Dynamic Metabolic Flux Analysis Demonstrated on Cultures Where the Limiting Substrate Is Changed from Carbon to Nitrogen andVice Versa

Abstract

The main requirement for metabolic flux analysis (MFA) is that the cells are in a pseudo-steady state, that there is no accumulation or depletion of intracellular metabolites. In the past, the applications of MFA were limited to the analysis of continuous cultures. This contribution introduces the concept of dynamic MFA and extends MFA so that it is applicable to transient cultures. Time series of concentration measurements are transformed into flux values. This transformation involves differentiation, which typically increases the noisiness of the data. Therefore, a noise-reducing step is needed. In this work, polynomial smoothing was used. As a test case, dynamic MFA is applied on Escherichia coli cultivations shifting from carbon limitation to nitrogen limitation and vice versa. After switching the limiting substrate from N to C, a lag phase was observed accompanied with an increase in maintenance energy requirement. This lag phase did not occur in the C- to N-limitation case.

Towards dynamic metabolic flux analysis in CHO cell cultures

“…Additional assumptions and approximations are needed to observe fluxes in fed-batch cultures using MFA [32]. To address these limitations, initial attempts have been undertaken in recent years to develop new and improved tools and techniques for dynamic metabolic flux analysis (DMFA) for measuring in vivo metabolic fluxes in systems that are not at metabolic steady state [31,[33][34][35]. These techniques promise to identify intracellular metabolic changes as a function of time.…”

Dynamic Flux Balance Analysis Models in SBML

“…Besides the whole-cell model which uses DFBA as a core module, many DFBA models have been constructed for different metabolic pathways. DFBA has been applied in small-scale examples (Varma and Palsson, 1994;Mahadevan et al, 2002;Luo et al, 2006), over mediumsize models (Pizarro et al, 2007;Lequeux et al, 2010;Meadows et al, 2010), and up to genome-scale DFBA applications (Hanly and Henson, 2011;Hjersted et al, 2007). For a recent overview, see Table 1 in (Höffner et al, 2013).…”

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

“…In the matrix S , the rows mean the metabolites, and the columns represent the reactions. The constraint‐based models are under the assumption of an internal quasi‐steady state (Lequeux et al, ). The mass balance equations are therefore given by .…”