Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks.

ABSTRACT: Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation

SUBMITTER: Lakrisenko P 

PROVIDER: S-EPMC9838866 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature