Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models

Bioinformatics
Olivia ErikssonJeanette Hellgren Kotaleski

Abstract

Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours. We used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a GSA was performed on the predictions using the posterior distribution as the possible values for the parameters. This methodology was applied on a relativel...Continue Reading

References

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Citations

Nov 22, 2020·Integrative and Comparative Biology·Nicholas A Battista
Aug 2, 2021·Computer Methods and Programs in Biomedicine·Jeremias GomesGeorge Teodoro
Oct 7, 2021·ELife·Timothy W ChurchMatthew G Gold

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Software Mentioned

R
GSA
VineCopula
ABC

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