Values as motivating factors for representatives of generation Z in the Czech Republic and Slovakia within the European context.

Generation Z is expected to officially surpass the Baby Boomers in the labor market by 2024 and to represent 30% of the global workforce by 2030. In the work environment, they are referred to oxymoronically as competitively ambivalent. Therefore, it is necessary to investigate the reasons for this behavior and to identify initiatives that would facilitate understanding between Generation Z and other generations.

BioParts-A Biological Parts Search Portal and Updates to the ICE Parts Registry Software Platform.

Capturing, storing, and sharing biological DNA parts data are integral parts of synthetic biology research. Here, we detail updates to the ICE biological parts registry software platform that enable these processes, describe our implementation of the Web of Registries concept using ICE, and establish Bioparts, a search portal for biological parts available in the public domain. The Web of Registries enables standalone ICE installations to securely connect and form a distributed parts database.

Repurposing a microfluidic formulation device for automated DNA construction.

Microfluidic applications have expanded greatly over the past decade. For the most part, however, each microfluidics platform is developed with a specific task in mind, rather than as a general-purpose device with a wide-range of functionality. Here, we show how a microfluidic system, originally developed to investigate protein phase behavior, can be modified and repurposed for another application, namely DNA construction.

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms.