Modeling the dynamics of E. coli populations in the three-dimensional turbulent field of a stirred-tank bioreactor—A structured–segregated approach

Lapin, A. D.; Schmid, Joachim; Reuß, Matthias

doi:10.1016/j.ces.2006.03.003

Cited by 121 publications

(93 citation statements)

References 27 publications

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“…As a first attempt, it would be straightforward to link phenotypic heterogeneity of a microbial population with the individual profile of environmental conditions experienced by each cell during the course of the bioprocess. However, this has not been done yet even if some very relevant work exists, both on the modeling of concentration time profile experienced by microbial cells during their revolution in a bioreactor [29,30] and on the heterogeneity of microbial populations as a function of bioreactor mixing performance [31,32]. Considering the complexity of both the numerical and the experimental methods needed to study the physico-biological interactions, only a few studies have considered both aspects together.…”

Section: Implication Of Microbial Heterogeneity In Bioprocessesmentioning

confidence: 98%

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Delvigne

Goffin

2013

Biotechnology Journal

130

102

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Heterogeneity or segregation of microbial populations has been the subject of much research, but the real impact of this phenomenon on bioprocesses remains poorly understood. The main reason for this lack of knowledge is the difficulty in monitoring microbial population heterogeneity under dynamic process conditions. The main concepts resulting in microbial population heterogeneity in the context of bioprocesses have been summarized by two distinct hypotheses. The first involves the individual history of microbial cells or the "path" followed during their residence time inside the process equipment. The second hypothesis involves a coordinated response by the microbial population as a bet-hedging strategy, in order to cope with process-related stresses. The respective contribution of each hypothesis to microbial heterogeneity in bioprocesses is still unclear. This illustrates the fact that, although microbial phenotypic heterogeneity has been thoroughly investigated at a fundamental level, the implications of this phenomenon in the context of microbial bioprocesses are still subject to debate. At this time, automated flow cytometry is the best technique for investigating microbial heterogeneity under process conditions. However, dedicated software and relevant biomarkers are needed for the proper integration of flow cytometry as a bioprocess control tool.

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Section: Implication Of Microbial Heterogeneity In Bioprocessesmentioning

confidence: 98%

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Delvigne

Goffin

2013

Biotechnology Journal

130

102

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“…In our previous study, the concentrations of these two alarmones were elevated during glucose limitation (27). It has already been suggested that these opposing regulations could result in the reduction of rpoS transcription during glucose-limited growth in bioreactors (36). However, other known regulatory mechanisms may also be worth considering (29).…”

Section: A) Glycolysis (Embden-meyerhof-parnas [Emp]) Pathway and Pppmentioning

confidence: 98%

Global Transcription and Metabolic Flux Analysis of Escherichia coli in Glucose-Limited Fed-Batch Cultivations

Lemuth

Hardiman

Winter

et al. 2008

Appl Environ Microbiol

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A time series of whole-genome transcription profiling of Escherichia coli K-12 W3110 was performed during a carbon-limited fed-batch process. The application of a constant feed rate led to the identification of a dynamic sequence of diverse carbon limitation responses (e.g., the hunger response) and at the same time provided a global view of how cellular and extracellular resources are used: the synthesis of high-affinity transporters guarantees maximal glucose influx, thereby preserving the phosphoenolpyruvate pool, and energy-dependent chemotaxis is reduced in order to provide a more economic "work mode." S -mediated stress and starvation responses were both found to be of only minor relevance. Thus, the experimental setup provided access to the hunger response and enabled the differentiation of the hunger response from the general starvation response. Our previous topological model of the global regulation of the E. coli central carbon metabolism through the crp, cra, and relA/spoT modulons is supported by correlating transcript levels and metabolic fluxes and can now be extended. The substrate is extensively oxidized in the tricarboxylic acid (TCA) cycle to enhance energy generation. However, the general rate of oxidative decarboxylation within the pentose phosphate pathway and the TCA cycle is restricted to a minimum. Fine regulation of the carbon flux through these pathways supplies sufficient precursors for biosyntheses. The pools of at least three precursors are probably regulated through activation of the (phosphoenolpyruvate-)glyoxylate shunt. The present work shows that detailed understanding of the genetic regulation of bacterial metabolism provides useful insights for manipulating the carbon flux in technical production processes.

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“…Thus, one has to know about the local and instantaneous values of the mass fluxes exchanged between the environment and the cells. Lapin et al 8 have shown that the prediction was possible if the main system for glucose uptake is the PTS. 59 However, there is still some work to be done to correlate the substrate uptake rate to the growth rate.…”

Section: Discussionmentioning

confidence: 98%

“…4,6 The characteristics of the fluctuating signal encountered by the cells along their trajectory within the reactor are essentially unpredictable but can be approached by numerical simulation. [7][8][9] However, as pointed out by Enfors et al, 10 the global performance of a large scale bioreactor is an integrated consequence of all these fluctuations on all micro-organisms. Thus, the modeling of the dynamic response of biological systems is equally crucial for a successful scale-up of bioprocesses.…”

Section: Introductionmentioning

confidence: 99%

A structured model for the simulation of bioreactors under transient conditions

Morchain

Fonade

2009

AIChE Journal

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Modeling the transient behavior of continuous culture is of primary importance for the scale-up of biological processes. Spatial heterogeneities increase with the reactor size and micro-organisms have to cope with a fluctuating environment along their trajectories within the bioreactor. In this article, a structured model for bioreactions expressed in terms of biological extensive variables is proposed. A biological variable is introduced to calculate the growth rate of the population. The value is updated on the basis of the difference between the composition in the liquid and biotic phase. The structured model is able to predict the transient behavior of different continuous cultures subject to various drastic perturbations. This performance is obtained with a minimum increase in the standard unstructured model complexity (one additional time constant). In the final part, the consequences of decoupling the growth rate from the substrate uptake rate are discussed. V

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Modeling the dynamics of E. coli populations in the three-dimensional turbulent field of a stirred-tank bioreactor—A structured–segregated approach

Cited by 121 publications

References 27 publications

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Global Transcription and Metabolic Flux Analysis of Escherichia coli in Glucose-Limited Fed-Batch Cultivations

A structured model for the simulation of bioreactors under transient conditions

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