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Systems Biology Software Infrastructure  (SBSI)  ISAB visit May 19 th   2011 Allan Clark, Nikos Tsorman, Neil Hanlon Richard Adams, Stephen Gilmore
Talk outline SBSI scope and purpose Progress since last ISAB Current priorities and future work www.sbsi.ed.ac.uk
SBSI objective ‘ A new infrastructure to streamline the connection between data, models, and analysis, allowing the updating of large scale data, models and analytic tools with greatly reduced overhead’ www.sbsi.ed.ac.uk
Goals of SBSI Facilitate the incorporation of experimental data into modelling approaches Develop computational capacity for analyzing larger models Allow integration of novel scientific methods Allow customization by external developers Initially, focus on parameter estimation problem www.sbsi.ed.ac.uk
Data and model results www.sbsi.ed.ac.uk How to get models to reproduce experimental data?
Parameter Estimation Problem Building predictive models – a challenging problem in Systems Biology Parameter estimation – critical stage in model development Multiple data sets for model calibration Global optimization needed due to complex cost landscapes  Genetic /evolutionary techniques perform well. Circadian clock modellers have existing high-quality time-series data to fit. www.sbsi.ed.ac.uk
Graphical Notation Network Inference Process Algebras Model analysis Existing knowledge High-resolution data High-throughput data New knowledge Static models Kinetic models Systems Biology Software Infrastructureℱ Kinetic Parameter Facility RNA metabolism Systems Biology Research, CSBE view Circadian clock C holesterol metabolism www.sbsi.ed.ac.uk
The SBSI Software Suite www.sbsi.ed.ac.uk
The SBSI Software Suite www.sbsi.ed.ac.uk Using SBSI we can fit to oscillating data ( green line).
The SBSI Software Suite www.sbsi.ed.ac.uk SBSIVisual client organizes & displays resources, access SBSINumerics.
Outreach & documentation Practical SBSI tutorial workshops at: ICSB2010, Edinburgh EraSysBio Summer School, 2010 Several internal training sessions Plant Systems Biology Summer School , 2011 Course material available for download from Sourceforge  (http://sourceforge.net/projects/sbsi/files/SBSIVisual/). Comprehensive HTML and pdf  user manuals for SBSINumerics and SBSIVisual Tutorial videos on YouTube ( channel sbsi23)  www.sbsi.ed.ac.uk
Integration - databases Data sources www.sbsi.ed.ac.uk Integration with Plasmo & Robust databases
Integration - databases Data sources Data Standards  High performance computing  Modelling languages Software www.sbsi.ed.ac.uk Plasmo search..
HPC access Data sources Data Standards  High performance computing  Modelling languages ROBuST ECDF Software www.sbsi.ed.ac.uk SBSI installed on Hector, the UK national supercomputer BioPepa
Community standards involvement Data Standards  www.sbsi.ed.ac.uk
SED-ML purpose Dagmar Waltemath -http://www.slideshare.net/dagwa/waltemath-onto-workshop-4326137 www.sbsi.ed.ac.uk
MIASE / SED-ML contributors > 21 collaborating institutions worldwide. http://sed-ml.org
SED-ML developments 2009 - present  www.sbsi.ed.ac.uk MIASE paper published 2011  http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1001122
SED-ML developments 2009 - present  www.sbsi.ed.ac.uk   SED-ML specification published
SED-ML developments 2009 - present  www.sbsi.ed.ac.uk   XML schema and  Java library released..
SED-ML support in SBSI www.sbsi.ed.ac.uk
Executing SED-ML www.sbsi.ed.ac.uk
Editing SED-ML www.sbsi.ed.ac.uk
Integration across projects Garuda collaborating institutions www.sbsi.ed.ac.uk Software CellDesigner
Integration across projects Garuda collaborating institutions www.sbsi.ed.ac.uk
Garuda functionality Knowledge www.sbsi.ed.ac.uk Led by Kitano group, SBI, Tokyo Pathway visualization Model creation  Model analysis Text mining Pathway  databases Molecular  databases
CellDesigner  / Garuda plugin www.sbsi.ed.ac.uk Download from www.celldesigner.org
Integration across projects Modelling languages Garuda collaborating institutions www.sbsi.ed.ac.uk BioPepa  appa – RuleBase Eclipse plugin works in SBSI
Integration across projects www.sbsi.ed.ac.uk The Kappa rule-based modelling environment
Vertical integration Web interface to SBSI REST-ful web service at https://mook.inf.ed.ac.uk:8083/sbsiservices/ Reuse of software components www.sbsi.ed.ac.uk
Coding challenge www.sbsi.ed.ac.uk How can a fixed number of developers continue to maintain and  develop new code?
Solution 1 – manage dependencies www.sbsi.ed.ac.uk Avoid cycles at all costs!
Solution 2 – continuous testing
Solution 3 – involve more developers www.sbsi.ed.ac.uk Plugin contributions can be independently developed, licensed and deployed.
Current work Supporting core users for parameter estimation (Circadian clock and cholesterol pathway modelling) Exploring collaboration with other computational groups in CSBE (Swain, Grima, Danos, Plotkin) for modelling language support in SBSI. Developing external collaborations  www.sbsi.ed.ac.uk
Acknowledgements Allan Clark Nikos Tsorman Neil Hanlon Richard Adams Stephen Gilmore Current Development Team Past developers Azusa Yamaguchi Millar Group Carl Troein Steve Watterson Maria-Louisa Guerriero Robert Smith Simon Bordage Martin Beaton Tomasz Zielinski Thanks for watching
  If you’re interested follow us on Twitter @CSBE_SBSI www.sbsi.ed.ac.uk

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Isab 11 for_slideshare

  • 1. Systems Biology Software Infrastructure (SBSI) ISAB visit May 19 th 2011 Allan Clark, Nikos Tsorman, Neil Hanlon Richard Adams, Stephen Gilmore
  • 2. Talk outline SBSI scope and purpose Progress since last ISAB Current priorities and future work www.sbsi.ed.ac.uk
  • 3. SBSI objective ‘ A new infrastructure to streamline the connection between data, models, and analysis, allowing the updating of large scale data, models and analytic tools with greatly reduced overhead’ www.sbsi.ed.ac.uk
  • 4. Goals of SBSI Facilitate the incorporation of experimental data into modelling approaches Develop computational capacity for analyzing larger models Allow integration of novel scientific methods Allow customization by external developers Initially, focus on parameter estimation problem www.sbsi.ed.ac.uk
  • 5. Data and model results www.sbsi.ed.ac.uk How to get models to reproduce experimental data?
  • 6. Parameter Estimation Problem Building predictive models – a challenging problem in Systems Biology Parameter estimation – critical stage in model development Multiple data sets for model calibration Global optimization needed due to complex cost landscapes Genetic /evolutionary techniques perform well. Circadian clock modellers have existing high-quality time-series data to fit. www.sbsi.ed.ac.uk
  • 7. Graphical Notation Network Inference Process Algebras Model analysis Existing knowledge High-resolution data High-throughput data New knowledge Static models Kinetic models Systems Biology Software Infrastructureℱ Kinetic Parameter Facility RNA metabolism Systems Biology Research, CSBE view Circadian clock C holesterol metabolism www.sbsi.ed.ac.uk
  • 8. The SBSI Software Suite www.sbsi.ed.ac.uk
  • 9. The SBSI Software Suite www.sbsi.ed.ac.uk Using SBSI we can fit to oscillating data ( green line).
  • 10. The SBSI Software Suite www.sbsi.ed.ac.uk SBSIVisual client organizes & displays resources, access SBSINumerics.
  • 11. Outreach & documentation Practical SBSI tutorial workshops at: ICSB2010, Edinburgh EraSysBio Summer School, 2010 Several internal training sessions Plant Systems Biology Summer School , 2011 Course material available for download from Sourceforge (http://sourceforge.net/projects/sbsi/files/SBSIVisual/). Comprehensive HTML and pdf user manuals for SBSINumerics and SBSIVisual Tutorial videos on YouTube ( channel sbsi23) www.sbsi.ed.ac.uk
  • 12. Integration - databases Data sources www.sbsi.ed.ac.uk Integration with Plasmo & Robust databases
  • 13. Integration - databases Data sources Data Standards High performance computing Modelling languages Software www.sbsi.ed.ac.uk Plasmo search..
  • 14. HPC access Data sources Data Standards High performance computing Modelling languages ROBuST ECDF Software www.sbsi.ed.ac.uk SBSI installed on Hector, the UK national supercomputer BioPepa
  • 15. Community standards involvement Data Standards www.sbsi.ed.ac.uk
  • 16. SED-ML purpose Dagmar Waltemath -http://www.slideshare.net/dagwa/waltemath-onto-workshop-4326137 www.sbsi.ed.ac.uk
  • 17. MIASE / SED-ML contributors > 21 collaborating institutions worldwide. http://sed-ml.org
  • 18. SED-ML developments 2009 - present www.sbsi.ed.ac.uk MIASE paper published 2011  http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1001122
  • 19. SED-ML developments 2009 - present www.sbsi.ed.ac.uk   SED-ML specification published
  • 20. SED-ML developments 2009 - present www.sbsi.ed.ac.uk   XML schema and Java library released..
  • 21. SED-ML support in SBSI www.sbsi.ed.ac.uk
  • 24. Integration across projects Garuda collaborating institutions www.sbsi.ed.ac.uk Software CellDesigner
  • 25. Integration across projects Garuda collaborating institutions www.sbsi.ed.ac.uk
  • 26. Garuda functionality Knowledge www.sbsi.ed.ac.uk Led by Kitano group, SBI, Tokyo Pathway visualization Model creation Model analysis Text mining Pathway databases Molecular databases
  • 27. CellDesigner / Garuda plugin www.sbsi.ed.ac.uk Download from www.celldesigner.org
  • 28. Integration across projects Modelling languages Garuda collaborating institutions www.sbsi.ed.ac.uk BioPepa  appa – RuleBase Eclipse plugin works in SBSI
  • 29. Integration across projects www.sbsi.ed.ac.uk The Kappa rule-based modelling environment
  • 30. Vertical integration Web interface to SBSI REST-ful web service at https://mook.inf.ed.ac.uk:8083/sbsiservices/ Reuse of software components www.sbsi.ed.ac.uk
  • 31. Coding challenge www.sbsi.ed.ac.uk How can a fixed number of developers continue to maintain and develop new code?
  • 32. Solution 1 – manage dependencies www.sbsi.ed.ac.uk Avoid cycles at all costs!
  • 33. Solution 2 – continuous testing
  • 34. Solution 3 – involve more developers www.sbsi.ed.ac.uk Plugin contributions can be independently developed, licensed and deployed.
  • 35. Current work Supporting core users for parameter estimation (Circadian clock and cholesterol pathway modelling) Exploring collaboration with other computational groups in CSBE (Swain, Grima, Danos, Plotkin) for modelling language support in SBSI. Developing external collaborations www.sbsi.ed.ac.uk
  • 36. Acknowledgements Allan Clark Nikos Tsorman Neil Hanlon Richard Adams Stephen Gilmore Current Development Team Past developers Azusa Yamaguchi Millar Group Carl Troein Steve Watterson Maria-Louisa Guerriero Robert Smith Simon Bordage Martin Beaton Tomasz Zielinski Thanks for watching
 If you’re interested follow us on Twitter @CSBE_SBSI www.sbsi.ed.ac.uk

Editor's Notes

  • #4: There is scope for a program that will link models, with experimental data that is perhaps in remote repositories, to the latest analytic tools, in a way that is straightforward for modellers to use.
  • #6: Given a set of data, how to fit model parameters for it to reproduce that data?
  • #7: Why are we tackling parameter estimation first? Predictive models are desirable e.g., for P4 medicine Search space dimensionality increases with each new parameter to fit Local minima are a big problem , therefore need global algorithms
  • #8: This shows some of the projects in CSBE and how SBSI fits in. This central panel shows the standard way of making progress in Systems Biology – starting with a static model, experimental data is used to refine and generate kinetic models, which can inform new experiments This process can use various analytical tools; we envision SBSI as a sort of lubricating agent to facilitate this activity.
  • #9: SBSINumerics – written in C++ for fast numerical algorithms SBSIVisual – client program for connecting with other projects, accessing SBSINumerics.
  • #10: SBSINumerics – written in C++ for fast numerical algorithms SBSIVisual – client program for connecting with other projects, accessing SBSINumerics.
  • #11: SBSINumerics – written in C++ for fast numerical algorithms SBSIVisual – client program for connecting with other projects, accessing SBSINumerics.
  • #12: Outreach events for SBSI
  • #13: Sought to increase interaction with databases and projects, both internal and external. 1) Databases –seek to ease incorporation of data & local models into modelling process
  • #14: Sought to increase interaction with databases and projects, both internal and external. 1) Databases –seek to ease incorporation of data & local models into modelling process
  • #15: SBSI is now installed on Hector
  • #16: SED-ML - community standard - SBML very successful , now the accepted exchange format - but doesn’t tell you what to do with the model - tedious manual process at present. - cmputational experiments will be be a greater proportion, models represent a huge investment of time effort and money -> need to maximise reuse.
  • #17: Promote model re-use Currently is a manual process. Post processing of raw results may also be needed to emphasize the biological significance. SEDML automates this.
  • #19: Jlibsedml in collaboration with Ion Moraru at University of Connecticut.
  • #20: Jlibsedml in collaboration with Ion Moraru at University of Connecticut.
  • #21: Jlibsedml in collaboration with Ion Moraru at University of Connecticut.
  • #22: 3. SEDML support is built into SBSI, systems biology software we’re developing in Edinburgh. Next release ( due early May) will implement the level1 version 1 final spec. Example screenshot shows that one can export a simulation configuration to SED-ML using the SBSI software (www.sbsi.ed.ac.uk) Configure simulation Configure output including SEDML export Export to SEDML archive or file.
  • #23: 1. Click on a SEDML file 2. Choose your output If the software can handle the modelling language and simulation type, it will produce the output. Screenshot shows the Elowitz repressilator example from the specification, reproducing the plots of normalized levels of protein.
  • #24: This is under development – a graphical editor for SEDML. SEDML files can get quite complicated to look at once there are several models and tasks defined Aims are to allow easy viewing, editing and annotation, validating input, help with generating Xpath etc., and viewing models and their changes. Will be available as standalone app, Eclipse plugin, or SBSI plugin. Screenshot shows simple example and simulation configuration dialog.
  • #25: Garuda – international collaborative project initiated and led by SBI Tokyo Aims to link software from different realms of systems biologyand ensure interoperability between applications.
  • #26: Garuda – international collaborative project initiated and led by SBI Tokyo Aims to link software from different realms of systems biologyand ensure interoperability between applications.
  • #27: Where SBSI fits in.
  • #28: Garuda /Cell Designer plugin
  • #29: .
  • #30: .
  • #31: Reuse and availability of existing code and resources
  • #32: How can a fixed number of developers continue to maintain and develop new code.
  • #33: Core modules are standard Java libraries and are resuable No cyclic dependencies.
  • #35: Get other people to develop code Needs SDK but some interest from Bioclipse and Netherlands Cancer Centre