Bernd Hitzmann

On-line infrared spectroscopy for bioprocess monitoring

One of the major aims of bioprocess engineering is the real-time monitoring of important process variables. This is the basis of precise process control and is essential for high productivity as well as the exact documentation of the overall production process. Infrared spectroscopy is a powerful analytical technique to analyze a wide variety of organic compounds. Thus, infrared sensors are ideal instruments for bioprocess monitoring. The sensors are non-invasive, have no time delay due to sensor response times, and have no influence on the bioprocess itself. No sampling is necessary, and several components can be analyzed simultaneously. In general, the direct monitoring of substrates, products, metabolites, as well as the biomass itself is possible. In this review article, insights are provided into the different applications of infrared spectroscopy for bioprocess monitoring and the complex data interpretation. Different analytical techniques are presented as well as example applications in different areas.

Future aspects of bioprocess monitoring.

Nature has the impressive ability to efficiently and precisely control biological processes by applying highly evolved principles and using minimal space and relatively simple building blocks. The challenge is to transfer these principles into technically applicable and precise analytical systems that can be used for many applications. This article summarizes some of the new approaches in sensor technology and control strategies for different bioprocesses such as fermentations, biotransformations, and downstream processes. It focuses on bio- and chemosensors, optical sensors, DNA and protein chip technology, software sensors, and modern aspects of data evaluation for improved process monitoring and control.

Chemometric modelling with two-dimensional fluorescence data for Claviceps purpurea bioprocess characterization

Modern bioprocess control requires fast data acquisition and in-time evaluation of bioprocess variables. On-line fluorescence spectroscopy and the application of chemometric methods accomplish these goals. In order to demonstrate how time-consuming off-line analysis methods can be replaced for bioprocess monitoring, fluorescence measurements were performed during different cultivations of the fungus Claviceps purpurea. To predict process variables like biomass, protein, and alkaloid concentrations, chemometric models were developed on the basis of the acquired fluorescence spectra. The results of these investigations are presented and the applicability of this approach for bioprocess monitoring is discussed.

Automatic Control of Bioprocesses

In this chapter, different approaches for open-loop and closed-loop control applied in bioprocess automation are discussed. Although in recent years many contributions dealing with closed-loop control have been published, only a minority were actually applied in real bioprocesses, the majority being simulations. As a result of the diversity of bioprocess requirements, a single control algorithm cannot be applied in all cases; rather, different approaches are necessary. Most publications combine different closed-loop control techniques to construct hybrid systems. These systems are supposed to combine the advantages of each approach into a well-performing control strategy. The majority of applications are soft sensors in combination with a proportional-integral-derivative (PID) controller. The fact that soft sensors have become this importance for control purposes demonstrates the lack of direct measurements or their large additional expense for robust and reliable online measurement systems. The importance of model predictive control is increasing; however, reliable and robust process models are required, as well as very powerful computers to address the computational needs. The lack of theoretical bioprocess models is compensated by hybrid systems combining theoretical models, fuzzy logic, and/or artificial neural network methodology. Although many authors suggest a possible transfer of their presented control application to other bioprocesses, the algorithms are mostly specialized to certain organisms or certain cultivation conditions as well as to a specific measurement system.

Bioanalytics: detailed insight into bioprocesses

The principles of bioanalytical systems for an on-line bioprocess monitoring are described within this paper. These sensor systems can be interfaced to the bioprocess in different ways according to the needs of the single bioprocess. Modular systems are necessary, which can fit exactly to the needs of the single process. Invasive as well as non-invasive bioanalytical tools are described and discussed in detail. Immunosensors give the possibility to monitor high molecular weight components within short time intervals. Non-invasive optical sensors allow the direct monitoring of various analytes such as oxygen pH for the complex fluorescence behavior of the bioprocess medium. These so-called fluorescence sensors offer the possibility to monitor intra- as well as extracellular components without interfering with the bioprocess. An industrial example for the application of bioanalytical tools for a process optimization are presented in this application. Here a biosensor system is used to optimize the downstreaming of molasses on a technical scale. The economic as well ecological advantages are discussed.

In-situ imaging sensors for bioprocess monitoring: state of the art

AbstractOver the last two decades, more and more applications of sophisticated sensor technology have been described in the literature on upstreaming and downstreaming for biotechnological processes (Middendorf et al. J Biotechnol 31:395–403, 1993; Lausch et al. J Chromatogr A 654:190–195, 1993; Scheper et al. Ann NY Acad Sci 506:431–445, 1987), in order to improve the quality and stability of these processes. Generally, biotechnological processes consist of complex three-phase systems—the cells (solid phase) are suspended in medium (liquid phase) and will be streamed by a gas phase. The chemical analysis of such processes has to observe all three phases. Furthermore, the bioanalytical processes used must monitor physical process values (e.g. temperature, shear force), chemical process values (e.g. pH), and biological process values (metabolic state of cell, morphology). In particular, for monitoring and estimation of relevant biological process variables, image-based inline sensors are used increasingly. Of special interest are sensors which can be installed in a bioreactor as sensor probes (e.g. pH probe). The cultivation medium is directly monitored in the process without any need for withdrawal of samples or bypassing. Important variables for the control of such processes are cell count, cell-size distribution (CSD), and the morphology of cells (Höpfner et al. Bioprocess Biosyst Eng 33:247–256, 2010). A major impetus for the development of these image-based techniques is the process analytical technology (PAT) initiative of the US Food and Drug Administration (FDA) (Scheper et al. Anal Chim Acta 163:111–118, 1984; Reardon and Scheper 1995; Schügerl et al. Trends Biotechnol 4:11–15, 1986). This contribution gives an overview of non-invasive, image-based, in-situ systems and their applications. The main focus is directed at the wide application area of in-situ microscopes. These inline image analysis systems enable the determination of indirect and direct cell variables in real time without sampling, but also have application potential in crystallization, material analysis, polymer research, and the petrochemical industry. FigurePhoto of an In-situ microscope manufactured by Sartorius Stedim Biotech (Göttingen, Germany)

Sensors in Disposable Bioreactors Status and Trends

For better control of productivity and product quality, detailed monitoring of various parameters is required. Since disposable bioreactors become more and more important for biotechnological applications, adequate sensors for this type of reactor are necessary. The required properties of sensors used in disposable reactors differ from those of sensors for multiuse reactors. For example, sensors which are in direct contact with the medium must be inexpensive, but do not need a long life-time, since they can be used only once.This chapter gives an overview on the state of the art and future trends in the field of sensors suited for use in disposable bioreactors. The main focus here is on in situ sensors, which can be based on optical, semiconductor and ultrasonic technologies, but current concepts for disposable sampling units are also reviewed.

In-Situ-Fluorescence-Probes: A Useful Tool for Non-invasive Bioprocess Monitoring

Optical sensors appear to be very promising for different applications in modern biotechnology. They offer the possibility to interface all the well known optical analysis techniques to bioprocesses via fiber optical cables. Thus, high sophisticated and sensitive optical analysis techniques can be coupled to a bioprocess via these light signal transporting fibers. A wide variety of sensor types for application in biotechnology has been described. Normally these sensors are non-invasive and the response times are nearly instantaneous. In particular, the use of glass fiber technology makes these sensors small, robust and reduces their costs.

Challenges in integrating biosensors and FIA for on-line monitoring and control

Several obstacles prevent the increased use of biosensors in industrial bioprocess monitoring and control. Among these are the absence of robust and reliable biosensors with the required specificity and sensitivity for use in complex media, and the absence of adequate sampling techniques. Progress with the integration of amperometric and potentiometric biosensors into flow injection analysis (FIA) systems may provide a route to developing automated, reliable, on-line systems for bioprocess control.

Retinitis pigmentosa: rapid neurodegeneration is governed by slow cell death mechanisms

For most neurodegenerative diseases the precise duration of an individual cell’s death is unknown, which is an obstacle when counteractive measures are being considered. To address this, we used the rd1 mouse model for retinal neurodegeneration, characterized by phosphodiesterase-6 (PDE6) dysfunction and photoreceptor death triggered by high cyclic guanosine-mono-phosphate (cGMP) levels. Using cellular data on cGMP accumulation, cell death, and survival, we created mathematical models to simulate the temporal development of the degeneration. We validated model predictions using organotypic retinal explant cultures derived from wild-type animals and exposed to the selective PDE6 inhibitor zaprinast. Together, photoreceptor data and modeling for the first time delineated three major cell death phases in a complex neuronal tissue: (1) initiation, taking up to 36 h, (2) execution, lasting another 40 h, and finally (3) clearance, lasting about 7 h. Surprisingly, photoreceptor neurodegeneration was noticeably slower than necrosis or apoptosis, suggesting a different mechanism of death for these neurons.