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...

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Bibliographic Details
Published in:Journal of the Royal Society interface Vol. 22; no. 231; p. 20250536
Main Authors: Simpson, Matthew J, Plank, Michael J
Format: Journal Article
Language:English
Published: England 01.10.2025
Subjects:
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
Online Access:Get more information
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Summary: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.
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ISSN:1742-5662
1742-5662
DOI:10.1098/rsif.2025.0536