ArticleEURASIP journal on bioinformatics & systems biology2011
Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models.
Article in EURASIP journal on bioinformatics & systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Overcoming the limits of traditional rate calculations from sparse concentration data: a probabilistic framework for bioprocess modeling.Bioprocess and biosystems engineering · 2026Article
- Uncertainty in model-based treatment decision support: Applied to aortic valve stenosis.International journal for numerical methods in biomedical engineering · 2020Article
- The double-edged sword role of fibroblasts in the interaction with cancer cells; an agent-based modeling approach.PloS one · 2020Article
- Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.Frontiers in genetics · 2019Review
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3 authors.
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Abstract
In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
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