Parameter identifiability and model selection for sigmoid population growth models

DOI10.1016/j.jtbi.2021.110998zbMath1483.92113OpenAlexW4226096213WikidataQ112821054 ScholiaQ112821054MaRDI QIDQ2670140

Alexander P. Browning, David J. Warne, Ruth E. Baker, Oliver J. Maclaren, Matthew J. Simpson

Publication date: 10 March 2022

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2021.110998

zbMATH Keywords

parameter estimationlogistic growthGompertz growthidentifiability analysisRichards growth

Mathematics Subject Classification ID

Population dynamics (general) (92D25)

Related Items (7)

Exact time-dependent solutions of a Fisher-KPP-like equation obtained with nonclassical symmetry analysis ⋮ Reliable and efficient parameter estimation using approximate continuum limit descriptions of stochastic models ⋮ Designing experimental conditions to use the Lotka-Volterra model to infer tumor cell line interaction types ⋮ Bayesian inference on the Allee effect in cancer cell line populations using time-lapse microscopy images ⋮ Simplified models of diffusion in radially-symmetric geometries ⋮ Profile likelihood-based parameter and predictive interval analysis guides model choice for ecological population dynamics ⋮ Modelling count data with partial differential equation models in biology

Uses Software

• Matlab
• R
• nlrwr
• fmincon
• GenSSI
• GitHub
• pllf

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Reproducibility of scratch assays is affected by the initial degree of confluence: experiments, modelling and model selection
• Determining identifiable parameter combinations using subset profiling
• A comparison and catalog of intrinsic tumor growth models
• Model selection and mixed-effects modeling of HIV infection dynamics
• Identifiability results for several classes of linear compartment models
• To explain or to predict?
• Nonlinear estimation
• Statistical tools for nonlinear regression. A practical guide with S-PLUS and R examples.
• Statistical inference in science
• Mathematical biology. Vol. 1: An introduction.
• Analysis of logistic growth models
• Extended logistic growth model for heterogeneous populations
• A unified approach to the Richards-model family for use in growth analyses: why we need only two model forms
• Model-based data analysis of tissue growth in thin 3D printed scaffolds
• A Bayesian computational approach to explore the optimal duration of a cell proliferation assay
• Using experimental data and information criteria to guide model selection for reaction-diffusion problems in mathematical biology
• Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability
• Nonlinear Regression with R
• Bayes Factors
• Principles of Statistical Inference
• A new look at the statistical model identification

This page was built for publication: Parameter identifiability and model selection for sigmoid population growth models