Growing E. coli to high cell density--a historical perspective on method development.

High-cell-density cultivation (HCDC) is required to improve microbial biomass and product formation substantially. An overview of HCDC is given for microorganisms including bacteria, archae and eukarya (yeasts). Problems encountered by HCDC and their possible solutions are discussed. Improvements of strains, different types of bioreactors and cultivation strategies for successful HCDC are described. Stirred-tank reactors with and without cell retention, a dialysis-membrane reactor, a gas-lift reactor and a membrane cyclone reactor used for HCDC are outlined. Recently modified traditional feeding strategies and new ones are included, in particular those for unlimited growth to very dense cultures. Emphasis is placed on robust fermentation control because of the growing industrial interest in this field. Therefore, developments in the application of multivariate statistical control, artificial neural networks, fuzzy control and knowledge-based supervision (expert systems) are summarized. Recent advances using Escherichia coli--the pioneer organism for HCDC--are outlined.

High-cell-density cultivation of microorganisms.

Progress in monitoring, modeling and control of bioprocesses during the last 20 years.

To increase product yields and to ensure consistent product quality, key issues of industrial fermentations, process optimization and scale up are aimed at maintaining optimum and homogenous reaction conditions minimizing microbial stress exposure and enhancing metabolic accuracy. For each individual product, process and facility, suitable strategies have to be elaborated by a comprehensive and detailed process characterization, identification of the most relevant process parameters influencing product yield and quality and their establishment as scale-up parameters to be kept constant as far as possible. Physical variables, which can only be restrictedly kept constant as single parameters, may be combined with other pertinent parameters to appropriate mathematical groups or dimensionless terms. Process characterization is preferably based on real-time or near real-time data collected by in situ and on-line measurements and may be facilitated by supportive approaches and tools like neural network based chemometric data analysis and modelling, clarification of the mixing and stream conditions through computational fluid dynamics and scale-down simulations. However, as fermentation facilities usually are not strictly designed according to scale-up criteria and the process conditions in the culture vessels thus may differ significantly and since any strategy and model can only insufficiently consider and reflect the highly complex interdependence and mutual interaction of fermentation parameters, successful scale up in most cases is not the result of a conclusive and straight-lined experimental strategy, but rather will be the outcome of a separate process development and optimization on each scale. This article gives an overview on the problems typically coming along with fermentation process optimization and scale up, and presents currently applied scale-up strategies while considering future technologies, with emphasis on Escherichia coli as one of the most commonly fermented organisms.

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Optimization and scale up of industrial fermentation processes

In the past decades, biotechnology has exploded to a cutting edge industry and its economical significance is exponentially growing. Bioprocess efficiency, productivity and optimization are essential. Efficient process control allows quality of the product to be maintained, but also reduces costs, increases profit, and reduces pollution of the environment. Real-time process monitoring is fundamental for good process control. Rapid development of bioprocess applications led to intensive search for new sensors able to provide real-time information about the process. Real-time sensors may operate in an in situ (in-line) and ex situ on-line set-up. This work reviews the approaches that have been used for bioprocess monitoring and is divided in two parts. First part deals with in situ methods with sensors placed inside the reactor and in direct contact with bulk medium. Second part discusses sample handling techniques (e.g. FIA and SIA) that allow sample withdrawal from the reactor and automatic handling, transport to the sensor, and ex situ, on-line monitoring.

Real-time bioprocess monitoring: Part I: In situ sensors

Bioprocess optimisation is often limited by an inability to measure biomass, nutrient and by-product concentrations in a time frame which allows process adjustments. Near-infrared (near-IR) spectroscopy can potentially be used to measure each of these components within 2 minutes of sampling, using an unprocessed whole broth sample. In the present study the use of near-IR spectroscopy for at-line (rapid off-line) monitoring of biomass, glycerol, ammonium, and acetate in a recombinant Escherichia coli fed batch process was investigated. The following robust correlation models were developed for these analytes using multiple least squares linear regression (MLR): [Glycerol],gl−1 =15.957 − 2219.270*
A(λ2274)−1705.041*
A(λ2172); [Acetate],gl−1=27.683 −1757.258*
A(λ2254)+296.903*
A(λ2340)−21.325*
A (λ620); [Ammonium],gl−1=−1310.502−47912.960*
A (λ2148)−135149.300*
A(λ1782)−27636.200*
A (λ830); and [Biomass],gl−1=14.034−3.548*
A(λ602/λ1134) −4286.050*
A(λ928). Using these models permitted rapid simultaneous analysis of all four analytes. This improved monitoring capability was used to develop a high cell density recombinant E. coli fed-batch process in which ammonium and acetate accumulation were minimised leading to higher cell densities. By manipulation of the C : N ratio in the complex feed, the toxic effects of ammonium accumulation upon the organism were minimised, thereby facilitating the application of a carbon limited feeding strategy. The effect of these C : N ratio medium changes, upon the near-IR measurement capability, was investigated. In this process, near-IR spectroscopy has been shown to be a powerful, accurate and precise method for simultaneously measuring several key process variables. Its accuracy, precision and utility for at-line measurement and control are evaluated, particularly in reference to processes where the initial medium composition may vary, leading to changes in the chemical matrix. The potential of near-infra red for online analysis and control is discussed.

The utility and performance of near-infra red spectroscopy in simultaneous monitoring of multiple components in a high cell density recombinant Escherichiacoli production process

At-Line Control and Fault Analysis In an Industrial High Cell Density Escherichia Coli Fermentation, Using NIR Spectroscopy

Near-infrared spectroscopy was used to determine biomass and glycerol concentrations in E.coli whole broth fermentation samples. For dry cell weight, a standard error of prediction (SEP) of 0.2 g/L and correlation coefficient (r) of 0.991 were obtained. The SEP and r for glycerol, carbon nutrient, were 0.3 g/L and 0.979. respectively. Off-line analysis was accomplished within 2 minutes of sampling and therefore provides the opportunity to monitor fermentations quickly enough to permit in-process development and troubleshooting.

G. Macaloney

Papers

The utility and performance of near-infra red spectroscopy in simultaneous monitoring of multiple components in a high cell density recombinant Escherichiacoli production process

At-Line Control and Fault Analysis In an Industrial High Cell Density Escherichia Coli Fermentation, Using NIR Spectroscopy

Monitoring biomass and glycerol in an Escherichia coli fermentation using near-infrared spectroscopy

The utility and performance of near-infra red spectroscopy in simultaneous monitoring of multiple components in a high cell density recombinant