Model Management in Systems Biology: Challenges – Approaches – Solutions
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Transcript of Model Management in Systems Biology: Challenges – Approaches – Solutions
SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCKS E Ssimulation experiment management system
Model Managementin Systems BiologyChallenges – Approaches – Solutions
MARTIN SCHARM, DAGMAR WALTEMATHDepartment of Systems Biology & Bioinformatics, University of Rostock
http://sems.uni-rostock.de
FAIRDOM Webinar 2016July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 1
Background
• Number of models is steadily increasing
• Models tend to get more complex
• Continuous development produces multipleversions
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 2
ModellingA typical workflow
Searchand
RetrieveCompare Evaluate
and SelectRunpr
ivate publicCreate
Model
Encode inStandardFormats
Submitand
Share
DefineAnalyses andExperiments
Model CreatorCurator
Model User
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 3
ModellingA typical workflow
COPASI
JWS
CellDesignerTellurium
Searchand
RetrieveCompare
CreateModel
Evaluateand Select
Runpriva
te public
Run
CreateModel
Encode inStandardFormats
Submitand
Share
DefineAnalyses andExperiments
Model CreatorCurator
Model User
Run
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 3
StandardsMake life easier
Dräger and Palsson: Improving collaboration by standardization efforts in systems biology. Front. Bioeng. Biotechnol. 2014; 2:61. 10.3389/fbioe.2014.00061
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 4
Generate an ExperimentEncoding the simulation study
Calzone et al. (2007): Dynamical modeling of syncytial mitoticcycles in Drosophila embryos. Mol Syst Biol. 3: 131
TM
Wee1n_1 MPFn_1 StgPn_1
mol/l
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MPFn_1 StgPn_1 Wee1n_1
mol/l
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[MPFc]|Time [MPFn]|Time [Stgc]|Time [Stgn]|Time [Wee1n]|Time [preMPFc]|Time [preMPFn]|Time
mol/l
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MLSED
as published MLSED
modified initialenvironment
MLSED
selected different species
adapted from Waltemath: Reproducible virtual experiments with SED-ML. Harmony 2016, Auckland, NZ
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 5
Generate an ExperimentEncoding the simulation study
Open Challenges
• click-able simulation studies
• hybrid diagrams in SBGN
• zooming for SBGN diagrams
• better links from SBML models to genomics data
• established standards perform already quite good for most cases, but don’tallow for encoding of every feature and very big studies
Waltemath et al.: Toward community standards and software for whole-cell modeling. IEEE Transactions on Biomedical Engineering. vol.PP, no.99, pp.1-1.10.1109/TBME.2016.2560762
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 6
Share your Research ResultsMaking research useful for the community
What belongs to a reproducible simulation study in systems biology?
• models encoding the biologyTM
• semantic annotations describing the model and its entities
• simulation descriptions defining environments and simulation setups MLSED
• experimental data feeding the model
• documentation on the model and its usage
• resulting data
⇒ plenty of heterogeneous data!
Problem: How to ship the data while preserving the links?
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 7
Share your Research ResultsMaking research useful for the community
Research Object
• different flavours, useful forany kind of research data
• excellent support for linkeddata and provenance
Combine Archive
• entailed for standards insystems biology
• good tool support in sysbiosoftware
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 8
Share your Research ResultsMaking research useful for the community
TM
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 9
Share your Research ResultsMaking research useful for the community
A Modeler’s Tale: the story about a researcher who wants to share his findings.
Wolfien, Bagnacani, Gebhardt, Scharm: A Modeler’s Tale. figshare (2016) 10.6084/m9.figshare.3423371.v1
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 10
Share your Research ResultsMaking research useful for the community
A fully featured COMBINE archive
Scharm, Touré: COMBINE Archive Show Case. figshare (2016). 10.6084/m9.figshare.3427271.v1see also github.com/SemsProject/CombineArchiveShowCase
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 11
Share your Research ResultsMaking research useful for the community
Open Challenges
• lack of tool support
• limited support for storing the provenance
• limited support for linking files
• lack of suitable guidelines for encoding of meta data
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 12
Public RepositoriesBiomodels
Biomodels Database: 575 curated SBML models in 4880 version
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 13
Public RepositoriesPMR2
The Physiome Model Repository: 5588 CellML models in 672 public repositories
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 14
Public RepositoriesFAIRDOMHub
The FAIRDOMHub is based on SEEK and manages data for whole consortia
ConsortiaConsortia
Grp 3
Grp 3
Grp 1
Grp 1
Grp 2
Grp 2
Natalie Stanford SEEKing our way to better presentation of data and models from scientific investigations. ICSB/NormSys workshop Melbourne 2014Wolstencroft et al.: SEEK: a systems biology data and model management platform. BMC Systems Biology (2015), Issue 9:33, pages 33. 10.1186/s12918-015-0174-y
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 15
Public RepositoriesFAIRDOMHub
SEEK uses an ISA structure to organise data.
Investigation
Study
Assay
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 15
Public Repositories
Open Challenges
• proper version control and access to specific versions
• track and extract of provenance information
• links between repositories
• support for quality checks
• one-click simulations
• export of COMBINE archives and Research Objects
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 16
Searching and Retrieving StudiesHow to get the data?
internet
internet
SEARCHubiquitin
internet
RESULTSEXPORT
EXPORT
EXPORT
EXPORT
Query databasefor annotations, persons,simulation descriptions
Retrieve informationabout models, simulations,figures, documentation
Export simulation studyas COMBINE archive
Download archiveand open the studywith your favouritesimulation tool
Open archive in CATto modify its contents andto share it with others
internet
API Commincationsenrich your studieswith simulation results
Simulate a Studywith just a single click
adapted from Scharm and Waltemath: Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit. Workshop on Datamanagement in Life Sciences, DMforLS 2015 @ BTW 2015, Hamburg, Germany. btw-2015.de/?dms
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 17
Searching and Retrieving StudiesHow to get the data?
Open Challenges
• ranking on different indices
• connection to existing repositories
• support for versions
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 18
CompareUnderstanding the differences
Dear Collaborator,
please find attached a fixedversion of your model!
Best regards,Researcher (GMT+7)
What happened to my model?
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 19
CompareUnderstanding the differences
The BiVeS tool identifies and communicates the differences
Scharm, Wolkenhauer and Waltemath: An algorithm to detect and communicate the differences in computational models describing biological systems.Bioinformatics (2016) 32 (4): 563-570. 10.1093/bioinformatics/btv484
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 20
CompareUnderstanding the differences
Open Challenges
• differences not really machine “understandable”, yet – see COMODI
• just available for versions of models
• how to compare different models?
• how to compare (versions of) simulation descriptions?
• how to compare (versions of) whole studies?Scharm, Waltemathet, Mendes, Wolkenhauer: COMODI: an ontology to characterise differences in versions of computational models in biology. Journal of BiomedicalSemantics (2016) 7:46 10.1186/s13326-016-0080-2
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 21
Evaluate and selectFunctional curation using a WebLab
A call for virtual experiments: Accelerating the scientific process.Cooper et al., Progress in biophysics and molecular biology (2014).
The Cardiac Electrophysiology Web Lab.Cooper et al., Biophysical Journal, Volume 110, Issue 2, 292 - 300
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 22
Evaluate and selectFunctional curation using a WebLab
Open Challenges
• exclusively available for cardiac models encoded in CellML
• lack of standard for protocols
• no method available to evaluate all models in a search result set
• lack of interoperability with other tools
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 23
SummaryAnd acknowledgements
Searchand
RetrieveCompare
CreateModel
Evaluateand Select
Runpriva
te public
Run
CreateModel
Encode inStandardFormats
Submitand
Share
DefineAnalyses andExperiments
Run
Pedro Mendes
Jacky Snoep
Claudine Chaouiya
Frank BergmannDavid Nickerson
Vasundra Touré
Brett Olivier
Stian Soiland-Reyes
Martin Peters
Natalie Stanford Stuart Owen
Viji Chelliah Tommy Yu
Mariam Nassar
Jonathan Cooper
Gary Mirams
Tom Gebhardt
Carole GobleDagmar Waltemath
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 24
SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCKS E Ssimulation experiment management system
That’s it!
SEMS task Force SBI Team
Tom GebhardtFabienne LambuschMariam NassarMartin Peters
Vasundra ToureDagmar WaltemathOlaf Wolkenhauer
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 25
SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCKS E Ssimulation experiment management system
References
• Dräger et al.: Improving collaboration by standardization efforts in systems biology. Front. Bioeng.Biotechnol. 2014; 2:61.
• Wolfien et al.: A Modeler’s Tale. figshare (2016) 10.6084/m9.figshare.3423371.v1• Wolstencroft et al.: SEEK: a systems biology data and model management platform. BMC
Systems Biology (2015), Issue 9:33, pages 33.• Scharm et al: Extracting reproducible simulation studies from model repositories using the
CombineArchive Toolkit. Workshop on Datamanagement in Life Sciences, DMforLS 2015 @ BTW2015, Hamburg, Germany.
• Scharm et al.: An algorithm to detect and communicate the differences in computational modelsdescribing biological systems. Bioinformatics (2016) 32 (4): 563-570.
• Scharm et al.: COMODI: an ontology to characterise differences in versions of computationalmodels in biology. Journal of Biomedical Semantics (2016) 7:46
• Cooper et al.: A call for virtual experiments: Accelerating the scientific process. Progress inbiophysics and molecular biology (2014).
• Cooper et al.: The Cardiac Electrophysiology Web Lab. Biophysical Journal, Volume 110, Issue 2,292 - 300
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 26