Geometric analysis enables biological insight from complex non-identifiable models using simple surrogates.

ABSTRACT: An enduring challenge in computational biology is to balance data quality and quantity with model complexity. Tools such as identifiability analysis and information criterion have been developed to harmonise this juxtaposition, yet cannot always resolve the mismatch between available data and the granularity required in mathematical models to answer important biological questions. Often, it is only simple phenomenological models, such as the logistic and Gompertz growth models, that are identifiable from standard experimental measurements. To draw insights from complex, non-identifiable models that incorporate key biological mechanisms of interest, we study the geometry of a map in parameter space from the complex model to a simple, identifiable, surrogate model. By studying how non-identi

SUBMITTER: Browning AP 

PROVIDER: S-EPMC9891533 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature