This repository accompanies the manuscript
Pieschner, Hasenauer, Fuchs (2021). "Identifiability analysis for models of the translation kinetics after mRNA transfection".
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.
In this project, we use simulated data generated as described below and experimental data that has been published in Fröhlich et al (2018,
https://doi.org/10.1038/s41540-018-0079-7, see data/experimental_data/README.md for further details).
This repository contains all relevant code for the data analysis.
The computational environment is described in the Docker file in container_image/dockerfile_r3.6.2_rstan_rmd.
The model and output files for the structural identifiability analysis with DAISY are contained in the folder Identifiability_analysis_with_DAISY. See Identifiability_analysis_with_DAISY/README.md for further details.
The figures for the simulation based assessment of parameter influence were generated in R with R_code/figures_and_tables/figs_simulate_trajectories_to_study_identifiability_with_param_from_Froehlich.R.
We use the open source software Stan to sample from the posterior distributions of the ODE or SDE model given the experimental or simulated data, respectively. The Stan models are defined in the Stan_model_code folder.
We use Stan through its interface rstan in R. All R code for generating simulated data, sampling from the posterior distribution, and aggregating the results is contained in the R_code folder.
Moreover, the R code to generate figures and table for the article is contained in R_code/figures_and_tables.
Most of the calculations were performed on a HPC cluster managed by SLURM. The bash scripts to submit the jobs are contained in the folder bash_scripts.
Additional summaries of all sampling results and for some individual trajectories are contained in the folder R_markdown.