A Dynamic Constraint-based Modelling (DCBM) Approach With Alternative Metabolic Objective Functions Predicts The impact of Oxidative Stress on Stored Red Blood Cells (RBCs)

Abstract

Mathematical metabolic modelling is a systematic endeavour to allow identifying the main causes of an observed metabolic change and to estimate the consequences of an imposed metabolic perturbation regarding a biosystem. Dynamic Constraint-based modelling (DCBM) has delivered promising results in metabolic engineering and in bioprocess design by providing mechanistically relevant systems-level knowledge of a network of bioreactions. Here, we seek to establish a DCBM approach that leverages convex optimization and nonlinear regression mathematical toolkit to estimate dynamic intracellular metabolic flux distributions in stored Red Blood Cells (RBCs) for transfusion purposes. First, we developed an ad-hoc metabolic network including 77 reactions and 74 metabolites, second, we adapted Flux Variability Analysis (FVA) technique to quantify the connection between exometabolomic dynamics and the dynamics of feasible intracellular reaction flux ranges. We have obtained fine-grained flux range dynamics of the intracellular reactions for the benchmark data published in (Bordbar et al., 2016). Then, we defined four objective functions regarding the accumulation of oxidative stress in stored RBCs for performing a dynamic Flux Balance Analysis (DFBA). In all four cases, time-resolved flux predictions were obtained respecting the imposed equality and inequality constraints. Last, we adapted a quadratic programming (QP) approach to calculate the Euclidean distance between the dynamic optimum flux vectors. The DCBM approach we have developed herein along with the developed metabolic network showed being suitable for the computational analysis of RBCs metabolic behaviour, and it is thought to be useful for other biosystems.