Relative sensitivity analysis of the predictive properties of sloppy models

Journal of Bioinformatics and Computational Biology
Ekaterina Myasnikova, Alexander Spirov

Abstract

Commonly among the model parameters characterizing complex biological systems are those that do not significantly influence the quality of the fit to experimental data, so-called "sloppy" parameters. The sloppiness can be mathematically expressed through saturating response functions (Hill's, sigmoid) thereby embodying biological mechanisms responsible for the system robustness to external perturbations. However, if a sloppy model is used for the prediction of the system behavior at the altered input (e.g. knock out mutations, natural expression variability), it may demonstrate the poor predictive power due to the ambiguity in the parameter estimates. We introduce a method of the predictive power evaluation under the parameter estimation uncertainty, Relative Sensitivity Analysis. The prediction problem is addressed in the context of gene circuit models describing the dynamics of segmentation gene expression in Drosophila embryo. Gene regulation in these models is introduced by a saturating sigmoid function of the concentrations of the regulatory gene products. We show how our approach can be applied to characterize the essential difference between the sensitivity properties of robust and non-robust solutions and select among t...Continue Reading

References

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Apr 10, 2014·Journal of Bioinformatics and Computational Biology·Ekaterina Myasnikova, Konstantin N Kozlov
Jul 15, 2015·The Journal of Chemical Physics·Mark K TranstrumJames P Sethna
Apr 29, 2016·Journal of Bioinformatics and Computational Biology·Alexander V SpirovDavid M Holloway

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Citations

May 10, 2018·Journal of Bioinformatics and Computational Biology·Mikhail Gelfand

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