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The   Systems Biology Software Infrastructure TiMet Workshop  May 7 th  2010, Edinburgh Richard Adams www.sbsi.ed.ac.uk http://sourceforge.net/projects/sbsi/
SBSI - Overall 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’
SBSI Contributors Core developers EPCC Test Models and  Evaluation  Project management Circadian clock modellers Stephen Gilmore PI Nikos Tsorman Neil Hanlon Galina Lebedeva Alexey Goltsov Azusa Yamaguchi Kevin Stratford  People previously  involved with SBSI Shakir Ali Anatoly Sorokin Treenut Saithong Stuart Moodie Ozgur Akman Igor Goryanin Biopepa integration Adam Duguid Richard Adams Requirements & Numerics  Andrew Millar Carl Troein
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 Infrastructure ™ Kinetic Parameter Facility Circadian clock RNA metabolism Interferon signalling Systems Biology Research, CSBE view ERB-b signalling
Initial use case : Parameter Estimation Problem ,[object Object],[object Object],[object Object],[object Object],[object Object]
SBSI Numerics optimization  ,[object Object],[object Object],[object Object],[object Object],Model.cpp, Datafiles, Parameter constraints SBML->C++ conversion ,[object Object],[object Object],[object Object],Eddie (ECDF) Output Using command line client Run on HPC  retrieve results Input
Integration of other CSBE projects BioPepa  ✔ Outline of SBSI design External model & experimental d ata sources BioModels  ✔ ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SBSI Numerics   core ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Eddie (ECDF) SBSI Numerics   SBSI Numerics   SBSI servers SBSI Numerics   SBSI  - complete system
Integration of other  CSBE projects BioPepa  ✔ EPE External model & experimental d ata sources ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SBSI Numerics   SBSI  - local mode
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SBSI Numerics   CellDesigner Eddie (ECDF) SBSI Numerics   SBSI Numerics   SBSI servers SBSI Numerics   A plugin for CellDesigner  CellDesigner  – SBSI plugin
Nactem CellDesigner Dunnart InSilicoIDE SBSI PathText Kleio Panther Pathways database autolayouts visualizes annotates Provides SBML models Optimizes? Sabio- RK database Kinetic  parameters Copasi Ananiadou/Tsujii/Kemper Mi (SRI) Funahashi/Ghosh Nomura (Osaka) updates EHMN Goryanin (Edinburgh) Boyd (Melbourne) 4-6 July Manchester, 8-9 October Edinburgh (ICSB), OIST early March 2011 Existing organisations/interactions Planned collaborations Gilmore (Edinburgh) GARUDA partners Proposed collaborations
Multiple Cost Function
Optimizing Circadian Clock models   with experimental data BIOMD055: “Extension of a genetic network model by iterative experimentation and mathematical analysis.” by  J. C. W. Locke, M. M. Southern, L. Kozma-Bognar, V. Hibberd, P. E. Brown, M. S. Turner, A. J. Millar (2005b). Molecular Systems Biology. 1:13 The model has 57 parameters and 13 states( equations). Fitting data is 2 of those states obtained by experiment. Using BG/L 128 nodes, it finished at 63140th  generation by  non-improvement criteria. The run time is 46 hours.  Multiple Cost Function is used up to 6740 generation,  after 6740th, only X2Cost is applied
Release code base on Sourceforge Establish SBSI Numerics on Hector Provide access to SBSI through CellDesigner Develop user base /community Publish! SBSI goals 2010
In the workspace you  can  store models, data, objective functions and results Editor view allows access to files Data visualization panel Step 1 – create a new  SBSI project Running  parameter optimisations…
Running  parameter optimisations… ,[object Object],[object Object],[object Object]
Step 3: choose parameters, constraints and initial values Running  parameter optimisations…
Running  parameter optimisations… Step 4: configure optimization algorithm
Step 5:  Compare simulation using best parameters,  with experimental data.

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Ti met may10

  • 1. The Systems Biology Software Infrastructure TiMet Workshop May 7 th 2010, Edinburgh Richard Adams www.sbsi.ed.ac.uk http://sourceforge.net/projects/sbsi/
  • 2. SBSI - Overall 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’
  • 3. SBSI Contributors Core developers EPCC Test Models and Evaluation Project management Circadian clock modellers Stephen Gilmore PI Nikos Tsorman Neil Hanlon Galina Lebedeva Alexey Goltsov Azusa Yamaguchi Kevin Stratford People previously involved with SBSI Shakir Ali Anatoly Sorokin Treenut Saithong Stuart Moodie Ozgur Akman Igor Goryanin Biopepa integration Adam Duguid Richard Adams Requirements & Numerics Andrew Millar Carl Troein
  • 4. 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 Infrastructure ™ Kinetic Parameter Facility Circadian clock RNA metabolism Interferon signalling Systems Biology Research, CSBE view ERB-b signalling
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10. Nactem CellDesigner Dunnart InSilicoIDE SBSI PathText Kleio Panther Pathways database autolayouts visualizes annotates Provides SBML models Optimizes? Sabio- RK database Kinetic parameters Copasi Ananiadou/Tsujii/Kemper Mi (SRI) Funahashi/Ghosh Nomura (Osaka) updates EHMN Goryanin (Edinburgh) Boyd (Melbourne) 4-6 July Manchester, 8-9 October Edinburgh (ICSB), OIST early March 2011 Existing organisations/interactions Planned collaborations Gilmore (Edinburgh) GARUDA partners Proposed collaborations
  • 12. Optimizing Circadian Clock models with experimental data BIOMD055: “Extension of a genetic network model by iterative experimentation and mathematical analysis.” by J. C. W. Locke, M. M. Southern, L. Kozma-Bognar, V. Hibberd, P. E. Brown, M. S. Turner, A. J. Millar (2005b). Molecular Systems Biology. 1:13 The model has 57 parameters and 13 states( equations). Fitting data is 2 of those states obtained by experiment. Using BG/L 128 nodes, it finished at 63140th generation by non-improvement criteria. The run time is 46 hours. Multiple Cost Function is used up to 6740 generation, after 6740th, only X2Cost is applied
  • 13. Release code base on Sourceforge Establish SBSI Numerics on Hector Provide access to SBSI through CellDesigner Develop user base /community Publish! SBSI goals 2010
  • 14. In the workspace you can store models, data, objective functions and results Editor view allows access to files Data visualization panel Step 1 – create a new SBSI project Running parameter optimisations…
  • 15.
  • 16. Step 3: choose parameters, constraints and initial values Running parameter optimisations…
  • 17. Running parameter optimisations… Step 4: configure optimization algorithm
  • 18. Step 5: Compare simulation using best parameters, with experimental data.

Hinweis der Redaktion

  1. Predictive models – desirable e.g., for P4 medicine Search space dimensionality increases with each new parameter to fit
  2. - Motivated from Kitano’s comments after ISAB last year Access CellDesigner user base Part of wider ‘Garuda’ concept.
  3. Garuda idea – technologies for all systems biology tasks Analogy with airline partners
  4. SBSI has a broad set of aims, we have initially chosen to focus on a key set that would be of early benefit. Client application easy to use Integration point for other software projects