Principles and approach to developing mammalian cell culture media for high cell density perfusion process leveraging established fed-batch media.

Perfusion medium was successfully developed based on our fed-batch platform basal and feed media. A systematic development approach was undertaken by first optimizing the ratios of fed-batch basal and feed media followed by targeted removal of unnecessary and redundant components. With this reduction in components, the medium could then be further concentrated by 2× to increase medium depth.

COMPLETE-MFA: complementary parallel labeling experiments technique for metabolic flux analysis.

We have developed a novel approach for measuring highly accurate and precise metabolic fluxes in living cells, termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. The COMPLETE-MFA method is based on combined analysis of multiple isotopic labeling experiments, where the synergy of using complementary tracers greatly improves the precision of estimated fluxes. In this work, we demonstrate the COMPLETE-MFA approach using all singly labeled glucose tracers, [1-(13)C], [2-(13)C], [3-(13)C], [4-(13)C], [5-(13)C], and [6-(13)C]glucose to determine precise metabolic fluxes for wild-type Escherichia coli.

Parallel labeling experiments with [U-13C]glucose validate E. coli metabolic network model for 13C metabolic flux analysis.

(13)C-metabolic flux analysis (MFA) is a widely used method for measuring intracellular metabolic fluxes in living cells. (13)C MFA relies on several key assumptions: (1) the assumed metabolic network model is complete, in that it accounts for all significant enzymatic and transport reactions; (2) (13)C-labeling measurements are accurate and precise; and (3) enzymes and transporters do not discriminate between (12)C- and (13)C-labeled metabolites. In this study, we tested these inherent assumptions of (13)C MFA for wild-type E.

Dynamic metabolic flux analysis (DMFA): a framework for determining fluxes at metabolic non-steady state.

Metabolic flux analysis (MFA) is a key tool for measuring in vivo metabolic fluxes in systems at metabolic steady state. Here, we present a new method for dynamic metabolic flux analysis (DMFA) of systems that are not at metabolic steady state. The advantages of our DMFA method are: (1) time-series of metabolite concentration data can be applied directly for estimating dynamic fluxes, making data smoothing and estimation of average extracellular rates unnecessary; (2) flux estimation is achieved without integration of ODEs, or iterations; (3) characteristic metabolic phases in the fermentation data are identified automatically by the algorithm, rather than selected manually/arbitrarily.