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Applying the Benchmark Models using Data2Dynamics

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Clemens Kreutz edited this page Oct 29, 2018 · 5 revisions

Data2Dynamics (D2D) is a Matlab-based modelling framework tailored to ODE-based modelling of cellular processes. D2D offers comprehensive functionality and is available at https://github.com/Data2Dynamics/d2d.

Data2Dynamics provides a large number of application models and toy models, including the presented benchmark collection. These models are stored in the D2D folder "D2D_LOCATION/d2d/arFramework3/Examples". Each subfolder for the individual models comprises the definition of the models as well as the mathematical link to the data in the D2D format.

In the following, we describe important tasks for usage of the benchmark collection in D2D:

Installation/setting up D2D

The following steps are required for setting up D2D:

  1. Download of Data2Dynamics, e.g. from https://github.com/Data2Dynamics/d2d/archive/master.zip
  2. Adding the D2D basic folder e.g. via addpath('D2D_LOCATION/d2d/arFramework3')
  3. Initialization of D2D via arInit

Since D2D generates C-code which is compiled for usage in Matlab, the so-called mex compiler has to be properly configured in Matlab.

Check the D2D wiki for further details about installation of Data2Dynamics.

Working with a specific benchmark model

The most straight forward way of using a benchmark model is switching the Matlab work folder to a specific benchmark model folder, e.g. via the Matlab command cd('D2D_LOCATION/d2d/arFramework3/Examples/Becker_Science2010').

D2D provides a short file termed Setup.m containing the commands for loading and compiling a model. Execution of this commands via Setup is sufficient for loading the model into the Matlab environment. After this step, basic D2D commands like plotting (via arPlot) or fitting (via arFit) can be applied.

Working with all benchmark models

D2D provides functionality for selecting the subset of models belonging to the benchmark collection, and for comprehensive compiling of these model files. The following procedure is suggested:

  1. arCopyBenchmarkModels copies all 20 model folders to the current working directory
  2. arCompileAllSetups performs compiling of all models in the subfolders of the working directory

Compiling the model files in many cases only takes few time. However, there are some models where compilation is time-consuming because a huge number of symbolic operations are necessary. Because compiling is required only once, we therefore recommend compilation of all models over night, e.g. via arCompileAllSetups. The time required for the individual models depends on the computer and on the operating system. The following models are computationally demanding:

  • Bachmann: 2-3 minutes on a standard PC/laptop
  • Chen: 10 hours
  • Crauste: 2-3 minutes
  • Fiedler: 15 minutes
  • Fujita: 15 minutes
  • Isensee: 30 minutes
  • Lucarelli: 15 minutes
  • Merkle: 30 minutes
  • Reelin: 1-2 minutes
  • Schwen: 1-2 minutes
  • Sobotta: 40 minutes

Simulated data

If simulated data is intended to be used for benchmarking purposes instead of the experimental data it can be generated via arSimuMeasurements. This function generates data points at the same experimental conditions with normally distributed errors with the same standard deviation as specified in the model or data.

Exporting a model in the bechmark collection format

arExportBenchmark provides the functionality to export an arbitrary D2D model loaded in the current Matlab workspace into the format which is used in this benchmark collection repository.

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