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Identifiability analysis for models of the translation kinetics after mRNA transfection

View ORCID ProfileSusanne Pieschner, View ORCID ProfileJan Hasenauer, View ORCID ProfileChristiane Fuchs
doi: https://doi.org/10.1101/2021.05.18.444633
Susanne Pieschner
1Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
2Department of Mathematics, Technical University Munich, Garching, Germany
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Jan Hasenauer
1Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
2Department of Mathematics, Technical University Munich, Garching, Germany
3Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
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Christiane Fuchs
1Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
2Department of Mathematics, Technical University Munich, Garching, Germany
4Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
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  • For correspondence: christiane.fuchs@uni-bielefeld.de
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Abstract

Mechanistic models are a powerful tool to gain insights into biological processes. The parameters of such models, e.g. kinetic rate constants, usually cannot be measured directly but need to be inferred from experimental data. In this article, we study dynamical models of the translation kinetics after mRNA transfection and analyze their parameter identifiability. Previous studies have considered ordinary differential equation (ODE) models of the process, and here we formulate a stochastic differential equation (SDE) model. For both model types, we consider structural identifiability based on the model equations and practical identifiability based on simulated as well as experimental data and find that the SDE model provides better parameter identifiability than the ODE model. Moreover, our analysis shows that even for those parameters of the ODE model that are considered to be identifiable, the obtained estimates are sometimes unreliable. Overall, our study clearly demonstrates the relevance of considering different modeling approaches and that stochastic models can provide more reliable and informative results.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 18, 2021.
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Identifiability analysis for models of the translation kinetics after mRNA transfection
Susanne Pieschner, Jan Hasenauer, Christiane Fuchs
bioRxiv 2021.05.18.444633; doi: https://doi.org/10.1101/2021.05.18.444633
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Identifiability analysis for models of the translation kinetics after mRNA transfection
Susanne Pieschner, Jan Hasenauer, Christiane Fuchs
bioRxiv 2021.05.18.444633; doi: https://doi.org/10.1101/2021.05.18.444633

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