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([email protected])
(2) Centre for Parallel ComputingUniversity of WestminsterLondon, UK([email protected])
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.
Steps of ODE based biochemical modelling
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).
Grid Architecture
WLDG(Westminster Local Desktop Grid)
SZDG – SZTAKI Desktop Grid WS-PGRADE portal (gUSE)
WLDG
BOINC Server
Components
WS-PGRADE
(User Interface/Portal)
gUse(Workflow
Processor & Services)
gUse DG Submitter
3G Bridge
BO
INC
T
ask
DB
Data Server
Sched-uler
BOINC Client
GenWrapper
Legacy Application
BOINC Client
GenWrapper
Legacy Application
BOINC Client
GenWrapper
Legacy Application
SZDG Server
Workflow Desciption
Workflow
WorkUnits
Tasks
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)
1
2
34
5
6
1
2
34
5
6
1. New Cavendish Street 576 nodes2. Marylebone Campus 559 nodes3. Regent Street 395 nodes4. Wells Street 31 nodes5. Little Tichfield Street 66 nodes6. Harrow Campus 254 nodes
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.
Questions