Wolfgang Wiechert

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.

Accelerated development of phototrophic bioprocesses : a conceptual framework

Phototrophic systems regained interest as feedstocks for bio-economy but their industrial exploitation mostly fails for economic reasons until today. Due to lacking high throughput photobioreactors and supporting methodologies, especially early stage screening suffers from low efficiency. In this context, a framework for accelerated phototrophic process development was designed. Targeting lipid production by Chlorella vulgaris as a microalgal model process, the full spectrum from strain maintenance, via cultivation and analytics to experimental design was addressed, while parallelized cultivation was focused as key technology. Contrary to well-established serial subculturing, strain maintenance was done by cryopreservation of glucose-adapted cells. Thus, an easy-to-use protocol was set up and optimized. According to specially developed growth pattern analysis, post-thawing viabilities of 63 ± 2 % were achieved and phototrophic pre-cultivation lead to highly reproducible adaptation to light. Enabling elevated throughput cultivation, a 48-well microtiter plate-based micro-photobioreactor was designed and growth was precisely and non-invasively monitored via scattered light. Strictly controlled conditions permitted a high comparability along the wells of a plate (± 5 %) while small scale and fast mixing ensured excellent light supply of the cultures. The system was shown to offer good scalability to established laboratory-scale photobioreactors. To handle samples from microscale cultivation, a dye-based assay was set up and assay conditions were optimized towards efficient and precise quantification of C. vulgaris’ intracellular lipid accumulation. Assay automation resulted in minimal hands-on-times while errors due to fluctuating performance of manual operators could be excluded. These technologies were merged into a framework for the accelerated development of phototrophic processes while Kriging-assisted experimental design was used to take full advantage of the improved experimental capacity. Within as little as four experimental rounds, the volumetric productivity of the model lipid production process was approx. tripled solely via medium optimization and synergistic multi-parameter interaction could be revealed. Though this framework was suitable to accelerate phototrophic process development, it may only be seen as an initial blueprint. Future improvement of the micro-photobioreactor and intensified robotic integration will enable more complex processes and thus extend the range of application from screening tasks to the acquisition of kinetic data concerning metabolism of phototrophic microorganisms or even simulation of complex environmental conditions. Abbreviations and formula symbols abbreviation denotation ACP acyl-carrier-protein ADP adenosine diphosphate ATP adenosine triphosphate CA cellulose acetate CCC central composite face centred design CCF composite circumscribed design C. vulgaris Chlorella vulgaris CoA coenzyme A CZ Czech Republic DMSO dimethyl sulfoxide DoE Design of Experiments EDTA ethylenediaminetetraacetic acid EI Expected Improvement enBBM enriched Bold’s Basal Medium enBBMopt optimized enriched Bold’s Basal Medium enBBMopt,min optimized and minimized enriched Bold’s Basal Medium enBBMref enriched Bold’s Basal Medium with reference composition FA fatty acid G Germany GC-ToF-MS gas chromatography time-of-flight mass spectrometry G3P 3-phosphoglycerate KriKit Kriging ToolKit LED light emitting diode MCMC Markov Chain Monte Carlo MES 2-(N-morpholino)ethanesulfonic acid continued on next page continuation from previous page MBR microbioreactor MTP microtiter plate NADP nicotinamide adenine dinucleotide phosphate NADPH nicotinamide adenine dinucleotide phosphate, reduced state PES polyether sulfone PS polystyrene PTFE polytetrafluorethylene PYR pyruvate STR stirred tank reactor SW Sweden TAGs triacylglycerides US United States symbol denotation dimension acc accuracy % a.u. arbitrary unit CDW cell dry weight g L DOT dissolved oxygen tension % f dilution factor h Planck’s constant 6.626 · 10 W s Iav average light intensity μmol m -2 s I0 incident light intensity μmol m -2 s k number of levels per input variable KI light excess inhibition constant μmol m -2 s KM light affinity constant μmol m -2 s KS substrate affinity constant g L -1 lagP lag phase coefficient for product formation continued on next page continuation from previous page lagμ lag phase coefficient m average amount of water transferred mg mb brutto tube weight mg mB mass of extracted biomass mg mi amount of transferred water mg mI maintenance coefficient mn netto tube weight mg mt target amount of water to be transferred mg n number of replicates NA Avogadro’s number 6.022 · 10 23 mol nexp number of required experiments ngen apparent generation number ni number of input variables (n)LC (neutral) lipid content % (w w) OD(λ) optical density (at wavelength λ) p probability value PAR(λ) photosynthetically active radiation (at wavelength λ) μmol m -2 s pH lat. potentia hydrogenii Pλ illumination intensity (at wavelength λ) W m -2 Pmax maximal photosynthesis rate prec precision % Prel relative photosynthesis rate Pvol volumetric productivity mg L -1 d qP product formation rate % (w w ) h R correlation coefficient Ref reference value continued on next page continuation from previous page S substrate concentration g L t process time h tacc,P duration of acceleration phase for product formation h tacc,μ duration of acceleration phase h tD doubling time h tlag duration of lag phase h tlag,P duration of lag phase for product formation h tlag,μ duration of lag phase h tprod time from start of production phase h tstat time to stationary phase h v viability % var variance % VL liquid volume mL v v volume per volume w v weight per volume w w weight per weight X biomass concentration g L YX/S biomass yield g g -1 Δngen differential apparent generation number ε light absorption coefficient g L λnm wavelength nm μ(max) (maximal) exponential growth rate h -1 or d List of tables and figures Table 1.1: Cultivation conditions influence C. vulgaris’ biochemical composition ....................3 Table 1.2: Selected physico-chemical properties of microalgal and mineral diesel .................5 Table 1.3: Lipid content and productivity of selected microalgae ............................................8 Table 1.4: Comparison of selected commercial MBR systems ............................................. 18 Table 2.1: Overview of chemicals ......................................................................................... 27 Table 2.2: Overview of devices ............................................................................................. 28 Table 2.3: Preparation of enBBMref from stock solutions ....................................................... 30 Table 3.1: Key design aspects of the photobioreactors used ................................................ 67 Table 3.2: Comparison of cell dry weight, neutral lipid content and volumetric productivity ... 73 Table 3.3: Target molecule addressing by excitation and emission wavelength variation ..... 77 Table 3.4: Initial evaluation of the medium components ....................................................... 89 Table 3.5: Impact of medium optimization by key performance indicating parameters .......... 96 Table 6.1: Experimental design and measured data used in section 3.5.2.1 ....................... 137 Table 6.2: Comparison of the initial composition of enBBMref, enBBMopt and enBBMopt,min .. 137 Table 6.3: Experimental design and measured data used in section 3.5.2.2 ....................... 138 Table 6.4: Experimental design and measured data used in section 3.5.3 ......................... 138 Table 6.5: Experimental design and measured data used in section 3.5.2.4 ....................... 139 Figure 1.1: Microscopic view of C. vulgaris 211-11b fixed to agar slides ................................2 Figure 1.2: Daughter cell formation during vegetative autosporulation of C. vulgaris ..............2 Figure 1.3: Lipid biosynthesis as integral component of microalgal carbon capture ................6 Figure 1.4: Exemplary photobioreactors for large-scale phototrophic cultivation .....................8 Figure 1.5: Kinetic relation between light intensity and growth rate ....................................... 12 Figure 1.6: Course of phototrophic batch cultivation with its characteristic phases ............... 13 Figure 1.7: Comparison of conventional and accelerated bioprocess development .............. 15 Figure 1.8: Automated microscale bioprocess platform ........................................................ 20 Figure 1.9: Schematic process model ................................................................................... 21 Figure 1.10: Schematic representation of central composite designs for nonlinear models .. 23 Figure 1.11: Framework for accelerated phototrophic bioprocess development ................... 26 Figure 2.1: Workflow for the automated quantification of intracellular neutral lipids .............. 37 Figure 3.1: Heterotrophic growth of C. vulgaris after freezing preservation ........................... 45 Figure 3.2: Comparison of cultivation times necessary to reach stationary phase ................ 46 Figure 3.3: Cell count and biovolume before and after cryopreservation procedures ............ 47 Figure 3.4: Growth pattern-deviated assessment of cell viability ........................................... 49 Figure 3.5: Post-thawing viability after cryopreservation of stationary phase cells ................ 52 Figure 3.6: Phototrophic pre-cultivation of C. vulgaris in shake flasks ................................... 53 Figure 3.7: Design of the micro-photobioreactor prototype ................................................... 57 Figure 3.8: Tailoring of the photo module spectrum .............................................................. 59 Figure 3.9: