Multi-wavelength (2D) fluorescence spectroscopy represents an important step towards exploiting the monitoring potential of microtiter plates (MTPs) during early-stage bioprocess development. In combination with multivariate data analysis (MVDA), important process information can be obtained, while repetitive, cost-intensive sample analytics can be reduced. This study provides a comprehensive experimental dataset of online and offline measurements for batch cultures of Hansenula polymorpha. In the first step, principal component analysis (PCA) was used to assess spectral data quality. Secondly, partial least-squares (PLS) regression models were generated, based on spectral data of two cultivation conditions and offline samples for glycerol, cell dry weight, and pH value. Thereby, the time-wise resolution increased 12-fold compared to the offline sampling interval of 6 h. The PLS models were validated using offline samples of a shorter sampling interval. Very good model transferability was shown during the PLS model application to the spectral data of cultures with six varying initial cultivation conditions. For all the predicted variables, a relative root-mean-square error (RMSE) below 6% was obtained. Based on the findings, the initial experimental strategy was re-evaluated and a more practical approach with minimised sampling effort and elevated experimental throughput was proposed. In conclusion, the study underlines the high potential of multi-wavelength (2D) fluorescence spectroscopy and provides an evaluation workflow for PLS modelling in microtiter plates.

Streptomyces species are intensively studied for their ability to produce a variety of natural products. However, conditions influencing and leading to product formation are often not completely recognized. Therefore, in this study, high-throughput online monitoring is presented as a powerful tool to gain indepth understanding of the cultivation of the model organism Streptomyces coelicolor A3(2). Through online measurements of oxygen transfer rate and autofluorescence, valuable information about availability of nutrients and product formation patterns of the pigments actinorhodin and undecylprodigiosin can be obtained and explained. Therefore, it is possible to determine the onset of pigmentation and to study in detail the influencing factors thereof. One factor identified in this study is the filling volume of the cultivation vessel. Slight variations led to varying pigmentation levels. By combining optical and metabolic online monitoring techniques, the correlation of the filling volume with pigmentation could be explained as a result of different growth trajectories caused by varying specific power inputs and their influence on the pellet formation of the filamentous system. Finally, experiments with the addition of supernatant from unpigmented and pigmented cultures could highlight the applicability of the presented approach to study quorum sensing and cell-cell interaction.

Understanding population dynamics is a key factor for optimizing co‐culture processes to produce valuable compounds. However, the measurement of independent population dynamics is difficult, especially for filamentous organisms and in presence of insoluble substrates like cellulose. We propose a workflow for fluorescence‐based online monitoring of individual population dynamics of two filamentous microorganisms. The fluorescent tagged target co‐culture is composed of the cellulolytic fungus Trichoderma reesei RUT‐C30—mCherry and the pigment‐producing bacterium Streptomyces coelicolor A3(2)—mNeonGreen (mNG) growing on insoluble cellulose as a substrate. To validate the system, the fluorescence‐to‐biomass and fluorescence‐to‐scattered‐light correlation of the two strains was characterized in depth under various conditions. Thereby, especially for complex filamentous microorganisms, microbial morphologies have to be considered. Another bias can arise from autofluorescence or pigments that can spectrally interfere with the fluorescence measurement. Green autofluorescence of both strains was uncoupled from different green fluorescent protein signals through a spectral unmixing approach, resulting in a specific signal only linked to the abundance of S. coelicolor A3(2)—mNG. As proof of principle, the population dynamics of the target co‐culture were measured at varying inoculation ratios in presence of insoluble cellulose particles. Thereby, the respective fluorescence signals reliably described the abundance of each partner, according to the variations in the inocula. With this method, conditions can be fine‐tuned for optimal growth of both partners along with natural product formation by the bacterium.

Microbial cocultures are used as a tool to stimulate natural product biosynthesis. However, studies often empirically combine different organisms without a deeper understanding of the population dynamics. As filamentous organisms offer a vast metabolic diversity, we developed a model filamentous coculture of the cellulolytic fungus Trichoderma reesei RUT‐C30 and the noncellulolytic bacterium Streptomyces coelicolor A3(2). The coculture was set up to use α‐cellulose as a carbon source. This established a dependency of S. coelicolor on hydrolysate sugars released by T. reesei cellulases. To provide detailed insight into coculture dynamics, we applied high‐throughput online monitoring of the respiration rate and fluorescence of the tagged strains. The respiration rate allowed us to distinguish the conditions of successful cellulase formation. Furthermore, to dissect the individual strain contributions, T. reesei and S. coelicolor were tagged with mCherry and mNeonGreen (mNG) fluorescence proteins, respectively. When evaluating varying inoculation ratios, it was observed that both partners outcompete the other when given a high inoculation advantage. Nonetheless, adequate proportions for simultaneous growth of both partners, cellulase, and pigment production could be determined. Finally, population dynamics were also tuned by modulating abiotic factors. Increased osmolality provided a growth advantage to S. coelicolor . In contrast, an increase in shaking frequency had a negative effect on S. coelicolor biomass formation, promoting T. reesei . This comprehensive analysis fills important knowledge gaps in the control of complex cocultures and accelerates the setup of other tailor‐made coculture bioprocesses.

A summary of the current status quo of the lifeline methodology is provided and promising recent developments are discussed. First is the emergence of GPU-driven Lattice-Boltzmann CFD, allowing to conduct dynamic simulations with fewer hydrodynamic assumptions, while reducing the computation time and need for compu-tational clusters [1]. Second are advances in compartment models, which allow fasterthan-real time calculations that open up possibilities for rigorous process optimization and digital twin applications [2]. Combining lifelines with state-of-the-art CFD and advanced compartment models, bioprocess engineers possess an exciting new toolbox for rapid process evaluation, optimization, and prospectively, operation.