ArticleIET systems biology2020
Application of conditional robust calibration to ordinary differential equations models in computational systems biology: a comparison of two sampling strategies.
Article in IET systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Bayesian parameter estimation for dynamical models in systems biology.PLoS computational biology · 2022Article
- Robustness analysis for quantitative assessment of vaccination effects and SARS-CoV-2 lineages in Italy.BMC infectious diseases · 2022Article
- Article
- Application of Response Surface Methodology for Optimizing the Therapeutic Activity of ZnO Nanoparticles Biosynthesized fromBiomimetics (Basel, Switzerland) · 2021Article
- Mathematical Modeling and Robustness Analysis to Unravel COVID-19 Transmission Dynamics: The Italy Case.Biology · 2020Article
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4 authors.
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Abstract
Mathematical modelling is a widely used technique for describing the temporal behaviour of biological systems. One of the most challenging topics in computational systems biology is the calibration of non-linear models; i.e. the estimation of their unknown parameters. The state-of-the-art methods in this field are the frequentist and Bayesian approaches. For both of them, the performance and accuracy of results greatly depend on the sampling technique employed. Here, the authors test a novel Bayesian procedure for parameter estimation, called conditional robust calibration (CRC), comparing two different sampling techniques: uniform and logarithmic Latin hypercube sampling. CRC is an iterative algorithm based on parameter space sampling and on the estimation of parameter density functions. They apply CRC with both sampling strategies to the three ordinary differential equations (ODEs) models of increasing complexity. They obtain a more precise and reliable solution through logarithmically spaced samples.
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