Difference between revisions of "Project 20 Benchmark Problems for Modelling Intracellular Processes"
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| − | == | + | == Summary == |
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Ordinary differential equation (ODE) models are frequently applied to describe intracellular biochemical processes. | Ordinary differential equation (ODE) models are frequently applied to describe intracellular biochemical processes. | ||
Here, we provide a set of 20 published benchmark models which should serve as test cases for assessing methodology for modelling. | Here, we provide a set of 20 published benchmark models which should serve as test cases for assessing methodology for modelling. | ||
| + | An overview about the benchmark models is also provided at the github repository [REF]. | ||
| − | + | == Download == | |
| − | |||
The 20 benchmark models are available with version control on github [REF]. | The 20 benchmark models are available with version control on github [REF]. | ||
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| + | == Studies based on the 20 Benchmark problems == | ||
Here, we summarize several outcomes of benchmark studies performed on the 20 benchmark problems. | Here, we summarize several outcomes of benchmark studies performed on the 20 benchmark problems. | ||
| − | + | === Optimization at the Log-Scale === | |
It has been shown [REF] that optimization algorithms have superior performance if parameters are optimized at the log-scale [REF]. | It has been shown [REF] that optimization algorithms have superior performance if parameters are optimized at the log-scale [REF]. | ||
| − | + | === Convexity === | |
It has been shown [REF] that the negative log-likelihood which has to be minimized for parameter estimation is more convex if the parameters are evaluated at the log-scale. It has been concluded that this fact is one reason for performance benefits of deterministic optimization algorithms if optimization is performed at the log-scale [REF]. | It has been shown [REF] that the negative log-likelihood which has to be minimized for parameter estimation is more convex if the parameters are evaluated at the log-scale. It has been concluded that this fact is one reason for performance benefits of deterministic optimization algorithms if optimization is performed at the log-scale [REF]. | ||
Revision as of 14:39, 9 August 2018
Contents
Summary
Ordinary differential equation (ODE) models are frequently applied to describe intracellular biochemical processes. Here, we provide a set of 20 published benchmark models which should serve as test cases for assessing methodology for modelling.
An overview about the benchmark models is also provided at the github repository [REF].
Download
The 20 benchmark models are available with version control on github [REF].
Studies based on the 20 Benchmark problems
Here, we summarize several outcomes of benchmark studies performed on the 20 benchmark problems.
Optimization at the Log-Scale
It has been shown [REF] that optimization algorithms have superior performance if parameters are optimized at the log-scale [REF].
Convexity
It has been shown [REF] that the negative log-likelihood which has to be minimized for parameter estimation is more convex if the parameters are evaluated at the log-scale. It has been concluded that this fact is one reason for performance benefits of deterministic optimization algorithms if optimization is performed at the log-scale [REF].
