ArticleBMC systems biology2012
Likelihood based observability analysis and confidence intervals for predictions of dynamic models.
Article in BMC systems biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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64 citing papers in PubMed.
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- Mathematical modeling for estimating influenza vaccine efficacy: A case study of the Valencian Community, Spain.Infectious Disease Modelling · 2024Article
- Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences.Royal Society open science · 2024Article
- Making Predictions Using Poorly Identified Mathematical Models.Bulletin of mathematical biology · 2024Article
- Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent.Bulletin of mathematical biology · 2024Article
- Modeling resistance to the broadly neutralizing antibody PGT121 in people living with HIV-1.PLoS computational biology · 2024Article
- Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling.Bulletin of mathematical biology · 2024Article
- Minimally sufficient experimental design using identifiability analysis.NPJ systems biology and applications · 2024Article
- Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.Journal of the Royal Society, Interface · 2024Review
- Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models.PLoS computational biology · 2023Article
- Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models.PLoS computational biology · 2023Article
- A Continuation Technique for Maximum Likelihood Estimators in Biological Models.Bulletin of mathematical biology · 2023Article
4 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
backgroundPredicting a system's behavior based on a mathematical model is a primary task in Systems Biology. If the model parameters are estimated from experimental data, the parameter uncertainty has to be translated into confidence intervals for model predictions. For dynamic models of biochemical networks, the nonlinearity in combination with the large number of parameters hampers the calculation of prediction confidence intervals and renders classical approaches as hardly feasible.
resultsIn this article reliable confidence intervals are calculated based on the prediction profile likelihood. Such prediction confidence intervals of the dynamic states can be utilized for a data-based observability analysis. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified model predictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted.
conclusionsThe presented methodology allows the propagation of uncertainty from experimental to model predictions. Although presented in the context of ordinary differential equations, the concept is general and also applicable to other types of models. Matlab code which can be used as a template to implement the method is provided at http://www.fdmold.uni-freiburg.de/∼ckreutz/PPL.
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