SEMS: Model search and ranked Retrieval (Ron Henkel)

29.11.2012 © 2009 UNIVERSITÄT ROSTOCK

Graph based storage and retrieval of computational models

Department of Systems Biology and Bioinformatics University of Rostock

www.sbi.uni-rostock.de

Ron Henkel, Martin Scharm, Dagmar Waltemath, Olaf Wolkenhauer

http://www.sbi.uni-rostock.de/�



Motivation

11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 2

Data from BioModels Database

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Models

Annotation

Motivation

• Models:

Grow in number and complexity Are provided with supplementary material Evolve over time


State of the Art

• Storage: Relational Databases Model files on Hard Disk Drive (HDD) Additional files (images, result sets, paper)

• Search:

SQL statements Facetted search Data browsing


State of the Art - Demo


Available Data for Ranked Retrieval


Model file Annotation & Ontologies A model‘s network

• Constituent names • Model code

• Biochemical background • Synonyms

• Model structure • Aggregate values



# aspect importance contained features 1 Administrative none ids, file name, version, formalism… 2 Person medium creator, encoder, submitter, publication author 3 Dates low creation and modification date 4 Publication high title, abstract, full-text, journal 5 Constituents very high compartment, species, reaction 6 User content very high keywords, tags, remarks, changes

• The concept is abstract and can be applied to different model formalisms. • Depending on the formalism the aspects can be refined into features. • The model constituents also contain the annotations. Henkel et al. (2010) BMC Bioinf

Biomodels Database – A Test Case

• Apache Lucene Framework

• Model Index 425 models, 140.977 terms

• Semantic Index 2261 URIs, 409.124 terms


http://www.ebi.ac.uk/biomodels-demo/

http://www.ebi.ac.uk/biomodels-demo/�



Demo


Improvements


• Ranking • Enhanced query possibilities

Required, optional and excluded criteria Allow full-text and Ontology queries

• Example: “Find cell cycle models”

Query BiomodelsDB Using IR Gold Standard cell cycle 135 173 n/a

“cell cycle” 14 26 28


29.11,2012 © 2009 UNIVERSITÄT ROSTOCK 11

Model based Annotation & Ontologies A model‘s network

• Model name • Model code

• Biochemical background • Allows to identify e.g. synonyms

• Include model structure • Aggregate values


A model‘s network

• Include model structure • Aggregate values

Mapping a Model to a Database

Advantages of Graph Databases

• Easy mapping of model structure • Fast browsing through models • Flexible and schema-free storage • Easy linking to models, simulation setups or results,

and external resources



Document

Model

P E CR S

SBO:0000268 uniprot:P07101

uniprot:Q03393 GO:0005737HGNC:8582

is

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is

isE

ncod

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asProductasReactant

asModifier


Preliminary Results

• All models stored in Biomodels DB were stored into the graph database

• Implemented storage and search in Jummp official demo release upcoming

• Added 140.811 models from path2models project done, but including annotation blows the memory database scales well and is reasonably fast


Demo


Future Work: Relate model versions

• Link successor and predecessor • Relate changed entities • Store the diff

• Enable version control for multi-

document models • Propagate changes for imported models



SEMS: Methods for Model & Simulation Management

• XML version control • Difference detection in XML

Waltemath et al., submitted

• Ranked model retrieval Henkel et al., 2010 (BMC Bioinf)

• Structure- and ontology-based search

Simulation VC SimulationSearch Simulation Storage

• Relational databases Waltemath et al., 2011 (DBSpektrum)

• Graph-based storage Henkel et al., 2012 (INFORMATIK)

• Standardized encoding of simulation setups Waltemath et al., 2011 (BMC SysBiol)

• Linking models and simulation descriptions Henkel et al., 2012 (INFORMATIK)

Model Search Model Version control Model Storage

Take Home Message

• Ranked retrieval is a necessary feature for model databases.

• The model’s inherent structure should be queryable. • Graph based storage reflects well a model‘s encoding

and evolution.


Thanks for your attention.

Questions?


[email protected]

mailto:[email protected]�

Education

SEMS: Model search and ranked Retrieval (Ron Henkel)