Dynamic metabolic flux analysis using a convex analysis approach: Application to hybridoma cell cultures in perfusion

Abstract

In recent years, dynamic metabolic flux analysis (DMFA) has been developed in order to evaluate the dynamic evolution of the metabolic fluxes. Most of the proposed approaches are dedicated to exactly determined or overdetermined systems. When an underdetermined system is considered, the literature suggests the use of dynamic flux balance analysis (DFBA). However the main challenge of this approach is to determine an appropriate objective function, which remains valid over the whole culture. In this work, we propose an alternative dynamic metabolic flux analysis based on convex analysis, DMFCA, which allows the determination of bounded intervals for the fluxes using the available knowledge of the metabolic network and information provided by the time evolution of extracellular component concentrations. Smoothing splines and mass balance differential equations are used to estimate the time evolution of the uptake and excretion rates from this experimental data. The main advantage of the proposed procedure is that it does not require additional constraints or objective functions, and provides relatively narrow intervals for the intracellular metabolic fluxes. DMFCA is applied to experimental data from hybridoma HB58 cell perfusion cultures, in order to investigate the influence of the operating mode (batch and perfusion) on the metabolic flux distribution.

How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review

“…The three EFMs define a polyhedral cone in the positive orthant, which contains all possible flux distributions. They correspond to a minimal bioreaction system, which provides an input-output representation of the cell metabolism (this kind of representation can be very useful to derive reduced-order macroscopic representations of the culture system [32][33][34], but this subject will be addressed later on in this paper; see Section 5.4).…”

“…The use of convex analysis and EFMs is investigated in [35] to determine intervals for the metabolic fluxes of CHO cells in batch cultures, which are decomposed into several time periods corresponding to different phases of the cell life cycle. This work is extended to more detailed metabolic networks in [62] and to the dynamic evolution of the flux spectrum, without assuming a decomposition in phases, in [34] where convex analysis is applied to hybridoma cultures switching from batch to perfusion mode.…”

“…These constraints express linear combinations of the metabolic fluxes as nonlinear functions of the extracellular concentrations, which correspond to the macroscopic reaction rate models. Another method that has often been used to define macroscopic reactions is the Elementary Flux Mode analysis [32][33][34]69]. As discussed above, EFMs are the shortest pathways from substrates to products.…”

Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures

“…Most industrial cultures display dynamic metabolic changes, such as a gradual decrease in specific cell growth rate, and the accumulation of by‐products and antibody production during batch and fed‐batch cell cultures. Thus, dynamic profiles of intracellular flux across the cultures are also needed (Ahn & Antoniewicz, ; Fernandes de Sousa, Bastin, Jolicoeur, & Vande Wouwer, ; Leighty & Antoniewicz, ). For this purpose, the dynamic metabolic flux analysis (DMFA) and dynamic flux balance analysis (DFBA) techniques have been proposed (Tepeli & Hortaçsu, ).…”

Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses

“…Results from literature, e.g., [3], indicate that including such metabolic network models in bioprocess optimization results in an improved process performance compared with the use of an unstructured macroscopic bioprocess model. In addition, the intracellular fluxes can be estimated rather accurately from extracellular measurements using a low-or medium-complexity metabolic network as in e.g., [4][5][6][7][8], enabling an enhanced bioprocess monitoring by monitoring the flux state, hence the metabolic state of the microorganisms in the biochemical process.…”