Stephan Noack

Customizable Visualization of Multi-omics Data in the Context of Biochemical Networks

This contribution presents a novel approach of visualization in the context of biochemical networks. In this field of application, simulation and experimental studies continuously produce a wide variety of data with a direct relation to nodes and edges of the networks. This leads to an urgent requirement of a data visualization method which is flexible and adaptable to many applications. The visualization software tool presented here allows creating network diagrams with a programmable style of network components. This was realized by defining the scripting language OVL in order to give a fast and intuitive access to visual properties of nodes and edges. Additionally, networks can be displayed in different abstraction levels. So, data visualization becomes highly customizable to individual requirements and preferences.

Real-time monitoring of fungal growth and morphogenesis at single-cell resolution

Development times for efficient large‐scale production, utilizing fungal species, are still very long. This is mainly due to the poor knowledge of many important variables related to fungal growth and morphogenesis. We specifically addressed this knowledge gap by combining a microfluidic cultivation device with time‐lapse live cell imaging. This combination facilitates (i) studying population heterogeneity at single‐cell resolution, (ii) monitoring of fungal morphogenesis in a high spatiotemporal manner under defined environmental conditions, and (iii) parallelization of experiments for statistical data analysis. Our analysis of Penicillium chrysogenum, the workhorse for antibiotic production worldwide, revealed significant heterogeneity in size, vitality and differentiation times between spore, mycelium and pellets when cultivated under industrially relevant conditions. For example, the swelling rate of single spores in complex medium ( μ=0.077±0.036h−1 ) and the formation rate of higher branched mycelia in defined glucose medium ( μ=0.046±0.031h−1 ) were estimated from broad time‐dependent cell size distributions, which in turn were derived from computational image analysis of 257 and 49 time‐lapse series, respectively. In order to speed up the development of new fungal production processes, a deeper understanding of these heterogeneities is required and the presented microfluidic single‐cell approach provides a solid technical foundation for such quantitative studies.