# Difference between revisions of "Project 20 Benchmark Problems for Modelling Intracellular Processes"

m (Ckreutz moved page 20 Benchmark Problem for Modelling Intracellular Processes to Project 20 Benchmark Problems for Modelling Intracellular Processes: Better name) |
(→20 Benchmark Problem for Modelling Intracellular Processes) |
||

Line 1: | Line 1: | ||

− | == | + | == Summary == |

− | |||

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]. | ||

− | |||

− | |||

− | |||

− | |||

− | |||

+ | == 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].