must be identical. That is how the two representations are linked and the instance data are found.

19. BioPAX: Synonyms <species id=“pyruvate” metaid=“pyruvate”>
<annotation xmlns:bp=“
<bp:smallMolecule rdf:ID=“#pyruvate” >
<bp:SYNONYMS>2-oxo-propionic acid</bp:SYNONYMS>
<bp:SYNONYMS>2-oxopropanoate</bp:SYNONYMS>
<bp:SYNONYMS>BTS</bp:SYNONYMS>
<bp:SYNONYMS>pyruvic acid</bp:SYNONYMS>
</bp:smallMolecule>
</annotation>
</species>
Example of integration. This example shows how multiple data sources can be linked via the synonyms. All known synonyms should be included for maximum coverage.

20. or have the world take over itself?)
Strategies
How we get to a Standard Pathway Representation? (Game plan: Take over the world
or have the world take over itself?)
Develop bridging technologies
Develop pathway representation standard within the Life Science community (BioPAX) (Social Engineering!)
Utilize Semantic Web Integration Technologies (LSID, RDF/OWL)

21. Exchange Formats in Pathway Data Space (Scope)
BioPAX
PSI-MI 2
SBML,
CellML
Genetic
Interactions
Molecular Interactions
Pro:Pro All:All
Interaction Networks
Molecular Non-molecular
Pro:Pro TF:Gene Genetic
Regulatory Pathways
Low Detail High Detail
Database Exchange
Formats
Simulation Model
Exchange Formats
Rate
Formulas
Metabolic Pathways
Low Detail High Detail
Biochemical
Reactions
Small Molecules
To design bridging technologies, we look at the landscape. This slide shows the coverage of exchange formats in the pathway data space highlighting the focus of their different domains.
BioPAX level 1 is broad and shallow allowing us to quickly build a format that can represent most the existing data types in databases today. Details will be added in subsequent levels. This is a practical approach.
Pathway databases and tools are not considered here, although each one is important and addresses specific use cases.
Graphic from Mike Cary & Gary Bader

22. BioPAX Objectives Accommodate existing database representations
Integration and exchange of pathway data
Interchange through a common (standard) representation
Provide a basis for future databases
Enable development of tools for searching and reasoning over the data
Also many other databases and tools have been published e.g. PATIKA We would like to learn from these as well.
Importantly: An exchange format needs to be a compatible superset of the databases, thus BioPAX will be copying certain structures from the databases in order to support them. A good reaction to BioPAX from a group that has developed their own ontology is that BioPAX looks like their specification.
Designed by the databases for themselves (DB-DB exchange) and for users.
Group emerged from this community, at this very meeting two years ago.

23. BioPAX Motivation >180 DBs and tools Application Database User
Designed by the databases for themselves (DB-DB exchange) and for users.
User
Before BioPAX
With BioPAX
Common format will make data more accessible, promoting data sharing and distributed curation efforts

24. BioPAX Biological PAthway eXchange
A data exchange ontology and format for biological pathway integration, aggregation and inference
Initiative arose from the community
BioPAX will provide a consistent format for pathway data so it will be easier for consumers of pathway data (e.g. tool developers, DB curators) to integrate data from multiple sources.

25. Biological pathways of the Cell
What is a Pathway?
Glycolysis
Protein-Protein
Apoptosis
Lac Operon
Also include gene regulation
Molecular
Interaction
Networks
Metabolic
Pathways
Signaling
Pathways
Gene
Regulation
BioPAX
Level 1
BioPAX
Level 2

26. Aggregation, Integration, Inference
Multiple kinds of pathway databases
metabolic
molecular interactions
signal transduction
Constructs designed for integration
DB References
XRefs (Publication, Unification, Relationship)
synonyms
provenance
OWL DL – to enable reasoning

27. BioPAX Biochemical Reaction
OWL
(schema)
Instances
(Individuals)
(data)
phosphoglucose
isomerase

28. BioPAX Ontology Conceptual framework based upon existing DB schemas:
aMAZE, BIND, EcoCyc, WIT, KEGG, Reactome, etc.
Allows wide range of detail, multiple levels of abstraction
BioPAX ontology in OWL (XML)
Designed for pathway database integration
Database ID
Unification X-REF
Relationship X-REF
Publication X-REF
Synonyms
Provenance

29. BioPAX uses other ontologies
Use pointers to existing ontologies to provide supplemental annotation where appropriate
Cellular location  GO Component
Cell type  Cell.obo
Organism  NCBI taxon DB
Incorporate other standards where appropriate
Chemical structure  SMILES, CML, INCHI

30. BioPAX Ontology: Overview
a set of
interactions
parts
This slide puts it all together
how the parts are known to interact
Level 1 v1.0 (July 7th, 2004)

31. BioPAX Ontology: Top Level
Entity
Pathway
Interaction
Physical Entity
Subclass (is a)
Contains (has a)
Pathway
A set of interactions
E.g. Glycolysis, MAPK, Apoptosis
Interaction
A set of entities and some relationship between them
E.g. Reaction, Molecular Association, Catalysis
Physical Entity
A building block of simple interactions
E.g. Small molecule, Protein, DNA, RNA
We may end up calling parts ‘Interactor’ instead of ‘Part’ to be compatible with the PSI format
Pathway is a name for a set of interactions. The cell is really a large network, but we tend to organize the network into pathways for our own understanding of the very complex network. For this reason, it is important to be able to have a set of interactions and give it a name (and other description)
Graphic from Gary Bader

32. BioPAX Ontology: Root Root class: Entity
Any concept referred to as a discrete biological unit when describing pathways. This is the root class for all biological concepts in the ontology, which include pathways, interactions and physical entities

33. Metabolic Pathways participants Interaction sub-classes Definition
An entity that defines a single biochemical interaction between two or more entities.
An interaction cannot be defined without the entities it relates.
Future BioPAX levels, this list may be extended to include other classes, such as genetic interactions.
participants

34. Metabolic Pathways Interaction sub-classes
Definition Two terms exist under interaction: Control and conversion. In future BioPAX levels, this list may be extended to include other classes, such as genetic interactions.
Examples Enzyme catalysis controls a biochemical reaction, transport catalysis controls transport, a small molecule that inhibits a pathway by an unknown mechanism controls the pathway.

35. BioPAX as a solution to Aggregation, Integration, Inference
Multiple kinds of pathway databases
metabolic
molecular interactions
signal transduction
gene regulatory
Constructs designed for integration
DB References
XRefs (Publication, Unification, Relationship)
Synonyms
Provenance (not yet implemented)
OWL DL – to enable reasoning
Be careful when using the word provenance. It turns out that there is an official way to do provenance in RDF, and we need to think more deeply about how to do this in BioPAX. Larry Hunter talks about this quite knowledgably.

36. Time Out (15 minutes)

37. Workshop Case Studies & Exercises (2 hrs 15 minutes)
Break into groups of triads and dyads
Case Study I (45 minutes)
Use Case 1: Inference of a Metabolic Flux Model from an Annotated Genome
Group Exercise 1
Case Study II (45 minutes)
Use Case 2: Integration of a metabolic flux model from two sources
Group Exercise 2
Case Study III (45 minutes)
Use Case 3: Multi-source aggregation Validation and Testing
Group Exercise 3

38. Methodology Define the goal of the integration
How will the integrated data be used?
This defines the level of integration from syntactic through semantic
Take stock of current resources
This defines your staring point
Data base sources, programmers, lab access, collaborators
Scope the work to get from B to A
Data Profiling
Resource Profiling

39. 3 Case Studies
Case study I: Semantic Inference of metabolic pathway data from an annotated genome.
Case study II: Semantic Integration of a metabolic flux model from two sources.
Case study III: Semantic Aggregation of pathway data from multiple sources

40. Case Study I: Inference of a Metabolic Flux Model from an Annotated Genome
Objective: To apply Biological knowledge to constrain the possible behaviors of a metabolic network.
Resources: Annotated Genome, Transport DB, Pathway databases, experimental community, published literature
Metabolic Flux analysis is a rigorous test of the knowledge contained in a Pathway Database. Failure of a metabolic flux model to accurately predict the growth rate of a single-cell organism under known nutrient conditions indicates the database may be incomplete or contain incorrect data. Through the semantic aggregation, integration and inference of pathway data, we hope to discover those errors and correct them wherever possible.

41. Genes make RNA make Protein
Legend:
Gene5
RNA 5 P5
Gene
RNA
Protein
Gene6
RNA 6 P6
Transporter
Gene7
RNA 7 P7
Enzyme
Gene8
RNA 8 P8
Transcription
Translation
Gene9
RNA 9 P9

42. Proteins catalyze biochemical reactions
Periplasm P1 P5 P9 P8 B
2 D
Cytoplasm E A E F D P2 P6
Legend:
Metabolites: A-F C A B
2 B
Transporter F
Enzyme P7 P3
Catalyzes A C C D
Reaction:

43. Biochemical reactions comprise a metabolic network
Uptake: R5 B E R4 R2 2B
Biomass: R8 2D
Uptake: R1 A R6
Given: sum of the fluxes which produce a metabolite = sum of fluxes which consume the metabolite for every metabolite.
Given uptake limits on R1 and R5, compute the maximum flux through R8. D R3 F R7
Legend: Exchange Intracellular
Objective C
Waste: R9

44. Metabolic Inference Subgoals
Infer genes from sequence and homology
Infer enzymatic reactions from Enzyme Commission (EC) numbers
Infer metabolic reaction network from enzymatic reactions and metabolites.
Infer pathway holes using network debugging algorithms
Propose candidate enzymes using pathway-hole filling algorithms
Add experimentally verified candidates to the annotated genome
Lather, rinse, repeat

45. Data Profiling of the Annotated Genome
Orphaned genes
Orphaned enzymes
Misannotated genes
Misannotated enzymes
Sequencing errors
BLAST Algorithm errors

46. Schema Level Errors Biochemical reaction Biochemical reaction
Enzyme (protein) that catalyzes the biochemical reaction
Gene that codes for the gene product (protein enzyme)

47. Semantic bugs revealed by chemical structure
EcoCyc 7.5 Pathway:
Riboflavin and FMN
and FAD biosynthesis
No place to go!
4-(1-D-ribitylamino)-5-amino-2,6-dihydroxypyrimidine:

48. Semantic bugs revealed by chemical structure
EcoCyc 8.0 Pathway:
Riboflavin and FMN
and FAD biosynthesis
Synonyms
4-(1-D-ribitylamino)-5-amino-2,6-dihydroxypyrimidine:

49. Data Profiling of Pathway/Genome Database
Unbalanced Reactions
Pathway holes
Unproducible metabolites
Generalized Metabolites
Unconsumable metabolites (toxins)
Add more slides on network debugging algorithms that infer pathway holes.
Add slides on algorithms that fill pathway holes.

50. Bugs in Network structure revealed by Forward and Backward chaining
Known Nutrient set
Fired Reaction
Unfired Reaction
Essential
compounds
Missing essential
compound
Biomass

51. Bugs in Network structure revealed by Forward and Backward chaining
Unproduced metabolite
Precursor metabolite
Essential
compounds
Missing essential
compound
Biomass

52. Case study II: Integration of a metabolic flux model from two sources
What is metabolic flux analysis?
How does one build a metabolic flux model?
What can go wrong in building a metabolic flux model?

53. What is Metabolic Flux Analysis?
Starts with the metabolic network
Assumes steady-state behavior
Constrain with Thermodynamics
Add Nutrient conditions
Choose an objective: Biomass growth
Predicts growth rate for mutant and wild-type organisms under different conditions.
Pathologic parses annotated genome files from GENBANK, looks for annotation tags such as accession number, EC number, etc.
Infers proteins, enzyme complexes, biochemical reactions, pathways, metabolic fluxes.
Pathologic also parses FASTA sequence to infer transcription units (operons)
Pathologic also infers the missing enzymes that appear as gaps in metabolic pathways using ‘hole filling’ algorithm (infers from metabolic reactions from other organisms (multi tier – evidence of which enzymes within which reactions of which pathways).

54. Start with the metabolic network
Objective v8 B A F 2D E C v3 v2
Waste: v9
Uptake: v5
Uptake: v1 v4 v7 v6 D 2B
Flux legend:
Exchange Intracellular
Objective

55. Stoichiometric Matrix: Representation of the metabolic network+1 -1 B -2 C D 2 E F
R1: → A
R2: A → B
R3: A → C
R4: B + E → 2D
R5: → E
R6: 2B → C + F
R7: C → D
R8: D →
R9: F →
Given: sum of the fluxes which produce a metabolite = sum of fluxes which consume the metabolite for every metabolite.
Given uptake limits on R1 and R5, compute the maximum flux through R8.

56. What is a metabolic flux?
Source fluxes
Metabolite Pool
Sink fluxes

57. What is a metabolic flux?
For a reaction of stoichiometry
R2: A → B
the rate of reaction, or flux is equal to:
For a reaction of stoichiometry
R4: B+E → 2D
the flux is equal to:

58. What is a metabolic flux?
For a reaction of stoichiometry
R4: B+E → 2D
The rate of reaction, or flux, is equal to:

59. At steady-state, nonlinear dynamics simplify to linear fluxes.B B k2 v2 P2 k1 v1 P1 A
Aext A
Aext k3 P3 v3 C C

60. At steady-state, the sum of the fluxes that produce a metabolite is equal to the sum of the fluxes that consume it. B v2 v1 A
Aext v3 C

61. Stoichiometric Matrix: more unknowns than equations+1 -1 B -2 C D 2 E F v1 v2 v3 v4 v5 v6 v7 v8 v9
Given: sum of the fluxes which produce a metabolite = sum of fluxes which consume the metabolite for every metabolite.
Given uptake limits on R1 and R5, compute the maximum flux through R8.

62. How to determine the metabolic capabilities of a network?
Uptake: v5 B E v4 v2 2B
Biomass: v8 2D
Uptake: v1 A v6
Given: sum of the fluxes which produce a metabolite = sum of fluxes which consume the metabolite for every metabolite.
Given uptake limits on R1 and R5, compute the maximum flux through R8. D v3 F v7
Flux legend:
Exchange Intracellular
Objective C
Waste: v9

63. Using Elementary modes to study the steady state-behaviorF v3 v7 C F v7 C R9 v9 v5 V1 v2 v3 v4 v5 v6 v7 v8 v9 A +1 -1 B -2 C D 2 E F
All metabolic capabilities in steady states are composed of elementary flux modes, which are the minimal sets of enzymes that can each generate valid steady states. B E v4 v2 2B 2D v1 A v6 v8 D v3 F v7 C v9

64. How to make predictions about the behavior of the metabolic network?
Uptake: v5 B E v4 v2 2B
Biomass: v8 2D
Uptake: v1 A v6
Given: sum of the fluxes which produce a metabolite = sum of fluxes which consume the metabolite for every metabolite.
Given uptake limits on R1 and R5, compute the maximum flux through R8. D v3 F v7
Flux legend:
Exchange Intracellular
Objective C
Waste: v9

65. Optimal wild-type flux distributionv5 10
Optimal Growth Flux B E v4 v2 2B 10 10 2D v1 A v8 10 v6 D 20 v3 F
Steady State assumption
Given uptake limits on R1, compute the maximum flux through R8.
What results is a genome-scale metabolic flux model that is capable of growth rate and internal flux predictions. v7 C v9

66. Optimal mutant flux distributionv5 B E v4 v2 2B
STOP 2D v1 A v8 10 v6 D 10 v3 10 10 F
By knocking out the gene whose product catalyzes R4, what is the maximum flux we can obtain through R8? C v7 v9

67. Suboptimal mutant flux distributionv5 B E v4 v2 2B
6.7
STOP 2D v1 A
3.3 v8 10 v6 D
6.7
Minimization of metabolic adjustment: Minimize the difference between the wild-type flux distribution and the mutant flux distribution. v3
3.3
6.7 F C v7
3.3 v9

68. Case II: Palsson JR904 good flux balance model implicit schema
literature curated biochemical reactions
904 enzymatic reactions
gene, enzyme-reaction associations

69. Case II: What sources of data are available to build a Metabolic Flux model?
Annotated Genome
Literature
Pathway Databases
Experimental measurements

70. (fluxes in [mmol/gr DM h] normalized to glucose uptake flux)
Model vs. Exper., Glucose limited
(fluxes in [mmol/gr DM h] normalized to glucose uptake flux)
(Segrè, Vitkup and Church, PNAS 2002)

71. ni (exper) ni (exper) ni (exper) Corr.coeff.=0.91 Corr.coeff.=0.97
Low Glucose Limited
High Glucose Limited
Nitrogen Limited
ni (exper)
ni (exper)
ni (exper)
Corr.coeff.=0.91
Corr.coeff.=0.97
Corr.coeff.=0.78

72. Min Adjust. (suboptimal)
Max growth (optimal)
Min Adjust. (suboptimal)
Corr .
coeff
.=0.564
250 P -
value=0.007
200 7 ) 8
150
theor 10 9 13
100 11 ( 14 3 1 12 i v 50 16 2 15 6 17 5 4 - 50 - 50 50
100
150
200
250 v (
exper ) i

73. The power of a model lies in its ability to distinguish between competing hypotheses

74. Case II: EcoCyc
good schema
Flux balance model doesn’t work

75. What happens if the steady-state behavior of the model fails to reproduce the steady-state behavior of the organism?
Genome
Pathologic
Nutrients &
Objective
Model
Definition
(SBML)
BioCyc
to SBML
Pathway/
Genome
Database
Transporter
prediction
The key insight that drives this project lies not in what these models can predict correctly, but in what they predict incorrectly, for by rigorously examining the assumptions underlying that model, new knowledge can be gained.
FBA &
MOMA
Flux
prediction

76. What happens if the steady-state behavior of the model fails to reproduce the steady-state behavior of the organism?
Genome
Pathologic
Nutrients &
Objective
Model
Definition
(SBML)
BioCyc
to SBML
Pathway/
Genome
Database
Transporter
prediction
The key insight that drives this project lies not in what these models can predict correctly, but in what they predict incorrectly, for by rigorously examining the assumptions underlying that model, new knowledge can be gained.
FBA &
MOMA
Network
Debugging
Flux
prediction

77. Case II: EcoCyc/JR904 Best of both worlds
Biological Objective: From nutrients create all essential compounds required for growth
True test of metabolic databases: Is the data good enough to predict growth rate under different nutrient conditions and effect of gene knockouts?

78. Case II: Schema level integration
Translation from BioCyc ontology to BioPAX ontology
Translation of implicit JR904 schema to BioPAX ontology
Integration of JR904 concepts with BioPAX ontology (flux limits)

79. Case II: Instance level
EcoCyc <-> JR904 Gene names
EcoCyc <-> JR904 Enzyme names
EcoCyc <-> JR904 Reaction names
EcoCyc <-> JR904 Reversibility/flux limits
EcoCyc <-> JR904 Gene->protein associations
EcoCyc <-> JR904 protein->enzyme complex
associations
EcoCyc <-> JR904 enzyme->reaction

80. Data Profiling of Flux Model
Incorrect constraints (reversibility)
Incorrect Nutrient conditions
Incorrect Biomass composition
Incorrect protein function predictions

81. Data profiling of Flux Predictions
Incorrect hypothesis
(FBA vs MOMA vs ROOM)
Incorrect network architecture
(Gene knockouts)
Incorrect modeling assumptions
(steady state assumption,
gene expression profiles)

82. Fixing the problems you find
Requires different amounts of time, money, and expertise
Enzyme Genomics project
Community annotation projects
Adopt-a-Genome project
High-throughput experiments
Pathway hole filling algorithms

83. Case III: Semantic Aggregation Case study
Prochlorococcus marinus MED4
Most abundant species in the ocean
Responsible for a significant portion of photosynthetic carbon fixation.
Iron hypothesis: Possible solution to global warming?
Need to understand details of metabolic network

84. Case III: Multi-source aggregation
Public
KEGG (metabolism)
BioCyc (metabolism)
WIT (metabolism)
TransportDB (transport proteins)
Local
RNA expression (microarrays)
protein expression (mass spec)

85. Case III: Goal Constrain metabolic flux model with
experimental measurements:
RNA expression
Protein expression
Metabolite concentrations
Flux measurements

86. Case III: Aggregation Problems
Higher Level: Orphan enzymes
Schema Level: Bridge ontologies
Instance Level: Object identity problem
Simulation Level: underdetermined system.
Solution:
BioPAX -> Biowarehouse
semantic test suite for data validation

87. Case III: Multi-source aggregation Validation and Testing
Joint-learning from multiple sources
Semantic test suite for data validation
Network debugging algorithms :

88. Time Out (15 minutes)

89. Lessons Learned (30 minutes)
What did you learn?
Discussion
“A good representation is the key to good problem solving” –Patrick Winston
“Standard is better than best”—Gerald J Sussman
“The great thing about standards is that there are so many from which to choose” --Unknown
“Above all, one must develop a feeling for the organism.”—Barbara McClintock
“Someone does it once, everybody benefits.” Eric Miller, W3C Semantic Web Activity Lead
Remember people, process, technology, however without people there isn’t any process or technology, so it’s all social engineering.

90. Lessons Not Yet Learned (Take home exercise)

91. How did we do? Please let us know how we can improve this tutorial.
Feedback
Our goal is to have you walk away with a clear understanding of how to approach any database integration project
To provide
A methodology to scope and plan the project
An understanding of what to expect
Some specific examples to illustrate what is common to all integration projects (data cleaning) and what specific to a particular task. (i.e. to provide you with examples to give a sense of it)
Some first hand experience at pedantic aggravation, irritation and interference
How did we do? Please let us know how we can improve this tutorial.

92. Thank You Joanne & Jeremy