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Outline
• What is MCell ?
• How to run MCell ?
• Resources
• Usage Scenario
• Summary
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What is MCell ?
A General Monte Carlo Simulator of Cellular Microphysiology
… MCell now makes it possible to incorporate high resolution ultrastructure into models of
ligand diffusion and signaling …
Thomas M. Bartol Jr.Computational Neurobiology
Laboratory
The Salk Institute
Joel R. StilesNeurobiology & Behavior
Cornell University
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What is MCell ?
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What is MCell ?
MCell uses
• Monte Carlo diffusion
• Chemical reaction algorithms in 3D
MCell simulates
• Release of ligands in solution
• Creation/destruction of ligands
• Ligand diffusion within spaces
• Chemical reactions undergone by ligand and effector
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What is MCell ?
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What is MCell ?
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What is MCell ?
Main biochemical interactions
• 3D diffusion of ligand moleculesbased on Brownian motion
• the average net flux from one region of space to another
depends on molecules mobility
depends on 3D concentration gradient between the regions
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What is MCell ?Different approaches to computing 3D gradients
With Voxels
Assume well-mixed condition
Use PDEs for average net changes
PROS:
• correct average system behavior
CONS:
• too complex for realistic structures
• output has no direct stochastic information
Monte Carlo approach
• Directly approximate the Brownian movements of the individual ligand
• Chemical reaction rates are solution rate const
PROS: • events are considered on a
molecule-by-molecule basis • the simulation results include
realistic stochastic noise
CONS: • complexity
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How to run MCell ?
Simulate the system behavior
• Running the same computation with different seeds
• Averaging all the instances
Each instance has • A pre-defined number of time
steps
• Input data
Input Data consists of • one or more MDL scripts
• files describing elements of the simulation
spatial geometry
effector location
chemicals' repartitions
Output files • resulting stochastic model
• visualization files
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Resources
Typical run now:
• 5 MBytes of input data per task
• 1000 tasks• 1 MBytes 2-D output files per
task• 10 MBytes 3-D output files
per task• usually 100 MBytes of RAM • require on the order of 10
minutes of processing on today's most powerful CPUs.
• Modeling ligands exchange, diffusion
Run envisioned:
• 50 MBytes of input data per task
• 1,000,000 tasks
• Tens of GBytes 2-D and 3-D output files per task
• RAM not easily available to an average user
• CPUs of MPPs.
• Modeling entire cells
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Resources
Salk Institute UCSD U. of Tennessee
Bartol and Sejnowski Casanova and Berman Dongarra and Wolski
MCell executes multiple instances of a given code on different
parameter set and collects (and perhaps processes) the results.
PROS:
each instance is independent from the others
each instance can be executed anywhere
Challenges:
1 tasks share common files 2 resource discovery
3 fault detection 4 fault recovery
5 scheduling
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Usage Scenario
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Usage Scenario
Security Requirements• data confidentiality• need for digital signatures, encryption, authorization• public vs. private information on application status and
executionPerformance Requirements
• network bandwidth• latency and jitter• CPU load• information service query time• disk capacity, speed• application timing formats
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Usage Scenario
Programming Model
• user interfaces (submit, monitor, steer runs)
• support for data analysis and visualization
Information Service Requirements
• frequency of information access
• application preferences on location, structure,
• representation, and format of IS information :
CPU RAM Disk
Network Queue waiting time
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Usage Scenario
Scheduling Requirements• resource reservation • application components, computation• data, intermediate files• remote instruments • tolerance to delays during execution
Remote Data Access requirements• publication, management, storage• streaming vs. batch processing
User Services• system status, its format• application needs for system services and tools
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Summary
The MCell development contributions: • larger problem size model for a class of science
applications
• parameter sweep application model for the Grid.
MCell needs:
• large-scale MCell runs
• further improvement and development of application scheduling mechanism
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Milestones
1. What are current problems and bottlenecks ?
2. Can one improve basic usage scenario ?
3. Current needs of application from GIS
4. What are requirements for
– job scheduling,
– job control
– storage infrastructure