The document discusses the Systems Biology Software Infrastructure (SBSI) project. SBSI aims to streamline the connection between biological data, models, and analysis. It allows for updating large-scale models and data with reduced overhead. The initial use case is parameter estimation for circadian clock models. SBSI includes tools for editing SBML models, running simulations, performing parameter optimizations, and visualizing results. It will integrate with other systems biology software and be accessible through the modeling tool CellDesigner. The goals for 2010 include releasing the SBSI code, providing access via Hector supercomputing, and growing the user base.

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. Initial use case : Parameter Estimation Problem Building predictive models – challenging problem in Systems Biology Parameter estimation – critical stage in model development Multiple data sets needed for model calibration Optimization of large scale models –computationally challenging Circadian clock modelling project requires model optimization.

6. SBSI Numerics optimization SBML model, Parameter constraints, Experimental Data files Configuration file Model.cpp, Datafiles, Parameter constraints SBML->C++ conversion Best parameters Cost function behaviour Time course with best parameters Eddie (ECDF) Output Using command line client Run on HPC retrieve results Input

7. Integration of other CSBE projects BioPepa ✔ Outline of SBSI design External model & experimental d ata sources BioModels ✔ SBSI Dispatcher (Task Manager ) Compile C codes Submit jobs to HPC ✔ Retrieve results ✔ Provide job status SBSI Numerics core SBSI Visual ✔ Desktop application ✔ Upload and edit SBML models Run simulations Configure and run optimisations ✔ Interact with external repositories ✔ Visualisation of data and results Eddie (ECDF) SBSI Numerics SBSI Numerics SBSI servers SBSI Numerics SBSI - complete system

8. Integration of other CSBE projects BioPepa ✔ EPE External model & experimental d ata sources SBSI Visual ✔ Desktop application ✔ Upload and edit SBML models Run simulations Configure and run optimisations ✔ Interact with external repositories ✔ Visualisation of data and results SBSI Numerics SBSI - local mode

9. SBSI Dispatcher (Task Manager ) Compile C codes Submit jobs to HPC ✔ Retrieve results ✔ Provide job status SBSI Numerics CellDesigner Eddie (ECDF) SBSI Numerics SBSI Numerics SBSI servers SBSI Numerics A plugin for CellDesigner CellDesigner – SBSI plugin

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

11. Multiple Cost Function

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. Running parameter optimisations… Step 2 – choose models, data and algorithm type multiple datasets can be selected

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.