PEtab—Interoperable specification of parameter estimation problems in systems biology

Schmiester, Leonard; Schälte, Yannik; Bergmann, Frank; Camba, Tacio; Dudkin, Erika; Egert, Janine; Fröhlich, Fabian; Fuhrmann, Lara; Hauber, Adrian L.; Kemmer, Svenja; Lakrisenko, Polina; Loos, Carolin; Merkt, Simon; Müller, Wolfgang G.; Pathirana, Dilan; Raimúndez, Elba; Refisch, Lukas; Rosenblatt, Marcus; Stapor, Paul; Städter, Philipp; Wang, Dantong; Wieland, Franz-Georg; Banga, Julio R.; Timmer, Jens; Villaverde, Alejandro F.; Sahle, Sven; Kreutz, Clemens; Hasenauer, Jan; Weindl, Daniel

doi:10.1371/journal.pcbi.1008646

Cited by 72 publications

(79 citation statements)

References 24 publications

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“…We implemented the gradient calculation method in the Python Parameter EStimation TOolbox (pyPESTO) [Schälte et al, 2020]. The qualitative data can be defined using an extension to the PEtab format, which is a standardized format for the definition of parameter estimation problems [Schmiester et al, 2021]. Model simulation was carried out using the AMICI toolbox [Fröhlich et al, 2020].…”

Section: Methodsmentioning

confidence: 99%

Efficient gradient-based parameter estimation for dynamic models using qualitative data

Schmiester

Weindl

Hasenauer

2021

Preprint

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Motivation: Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence. Results: Here, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. Additionally, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data. Availability: The proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613.

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Section: Methodsmentioning

confidence: 99%

Efficient gradient-based parameter estimation for dynamic models using qualitative data

Schmiester

Weindl

Hasenauer

2021

Preprint

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“…Therefore, the system states must be mapped to measured quantities by observable functions, and a measurement noise model must be specified. The PEtab format was recently introduced to complement SBML by tab-separated value files specifying observables, measurements, experimental conditions, and estimated parameters (Schmiester et al, 2021). Currently 9 software toolboxes support PEtab as an input format, among them COPASI, d2d, dMod and AM-ICI/pyPESTO.…”

Section: Statement Of Needmentioning

confidence: 99%

yaml2sbml: Human-readable and -writable specification of ODE models and their conversion to SBML

Vanhoefer¹,

Matos²,

Pathirana³

et al. 2021

JOSS

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Ordinary differential equations (ODE) models are used throughout natural sciences to describe dynamic processes. In systems biology, ODEs are mostly stored and exchanged using the Systems Biology Markup Language (SBML), a widely adopted community standard based on XML. The Parameter Estimation table (PEtab) format extends SBML to parameter estimation problems. A large number of software tools support simulation of SBML models and parameter estimation for PEtab problems. Specifying ODE models in SBML and parameter estimation problems in PEtab provides access to these tools. However, SBML is considered to be neither human-readable nor human-writable. An easy-to-use approach to construct the SBML/PEtab models tailored to ODE models will facilitate model generation.

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“…The compartment model is encoded in the Systems Biology Markup Language (SBML, Hucka et al (2003)), a widely used standard in the systems biology community. The datasets used for fitting, together with parameter priors and bounds, are stored in the PEtab format (Schmiester et al 2021), a standard format for the formulation of estimation problems. Both formats are supported by various simulation and analysis tools, which aids accessibility and reusability of model and results.…”

Section: Computational Modelling Pipelinementioning

confidence: 99%

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts

Contento

Castelletti²,

Raimúndez

et al. 2021

Preprint

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Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases. Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach. The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses.

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PEtab—Interoperable specification of parameter estimation problems in systems biology

Cited by 72 publications

References 24 publications

Efficient gradient-based parameter estimation for dynamic models using qualitative data

Efficient gradient-based parameter estimation for dynamic models using qualitative data

yaml2sbml: Human-readable and -writable specification of ODE models and their conversion to SBML

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts

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