Presentation on theme: "Investigating Approaches to Speeding Up Systems Biology Using BOINC-Based Desktop Grids Simon J E Taylor (1) Mohammadmersad Ghorbani (1) David Gilbert."— Presentation transcript:
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
Overview Systems Biology Description of Application Grid enabling core component of application Experiments Conclusion
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.
Model Models are described by Systems Biology Mark up Language (SBML)
Complexity of MAPK model Contains 732 species, ~ 244 parameters Graph generated by CellDesigner from MAPK.xml
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.
Simulation output Output of simulation is a text file which can be used in Excel or other analytical tools
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).
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.
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
Control Flow of the Ported Application Figure : Control Flow of the Ported SIMAP Application
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)
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
Experiment 3 Speedup dynamic -100*100 simulations 30 min to complete ~50% of jobs 2 h and 30 min to complete others
Experiment 4 Dynamics of jobs completion for different simulation/job
Experiment 4 Point Speedup for different simulation/job 6400 simulations.
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.