Inference and prediction for stochastic models of biological populations undergoing migration and proliferation

Parameter inference is a critical step in the process of interpreting biological data using mathematical models. Inference provides a means of deriving quantitative, mechanistic insights from sparse, noisy data. While methods for parameter inference, parameter identifiability and model prediction ar...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of the Royal Society interface Ročník 22; číslo 231; s. 20250536
Hlavní autoři: Simpson, Matthew J, Plank, Michael J
Médium: Journal Article
Jazyk:angličtina
Vydáno: England 01.10.2025
Témata:
Animal Migration - physiology
Animals
Cell Proliferation
Humans
Models, Biological
Population Dynamics
Stochastic Processes
random walk
uncertainty quantification
continuum limit
cell proliferation
model prediction
parameter identifiability
cell migration
parameter estimation
partial differential equation
stochastic model
ISSN:1742-5662, 1742-5662
On-line přístup:Zjistit podrobnosti o přístupu
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Parameter inference is a critical step in the process of interpreting biological data using mathematical models. Inference provides a means of deriving quantitative, mechanistic insights from sparse, noisy data. While methods for parameter inference, parameter identifiability and model prediction are well developed for deterministic continuum models, working with biological applications often requires stochastic modelling approaches to capture inherent variability and randomness that can be prominent in biological measurements and data. Random walk models are especially useful for capturing spatio-temporal processes, such as ecological population dynamics, molecular transport phenomena and collective behaviour associated with multicellular phenomena. This review focuses on parameter inference, identifiability analysis and model prediction for a suite of biologically inspired, stochastic agent-based models relevent to animal dispersal and populations of biological cells. With a particular emphasis on model prediction, we highlight roles for numerical optimization and automatic differentiation. Open-source Julia code is provided to support scientific reproducibility. We encourage readers to use this code directly or adapt it to suit their interests and applications.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ISSN:1742-5662
1742-5662
DOI:10.1098/rsif.2025.0536