I gave this talk as a webinar in the FAIRDOM webinar series 2016. The recordings of the webinar are available from http://fair-dom.org/knowledgehub/webinars-2/martin-scharm/

1. SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCK
S E Ssimulation experiment management system
Model Management
in Systems Biology
Challenges – Approaches – Solutions
MARTIN SCHARM, DAGMAR WALTEMATH
Department of Systems Biology & Bioinformatics, University of Rostock
http://sems.uni-rostock.de
FAIRDOM Webinar 2016
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 1

2. Background
• Number of models is steadily increasing
• Models tend to get more complex
• Continuous development produces multiple
versions
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 2

3. Modelling
A typical workflow
Search
and
Retrieve
Compare
Evaluate
and Select
Run
private
public
Create
Model
Encode in
Standard
Formats
Submit
and
Share
Define
Analyses and
Experiments
Model Creator
Curator
Model User
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 3

4. Modelling
A typical workflow
COPASI
JWS
CellDesigner
Tellurium
Search
and
Retrieve
Compare
Create
Model
Evaluate
and Select
Run
private
public
Run
Create
Model
Encode in
Standard
Formats
Submit
and
Share
Define
Analyses and
Experiments
Model Creator
Curator
Model User
Run
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 3

5. Standards
Make 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

6. Generate an Experiment
Encoding the simulation study
Calzone et al. (2007): Dynamical modeling of syncytial mitotic
cycles in Drosophila embryos. Mol Syst Biol. 3: 131
TM
Wee1n_1 MPFn_1 StgPn_1
mol/l
0
0.2
0.4
0.6
0.8
1
1.2
1.4
s
0 50 100 150 200 250
MPFn_1 StgPn_1 Wee1n_1
mol/l
0
0.2
0.4
0.6
0.8
1
1.2
s
0 50 100 150 200 250
[MPFc]|Time [MPFn]|Time [Stgc]|Time [Stgn]|Time [Wee1n]|Time [preMPFc]|Time [preMPFn]|Time
mol/l
0
0.5
1
1.5
2
s
0 50 100 150 200 250
MLSED
as published MLSED
modified initial
environment
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

7. Generate an Experiment
Encoding 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’t
allow 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

8. Share your Research Results
Making research useful for the community
What belongs to a reproducible simulation study in systems biology?
• models encoding the biology
TM
• 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

9. Share your Research Results
Making research useful for the community
Research Object
• different flavours, useful for
any kind of research data
• excellent support for linked
data and provenance
Combine Archive
• entailed for standards in
systems biology
• good tool support in sysbio
software
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 8

10. Share your Research Results
Making research useful for the community
TM
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 9

11. Share your Research Results
Making 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

12. Share your Research Results
Making research useful for the community
A fully featured COMBINE archive
Scharm, Touré: COMBINE Archive Show Case. figshare (2016). 10.6084/m9.figshare.3427271.v1
see also github.com/SemsProject/CombineArchiveShowCase
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 11

13. Share your Research Results
Making 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

14. Public Repositories
Biomodels
Biomodels Database: 575 curated SBML models in 4880 version
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 13

15. Public Repositories
PMR2
The Physiome Model Repository: 5588 CellML models in 672 public repositories
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 14

16. Public Repositories
FAIRDOMHub
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 2014
Wolstencroft 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

17. Public Repositories
FAIRDOMHub
SEEK uses an ISA structure to organise data.
Investigation
Study
Assay
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 15

18. 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

19. Searching and Retrieving Studies
How to get the data?
internet
internet
SEARCHubiquitin
internet
RESULTS
EXPORT
EXPORT
EXPORT
EXPORT
Query database
for annotations, persons,
simulation descriptions
Retrieve information
about models, simulations,
figures, documentation
Export simulation study
as COMBINE archive
Download archive
and open the study
with your favourite
simulation tool
Open archive in CAT
to modify its contents and
to share it with others
internet
API Commincations
enrich your studies
with simulation results
Simulate a Study
with just a single click
adapted from Scharm and Waltemath: Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit. Workshop on Data
management 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

20. Searching and Retrieving Studies
How 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

21. Compare
Understanding the differences
Dear Collaborator,
please find attached a fixed
version 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

22. Compare
Understanding 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

23. Compare
Understanding 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 Biomedical
Semantics (2016) 7:46 10.1186/s13326-016-0080-2
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 21

24. Evaluate and select
Functional 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

25. Evaluate and select
Functional 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

26. Summary
And acknowledgements
Search
and
Retrieve
Compare
Create
Model
Evaluate
and Select
Run
private
public
Run
Create
Model
Encode in
Standard
Formats
Submit
and
Share
Define
Analyses and
Experiments
Run
Pedro Mendes
Jacky Snoep
Claudine Chaouiya
Frank Bergmann
David 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

27. SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCK
S E Ssimulation experiment management system
That’s it!
SEMS task Force SBI Team
Tom Gebhardt
Fabienne Lambusch
Mariam Nassar
Martin Peters
Vasundra Toure
Dagmar Waltemath
Olaf Wolkenhauer
July, 2016 Model Management in Systems Biology | Martin Scharm, Dagmar Waltemath 25

28. SYSTEMS BIOLOGY
BIOINFORMATICS
ROSTOCK
S 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 @ BTW
2015, Hamburg, Germany.
• Scharm et al.: An algorithm to detect and communicate the differences in computational models
describing biological systems. Bioinformatics (2016) 32 (4): 563-570.
• Scharm et al.: COMODI: an ontology to characterise differences in versions of computational
models in biology. Journal of Biomedical Semantics (2016) 7:46
• Cooper et al.: A call for virtual experiments: Accelerating the scientific process. Progress in
biophysics 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