Scale-up of cell culture bioreactors using biomechatronic design

Scale-up of cell culture bioreactors is a challenging engineering work that requires wide competence in cell biology, mechanical engineering and bioprocess design. In this article, a new approach for cell culture bioreactor scale-up is suggested that is based on biomechatronic design methodology. The approach differs from traditional biochemical engineering methodology by applying a sequential design procedure where the needs of the users and alternative design solutions are systematically analysed. The procedure is based on the biological and technical functions of the scaled-up bioreactor that are derived in functional maps, concept generation charts and scoring and interaction matrices. Basic reactor engineering properties, such as mass and heat transfer and kinetics are integrated in the procedure. The methodology results in the generation of alternative design solutions that are thoroughly ranked with help of the user needs. Examples from monoclonal antibodies and recombinant protein production illuminate the steps of the procedure. The methodology provides engineering teams with additional tools that can significantly facilitate the design of new production methods for cell culture processes.     

Reversible and specific interaction of dehydrogenases with a coenzyme-coated surface continuously monitored with a reflectometer

Reversible affinity binding of NAD-dependent dehydrogenase to an NAD-coated silicon surface ("NAD biochip") has been accomplished. The silicon surface, which is favorable for use with optical techniques because of its excellent reflection properties, was precoated with a polymer to prevent nonspecific and irreversible adsorption. Using a new reflectometry technique based on measurement of the polarization change of light reflected upon the biochip, continuous monitoring of the affinity binding and subsequent desorption of alcohol dehydrogenase and lactate dehydrogenase from the NAD surface were possible; allowing repeated use of the same NAD chip--an advantage when the assay was carried out in a continuous reflectometer. With a flow rate of 0.5 ml/min, response times on the order of 30 s were obtained.     

On-line multi-analyzer monitoring of biomass, glucose and acetate for growth rate control of a Vibrio cholerae fed-batch cultivation

In situ near-infrared (NIR) spectroscopy and in-line electronic nose (EN) mapping were used to monitor and control a cholera-toxin producing Vibrio cholerae fed-batch cultivation carried out with a laboratory method as well as with a production method. Prediction models for biomass, glucose and acetate using NIR spectroscopy were developed based on spectral identification and partial-least squares (PLS) regression resulting in high correlation to reference data (standard errors of prediction for biomass, glucose and acetate were 0.20 gl(-1), 0.26 gl(-1) and 0.28 gl(-1)). A compensation algorithm for aerated bioreactor disturbances was integrated in the model computation, which in particular improved the prediction by the biomass model. First, the NIR data were applied together with EN in-line data selected by principal component analysis (PCA) for generating a trajectory representation of the fed-batch cultivation. A correlation between the culture progression and EN signals was demonstrated, which proved to be beneficial in monitoring the culture quality. It was shown that a deviation from a normal cultivation behavior could easily be recognized and that the trajectory was able to alarm a bacterial contamination. Second, the NIR data indicated the potential of predicting the concentration of formed cholera toxin with a model prediction error of 0.020 gl(-1). Third, the on-line biomass prediction based on the NIR model was used to control the overflow metabolism acetate formation of the V. cholerae culture. The controller compared actual specific growth rate as estimated from the prediction with the critical acetate formation growth rate, and from that difference adjusted the glucose feed rate.     

Detection of biospecific interactions using amplified ellipsometry

Amplified detection of biomolecules and biological interactions using an optical surface technique, ellipsometry, is demonstrated for two biosystems--immunoglobulin G with anti-immunoglobulin G (IgG) and the lectin concanavalin A (Con A) with yeast cells. In order to improve the sensitivity of the ellipsometer signal, an amplifier conjugate is formed by binding the affinity ligand to a 12-nm silica particle which is readily detected by the ellipsometer. Thus by using conjugates of IgG-silica and Con A-silica, amplifications of five to seven times have been obtained enabling detection of less than 20 pg/mm2 of biomolecular material.     

Probabilistic Identification of Cerebellar Cortical Neurones across Species

Despite our fine-grain anatomical knowledge of the cerebellar cortex, electrophysiological studies of circuit information processing over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cell types. We approached this problem by assessing the spontaneous activity signatures of identified cerebellar cortical neurones. A range of statistics describing firing frequency and irregularity were then used, individually and in combination, to build Gaussian Process Classifiers (GPC) leading to a probabilistic classification of each neurone type and the computation of equi-probable decision boundaries between cell classes. Firing frequency statistics were useful for separating Purkinje cells from granular layer units, whilst firing irregularity measures proved most useful for distinguishing cells within granular layer cell classes. Considered as single statistics, we achieved classification accuracies of 72.5% and 92.7% for granular layer and molecular layer units respectively. Combining statistics to form twin-variate GPC models substantially improved classification accuracies with the combination of mean spike frequency and log-interval entropy offering classification accuracies of 92.7% and 99.2% for our molecular and granular layer models, respectively. A cross-species comparison was performed, using data drawn from anaesthetised mice and decerebrate cats, where our models offered 80% and 100% classification accuracy. We then used our models to assess non-identified data from awake monkeys and rabbits in order to highlight subsets of neurones with the greatest degree of similarity to identified cell classes. In this way, our GPC-based approach for tentatively identifying neurones from their spontaneous activity signatures, in the absence of an established ground-truth, nonetheless affords the experimenter a statistically robust means of grouping cells with properties matching known cell classes. Our approach therefore may have broad application to a variety of future cerebellar cortical investigations, particularly in awake animals where opportunities for definitive cell identification are limited.     

Automated ensemble modeling with modelMaGe: analyzing feedback mechanisms in the Sho1 branch of the HOG pathway

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback.modelMaGe is an open source project and is distributed under the Gnu General Public License (GPL) and is available from http://modelmage.org.     

In vivo analysis of inhibitory synaptic inputs and rebounds in deep cerebellar nuclear neurons

Neuronal function depends on the properties of the synaptic inputs the neuron receive and on its intrinsic responsive properties. However, the conditions for synaptic integration and activation of intrinsic responses may to a large extent depend on the level of background synaptic input. In this respect, the deep cerebellar nuclear (DCN) neurons are of particular interest: they feature a massive background synaptic input and an intrinsic, postinhibitory rebound depolarization with profound effects on the synaptic integration. Using in vivo whole cell patch clamp recordings from DCN cells in the cat, we find that the background of Purkinje cell input provides a tonic inhibitory synaptic noise in the DCN cell. Under these conditions, individual Purkinje cells appear to have a near negligible influence on the DCN cell and clear-cut rebounds are difficult to induce. Peripheral input that drives the simple spike output of the afferent PCs to the DCN cell generates a relatively strong DCN cell inhibition, but do not induce rebounds. In contrast, synchronized climbing fiber activation, which leads to a synchronized input from a large number of Purkinje cells, can induce profound rebound responses. In light of what is known about climbing fiber activation under behaviour, the present findings suggest that DCN cell rebound responses may be an unusual event. Our results also suggest that cortical modulation of DCN cell output require a substantial co-modulation of a large proportion of the PCs that innervate the cell, which is a possible rationale for the existence of the cerebellar microcomplex.