Dataset Information

GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models.

ABSTRACT: Motivation:Mathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed. Results:We introduce GenSSI 2.0, an advancement of the software toolbox GenSSI (Generating Series for testing Structural Identifiability). GenSSI 2.0 is the first toolbox for structural identifiability analysis to implement Systems Biology Markup Language import, state/parameter transformations and multi-experiment structural identifiability analysis. In addition, GenSSI 2.0 supports a range of MATLAB versions and is computationally more efficient than its previous version, enabling the analysis of more complex models. Availability and implementation:GenSSI 2.0 is an open-source MATLAB toolbox and available at https://github.com/genssi-developer/GenSSI. Contact:thomas.ligon@physik.uni-muenchen.de or jan.hasenauer@helmholtz-muenchen.de. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Ligon TS

PROVIDER: S-EPMC5905618 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models.

Ligon Thomas S TS Fröhlich Fabian F Chis Oana T OT Banga Julio R JR Balsa-Canto Eva E Hasenauer Jan J

Bioinformatics (Oxford, England) 20180401 8

<h4>Motivation</h4>Mathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed.<h4>Results</h4>We introduce GenSSI 2.0, an advancement of the software toolbox GenSSI ( ...[more]

PMID: 29206901

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Project description:The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights – as well as the possibility of controlling the system – may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements. Highlights • Structural identifiability and observability are desirable model properties.• They describe a model’s ability to inform about unmeasured parameters and states.• We collect and analyse hundreds of compartmental models of the COVID-19 pandemics.• We show which parameters and states can be determined from output measurements.• We discuss how to choose the most informative model for the available knowledge.

Dataset Information

GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models.

Publications

GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models.

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