Biotechnology process development involves strain testing and improvement steps aimed at increasing yields and productivity. This necessitates the high-throughput screening of many potential strain candidates, a task currently mainly performed in shake flasks or microtiter plates. However, these methods have some drawbacks, such as the low data density (usually only end-point measurements) and the lack of control over cultivation conditions in standard shake flasks. Microbioreactors can offer the flexibility and controllability of bench-scale reactors and thus deliver results that are more comparable to large-scale fermentations, but with the additional advantages of small size, availability of online cultivation data and the potential for automation. Current microbioreactor technology is analyzed in this review paper, focusing on its industrial applicability, and directions for future research are presented.

The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model-building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base-consumption were found low compared to the large uncertainty observed in the antibiotic and off-gas CO(2) predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases - meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass-transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes.

The synthesis of secondary metabolites by microorganisms, specifically antibiotics, is of great scientific and economic importance. The onset (control and regulation) of secondary metabolite formation has and still is intriguing scientists both in industry and academia. Despite many studies, there is little known about the molecular mechanisms underlying the regulation of secondary metabolism. With the recent developments in genomics and further development of advanced post-genomic techniques, it will be possible to apply a more holistic analysis to the regulation of antibiotic production in microorganisms. Here we review current knowledge about the control and regulation of secondary metabolites, with a focus on antibiotics. We will also review developments in the genomics of antibiotic-producing microorganisms, and discuss the use of systems biology for gaining a better understanding of the networks involved in regulation of antibiotic production.

Near-infrared (NIR) spectroscopy can potentially provide on-line information on substrate, biomass, product, and metabolite concentrations in fermentation processes, which could be useful for improved monitoring or control. However, several factors can negatively influence the quality of chemometric models built for interpretation of the spectra, thus impairing the analyte concentration predictions. The aim of this review was to provide an overview of necessary conditions and challenges that one has to face when developing a NIR application for monitoring of cell culture or fermentation processes. Important practical aspects are introduced, such as sampling, modeling of biomass concentration, influence of microorganism morphology on the spectra, effects of the hydrodynamic conditions in the fermenter, temperature influence, instrument settings, and signal optimization. Several examples from the literature are provided, which will hopefully guide the reader interested in the topic. Furthermore, the general procedure used for the development of calibration models is presented, and the influence of microorganism metabolism-potential source of correlation between analytes-is commented. Other important issues such as wavelength selection and evaluation of robustness are shortly introduced. Finally, some examples of potential applications of NIR monitoring are provided, including the implementation of control strategies, the combination with other monitoring tools (the so-called sensor fusion), and the description of process trajectories. On the basis of the review, we conclude that acceptance of NIR spectroscopy as a standard monitoring tool by the fermentation industry will necessitate considerably more on-line studies using industrially relevant-and highly challenging-fermentation conditions (high aeration intensity, high biomass concentration and viscosity, and filamentous production strain).

BackgroundTraditionally average values of the whole population are considered when analysing microbial cell cultivations. However, a typical microbial population in a bioreactor is heterogeneous in most phenotypes measurable at a single-cell level. There are indications that such heterogeneity may be unfavourable on the one hand (reduces yields and productivities), but also beneficial on the other hand (facilitates quick adaptation to new conditions - i.e. increases the robustness of the fermentation process). Understanding and control of microbial population heterogeneity is thus of major importance for improving microbial cell factory processes.ResultsIn this work, a dual reporter system was developed and applied to map growth and cell fitness heterogeneities within budding yeast populations during aerobic cultivation in well-mixed bioreactors. The reporter strain, which was based on the expression of green fluorescent protein (GFP) under the control of the ribosomal protein RPL22a promoter, made it possible to distinguish cell growth phases by the level of fluorescence intensity. Furthermore, by exploiting the strong correlation of intracellular GFP level and cell membrane integrity it was possible to distinguish subpopulations with high and low cell membrane robustness and hence ability to withstand freeze-thaw stress. A strong inverse correlation between growth and cell membrane robustness was observed, which further supports the hypothesis that cellular resources are limited and need to be distributed as a trade-off between two functions: growth and robustness. In addition, the trade-off was shown to vary within the population, and the occurrence of two distinct subpopulations shifting between these two antagonistic modes of cell operation could be distinguished.ConclusionsThe reporter strain enabled mapping of population heterogeneities in growth and cell membrane robustness towards freeze-thaw stress at different phases of cell cultivation. The described reporter system is a valuable tool for understanding the effect of environmental conditions on population heterogeneity of microbial cells and thereby to understand cell responses during industrial process-like conditions. It may be applied to identify more robust subpopulations, and for developing novel strategies for strain improvement and process design for more effective bioprocessing.