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The power of identifiability analysis for dynamic modeling in animal science: A practitioner approach

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Abstract

Constructing dynamic mathematical models of biological systems requires estimating unknown parameters from available experimental data, usually using a statistical fitting procedure. This procedure is usually called parameter identification, parameter estimation, model fitting, or model calibration. In animal science, parameter identification is often performed without analytic considerations on the possibility of determining unique values of the model parameters. These analytic studies are related to the notion of structural identifiability. The structural identifiability analysis is a powerful tool for model construction because it informs whether the parameter identification problem is well-posed. In case of lack of identifiability, structural identifiability analysis is helpful to determine which actions (e.g., model reparameterization, choice of new data measurements) are needed to render the model parameters identifiable (when possible). The mathematical technicalities associated with structural identifiability analysis are very sophisticated. However, the development of dedicated, freely available software tools enables the application of identifiability analysis without needing to be an expert in mathematics and computer programming. We refer to such a non-expert user as a practitioner for hands-on purposes. In this paper, we propose to adopt a practitioner approach that takes advantage of available software tools to integrate identifiability analysis in the modeling practice in the animal science field. The application of structural identifiability implies switching our regard of the parameter identification problem as a downstream process (after data collection) to an upstream process (before data collection). This upstream approach will substantially improve the workflow of model construction toward robust and valuable models in animal science. Illustrative examples with different levels of complexity support our work. The source codes of the examples are provided for learning purposes and to promote open science practices.
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hal-03878727 , version 1 (30-11-2022)

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  • HAL Id : hal-03878727 , version 1

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Rafael Munoz Tamayo, Luis O. Tedeschi. The power of identifiability analysis for dynamic modeling in animal science: A practitioner approach. 2022. ⟨hal-03878727⟩
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