RT Journal Article
SR Electronic
T1 Identifiability analysis for models of the translation kinetics after mRNA transfection
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.18.444633
DO 10.1101/2021.05.18.444633
A1 Pieschner, Susanne
A1 Hasenauer, Jan
A1 Fuchs, Christiane
YR 2021
UL http://biorxiv.org/content/early/2021/05/18/2021.05.18.444633.abstract
AB 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 StatementThe authors have declared no competing interest.