REDEMPTION - Reduced Dimension Ensemble Modeling and Parameter Estimation

Main content

REDEMPTION (REduced Dimension Ensemble Modeling and Parameter estimaTION) is a MATLAB toolbox for the identification of parameters and parameter ensembles of ODE models from time-series data. The toolbox is based on incremental parameter estimation (IPE) and integrated flux parameter estimation (IFPE) methods [1-3], in which the data fitting (parameter estimation) problem is formulated as a nested optimization of reduced dimension. REDEMPTION provides a user-friendly interface for model description (using Power-law, Lin-log kinetics or from SBML format), parameter estimation, ensemble modelling and visualization of results. For computational speed-up, the toolbox also offers a parallelization option using MATLAB Parallel Computing Toolbox. Please cite [4] if you use this toolbox.

Created by: Liu Yang

Designed by: Liu Yang and Rudiyanto Gunawan

Required MATLAB toolboxes

Recommended third-party MATLAB toolboxes

REDEMPTION was implemented and tested on MATLAB 2012a platform.

Last Update, Download & Installation

Current version: 07.08.2015

Download & unzip the (ZIP, 6.1 MB) file.

We welcome suggestions for additional features that you would like to see on REDEMPTION.


Redistribution and use in P-code form is permitted provided agreeing to the License (TXT, 2 KB).


  1. Jia, G., G. Stephanopoulos, and Gunawan, R. Incremental parameter estimation of kinetic metabolic network models. BMC Systems Biology, 2012. 6: p. 142. abstract
  2. Jia, G., G. Stephanopoulos, and Gunawan, R. Ensemble kinetic modeling of metabolic networks from dynamic metabolic profiles. Metabolites, 2012. 2(4): p. 891-912. abstract
  3. Liu, Y., and Gunawan, R. Parameter estimation of dynamic biological network models using integrated fluxes. BMC Systems Biology, 2014. 8: p. 127. abstract
  4. Liu, Y., Manesso, E. and Gunawan R. REDEMPTION: Reduced Dimension Ensemble Modeling and Parameter Estimation. Bioinformatics 31 (20): 3387-3389 (2015). abstract
  5. Zamora-Sillero, E., et al., Efficient characterization of high-dimensional parameter spaces for systems biology. BMC Systems Biology, 2011. 5: p. 142.
  6. Egea, J.A., R. Marti, and J.R. Banga, An evolutionary method for complex-process optimization. Computers & Operations Research, 2010. 37(2): p. 315-324.
  7. Hindmarsh, A.C., et al., SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers. Acm Transactions on Mathematical Software, 2005. 31(3): p. 363-396.
  8. Keating, S.M., et al., SBMLToolbox: an SBML toolbox for MATLAB users. Bioinformatics, 2006. 22(10): p. 1275-7.
  9. Bornstein, B. J., Keating, S. M., Jouraku, A., and Hucka M. (2008) LibSBML: An API Library for SBML. Bioinformatics, 24(6):880–881.


This work is supported by funding from Swiss National Science Foundation.

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