1. Multiobjective optimization with modeFRONTIER applied to systems biology.
Adam Thorp, EnginSoft Nordic AB. Elin Nyman, Linköping University & MathCore
...
Multiobjective optimization with modeFRONTIER applied to systems biology
Adam Thorp, EnginSoft Nordic AB Elin Nyman, Linköping University & MathCore Engineering AB
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Introducing multidisciplinary optimization
Conference Proceedings - 2010 modeFRONTIER International Users' Meeting
The big trends
Conference Proceedings - 2010 modeFRONTIER International Users' Meeting
EnginSoft • Software – 2nd largest CAE software company in EU
• Services – Strong multi-disciplinary competences
• Research – 20 EU projects completed, 6 ongoing – Coordinates several
Defining modeFRONTIER
Using smart algorithms and automation, modeFRONTIER helps engineers to – Find better designs – Reduce project time – Understand complex relations
The concept behind modeFRONTIER
Design of Experiments
Metamodeling
Optimization
Statistical Analysis
Robust Design
Multivariate Analysis
Process Integration
Job Control
Decision Making
Data Management
Supporting the design process modeFRONTIER
Hardware-in-the-loop
Physical measurements
Computer simulations
Scaling up (or down) modeFRONTIER adapts to your working environment
From laptops and workstations …
JAVA-based code runs in Windows, Linux or mixed environments
GridGain provides built-in functionality for Cloud Computing
… to large clusters
MathModelica Environment Modeling
Simulation
Documentation
Analysis
MathModelica: Examples of different systems
MathModelica: Examples of different systems
Challenges in Systems Biology
Biological data are noisy and of limited amount
Biological models are large with non-unique parameter values
Approach to draw conclusions Collect many different parameters describing the data
Search for unique behaviours that are shared along the acceptable parameters Use these predictions to collect more data
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Example model
Early insulin signaling events in fat cells Well studied, decent amount of experimental data Model formulated and simulated in MathModelica using the BioChem library
MathModelica simulator MathModelica generates a standalone executable for each simulation
Input File (XML)
myModel.exe
result file (.MAT)
Input File contains: start time, stop time, parameter values, initial values, solver settings, etc. Output file contains: Variable trajectories for all variables of the model, parameters, etc.
Running Modelica through modeFRONTIER Model parameter values are written to input files for MathModelica executable
Scheduler: Optimization algorithm and initial points
MathModelica results (.mat files) are read by Octave script
Output from model (errors between simulation and measurement data) Goal functions (minimize model error) and constraints Matlab integration available
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Optimization from no-prior-knowledge initial setup
Designs in orange indicate violated constraints
Individual vs total error
Total error
Errors for each individual case
Variability Primary goal: identify multiple solution modes with different parameter values but similar error values
Clustering – automatically identify sets of similar solutions
Cluster centroids – identify key model behavior
Strong overlap: no distinction between clusters for parameter v1dk1
Cluster scatter
Clusters are well separated in v1rk1 but not in error value: Multiple solution modes with similar error
Results and conclusions • We broaden the range of acceptable solutions by changing the optimization settings • We achieve a stronger basis for biological insights and conlusions such as rejections of hypotheses and predictions
Results and conclusions • modeFRONTIER and MathModelica automatically locates and identifies multiple variants of the model with similar error values • We achieve a stronger basis for biological insights and conlusions such as rejections of hypotheses and predictions
www.mathcore.com
[email protected] +46 13 32 85 00 www.enginsoft.se
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