This chapter deals with fermentation processes, converting low cost renewable feedstocks into valuable bio-products, with the help of microorganisms or mammalian cells in bioreactors or fermenters. In industrial vessels, the volumetric conversion rate, i.e. the fermentation intensity, is limited by a transport step: mass transfer, liquid mixing or cooling. In special processes where the growth of the cells is marginal, intensification is possible by active cell retention. A comparison with chemical process intensification reveals that the same four main principles are valid, i.e. (1) maximize the rate at optimal selectivity, (2) minimize the impact of substrate concentration gradients, shear rate gradients and other local differences, (3) relieve the transport limitations and (4) arrange smart integration of operation steps of which cell retention is the most important. In essence, optimized microorganisms in fermentations can be viewed as efficient, smartly integrated cell factories. The main principles are illustrated with four intensification examples, showing that debottlenecking of the oxygen transfer capacity is most important, followed by liquid mixing. The limits of fermentation intensity have been estimated for fed-batch fermentations supplied with air or pure oxygen and point at significant optimization space. In contrast, aerobic continuous fermentation is expected to remain difficult due to fundamental restrictions.

The objective of this research study is to be able to scale-up and optimize aerobic fermentation processes via computer simulations. As an example the production of penicillin by P. chrysogenum is studied. In vivo kinetics of the cell can be understood by conducting stimulus response and scale down experiments. These experiments provide dynamic information that can be used to estimate parameters of enzyme kinetic models. The transport phenomena inside the bioreactor can by understood by solving hydrodynamic model using computational fluid dynamics (CFD). Thus, with information from intracellular and extracellular environments, an integrated model is solved using CFD. In these simulations, the multiphase flow and transport phenomena taking place inside the bioreactor are simulated, and integrated with the microbial reaction kinetics. The micro-organisms inside the reactor are followed along their trajectories, where they experience a changing environment. The dynamic response of the cells to this environment dictates the productivity and selectivity of the cells. By simulating both the flow in the reactor and the response of the cells, the entire process inside the bioreactor can be assessed and scaled-up and optimization can be done without the need of costly experiments, both in time and financial resources.

In this study, prolonged chemostat cultivation is applied to investigate in vivo enzyme kinetics of Saccharomyces cerevisiae. S. cerevisiae was grown in carbon-limited aerobic chemostats for 70–95 generations, during which multiple steady states were observed, characterized by constant intracellular fluxes but significant changes in intracellular metabolite concentrations and enzyme capacities. We provide evidence for two relevant kinetic mechanisms for sustaining constant fluxes: in vivo near-equilibrium of reversible reactions and tight regulation of irreversible reactions by coordinated changes of metabolic effectors. Using linear-logarithmic kinetics, we illustrate that these multiple steady-state measurements provide linear constraints between elasticity parameters instead of their absolute values. Upon perturbation by a glucose pulse, glucose uptake and ethanol excretion in prolonged cultures were remarkably lower, compared to a reference culture perturbed at 10 generations. Metabolome measurements during the transient indicate that the differences might be due to a reduced ATP regeneration capacity in prolonged cultures.

Metabolic-flux analyses in microorganisms are increasingly based on 13C-labeling data. In this paper a new approach for the measurement of 13C-label distributions is presented: rapid sampling and quenching of microorganisms from a cultivation, followed by extraction and detection by liquid chromatography-mass spectrometry of free intracellular metabolites. This approach allows the direct assessment of mass isotopomer distributions of primary metabolites. The method is applied to the glycolytic and pentose phosphate pathways of Saccharomyces cerevisiae strain CEN.PK113-7D grown in an aerobic, glucose-limited chemostat culture. Detailed investigations of the measured mass isotopomer distributions demonstrate the accuracy and information-richness of the obtained data. The mass fractions are fitted with a cumomer model to yield the metabolic fluxes. It is estimated that 24% of the consumed glucose is catabolized via the pentose phosphate pathway. Furthermore, it is found that turnover of storage carbohydrates occurs. Inclusion of this turnover in the model leads to a large confidence interval of the estimated split ratio.

The currently applied reaction structure in stoichiometric flux balance models for the nonoxidative branch of the pentose phosphate pathway is not in accordance with the established ping-pong kinetic mechanism of the enzymes transketolase (EC 2.2.1.1) and transaldolase (EC 2.2.1.2). Based upon the ping-pong mechanism, the traditional reactions of the nonoxidative branch of the pentose phosphate pathway are replaced by metabolite specific, reversible, glycolaldehyde moiety (C2) and dihydroxyacetone moiety (C3) fragments producing and consuming half-reactions. It is shown that a stoichiometric model based upon these half-reactions is fundamentally different from the currently applied stoichiometric models with respect to the number of independent C2 and C3 fragment pools in the pentose phosphate pathway and can lead to different label distributions for 13C-tracer experiments. To investigate the actual impact of the new reaction structure on the estimated flux patterns within a cell, mass isotopomer measurements from a previously published 13C-based metabolic flux analysis of Saccharomyces cerevisiae were used. Different flux patterns were found. From a genetic point of view, it is well known that several micro-organisms, including Escherichia coli and S. cerevisiae, contain multiple genes encoding isoenzymes of transketolase and transaldolase. However, the extent to which these gene products are also actively expressed remains unknown. It is shown that the newly proposed stoichiometric model allows study of the effect of isoenzymes on the 13C-label distribution in the nonoxidative branch of the pentose phosphate pathway by extending the half-reaction based stoichiometric model with two distinct transketolase enzymes instead of one. Results show that the inclusion of isoenzymes affects the ensuing flux estimates.

First, we report the application of stable isotope dilution theory in metabolome characterization of aerobic glucose limited chemostat culture of S. cerevisiae CEN.PK 113-7D using liquid chromatography - electrospray ionization MS/MS (LC-ESI-MS/MS). A glucose-limited chemostat culture of S. cerevisiae was grown to steady state at a specific growth rate (μ) = 0.05 h^-1 in a medium containing only naturally labeled (99% U-¹²C, 1% U- ¹³C) carbon source. Upon reaching steady state, defined as 5 volume changes, the culture medium was switched to chemically identical medium except that the carbon source was replaced with 100% uniformly (U) ¹³C labeled stable carbon isotope, fed for 4 h, with sampling every hour. We observed that within a period of 1 h ∼80% of the measured glycolytic metabolites were U-¹³C-labeled. Surprisingly, during the next 3 h no significant increase of the U-¹³C-labeled metabolites occurred. Second, we demonstrate for the first time the LC-ESI-MS/MS-based quantification of intracellular metabolite concentrations using U-¹³C-labeled metabolite extracts from chemostat cultivated S. cerevisiae cells, harvested after 4 h of feeding with 100% U-¹³C-labeled medium, as internal standard. This method is hereby termed "Mass Isotopomer Ratio Analysis of U-¹³C Labeled Extracts" (MIRACLE). With this method each metabolite concentration is quantified relative to the concentration of its U-¹³C-labeled equivalent, thereby eliminating drawbacks of LC-ESI-MS/MS analysis such as nonlinear response and matrix effects and thus leads to a significant reduction of experimental error and work load (i.e., no spiking and standard additions). By coextracting a known amount of U- ¹³C labeled cells with the unlabeled samples, metabolite losses occurring during the sample extraction procedure are corrected for.

A well-established way of determining metabolic fluxes is to measure 2D [13C,1H] COSY NMR spectra of components of biomass grown on uniformly 13C-labeled carbon sources. When using the entire set of measured data to simultaneously determine all fluxes in a proposed metabolic network model, the 13C-labeling distribution in all measured compounds has to be simulated. This requires very large sets of isotopomer or cumomer balances. This article introduces the new concept of bondomers; entities that only vary in the numbers and positions of C–C bonds that have remained intact since the medium substrate molecule entered the metabolism. Bondomers are shown to have many analogies to isotopomers. One of these is that bondomers can be transformed to cumulative bondomers, just like isotopomers can be transformed to cumomers. Similarly to cumomers, cumulative bondomers allow an analytical solution of the entire set of balances describing a metabolic network. The main difference is that cumulative bondomer models are considerably smaller than corresponding cumomer models. This saves computational time, allows easier identifiability analysis, and yields new insights in the information content of 2D [13C,1H] COSY NMR data. We illustrate the theoretical concepts by means of a realistic example of the glycolytic and pentose phosphate pathways. The combinations of 2D [13C,1H] COSY NMR data that allow identification of all metabolic fluxes in these pathways are analyzed, and it is found that the NMR data contain less information than was previously expected.

The 13C-labeling technique was introduced in the field of metabolic engineering as a tool for determining fluxes that could not be found using the ‘classical’ method of flux balancing. An a priori flux identifiability analysis is required in order to determine whether a 13C-labeling experiment allows the identification of all the fluxes. In this article, we propose a method for identifiability analysis that is based on the recently introduced ‘cumomer’ concept. The method improves upon previous identifiability methods in that it provides a way of systematically reducing the metabolic network on the basis of structural elements that constitute a network and to use the implicit function theorem to analytically determine whether the fluxes in the reduced network are theoretically identifiable for various types of real measurement data. Application of the method to a realistic flux identification problem shows both the potential of the method in yielding new, interesting conclusions regarding the identifiability and its practical limitations that are caused by the fact that symbolic calculations grow fast with the dimension of the studied system.