Distilling identifiable and interpretable dynamic models from biological data.

ABSTRACT: Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in computational biology. Currently, many research efforts are focused on model discovery, i.e. automating the development of interpretable models from data. One of the main frameworks is sparse regression, where the sparse identification of nonlinear dynamics (SINDy) algorithm and its variants have enjoyed great success. SINDy-PI is an extension which allows the discovery of rational nonlinear terms, thus enabling the identification of kinetic functions common in biochemical networks, suc

SUBMITTER: Massonis G 

PROVIDER: S-EPMC10615316 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature