The extended Elution by characteristic Point Method to determine Adsorption Isotherms of Labyrinthopeptin for Purification via Ion-Exchange Chromatography

Ion-exchange chromatography (IEC) is the most frequently used technique to purify proteins and therefore, plays an important role in process development for therapeutical proteins. To improve the purification using ion-exchange chromatography, adequate characterization of adsorption isotherms is obligatory. The Elution by Characteristic Point method (ECP) can be used to determine adsorption isotherms applying only minor amounts of sample material to the chromatography column. Here, the applicability of the extended ECP method to determine adsorption isotherms of the model protein bovine serum albumin (BSA) using bovine hemoglobin (bHb) as tracer substance to quantify all nonidealities of the system is shown. The resulting isotherm was validated using the static batch approach. In the next step, the gained knowledge is used to measure isotherms of Labyrinthopeptins A1 and A2, which show promising activity against retroviruses like herpes simplex virus or human immunodeficiency virus

Social infrastructure as a basis for the university's development

Сохраненная и динамично развиваемая инфраструктура университета является надежным фундаментом для создания эффективной системы социальной работы со студентами и сотрудниками, что позволяет удовлетворить их потребности в организации досуга, формировании условий для ведения здорового образа жизни, профессионального развития и дополнительных профессиональных и социальных компетенций.The University's retained and dynamically developed infrastructure is a sustainable foundation for establishing the efficient system of social work with students and staff,which allows satisfying their needs in leisure-time activities, creating conditions for keeping a healthy way of life, professional progress, and acquiring additional vocationaland social competences

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Modeling of biocatalytic reactions: A workflow for model calibration, selection and validation using Bayesian statistics

We present a workflow for kinetic modeling of biocatalytic reactions which combines methods from Bayesian learning and uncertainty quantification for model calibration, model selection, evaluation, and model reduction in a consistent statistical framework. Our workflow is particularly tailored to sparse data settings in which a considerable variability of the parameters remains after the models have been adapted to available data, a ubiquitous problem in many real‐world applications. Our workflow is exemplified on an enzyme‐catalyzed two‐substrate reaction mechanism describing the symmetric carboligation of 3,5‐dimethoxy‐benzaldehyde to (R )‐3,3′,5,5′‐tetramethoxybenzoin catalyzed by benzaldehyde lyase from Pseudomonas fluorescens . Results indicate a substrate‐dependent inactivation of enzyme, which is in accordance with other recent studies