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.

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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.@en

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.

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