A protocol for dynamic model calibration

TL;DR

This paper presents a comprehensive protocol for calibrating dynamic biological models using differential equations, addressing challenges like parameter identifiability and computational complexity, with practical guidance and reproducible code.

Contribution

It introduces a detailed, step-by-step calibration protocol for biological models, including illustrative examples and complete code for reproducibility.

Findings

The protocol effectively guides model calibration in biological systems.

Application to two models demonstrates its practicality.

Provides a reproducible framework for non-expert users.

Abstract

Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and non-measurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data, and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For non-expert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models, and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.