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

http://www.sbi.uni-rostock.de/team/single/martin-scharm/

http://www.sbi.uni-rostock.de/team/single/dagmar-waltemath/

http://sems.uni-rostock.de



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




ModellingA typical workflow

COPASI

JWS

CellDesignerTellurium

Searchand

RetrieveCompare

CreateModel

Evaluateand Select

Runpriva

te public

Run

CreateModel


Submitand

Share


Model CreatorCurator

Model User

Run




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


https://dx.doi.org/10.3389/fbioe.2014.00061



Generate an ExperimentEncoding the simulation study

Calzone et al. (2007): Dynamical modeling of syncytial mitoticcycles in Drosophila embryos. Mol Syst Biol. 3: 131

TM

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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




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


https://dx.doi.org/10.1109/TBME.2016.2560762



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?





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





TM





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


https://dx.doi.org/10.6084/m9.figshare.3423371.v1




A fully featured COMBINE archive

Scharm, Touré: COMBINE Archive Show Case. figshare (2016). 10.6084/m9.figshare.3427271.v1see also github.com/SemsProject/CombineArchiveShowCase



https://github.com/SemsProject/CombineArchiveShowCase




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




Public RepositoriesBiomodels

Biomodels Database: 575 curated SBML models in 4880 version




Public RepositoriesPMR2

The Physiome Model Repository: 5588 CellML models in 672 public repositories




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


https://dx.doi.org/10.1186/s12918-015-0174-y



Public RepositoriesFAIRDOMHub

SEEK uses an ISA structure to organise data.

Investigation

Study

Assay




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




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


http://btw-2015.de/?dms



Searching and Retrieving StudiesHow to get the data?

Open Challenges

• ranking on different indices

• connection to existing repositories

• support for versions




CompareUnderstanding the differences

Dear Collaborator,

please find attached a fixedversion of your model!

Best regards,Researcher (GMT+7)

What happened to my model?





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


https://dx.doi.org/10.1093/bioinformatics/btv484




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


https://dx.doi.org/10.1186/s13326-016-0080-2



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




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




SummaryAnd acknowledgements

Searchand

RetrieveCompare

CreateModel

Evaluateand Select

Runpriva

te public

Run

CreateModel


Submitand

Share


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




SYSTEMS BIOLOGY

BIOINFORMATICS


That’s it!

SEMS task Force SBI Team

Tom GebhardtFabienne LambuschMariam NassarMartin Peters

Vasundra ToureDagmar WaltemathOlaf Wolkenhauer




SYSTEMS BIOLOGY

BIOINFORMATICS


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





model management in systems biology: challenges – approaches – solutions

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