Modeling population dynamics in a microbial consortium under control of a synthetic pheromone‐mediated communication system

Microbial consortia can be used to catalyze complex biotransformations. Tools to control the behavior of these consortia in a technical environment are currently lacking. In the present study, a synthetic biology approach was used to build a model consortium of two Saccharomyces cerevisiae strains where growth and expression of the fluorescent marker protein EGFP by the receiver strain is controlled by the concentration of α‐factor pheromone, which is produced by the emitter strain. We have developed a quantitative experimental and theoretical framework to describe population dynamics in the model consortium. We measured biomass growth and metabolite production in controlled bioreactor experiments, and used flow cytometry to monitor changes of the subpopulations and protein expression under different cultivation conditions. This dataset was used to parameterize a segregated mathematical model, which took into account fundamental growth processes, pheromone‐induced growth arrest and EGFP production, as well as pheromone desensitization after extended exposure. The model was able to predict the growth dynamics of single‐strain cultures and the consortium quantitatively and provides a basis for using this approach in actual biotransformations.     

Smart process development: Application of machine-learning and integrated process modeling for inclusion body purification processes

The development of a biopharmaceutical production process usually occurs sequentially, and tedious optimization of each individual unit operation is very time-consuming. Here, the conditions established as optimal for one-step serve as input for the following step. Yet, this strategy does not consider potential interactions between a priori distant process steps and therefore cannot guarantee for optimal overall process performance. To overcome these limitations, we established a smart approach to develop and utilize integrated process models using machine learning techniques and genetic algorithms. We evaluated the application of the data-driven models to explore potential efficiency increases and compared them to a conventional development approach for one of our development products. First, we developed a data-driven integrated process model using gradient boosting machines and Gaussian processes as machine learning techniques and a genetic algorithm as recommendation engine for two downstream unit operations, namely solubilization and refolding. Through projection of the results into our large-scale facility, we predicted a twofold increase in productivity. Second, we extended the model to a three-step model by including the capture chromatography. Here, depending on the selected baseline-process chosen for comparison, we obtained between 50% and 100% increase in productivity. These data show the successful application of machine learning techniques and optimization algorithms for downstream process development. Finally, our results highlight the importance of considering integrated process models for the whole process chain, including all unit operations.     

Carbanilation of cereal beta-glucans for molecular weight determination and conformational studies

Cereal beta-glucans can form aggregates in aqueous solution. The presence of aggregates in cereal beta-glucan solutions led to inaccurate determination of molecular weights and it was believed that intermolecular hydrogen bonding caused the aggregation. To eliminate aggregates, a carbanilation method for molecular weight determination of cereal beta-glucans was developed. Wheat beta-glucan samples were selected for investigation. The carbanilation method can prevent intermolecular hydrogen bonding by blocking hydroxyl groups with phenyl carbamate groups. The carbanilates of cereal beta-glucans were prepared by the reaction of cereal beta-glucans with phenylisocyanate catalyzed by DMSO and pyridine. To avoid degradation during the carbanilation reaction, relatively mild conditions were used, which led to incomplete substitution (DS: approximately 2). However, after the carbanilation reaction, the carbanilates dissolved completely in 1,4-dioxane solution without any detectable aggregates, which allowed accurate molecular weight determination. The degree of substitution (DS) of carbanilates was determined by both a nitrogen content method and an FT-IR method. The FT-IR method proved to be the more effective for DS estimation. Using this method, the converted molecular weights of cereal beta-glucans were in good agreement with the results measured in 0.5M NaOH solution, which previously was shown to be a good solvent for cereal beta-glucans. After the carbanilation reaction, conformational changes of carbanilates were studied by static and dynamic light scattering techniques. The fractal dimension (d(f)=2.27) and the structure sensitive parameters (rho >2) suggested a porous globular structure for partially carbanilated beta-glucans.     

The Mass Distance Fingerprint: a statistical framework for de novo detection of predominant modifications using high-accuracy mass spectrometry

We describe a statistical measure, Mass Distance Fingerprint, for automatic de novo detection of predominant peptide mass distances, i.e., putative protein modifications. The method's focus is to globally detect mass differences, not to assign peptide sequences or modifications to individual spectra. The Mass Distance Fingerprint is calculated from high accuracy measured peptide masses. For the data sets used in this study, known mass differences are detected at electron mass accuracy or better. The proposed method is novel because it works independently of protein sequence databases and without any prior knowledge about modifications. Both modified and unmodified peptides have to be present in the sample to be detected. The method can be used for automated detection of chemical/post-translational modifications, quality control of experiments and labeling approaches, and to control the modification settings of protein identification tools. The algorithm is implemented as a web application and is distributed as open source software.     

The effects of alternating tangential flow (ATF) residence time,hydrodynamic stress,and filtration flux on high-density perfusion cell culture

In this study, we investigated the effects of alternating tangential flow (ATF) cell separation on high-density perfusion cultures. We have developed methods to estimate theoretical residence times of cells in the ATF system and discovered that long residence times (above 75 s) correlate with decreased growth, metabolism, and productivity. We have calculated energy dissipation rates in the ATF transfer line and filter and empirically studied the impacts of increased exchange rates on cell culture, determining that increased hydrodynamic stress can lead to decreased cell size, lactate production, and specific productivity. Finally, we have conducted experiments to understand the relationship between filtration fluxes and ATF membrane fouling, finding that at fluxes above 60 L·m^–2·day ^–1, protein sieving coefficients see significant rates of decrease (greater than 1% per day). While most of these studies have been conducted with one cell line at one target viable cell density (40 million cells/ml), the general, directional knowledge arising from this study should be applicable to other conditions and programs, ultimately leading to more robust and well-designed perfusion processes.     

Impact of human activities on the reproduction of Hooded Vultures Necrosyrtes monachus in Burkina Faso

During the last decades, the critically endangered Hooded Vulture Necrosyrtes monachus has strongly declined across its African range. Although direct persecution has been suggested as a major cause of this decline, little is known about the impact of humans on reproductive output in West Africa. We studied the impact of human activities on the reproductive output of Hooded Vultures in the Garango area of Burkina Faso. Twenty and 56 nesting attempts were monitored, respectively, during the breeding season in 2013/14 and 2014/15, to determine reproductive success and identify causes of nest failure. Annual breeding success varied between 0.68 and 0.71 chicks fledged per breeding pair per year and productivity was assessed at 0.57 chicks fledged per territorial pair in 2014/15. The main threats imposed by humans were poaching of eggs, chicks and collection of nest materials, leading to 20% (13 out of 64 breeding attempts) of nest failures over the two years. An additional important reason for nest failure was the pruning and (partial) cutting of nest trees. Despite this high level of human interference, we found that Hooded Vulture nest success increased with proximity to human settlements, probably because breeding vultures benefit from protection by people against persecution and disturbance.