Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states.

ABSTRACT: Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based optimization is used. In many cases, steady-state computation is a part of model simulation, either due to steady-state data or an assumption that the system is at steady state at the initial time point. Various methods are available for steady-state and gradient computation. Yet, the most efficient pair of methods (one for steady states, one for gradients) for a particular model is often not clear. In order to facilitate the selection of methods, we explore six method pairs for computing the steady state and sensitivities at steady state using six real-world

SUBMITTER: Lakrisenko P 

PROVIDER: S-EPMC11498742 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature