1. Investigating Approaches to Speeding Up Systems Biology Using BOINC-Based Desktop Grids Simon J E Taylor (1) Mohammadmersad Ghorbani (1) David Gilbert (1) Annette Payne (1) Tamas Kiss (2) Daniel Farkas (2) (1) ICT Innovation Group/Centre for Synthetic and Systems Biology Department of Information Systems and Computing Brunel University, UK (X.Y@brunel.ac.uk)X.Y@brunel.ac.uk (2) Centre for Parallel Computing University of Westminster London, UK (initial.Y@wmin.ac.uk)initial.Y@wmin.ac.uk

2. Overview  Systems Biology  Description of Application  Grid enabling core component of application  Experiments  Conclusion

3. Systems Biology  Systems biology addresses the systematic study of biological and biochemical systems in terms of complex interactions rather than individual molecular components. Computational modelling is used to construct and simulate an abstract model of a biological system for analysis.

4. Steps of ODE based biochemical modelling

5. Model  Models are described by Systems Biology Mark up Language (SBML)

6. Complexity of MAPK model  Contains 732 species, ~ 244 parameters  Graph generated by CellDesigner from MAPK.xml

7. Simulation  Simulation : Solving the system of differential equations in ODE (ordinary differential equation) based models.  SBMLODEsolver extract parameter (kinetic rates) and ODEs and compute the concentrations of species. Result will show changes of concentration of species during time.

8. Simulation output  Output of simulation is a text file which can be used in Excel or other analytical tools

9. Why use Grid  Parameter scanning runs simulation over different parameter range.  e.g. parameter scanning of MAPK model  11 hours for 2 parameter. 3 months for 3 parameter to run on (Typical desktop PC).

10. Grid Architecture  WLDG(Westminster Local Desktop Grid) SZDG – SZTAKI Desktop Grid WS-PGRADE portal (gUSE)

11. WLDG

12. Job (work unit) Description  Inputs SBML model which is basically xml file Instruction to run ODEsolver n times for range of parameters. (batching simulations in job) Size: 1 MB.  Output Zip file contains results for all jobs Size:1.5 MB.

13. Workflow  Generator generate xml files for BioNessie application (port 1 of generator to port 0 of Bionessie)  Input Port 2 of bionessie can be set for number of simulations/job

14. Control Flow of the Ported Application Figure : Control Flow of the Ported SIMAP Application

15. The University of Westminster Local DG Over 1500 Windows PCs from 6 different campuses Lifecycle of a node: 1.PCs basically used by students/staff 2.If unused, switch to Desktop Grid mode 3.No more work from DG server -> shutdown (green solution)

16. Experimentation  Experiment 1: Several run for different job and simulation sizes Results : jobs completion highly variable -> Exp 3  Experiment 2 Fix number of simulations and different simulations/job number. Results: Speedup for some simulation/job number.  Exp1->Experiment 3 Calculating point speed up at time steps.  Exp2&Exp3->Experiment 4 Comparing point based speed up for fix number of simulations and different number of simulation per job

17. Experiment 3  Speedup dynamic -100*100 simulations  30 min to complete ~50% of jobs  2 h and 30 min to complete others

18. Experiment 4  Dynamics of jobs completion for different simulation/job

19. Experiment 4  Point Speedup for different simulation/job 6400 simulations.

20. Conclusions  WLDG meets the requirement of parameter scanning application at design and implementation level.  Batching of jobs show speedups for some simulation/job size.  Further experiment may show optimal value for simulation/job for different job numbers.

21. Questions