Substrate gradients in bioreactors: origin and consequences

Larsson, Gen; Törnkvist, M.; Wernersson, Eva Ståhl; Trägårdh, Christian; Noorman, Henk; Enfors, Sven‐Olof

doi:10.1007/bf00369471

Cited by 174 publications

(139 citation statements)

References 7 publications

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“…These results demonstrate for the first time, that even short-term exposure of a small compartment of the total cell count to elevated pH values, as it is expected to occur in large-scale mammalian cell culture, can affect overall process performance. Thus, sub-surface base addition in large-scale could most probably reduce these negative effects as suggested by other authors and applied by Khan [6,50,51]. The two-compartment system could be further used to mimic the industrial large-scale process.…”

Section: Simulation Of Ph Inhomogeneities In a Fedbatch Process Usingmentioning

confidence: 87%

The impact of pH inhomogeneities on CHO cell physiology and fed‐batch process performance – two‐compartment scale‐down modelling and intracellular pH excursion

Brunner

Braun

Doppler

et al. 2017

Biotechnology Journal

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Due to high mixing times and base addition from top of the vessel, pH inhomogeneities are most likely to occur during large-scale mammalian processes. The goal of this study was to set-up a scale-down model of a 10-12 m stirred tank bioreactor and to investigate the effect of pH perturbations on CHO cell physiology and process performance. Short-term changes in extracellular pH are hypothesized to affect intracellular pH and thus cell physiology. Therefore, batch fermentations, including pH shifts to 9.0 and 7.8, in regular one-compartment systems are conducted. The short-term adaption of the cells intracellular pH are showed an immediate increase due to elevated extracellular pH. With this basis of fundamental knowledge, a two-compartment system is established which is capable of simulating defined pH inhomogeneities. In contrast to state-of-the-art literature, the scale-down model is included parameters (e.g. volume of the inhomogeneous zone) as they might occur during large-scale processes. pH inhomogeneity studies in the two-compartment system are performed with simulation of temporary pH zones of pH 9.0. The specific growth rate especially during the exponential growth phase is strongly affected resulting in a decreased maximum viable cell density and final product titer. The gathered results indicate that even short-term exposure of cells to elevated pH values during large-scale processes can affect cell physiology and overall process performance. In particular, it could be shown for the first time that pH perturbations, which might occur during the early process phase, have to be considered in scale-down models of mammalian processes.

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Section: Simulation Of Ph Inhomogeneities In a Fedbatch Process Usingmentioning

confidence: 87%

The impact of pH inhomogeneities on CHO cell physiology and fed‐batch process performance – two‐compartment scale‐down modelling and intracellular pH excursion

Brunner

Braun

Doppler

et al. 2017

Biotechnology Journal

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“…This is mostly relevant to mixing and mass transfer issues, which are scale-up parameters that are not accounted for in laboratory-scale strain development (Wehrs et al, 2019). Microbes or cells in industrial-scale bioreactors are periodically forced by flow field dynamics to experience feast/famine environments, for example, spatial-temporal gradients of substrate, dissolved oxygen, pH, and so forth (Lara et al, 2006;Larsson et al, 1996), which likely give rise to phenotypic heterogeneity and global reduction of production capacity in the context of industrial bioprocessing. This can be indeed anticipated that as response to changing nutrient (e.g., glucose, oxygen) availability in industrial-scale bioreactors, complex, multilayered regulatory programs (e.g., short-term stringent regulation, long-term repeatedly switched on/off of related genes, etc.)…”

Section: Metabolomics-assisted Systems Biology and Synthetic Biologmentioning

confidence: 99%

“…As the environment itself is a result of both the hydrodynamics and metabolic dynamics (uptake/excretion), the simulations require the inclusion of a model representing the microbes. For a gross insight in the environment, this can be a simple Monod-type model Larsson et al, 1996), but to include the organism's response, structured models accounting for population heterogeneity are required. There are two approaches towards including population heterogeneity.…”

Section: Through the Organism's Eyes: The Interaction Between Hydromentioning

confidence: 99%

“…Thereinto, integrated metabolic models describing the biophase have been developing from simple saturation-type kinetics of sugar uptake model (Larsson et al, 1996) to recently 9-pool metabolic structured model Tang et al, 2017). As metabolic modeling progressed, more mechanistic insight into the in vivo kinetics can be obtained and more important, cell population dynamics and complete bioprocess performance in different scales of fermentors can be in silico simulated before bioprocess scaling-up .…”

Section: Introductionmentioning

confidence: 99%

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Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

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“…In addition, in large scale fermentations the mixing and mass transfer are insufficient, which causes heterogeneity. It has been shown that substrate gradients exist both in time and space in a Saccharomyces cerevisiae process in a 30 m3 reactor [1]. Hence, the organisms are continuously exposed to oscillating conditions due to a number of reasons.…”

Section: Introductionmentioning

confidence: 99%

Protein release and foaming in Escherichia coli cultures grown in minimal medium

Törnkvist

Larsson

Enfors

1996

Bioprocess Engineering

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Protein release was studied in Escherichia coli cultivations in minimal medium under different conditions. The energy source concentration was oscillating either due to the cultivation technique or due to an applied on/off feed rate concept in fed-batch cultivations. It was found that the magnitude of protein release was dependent on the cultivation technique and the strain. The use of batch technique resulted in highest specific rate of protein release compared to fed-batch cultivations. No dependence of protein release on oscillating glucose concentration could be distinguished with oscillating periods of minutes of carbon starvation. Proteins released by cells acted as foaming agents and caused stabilisation of foam, during cultivation of Escherichia coli grown in minimal medium. Since the total cell protein was reflected in the medium the protein release is considered to be caused by cell lysis. However, only a few dominating proteins were present in the foam.

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Substrate gradients in bioreactors: origin and consequences

Cited by 174 publications

References 7 publications

The impact of pH inhomogeneities on CHO cell physiology and fed‐batch process performance – two‐compartment scale‐down modelling and intracellular pH excursion

The impact of pH inhomogeneities on CHO cell physiology and fed‐batch process performance – two‐compartment scale‐down modelling and intracellular pH excursion

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Protein release and foaming in Escherichia coli cultures grown in minimal medium

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