@phdthesis{Schifferle2024,
  author    = {Schifferle, Lukas},
  title     = {Optical properties of (Mg,Fe)O at high pressure},
  doi       = {10.25932/publishup-62216},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-622166},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XIV, 90},
  year      = {2024},
  abstract  = {Large parts of the Earth's interior are inaccessible to direct observation, yet global geodynamic processes are governed by the physical material properties under extreme pressure and temperature conditions. It is therefore essential to investigate the deep Earth's physical properties through in-situ laboratory experiments. With this goal in mind, the optical properties of mantle minerals at high pressure offer a unique way to determine a variety of physical properties, in a straight-forward, reproducible, and time-effective manner, thus providing valuable insights into the physical processes of the deep Earth. This thesis focusses on the system Mg-Fe-O, specifically on the optical properties of periclase (MgO) and its iron-bearing variant ferropericlase ((Mg,Fe)O), forming a major planetary building block. The primary objective is to establish links between physical material properties and optical properties. In particular the spin transition in ferropericlase, the second-most abundant phase of the lower mantle, is known to change the physical material properties. Although the spin transition region likely extends down to the core-mantle boundary, the ef-fects of the mixed-spin state, where both high- and low-spin state are present, remains poorly constrained. In the studies presented herein, we show how optical properties are linked to physical properties such as electrical conductivity, radiative thermal conductivity and viscosity. We also show how the optical properties reveal changes in the chemical bonding. Furthermore, we unveil how the chemical bonding, the optical and other physical properties are affected by the iron spin transition. We find opposing trends in the pres-sure dependence of the refractive index of MgO and (Mg,Fe)O. From 1 atm to ~140 GPa, the refractive index of MgO decreases by ~2.4\% from 1.737 to 1.696 (±0.017). In contrast, the refractive index of (Mg0.87Fe0.13)O (Fp13) and (Mg0.76Fe0.24)O (Fp24) ferropericlase increases with pressure, likely because Fe Fe interactions between adjacent iron sites hinder a strong decrease of polarizability, as it is observed with increasing density in the case of pure MgO. An analysis of the index dispersion in MgO (decreasing by ~23\% from 1 atm to ~103 GPa) reflects a widening of the band gap from ~7.4 eV at 1 atm to ~8.5 (±0.6) eV at ~103 GPa. The index dispersion (between 550 and 870 nm) of Fp13 reveals a decrease by a factor of ~3 over the spin transition range (~44-100 GPa). We show that the electrical band gap of ferropericlase significantly widens up to ~4.7 eV in the mixed spin region, equivalent to an increase by a factor of ~1.7. We propose that this is due to a lower electron mobility between adjacent Fe2+ sites of opposite spin, explaining the previously observed low electrical conductivity in the mixed spin region. From the study of absorbance spectra in Fp13, we show an increasing covalency of the Fe-O bond with pressure for high-spin ferropericlase, whereas in the low-spin state a trend to a more ionic nature of the Fe-O bond is observed, indicating a bond weakening effect of the spin transition. We found that the spin transition is ultimately caused by both an increase of the ligand field-splitting energy and a decreasing spin-pairing energy of high-spin Fe2+.},
  language  = {en}
}
@phdthesis{Arend2024,
  author    = {Arend, Marius},
  title     = {Comparing genome-scale models of protein-constrained metabolism in heterotrophic and photosynthetic microorganisms},
  doi       = {10.25932/publishup-65147},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-651470},
  school      = {Universit{\"a}t Potsdam},
  pages     = {150},
  year      = {2024},
  abstract  = {Genome-scale metabolic models are mathematical representations of all known reactions occurring in a cell. Combined with constraints based on physiological measurements, these models have been used to accurately predict metabolic fluxes and effects of perturbations (e.g. knock-outs) and to inform metabolic engineering strategies. Recently, protein-constrained models have been shown to increase predictive potential (especially in overflow metabolism), while alleviating the need for measurement of nutrient uptake rates. The resulting modelling frameworks quantify the upkeep cost of a certain metabolic flux as the minimum amount of enzyme required for catalysis. These improvements are based on the use of in vitro turnover numbers or in vivo apparent catalytic rates of enzymes for model parameterization. In this thesis several tools for the estimation and refinement of these parameters based on in vivo proteomics data of Escherichia coli, Saccharomyces cerevisiae, and Chlamydomonas reinhardtii have been developed and applied. The difference between in vitro and in vivo catalytic rate measures for the three microorganisms was systematically analyzed. The results for the facultatively heterotrophic microalga C. reinhardtii considerably expanded the apparent catalytic rate estimates for photosynthetic organisms. Our general finding pointed at a global reduction of enzyme efficiency in heterotrophy compared to other growth scenarios. Independent of the modelled organism, in vivo estimates were shown to improve accuracy of predictions of protein abundances compared to in vitro values for turnover numbers. To further improve the protein abundance predictions, machine learning models were trained that integrate features derived from protein-constrained modelling and codon usage. Combining the two types of features outperformed single feature models and yielded good prediction results without relying on experimental transcriptomic data. The presented work reports valuable advances in the prediction of enzyme allocation in unseen scenarios using protein constrained metabolic models. It marks the first successful application of this modelling framework in the biotechnological important taxon of green microalgae, substantially increasing our knowledge of the enzyme catalytic landscape of phototrophic microorganisms.},
  language  = {en}
}
@article{ZurellKoenigMalchowetal.2022,
  author    = {Zurell, Damaris and K{\"o}nig, Christian and Malchow, Anne-Kathleen and Kapitza, Simon and Bocedi, Greta and Travis, Justin M. J. and Fandos, Guillermo},
  title     = {Spatially explicit models for decision-making in animal conservation and restoration},
  series = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos},
  journal   = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos},
  number    = {4},
  publisher = {Wiley-Blackwell},
  address   = {Oxford},
  issn      = {1600-0587},
  doi       = {10.1111/ecog.05787},
  pages     = {1 -- 16},
  year      = {2022},
  abstract  = {Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79\%), towards the species and population level (80\%) and towards conservation (rather than restoration) applications (71\%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10\% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier-to-use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.},
  language  = {en}
}