A Systematic Computational Framework for Practical Identifiability Analysis in Mathematical Models Arising from Biology.

ABSTRACT: Practical identifiability is a critical concern in data-driven modeling of mathematical systems. In this paper, we propose a novel framework for practical identifiability analysis to evaluate parameter identifiability in mathematical models of biological systems. Starting with a rigorous mathematical definition of practical identifiability, we demonstrate its equivalence to the invertibility of the Fisher Information Matrix. Our framework establishes the relationship between practical identifiability and coordinate identifiability, introducing a novel metric that simplifies and accelerates the evaluation of parameter identifiability compared to the profile likelihood method. Additionally, we introduce new regularization terms to address non-identifiable parameters, enabling uncertainty qua

SUBMITTER: Wang S 

PROVIDER: S-EPMC11722522 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature