Estimation of Specific Fluxes in Metabolic Networks using Non-linear Dynamic Optimization

Abstract

Metabolic network models describing the biochemical reaction network and material fluxes inside micro-organisms open interesting routes for the model based optimization of bioprocesses. Dynamic metabolic flux analysis (dMFA) has lately been studied as an extension of regular metabolic flux analysis (MFA), rendering a dynamic view of the fluxes, also in non-stationary conditions. Recent dMFA implementations suffer from some drawbacks, though. More specifically, the fluxes are not estimated as specific fluxes, which are more biologically relevant. The flux profiles are not smooth, and additional constraints cannot be taken into account. In this work, a new methodology based on a B-spline parameterization of the fluxes is presented. These are estimated using state-of-the-art dynamic optimization tools, i.e., orthogonal collocation, an interior-point optimizer and automatic differentiation. The approach is validated on a small-scale network, and proven to yield an accurate representation of the metabolic fluxes.