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Daniel Weindl edited this page Mar 24, 2017 · 3 revisions

PESTO - Parameter EStimation TOolbox

PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers state-of-the art algorithms for optimization and uncertainty analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, which provides an objective function in MATLAB.

PESTO features include:

  • Multistart optimization
  • Sampling routines
  • Profile-likelihood analysis
  • Visualization routines
  • and more

These functions are demonstrated in several systems biology examples included in the examples/ directory and further documentation is available in doc/PESTO-doc.pdf.

References

PESTO has been used in a number of computational biology research projects:

  • Fröhlich, F.; Theis, F. J.; Rädler, J. O. & Hasenauer, J. Parameter estimation for dynamical systems with discrete events and logical operations. Bioinf., 2016

  • Fröhlich, F.; Kaltenbacher, B.; Theis, F. J. & Hasenauer, J. Scalable parameter estimation for genome-scale biochemical reaction networks, PLoS Comput. Biol., 2017, 13, 1-18

  • Hross, S.; Fiedler, A.; Theis, F. J. & Hasenauer, J. Findeisen, R.; Bullinger, E.; Balsa-Canto, E. & Bernaerts, K. (Eds.) Quantitative comparison of competing PDE models for Pom1p dynamics in fission yeast, Proceedings 6th IFAC Conference on Foundations of Systems Biology in Engineering, 2016, 49, 264-269

  • Loos, C.; Fiedler, A. & Hasenauer, J. Bartocci, E.; Lio, P. & Paoletti, N. (Eds.) Parameter estimation for reaction rate equation constrained mixture models, Proceedings of the 13th Computational Methods in Systems Biology, Cambridge, UK, 2016, 9859, 186-200

  • Geissen, E.-M.; Hasenauer, J.; Heinrich, S.; Hauf, S.; Theis, F. J. & Radde, N. MEMO -- Multi-experiment mixture model analysis of censored data, Bioinf., 2016, 32, 2464-2472

  • Hross, S. & Hasenauer, J. Analysis of CFSE time-series data using division-, age- and label-structured population models, Bioinf., 2016, 32, 2321-2329

  • Fröhlich, F.; Thomas, P.; Kazeroonian, A.; Theis, F. J.; Grima, R. & Hasenauer, J. Inference for stochastic chemical kinetics using moment equations and system size expansion, PLoS Comput. Biol., 2016, 12, e1005030

  • Boiger, R.; Hasenauer, J.; Hross, S. & Kaltenbacher, B. Integration based profile likelihood calculation for PDE constrained parameter estimation problems, Inverse Prob., 2016, 32, 125009

  • Fröhlich, F.; Hross, S.; Theis, F. J. & Hasenauer, J. Mendes, P.; Dada, J. O. & Smallbone, K. O'Neill. (Eds.) Radial basis function approximation of Bayesian parameter posterior densities for uncertainty analysis, Proceedings of the 12th International Conference on Computational Methods in Systems Biology (CMSB 2014), 2014, 73-85

  • Fröhlich, F.; Theis, F. J. & Hasenauer, J. Mendes, P.; Dada, J. O. & Smallbone, K. O'Neill. (Eds.) Uncertainty analysis for non-identifiable dynamical systems: Profile likelihoods, bootstrapping and more. Proceedings of the 12th International Conference on Computational Methods in Systems Biology (CMSB 2014), 2014, 61-72

  • Hasenauer, J.; Hasenauer, C.; Hucho, T. & Theis, F. J. ODE constrained mixture modelling: A method for unraveling subpopulation structures and dynamics, PLoS Comput. Biol., 2014, 10, e1003686

  • Heinrich, S.; Geissen, E.-M..; Kamenz, J.; Trautmann, S.; Widmer, C.; Drewe, P.; Knop, M.; Radde, N.; Hasenauer, J. & Hauf, S. Determinants for robustness in spindle assembly checkpoint signalling, Nature Cell Biology, 2013, 15, 1328-1339

  • Hock, S.; Hasenauer, J. & Theis, F. J. Modeling of 2D diffusion processes based on imaging data: Parameter estimation and practical identifiability analysis, BMC Bioinf., 2013, 14(Suppl 10)

  • Kazeroonian, A.; Hasenauer, J. & Theis, F. J. Autio, R.; Shmulevich, I.; Strimmer, K.; Wiuf, C.; Sarbu, S. & Yli-Harja, O. (Eds.) Parameter estimation for stochastic biochemical processes: A comparison of moment equation and finite state projection, Proceedings of 10th International Workshop on Computational Systems Biology, 2013, 66-73

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