This study aims at providing a methodology based on both data- and knowledge-driven approaches to build dynamic macroscopic models of cell cultures. This methodology proceeds in three steps. A principal component analysis is first applied in order to determine the minimum number of macroreactions necessary to faithfully describe the available data. These reactions are then selected among the elementary flux modes associated with a chosen metabolic network through the definition of an original linear programming problem. Kinetic laws are finally identified so as to reproduce the measurement data. The proposed methodology is illustrated using four different perfusion cultures of hybridoma cells and demonstrates a good capacity to select macroreactions capable of reproducing well the complex experimental data, even with the use of simple kinetic laws and without re-identifying the stoichiometry.

In this work, two metabolic networks representing the metabolism of CHO cells in fed-batch cultures are considered. The first metabolic network is relatively detailed and underdetermined with the available extracellular measurements, while the second is a reduced version of the former and is overdetermined. A dynamic metabolic flux analysis based on convex analysis (DMFCA) is applied to the detailed network, which allows the computation of the time evolution of bounded intervals. On the other hand, a linear optimization problem is solved for the reduced-size network, with either positivity constraints or box constraints inferred from DMFCA. In all cases, smoothing splines and mass balance differential equations are used to infer the time evolution of the uptake and excretion rates from experimental data. The analysis allows to get insight into CHO metabolism as well as to investigate the influence of the size of the metabolic network.

In this work, two metabolic networks representing the metabolism of CHO cells in fed-batch cultures are considered. The first metabolic network is relatively detailed and underdetermined with the available extracellular measurements, while the second is a reduced version of the former and is overdetermined. A dynamic metabolic flux analysis based on convex analysis (DMFCA) is applied to the detailed network, which allows the computation of the time evolution of bounded intervals. On the other hand, a linear optimization problem is solved for the reduced-size network, with either positivity constraints or box constraints inferred from DMFCA. In all cases, smoothing splines and mass balance differential equations are used to infer the time evolution of the uptake and excretion rates from experimental data. The analysis allows to get insight into CHO metabolism as well as to investigate the influence of the size of the metabolic network.

In this work, a dynamic metabolic flux analysis based on convex analysis (DMFCA) is applied to CHO-DXB11 cell fed-batch cultures. This approach exploits all the available knowledge of the metabolic network and the time evolution of extracellular component concentrations, to determine bounded intervals for the fluxes continuously over time. Smoothing splines and mass balance differential equations are used to estimate the time evolution of the uptake and excretion rates from experimental data. Furthermore, the method is suitable for underdetermined systems, and does not require the definition of ad-hoc objective functions to be optimized. Moreover the metabolic network considered in this work allows an estimation of the carbon dioxide flux.

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.

In this work, metabolic flux analysis (MFA) is applied to determine the flux distribution within a metabolic network representing the metabolism of a hybridoma cell line. Even though a relatively large set of extracellular measurements are available, mass balancing leads to an underdetermined system and a unique solution cannot be computed. To tackle this problem, a convex analysis approach is therefore used to compute the metabolic fluxes as positive bounded intervals, with the help of the toolbox METATOOL. Our main objective is to investigate the influence of the number of extracellular measurements and the culture operating mode, i.e., batch or perfusion, on the flux distribution. Our study is based on sets of experimental data from hybridoma HB58 cell batch/perfusion cultures.

The monitoring and optimization of hybridoma cell fed-batch cultures depend on the availability of appropriate on-line sensors for the main culture components. A simple and efficient approach to maintain hybridoma cultures in the optimal operating conditions is to regulate the substrate concentrations at the critical values (G=Gcrit and/or Gn=Gncrit) such as to control the hybridoma cells at the critical metabolism state. However, reliable glucose and glutamine probes are currently rare and/or very expensive on the market and it is necessary to design software sensors which are at same time cheap and reliable and that can be used for online measurement. In this study, the overflow metabolism model is used to develop an extended Kalman filter for online estimation of glucose and glutamine in hybridoma cell fed-batch cultures based on the considered available measurements (biomasses (on-line), lactate and ammonia (on-line or off-line)). The observability conditions are examined, and the performances are analysed with simulations of hybridoma cell fed-batch cultures. Glutamine estimation sensitivity is enforced by minimizing a cost function combining a usual least-squares criterion with a state estimation sensitivity criterion.