Tutorial 9

9/3 1:30 - 4:00, Hewlett 101

MATLAB Toolbox PottersWheel 2.0 - Introduction into Dynamical Modeling and Multi-Experiment Fitting

Thomas Maiwald

Harvard Medical School, Department of Systems Biology, Boston, MA, USA.

The Matlab® toolbox PottersWheel provides an intuitive and yet powerful framework for data-based modeling of dynamical systems which can be expressed as sets of ordinary differential and algebraic equations, e.g. as a chemical reaction network [1]. The tutorial introduces the methodological approach of the program and exemplifies its use on models and experimental data of biochemical cellular systems. The key functionality of PottersWheel is multi-experiment fitting, where a mathematical model is fitted simultaneously to several data sets from different experimental conditions in order to improve the estimation of unknown model parameters, to check the validity of a given model, and to discriminate competing model hypotheses. Stochastic, deterministic, and hybrid optimization techniques can be applied to generate single fits or fit-sequences allowing for parameter identifiability analysis [2]. Chi-square and Likelihood ratio tests rule out insufficient models and indicate what can be concluded from experimental data given a certain noise level. Interactive design of external driving input functions optimizes the expected information of future experiments. Models are either created based on custom text files, PotterWheel model definition files, or by drag and drop via a graphical model designer. Dynamically generated C MEX functions and the use of FORTRAN integrators provide fast simulation and fitting procedures. PottersWheel comprises 200.000 lines of Matlab and C code, includes numerous graphical user interfaces, and is freely available for academic usage from www.potterswheel.de. It is intensively used by experimentalists and modelers since 2005. A comprehensive application programming interface is available for customization and use within own Matlab scripts.


  1. Thomas Maiwald and Jens Timmer. Dynamical modeling and multi-experiment fitting with PottersWheel, Bioinformatics 2008, 24:2037-2043.
  2. Stefan Hengl, Clemens Kreutz, Jens Timmer, Thomas Maiwald. Data-based identifiability analysis of nonlinear dynamical models, Bioinformatics 2007, 23, 2612-2618.

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