Correction to: Application of a multiphase microreactor chemostat for the determination of reaction kinetics of Staphylococcus carnosus.

Unfortunately, in the "How to Cite as" section, the given and the family name of the author was incorrectly published, the correct name is Lladó Maldonado. S.

Application of a multiphase microreactor chemostat for the determination of reaction kinetics of Staphylococcus carnosus.

Bioreactors at the microliter scale offer a promising approach to accelerate bioprocess development. Advantages of such microbioreactors include a reduction in the use of expensive reagents. In this study, a chemostat operation mode of a cuvette-based microbubble column bioreactor made of polystyrene (working volume of 550 µL) was demonstrated. Aeration occurs through a nozzle (Ø ≤ 100 µm) and supports submerged whole-cell cultivation of Staphylococcus carnosus. Stationary concentrations of biomass and glucose were determined in the dilution rate regime ranging from 0.12 to 0.80 1/h with a glucose feed concentration of 1 g/L. For the first time, reaction kinetics of S. carnosus were estimated from data obtained from continuous cultivation. The maximal specific growth rate (µmax = 0.824 1/h), Monod constant (KS = 34 × 10- 3gS/L), substrate-related biomass yield coefficient (YX/S = 0.315 gCDW/gS), and maintenance coefficient (mS = 0.0035 gS/(gCDW·h)) were determined. These parameters are now available for further studies in the field of synthetic biology.

Segmentation of phase contrast microscopy images based on multi-scale local Basic Image Features histograms.

Phase contrast microscopy (PCM) is routinely used for the inspection of adherent cell cultures in all fields of biology and biomedicine. Key decisions for experimental protocols are often taken by an operator based on typically qualitative observations. However, automated processing and analysis of PCM images remain challenging due to the low contrast between foreground objects (cells) and background as well as various imaging artefacts. We propose a trainable pixel-wise segmentation approach whereby image structures and symmetries are encoded in the form of multi-scale Basic Image Features local histograms, and classification of them is learned by random decision trees. This approach was validated for segmentation of cell versus background, and discrimination between two different cell types. Performance close to that of state-of-the-art specialised algorithms was achieved despite the general nature of the method. The low processing time ( < 4 s per 1280 × 960 pixel images) is suitable for batch processing of experimental data as well as for interactive segmentation applications.

Experimental methods and modeling techniques for description of cell population heterogeneity.

With the continuous development, in the last decades, of analytical techniques providing complex information at single cell level, the study of cell heterogeneity has been the focus of several research projects within analytical biotechnology. Nonetheless, the complex interplay between environmental changes and cellular responses is yet not fully understood, and the integration of this new knowledge into the strategies for design, operation and control of bioprocesses is far from being an established reality. Indeed, the impact of cell heterogeneity on productivity of large scale cultivations is acknowledged but seldom accounted for. In order to include population heterogeneity mechanisms in the development of novel bioprocess control strategies, a reliable mathematical description of such phenomena has to be developed. With this review, we search to summarize the potential of currently available methods for monitoring cell population heterogeneity as well as model frameworks suitable for describing dynamic heterogeneous cell populations. We will furthermore underline the highly important coordination between experimental and modeling efforts necessary to attain a reliable quantitative description of cell heterogeneity, which is a necessity if such models are to contribute to the development of improved control of bioprocesses.