Estimating and Assessing Differential Equation Models with Time-Course Data.

ABSTRACT: Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This Article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy, and some components of the system may not be observed. Furthermore, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories, including unobserved components, with appropriate uncertain

SUBMITTER: Wong SWK 

PROVIDER: S-EPMC10041644 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature