Automatic bioprocess control. 5. Biologically and technically caused effects during cultivation

Current good manufacturing practice in plant automation of biological production processes

The production of biologicals is subject to strict governmental regulations. These are drawn up in current good manufacturing practices (cGMP), a.o. by the U.S. Food and Drug Administration. To implement cGMP in a production facility, plant automation becomes an essential tool. For this purpose Manufacturing Execution Systems (MES) have been developed that control all operations inside a production facility. The introduction of these recipe-driven control systems that follow ISA S88 standards for batch processes has made it possible to implement cGMP regulations in the control strategy of biological production processes. Next to this, an MES offers additional features such as stock management, planning and routing tools, process-dependent control, implementation of software sensors and predictive models, application of historical data and on-line statistical techniques for trend analysis and detection of instrumentation failures. This paper focuses on the development of new production strategies in which cGMP guidelines are an essential part.

Excess turbulence as a cause of turbohypobiosis in cultivation of microorganisms

The present review describes the influence of different types of mixing systems under excess turbulence conditions on microorganisms. Turbohypobiosis phenomena were described by applying a method for measurement of the kinetic energy of flow fluctuations based on the piezoeffect. It can be assumed that the shear stress effect (the state of turbohypobiosis) plays a role mainly when alternative mechanisms in cells cannot ensure a normal physiological state under stress conditions. Practically any system (inner construction of a bioreactor, culture and cultivation conditions, including mixing) requires its own optimisation to achieve the final goal, namely, the maximum product and/or biomass yields from substrate (YP/S or/and YX/S), respectively. Data on the biotechnological performance of cultivation as well as power input, kinetic energy (e) of flow fluctuations, air consumption rate, rotational speed, tip speed, etc. do not correlate directly if the mixing systems (impellers-baffles) are dissimilar. Even the widely used specific power consumption cannot be relied upon for scaling up the cultivation performance using dissimilar mixing systems. A biochemical explanation for substrate and product transport via cell walls, carbon pathways, energy generation and utilisation, etc. furnishes insight into cellular interactions with turbulence of different origin for different types of microorganisms (single cells, mycelia forming cells, etc.).

Instrumentation of biotechnological processes

Modern bioprocesses are monitored by on-line sensing devices mounted either in situ or externally. In addition to sensor probes, more and more analytical subsystems are being exploited to monitor the state of a bioprocess on-line and in real time. Some of these subsystems deliver signals that are useful for documentation only, other, less delayed systems generate signals useful for closed loop process control. Various conventional and non-conventional monitoring instruments are evaluated; their usefulness, benefits and associated pitfalls are discussed.

Oscillatory, stochastic and chaotic growth rate fluctuations in permittistatically controlled yeast cultures

We describe a continuous culture system related to the turbidostat, but using a feedback system based on biomass estimation from the dielectric permittivity of the cell suspension rather than its optical density. It is shown that this system provides an excellent method of maintaining a constant biomass level within a fermentor. The computer-controlled system was able to effect the essentially continuous registration of growth rate by monitoring the rate of medium addition via the time-dependent activity of the pump. At some biomass setpoints for aerobically grown cultures of baker's yeast substantial time-dependent fluctuations in the growth rate of the culture were thereby observed. At some biomass setpoints, however, or under anaerobic conditions, or when using a non-Crabtree yeast, the growth rate was constant, indicating that the fluctuations were inherent to the biological system and not simply a property of the fermentor and control system. A variety of time series analyses (Fourier transformations, Hurst and Lyapunov exponents, the determination of embedding dimension, and non-linear time series predictions based on the methodology of Sugihara and May) were used to demonstrate, for the first time, that as well as stochastic and periodic components these fluctuations exhibited deterministic chaos. 'Trivial predictors' were unable to give accurate predictions of the growth rate in these cultures. The growth rate fluctuations were studied further by means of offline measurements of changes in percentage viability, bud count, and in the external ethanol and glucose concentrations; these data and other evidence suggested that the growth rate fluctuations were closely linked to the primary respiro-fermentative metabolism of this organism. The identification of chaotic growth rates in cell cultures suggests that there may be novel methods for controlling the growth of such cultures.

Fermentation monitoring and process control

During the past year, several papers describing the potential of new sensor devices for application in real bioprocesses have been published. Biosensors, optical sensors, and immunosensors are all gaining in importance. At present, the development of correct/adequate interfacing of biosensors to bioprocesses is the major limitation on progress. On the basis of new analytical data, a more precise modeling and control of fermentations can now be performed. Recent research efforts attest to the increasing importance of this area in biotechnology.

Automatic bioprocess control. 4. A prototype batch of Saccharomyces cerevisiae

The recent investigations in our high performance bioreactors have shown that living cells can be extremely sensitive to physical-chemical environmental conditions and their changes. Consequently, the relationship bioreactor-living cell must thoroughly be investigated in order to discuss both: whether bioreactor characteristics are limiting/dominating during cultivation and to what extent controlled changes of the cellular environment can lead the cells to a desired physiological state. For these investigations, a generally accepted biological test organism would be helpful, of which the requirements and reactions under certain conditions are well known. Saccharomyces cerevisiae is a well known, very robust but nevertheless sensitive organism, eligible for this purpose. In this article a typical batch cultivation on glucose is presented, collected from approx. 300 experiments. Regarding metabolite production and consumption, seven different phases are distinguished on the basis of approx. 20 sensor signals and their metabolic background is discussed. Prerequisite, however, was an exhaustive knowledge upon extracellular conditions, a task which could successfully be fulfilled with the highly automated equipment introduced in the preceding articles of this series.