David Nickerson 1 (with help from Dagmar Waltemath 2 & Frank Bergmann 3 )
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David Nickerson 1 (with help from Dagmar Waltemath 2 & Frank Bergmann 3 ) 1 Auckland Bioengineering Institute, University of Auckland 2 University of Rostock 3 California Institute of Technology
description
David Nickerson 1 (with help from Dagmar Waltemath 2 & Frank Bergmann 3 ) 1 Auckland Bioengineering Institute, University of Auckland 2 University of Rostock 3 California Institute of Technology. SED-ML Motivation. Biological. publication. repository. Simulation. tool. ?. models. - PowerPoint PPT Presentation
Transcript of David Nickerson 1 (with help from Dagmar Waltemath 2 & Frank Bergmann 3 )
David Nickerson1 (with help from Dagmar Waltemath2 & Frank Bergmann3)1Auckland Bioengineering Institute, University of Auckland2University of Rostock3California Institute of Technology
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SED-ML Motivation
Simulationtool
models
Biologicalpublicationrepository
?Simulation result
SED-ML Motivation
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Simulation
Simulation results (SBW Workbench)
BIOMD0000000139 , BIOMD0000000140
models
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Example
First attempt to run the model, measuring the spiking rate v over time
load SBML into the simulation tool COPASI use parametrisation as given in the SBML
file define output variables (v) run the time course
1 ms (standard) 100ms 1000ms
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Example
Fig.: COPASI simulation, duration: 140ms, step size: 0.14
Second attempt to run the model, adjusting simulation step size and duration
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Example
Fig.: COPASI, adjusted parameter values (a=0.02, b=0.2 c=-55, d=4)
Third attempt to run the model, updating initial model parameters
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Example
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Example
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SED-ML Level 1 Version 1
Figure: SED-ML structure (Waltemath et al., 2011)
UniformTimeCourseSimulation
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SED-ML Level 1 Version 1
• Carry out multiple time course simulations• Collect results from these simulations• Combine results from these simulations• Report / Graph the results
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SED-ML Level 1 Version 2
ModelSimulation
Task
Data Generators
Reports
• UniformTimeCourseSimulation• oneStep• steadyState
• Task• RepeatedTask
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SED-ML Level 1 Version 2
Time Course Parameter ScanRepeated Stochastic Traces
Pulse ExperimentsParameter Scan
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SED-ML next version
• Focus on the integration of “data” with SED-ML, e.g.,– experimental data for use in model fitting,
parameter estimation– simulation data for testing implementations
• Adoption of NuML as standard data description format– https://code.google.com/p/numl/– XML description of underlying data (initially
CSV).– provides a common data abstraction layer
for SED-ML to utilise.
