Category Cross-Omics>Pathway Analysis/Tools

Abstract PottersWheel is a multi-experiment fitting MATLAB® (see Note 1) toolbox for mathematical modeling in Systems Biology.

It allows reaction network or ordinary differential equation (ODE) based modeling and fits a model to several data sets at once (multi- experiment fitting).

PottersWheel is numerically fast, based on FORTRAN integrators and C MEX ODE files and is accessible via graphical user interfaces, from the command line and scripts.

It determines parameter identifiability and confidence intervals, imports and exports Systems Biology Markup Language (SBML) models and supports ‘biochemical network’ modeling.

PottersWheel originates from Systems Biology, but is applicable to arbitrary ODE based modeling.

PottersWheel's features/capabilities include:

Model creation --

1) Chemical reaction based (ODE creation by the PottersWheel chemical compiler);

2) ODE based (optionally with reaction network reconstruction);

3) Import from SBML models;

4) Rule based modeling to cope with combinatorial complexity;

5) Algebraic equations (assignment rules, start value assignments, and events); and

6) Support of model families without redundant modeling.

Model integration --

1) The differential equations of a model are dynamically compiled as C++ files;

2) Six (6) different FORTRAN integrators are supported; and

3) Very fast model integration allows for a ‘real-time’ modeling and fitting experience.

Model investigation --

1) Equalizer allows you to change parameter values via sliders in real time;

2) Input Designer allows you to change the characteristics of external driving functions, like a continuous, pulsed, or ramp stimulation; and

3) 'Automatic investigations' like sensitivity analyses, including control coefficients.

External driven input --

1) Strong support of external input functions which drive the system dynamically; and

2) Input is either presented analytically or an experimental data set is interpolated with a smoothing spline (A spline is a mathematical function used for interpolation or smoothing).

Experimental data --

1) Strong support to incorporate external data saved in MS Excel or ASCII format;

2) Automatic detection of data column names and mapping dialog for model observations;

3) Automatic use of already mapped data sets;

4) Support of multiple stimulations within one experiment;

5) Dose- orstimulus-dependent view is possible;

6) Estimation of experimental standard deviation; and

7) Outliers dialog to remove single data points after visual inspection.

Fitting --

1) Support of advanced deterministic and stochastic optimization algorithms;

2) Multi-Experiment fitting:

3) Several fit sequences available, like n fits each starting from the last fit with disturbed parameter values;

4) Fit sequence starting from quasi random number generated locations within the complete parameter space; and 5) Qualitative or soft constraints can be specified.

Fit sequence analysis --

1) Best fit selection;

2) Histograms and box plots of fitted parameter values;

3) Linear correlation analysis including principal component analysis (PCA) in order to detect linear non-identifiabilities;

Note: see Plug-Ins (below...) for detection of non-linear non- identifiabilities.

4) Hierarchical clustering of the parameter space;

5) Analysis of custom derived parameters, which are functions of the parameters; and

6) ScatterMan: Manual investigation of pairs and triples of fitted parameter values.

Single fit analysis --

1) Detailed residual analysis; and

2) Statistical tests for chi square values.

Reporting --

1) Each analysis or fit can be appended as a section to a report;

2) Optional with graphical model visualization and a list of reactions;

3) The order of sections can be changed at any time;

4) All figures of a section can be investigated separately; and 5) Support of PDF-Latex, MS Word, and HTML reports.

Sessions --

1) The complete current working state can be saved and reloaded at any time;

2) Exchange of sessions between researches to exactly reproduce modeling efforts; and

3) A modeling session comprises an arbitrary set of repository models and data sets, and currently combined model-data couples for fitting.

Application Programming Interface (API) and macros --

1) Rich set of MATLAB functions to use PottersWheel functionalities within custom MATLAB programs;

2) Macros support an automated and documented way to model efficiently; and

3) Comprehensive, up-to-date documentation via 'help FunctionName' or on-line.

Plug-Ins --

Stefan Hengl's MOTA algorithm in order to detect non-identifiabilities if an arbitrary number of model parameters share a linear or non-linear functional relationship; (see Data-Based Identifiability Analysis of Nonlinear Dynamical Models. Bioinformatics, 2007, 23: 2612-8).

Note 1: MATLAB is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and FORTRAN.

Note 2: PottersWheel contains more than 150,000 lines of MATLAB and C code, is freely available for academic usage, and is intensively used by biologists and modelers since 2005.

System Requirements

1) MATLAB 7.1 SP3

2) MATLAB Optimization Toolbox is recommended.

3) Windows, Linux, or Macintosh


PottersWheel has been developed by Thomas Maiwald during his PhD at the Freiburg Center of Data Analysis and Modeling (FDM) at the University of Freiburg, in the group of Prof. Jens Timmer supported by the German HepatoSys initiative.

Manufacturer Web Site PottersWheel

Price Contact manufacturer.

G6G Abstract Number 20361

G6G Manufacturer Number 104010