Uncertainty propagation for deterministic models of biochemical networks using moment equations and the extended Kalman filter.

ABSTRACT: Differential equation models of biochemical networks are frequently associated with a large degree of uncertainty in parameters and/or initial conditions. However, estimating the impact of this uncertainty on model predictions via Monte Carlo simulation is computationally demanding. A more efficient approach could be to track a system of low-order statistical moments of the state. Unfortunately, when the underlying model is nonlinear, the system of moment equations is infinite-dimensional and cannot be solved without a moment closure approximation which may introduce bias in the moment dynamics. Here, we present a new method to study the time evolution of the desired moments for nonlinear systems with polynomial rate laws. Our approach is based on solving a system of low-order moment equat

SUBMITTER: Kurdyaeva T 

PROVIDER: S-EPMC8331248 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature