Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions.

ABSTRACT: We present a novel surrogate modeling method that can be used to accelerate the solution of uncertainty quantification (UQ) problems arising in nonlinear and non-smooth models of biological systems. In particular, we focus on dynamic flux balance analysis (DFBA) models that couple intracellular fluxes, found from the solution of a constrained metabolic network model of the cellular metabolism, to the time-varying nature of the extracellular substrate and product concentrations. DFBA models are generally computationally expensive and present unique challenges to UQ, as they entail dynamic simulations with discrete events that correspond to switches in the active set of the solution of the constrained intracellular model. The proposed non-smooth polynomial chaos expansion (nsPCE) method is a

SUBMITTER: Paulson JA 

PROVIDER: S-EPMC6742419 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature