Dataset Information

PESTO: Parameter EStimation TOolbox.

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.

SUBMITTER: Stapor P

PROVIDER: S-EPMC5860618 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Publications

PESTO: Parameter EStimation TOolbox.

Stapor Paul P Weindl Daniel D Ballnus Benjamin B Hug Sabine S Loos Carolin C Fiedler Anna A Krause Sabrina S Hroß Sabrina S Fröhlich Fabian F Hasenauer Jan J

Bioinformatics (Oxford, England) 20180201 4

<h4>Summary</h4>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.<h4>Availability and implementation</h4>PESTO is a MATLAB toolb ...[more]

PMID: 29069312

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Project description:BackgroundDynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability. As a consequence, the use of standard estimation methods (such as gradient-based local ones) will often result in wrong solutions. Overfitting can be particularly problematic, since it produces very good calibrations, giving the impression of an excellent result. However, overfitted models exhibit poor predictive power. Here, we present a novel automated approach to overcome these pitfalls. Its workflow makes use of two sequential optimisation steps incorporating three key algorithms: (1) sampling strategies to systematically tighten the parameter bounds reducing the search space, (2) efficient global optimisation to avoid convergence to local solutions, (3) an advanced regularisation technique to fight overfitting. In addition, this workflow incorporates tests for structural and practical identifiability.ResultsWe successfully evaluate this novel approach considering four difficult case studies regarding the calibration of well-known biological oscillators (Goodwin, FitzHugh-Nagumo, Repressilator and a metabolic oscillator). In contrast, we show how local gradient-based approaches, even if used in multi-start fashion, are unable to avoid the above-mentioned pitfalls.ConclusionsOur approach results in more efficient estimations (thanks to the bounding strategy) which are able to escape convergence to local optima (thanks to the global optimisation approach). Further, the use of regularisation allows us to avoid overfitting, resulting in more generalisable calibrated models (i.e. models with greater predictive power).

Dataset Information

PESTO: Parameter EStimation TOolbox.

Summary

Availability and implementation

Contact

Supplementary information

Publications

PESTO: Parameter EStimation TOolbox.

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