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Publication

Bayesian multi-purpose modelling of plant growth and development across scales

(2024) Viswanathan, Michelle; Streck, Thilo

Crop models are invaluable tools for predicting the impact of climate change on crop production and assessing the fate of agrochemicals in the environment. To ensure robust predictions of crop yield, for example, models are usually calibrated to observations of plant growth and phenological development using different methods. However, various sources of uncertainty exist in the model inputs, parameters, equations, observations, etc., which need to be quantified, especially when model predictions influence decision-making. Bayesian inference is suitable for this purpose since it enables different uncertainties to be taken into account, while also incorporating prior knowledge. Thus, Bayesian methods are used for model calibration to improve the model and enhance prediction quality. However, this improvement in the model and its prediction quality does not always occur due to the presence of model errors. These errors are a result of incomplete knowledge or simplifying assumptions made to reduce model complexity and computational costs. For instance, crop models are used for regional scale simulations thereby assuming that these point-based models are able to represent processes that act at regional scale. Additionally, simple statistical assumptions are made about uncertainty in model errors during Bayesian calibration. In this work, the problems arising from such applications are analysed and other Bayesian approaches are investigated as potential solutions. A conceptually simple Bayesian approach of sequentially updating a maize phenology model, an important component in plant models, was investigated as yearly observation data were gathered. In this approach, model parameters and their uncertainty were estimated while accounting for observation uncertainty. As the model was calibrated to increasing amounts of observation data, the uncertainty in the model parameters reduced as expected. However, the prediction quality of the calibrated model did not always improve in spite of more data being available for potentially improving the model. This discrepancy was attributed to the presence of errors in the model structure, possibly due to missing environmental dependencies that were ignored during calibration. As a potential solution, the model was calibrated using Bayesian multi-level modelling which could account for model errors. Furthermore, this approach accounted for the hierarchical data structure of cultivars nested within maize ripening groups, thus simultaneously obtaining model parameter estimates for the species, ripening groups and cultivars. Applying this approach improved the model's calibration quality and further aided in identifying possible model deficits related to temperature effects in the post-flowering phase of development and soil moisture. As another potential solution, an alternative calibration strategy was tested which accounted for model errors by relaxing the strict statistical assumptions in classical Bayesian inference. This was done by first acknowledging that due to model errors, different data sets may yield diverse solutions to the calibration problem. Thus, instead of fitting the model to all data sets together and finding a compromise solution, a fit was found to each data set. This was implemented by modifying the likelihood, a term that accounts for information content of the data. An additive rather than the classical multiplicative strategy was used to combine likelihood values from different data sets. This approach resulted in conservative but more reliable predictions than the classical approach in most cases. The classical approach resulted in better predictions only when the prediction target represented an average of the calibration data. The above-mentioned results show that Bayesian methods with representative error assumptions lead to improved model performance and a more realistic quantification of uncertainties. This is a step towards the effective application of process-based crop models for developing suitable adaptation and mitigation strategies.

Publication

tsCRISPR based identification of Rab proteins required for the recycling of Drosophila TRPL ion channel

(2024) Zeger, Matthias; Stanisławczyk, Lena Sarah; Bulić,Marija; Binder, Andrea Maria; Huber, Armin

In polarized cells, the precise regulation of protein transport to and from the plasma membrane is crucial to maintain cellular function. Dysregulation of intracellular protein transport in neurons can lead to neurodegenerative diseases such as Retinitis Pigmentosa, Alzheimer’s and Parkinson’s disease. Here we used the light-dependent transport of the TRPL (transient receptor potential-like) ion channel in Drosophila photoreceptor cells to study the role of Rab proteins in TRPL recycling. TRPL is located in the rhabdomeric membrane of dark-adapted flies, but it is transported out of the rhabdomere upon light exposure and localizes at the Endoplasmatic Reticulum within 12 h. Upon subsequent dark adaptation, TRPL is recycled back to the rhabdomeric membrane within 90 min. To screen for Rab proteins involved in TRPL recycling, we established a tissue specific (ts) CRISPR/Cas9-mediated knock- out of individual Rab genes in Drosophila photoreceptors and assessed TRPL localization using an eGFP tagged TRPL protein in the intact eyes of these mutants. We observed severe TRPL recycling defects in the knockouts of Rab3, Rab4, Rab7, Rab32, and RabX2. Using immunohistochemistry, we further showed that Rab3 and RabX2 each play a significant role in TRPL recycling and also influence TRPL transport. We localized Rab3 to the late endosome in Drosophila photoreceptors and observed disruption of TRPL transport to the ER in Rab3 knock-out mutants. TRPL transport from the ER to the rhabdomere ensues from the trans-Golgi where RabX2 is located. We observed accumulated TRPL at the trans-Golgi in RabX2 knock-out mutants. In summary, our study reveals the requirement of specific Rab proteins for different steps of TRPL transport in photoreceptor cells and provides evidence for a unique retrograde recycling pathway of TRPL from the ER via the trans-Golgi

Publication

Nachhaltigkeitsexzellenz in der Landwirtschaft: Mehr Sichtbarkeit für die versteckten Leuchttürme der Alltagspraxis

(2024-09) Gebhardt, Beate; Hellstern, Laura

Im Projekt NEAL wurde die Bedeutung von exzellenter Mikro-Nachhaltigkeit und die Rolle von Nachhaltigkeitsawards sowie weiterer unterstützender Instrumente einer nachhaltigen Transformation in der Landwirtschaft untersucht. Die Erkenntnisse des Projektes sollen landwirtschaftlichen Unternehmen, Verbänden sowie weiteren relevanten Akteuren der land¬wirtschaftlichen Wertschöpfungsketten eine Orientierung geben in den Fragen: • Welche Nachhaltigkeitsthemen benötigen in Zukunft ein größeres Augenmerk? • Wie können Landwirt*innen in ihrer Nachhaltigkeitstransformation gefördert werden? • Welche Rolle spielen Nachhaltigkeitsawards in der Nachhaltigkeitstransformation? Für das Forschungsprojekt NEAL wurden dazu (a) 310 Landwirt*innen und 59 landwirtschaftsnahe Verbände in einem bundesweiten Nachhaltigkeits-Crowd-Screening im Frühjahr 2022 online befragt und hierbei insgesamt 236 herausragende, awardwürdige Nachhaltigkeitsaktivitäten identifiziert, die in der Landwirtschaft bereits umgesetzt oder geplant werden. Mittels Awards-Matching und Clustering wurde (b) der webbasierte, interaktive CSR-Award Finder mit über 150 Wettbewerben erstellt und Ende 2022 online gestellt. Der CSR-Award Finder macht die Welt der Awards für Unternehmen übersichtlicher und einfacher zugänglich, insbesondere erleichtert dies den Zugang kleiner Unternehmen und landwirtschaftliche Betriebe. Zentrale Aussagen der Studie lauten: 1. Nachhaltigkeit ist für Landwirt*innen ein relevantes Thema und viele „versteckte“ nachhaltige Tätigkeiten werden auf den befragten Betrieben bereits umgesetzt. 2. Bio-Betriebe zeigen sich als Vorreiter von Nachhaltigkeitsexzellenz in der Landwirtschaft. 3. Bodennutzung, Biodiversität, regionale Wertschöpfung und Tierwohl sind wichtige Bereiche, in denen viele nachhaltigkeitsbezogene Maßnahmen von Landwirt*innen umgesetzt und als besonders hervorgehoben werden. 4. „Blinde Flecken“ in Nachhaltigkeitsansätzen korrespondieren mit Bewertungen der Landwirtschaft.. 5. Leuchttürme der Mikro-Ebene kommen auf der Makro-Ebene kaum an. 6. Eine heterogene und multifunktionale Landwirtschaft benötigt vielfältige, multiple Instrumente zur Förderung von Nachhaltigkeit auf Betriebsebene. 7. Nachhaltigkeitstransformation in der Landwirtschaft benötigt mehr gemeinsame An-strengungen. 8. Nachhaltigkeitsexzellenz in der Landwirtschaft benötigt mehr Mut und Sichtbarkeit. Die Ergebnisse im Projekt NEAL unterstreichen: Ein Ansatz alleine ist nicht ausreichend. Aufgrund der Heterogenität der landwirtschaftlichen Betriebe gilt dies gerade auch für die Landwirtschaft. Sustainable Finance und Awards sind dabei zwei verschiedene Ansätze bzw. Instrumente, die beide als wichtig und unterstützend gelten, um die Nachhaltigkeitstransformation landwirtschaftlicher Systeme und Betriebe voranzubringen. Beiden Ansätzen wird bescheinigt, ein wichtiges Instrument unter vielen zu sein, aber singulär einen eher geringen Hebel zu haben, da nicht alle landwirtschaftlichen Betriebe damit eingebunden werden können oder sich dadurch angesprochen fühlen. Die Ergebnisse im Projekt NEAL zeigen außerdem, Awards sind in die Toolbox der bekannten Instrumente und Anreize zur Stärkung der Nachhaltigkeitstransformation einzubinden. Sie stehen damit neben ökonomischen Anreizen im Markt oder regulativen Anreize, die vom Staat gesetzt werden. Awards setzen am Positiven und an der Sichtbarmachung des Vorbildhaften und Innovativen in der Landwirtschaft an. Sie können damit das verborgene Besondere, die nachhaltigen Aktivitäten und die versteckten Leuchttürme in der Landwirtschaft, nach außen tragen und zum Leuchten bringen, und damit die Nachhaltigkeits-Motivation der Landwirt*innen erheblich steigern.

Publication

Metabolic rewiring enables ammonium assimilation via a non‐canonical fumarate‐based pathway

(2024) Mardoukhi, Mohammad Saba Yousef; Rapp, Johanna; Irisarri, Iker; Gunka, Katrin; Link, Hannes; Marienhagen, Jan; de Vries, Jan; Stülke, Jörg; Commichau, Fabian M.

Glutamate serves as the major cellular amino group donor. In Bacillus subtilis, glutamate is synthesized by the combined action of the glutamine synthetase and the glutamate synthase (GOGAT). The glutamate dehydrogenases are devoted to glutamate degradation in vivo. To keep the cellular glutamate concentration high, the genes and the encoded enzymes involved in glutamate biosynthesis and degradation need to be tightly regulated depending on the available carbon and nitrogen sources. Serendipitously, we found that the inactivation of the ansR and citG genes encoding the repressor of the ansAB genes and the fumarase, respectively, enables the GOGAT-deficient B. subtilis mutant to synthesize glutamate via a non-canonical fumarate-based ammonium assimilation pathway. We also show that the de-repression of the ansAB genes is sufficient to restore aspartate prototrophy of an aspB aspartate transaminase mutant. Moreover, in the presence of arginine, B. subtilis mutants lacking fumarase activity show a growth defect that can be relieved by aspB overexpression, by reducing arginine uptake and by decreasing the metabolic flux through the TCA cycle.

Publication

Navigating the biocosmos: Cornerstones of a bioeconomic utopia

(2023-06-11) Onyeali, Wolfgang; Schlaile, Michael P.; Winkler, Bastian

One important insight from complexity science is that the future is open, and that this openness is an opportunity for us to participate in its shaping. The bioeconomy has been part of this process of “future-making”. But instead of a fertile ecosystem of imagined futures, a dry monoculture of ideas seems to dominate the landscape, promising salvation through technology. With this article, weintend to contribute to regenerating the ecological foundations of the bioeconomy. What would it entail if we were to merge with the biosphere instead of machines? To lay the cornerstones of a bioeconomic utopia, we explore the basic principles of self-organization that underlie biological, ecological, social, and psychological processes alike. All these are self-assembling and self-regulating elastic structures that exist at the edge of chaos and order. We then revisit the Promethean problem that lies at the foundation of bioeconomic thought and discuss how, during industrialization, the principles of spontaneous self-organization were replaced by the linear processes of the assembly line. We ultimately propose a bioeconomy based on human needs with the household as the basic unit: the biocosmos. The biocosmos is an agroecological habitat system of irreducible complexity, a newhumanniche embedded into the local ecosystem.