PESTO: Parameter EStimation TOolbox

Paul Stapor; Daniel Weindl; Benjamin Ballnus; Sabine Hug; Carolin Loos; Anna Fiedler; Sabrina Krause; Sabrina Hroß; Fabian Fröhlich; Jan Hasenauer

doi:10.1093/bioinformatics/btx676

PESTO: Parameter EStimation TOolbox

Bioinformatics. 2018 Feb 15;34(4):705-707. doi: 10.1093/bioinformatics/btx676.

Authors

Paul Stapor^{1

2}, Daniel Weindl¹, Benjamin Ballnus^{1

2}, Sabine Hug^{1

2}, Carolin Loos^{1

2}, Anna Fiedler^{1

2}, Sabrina Krause^{1

2}, Sabrina Hroß^{1

2}, Fabian Fröhlich^{1

2}, Jan Hasenauer^{1

2}

Affiliations

¹ Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
² Center for Mathematics, Technische Universität München, 85748 Garching, Germany.

Abstract

Summary: PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability 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, for which the user can provide a deterministic objective function in MATLAB.

Availability and implementation: PESTO is a MATLAB toolbox, freely available under the BSD license. The source code, along with extensive documentation and example code, can be downloaded from https://github.com/ICB-DCM/PESTO/.

Contact: jan.hasenauer@helmholtz-muenchen.de.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Computational Biology / methods*
Software*