Model Management for Systems biology ProjectsDagmar Waltemath (University of Rostock)

1st RSGLux congress. Belval, Luxembourg. November 2016

All comic-style graphics in this presentation were done either by Anna Zhukova or by Martin Peters. Thank you very much!

Disclaimer

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Who I am and what I do

Projects. SEMS | de.NBI data management for German Bioinformatics network | SBGN-ED+

Community work. Standard development | COMBINE coordinator | SBML editor

Research interests. Reproducibility of modeling results | Sustainability of scientific outcomes

Other things. Education of young scientists | Open Access & open data | Gender equality in science

SEMS@University of Rostock, Germany (2015)

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Model management. Or: How I got into this reproducibility topic...

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Reproduce simulations

Ship & archive modeling results

Detect differences

Understand model evolution

Develop management strategies for models

2008 2012 2014 2016

Why many want data managed

I need support in organising the data for my thesis.

Funders say I must make all project data available for the next 10 years.

I need to share parts of my data with collaborators and want to keep track.

These are only some examples.5

...and why they still don’t do it.

This takes time.

The software does not support the format I need for my data.

I do not want to share my data. I want full control.

These are only some examples – there are many, many more. 6

50+ %of research studies are not reproducible*!

But why they should …

7*study performed by Bayer (2011) to check replicability of 67 results in cancer studies. More in: Waltemath & Wolkenhauer (2016) IEEE TBME

Problem: Many data items Characteristics of the data

– Heterogeneous

– Big

– Distributed

– Complex

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Problem: Many data items Characteristics of the data

– Heterogeneous

– Big

– Distributed

– Complex

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Requirements of the field

– Long-term availability

– Thorough documentation/trust

– High data quality

– Interoperability & reusability

How do we manage the data

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… once we have it?

science sucks - sterni4ever

Use & follow a data management planData management

● procedures and actions that help to store, preserve, organize and control the data generated during a (research) project.

Examples & resources

● Data management plans provided by funders, e.g. NIH

● Checklist for a data management plan

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Use & follow a data management planKey principles

● Avoid re-collection of data

● Keep control of data at all steps of the data life cycle

● Justify data collection Specify the collected data

● Perform data audit

● Archive the data

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Is the data archived properly? What are the planned

destruction mechanisms?

What kind of data is collected? How was it processed?

Is the data fit for purpose and held securely?

Is the data useful and the data collection effective?

Use a dedicated model management system

Benefits

– Your data is organised and documented.

– Your data is kept safe (backup) and secure.

– User and sharing management for small and large projects, and for work groups.

– Management functionality comes for free, e.g. interlinks to other databases, version control, search!

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Use a dedicated model management system

Example: FAIRDOMHub

– Data & model management for Systems Biology

– Follows the FAIR principles (Wilkinson et al 2016)

– User support, PALs meetings, online tutorials

– Project based instances, ISAtab, but flexible

14More information at: https://www.fairdomhub.org/

Use a dedicated model management system

15More information at: https://www.fairdomhub.org/

Use a dedicated model management system

16More information at: https://www.fairdomhub.org/

Use a dedicated model management system

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Version 2 Version 4

More information at: https://www.fairdomhub.org/

Use standards for data sharing and interoperability

18Fig.: Mosaic of standards, adapted from Chelliah et al (2009) DILS

Guidelines, ontologies and standards for modeling & simulation of biological systems.

Use standards for data sharing and interoperability

19Figure: Draeger and Palsson (2014). More on COMBINE at: http://co.mbine.org

Help developing standardsAccess to all specificationsTutorials, forums, mailing listsEvents

Guidelines, ontologies and standards for modeling & simulation of biological systems.

Publish, share & archive your study in a model repository

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CuratedOpen

Standard formats

Repositories: BiGG, BioModels, JWS Online Model Database, Physiome Model Repository

Publish, share & archive your study in a model repository

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CuratedOpen

Standard formats

Repositories: BiGG, BioModels, JWS Online Model Database, Physiome Model Repository

Care for your models’ quality

● MIASE and MIRIAM Guidelines → read, understand, implement.

● COMBINE annotations (RDF / OWL / Bio-ontologies)

– To annotate models: COPASI, libSBML

– To annotate simulations: SED-ML Web Tools, JWS Online Simulator

– Specifically: Add SBO terms wherever possible to improve later conversion between standards*

22*Format converters for COMBINE standards Rodriquez et al (2016)

Semanticannotations

to bio-ontologiesQ

ualit

y en

hanc

er

Care for your models’ quality

● Open publication in model repositories, e.g.: in BioModels, JWS Online Model Database, Physiome Model Repository

● Full documentation of provenance, e.g.: Research Object frameworkExport and publish study as COMBINE Archive, e.g.: using COMBINE Archive Web, JWS Online, SED-ML Web Tools

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Documented, reproducible

simulation studyQ

ualit

y en

hanc

er

Link: JWS Online Simulation Database. Peters et al (2016, under revision)

Care for your models’ quality

● Functional curation (testing models under a range of perturbations), e.g.: in the Cardiac Electrophysiology Web Lab

● Documentation of origin for all parameter values

● Linking model – simulation studies – experimental data – conditions – simulation data – publication

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Validation of modelbehavior

Qua

lity

enha

ncer

Care for your models’ quality

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Validation of modelbehavior

Qua

lity

enha

ncer

Figures: Electrophysiology Web Lab Cooper et al (2016)

In summary: Make your study valuable & sustainable

Check reproducibility prior to publication!

26Steps towards making a study reproducible: Henkel et al (2013), Springer – closed access :(

If your work is available,

documented, and open

We can index it, so it can be retrieved by

others.

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Collecting & integrating modeling dataMASYMOS: Store models

28Figure (left): Visualising database content for 6 BioModels & versions (courtesy M. Peters), Figure (right): Henkel et al (2013) DATABASE

Collecting & integrating modeling data

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JWS Online: Find simulations

Figure: Peters et al (2016) under revision

Provenance – who changed what when where and why?BiVeS: Keep track of changes in a model

30More information in: Scharm et al (2015) BIOINFORMATICS, https://sems.uni-rostock.de/projects/bives/

Provenance – who changed what when where and why?

31Figure: courtesy V. Touré, Scharm et al (in preparation), http://most.sems.uni-rostock.de

version 3 05-06-2006

version 5 05-01-2007

version 4 03-10-2006

BIVES diff 3-4

BIVES diff 4-5

version 13 26-01-2010

version 15 15-04-2011

version 14 30-09-2010

BIVES diff 13-14

BIVES diff 14-15

MOST: Keep track of changes in public model repositories

Reusable models

Fully featured COMBINE archive

Example of a complete COMBINE archive (BIOM 144).

Recon 2

Reconstruction of human metabolism reuses existing networks.

Whole cell model

Based on >170 publications. All model-related data & code available.

These are only some examples. Much to explore on BioModels, FAIRDOMHub, biosharing, ...32

Thank you!Contact me if you want:

• help with our tools

• help with COMBINE standards

• set up a FAIRDOMHub project

• get involved in all the exciting efforts.

Ron HenkelMASYSMOS

Martin PetersM2CAT, JWS, MASYMOS

Martin ScharmBiVeS, Web Lab

Tom GebhardtMOST

Vasundra TouréSBGN-ED

Mariam NassarRanking, MASYMOS@dagmarwaltemath

Orcid: 0000-0002-5886-5563