@phdthesis{Schwahn2018,
  author    = {Schwahn, Kevin},
  title     = {Data driven approaches to infer the regulatory mechanism shaping and constraining levels of metabolites in metabolic networks},
  doi       = {10.25932/publishup-42324},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423240},
  school      = {Universit{\"a}t Potsdam},
  pages     = {109},
  year      = {2018},
  abstract  = {Systems biology aims at investigating biological systems in its entirety by gathering and analyzing large-scale data sets about the underlying components. Computational systems biology approaches use these large-scale data sets to create models at different scales and cellular levels. In addition, it is concerned with generating and testing hypotheses about biological processes. However, such approaches are inevitably leading to computational challenges due to the high dimensionality of the data and the differences in the dimension of data from different cellular layers. This thesis focuses on the investigation and development of computational approaches to analyze metabolite profiles in the context of cellular networks. This leads to determining what aspects of the network functionality are reflected in the metabolite levels. With these methods at hand, this thesis aims to answer three questions: (1) how observability of biological systems is manifested in metabolite profiles and if it can be used for phenotypical comparisons; (2) how to identify couplings of reaction rates from metabolic profiles alone; and (3) which regulatory mechanism that affect metabolite levels can be distinguished by integrating transcriptomics and metabolomics read-outs. I showed that sensor metabolites, identified by an approach from observability theory, are more correlated to each other than non-sensors. The greater correlations between sensor metabolites were detected both with publicly available metabolite profiles and synthetic data simulated from a medium-scale kinetic model. I demonstrated through robustness analysis that correlation was due to the position of the sensor metabolites in the network and persisted irrespectively of the experimental conditions. Sensor metabolites are therefore potential candidates for phenotypical comparisons between conditions through targeted metabolic analysis. Furthermore, I demonstrated that the coupling of metabolic reaction rates can be investigated from a purely data-driven perspective, assuming that metabolic reactions can be described by mass action kinetics. Employing metabolite profiles from domesticated and wild wheat and tomato species, I showed that the process of domestication is associated with a loss of regulatory control on the level of reaction rate coupling. I also found that the same metabolic pathways in Arabidopsis thaliana and Escherichia coli exhibit differences in the number of reaction rate couplings. I designed a novel method for the identification and categorization of transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approach determines the partial correlation of metabolites with control by the principal components of the transcript levels. The principle components contain the majority of the transcriptomic information allowing to partial out the effect of the transcriptional layer from the metabolite profiles. Depending whether the correlation between metabolites persists upon controlling for the effect of the transcriptional layer, the approach allows us to group metabolite pairs into being associated due to post-transcriptional or transcriptional regulation, respectively. I showed that the classification of metabolite pairs into those that are associated due to transcriptional or post-transcriptional regulation are in agreement with existing literature and findings from a Bayesian inference approach. The approaches developed, implemented, and investigated in this thesis open novel ways to jointly study metabolomics and transcriptomics data as well as to place metabolic profiles in the network context. The results from these approaches have the potential to provide further insights into the regulatory machinery in a biological system.},
  language  = {en}
}
@phdthesis{Jueppner2014,
  author    = {J{\"u}ppner, Jessica},
  title     = {Characterization of metabolomic dynamics in synchronized Chlamydomonas reinhardtii cell cultures and the impact of TOR inhibition on cell cycle, proliferation and growth},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-76923},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VI, 153},
  year      = {2014},
  abstract  = {The adaptation of cell growth and proliferation to environmental changes is essential for the surviving of biological systems. The evolutionary conserved Ser/Thr protein kinase "Target of Rapamycin" (TOR) has emerged as a major signaling node that integrates the sensing of numerous growth signals to the coordinated regulation of cellular metabolism and growth. Although the TOR signaling pathway has been widely studied in heterotrophic organisms, the research on TOR in photosynthetic eukaryotes has been hampered by the reported land plant resistance to rapamycin. Thus, the finding that Chlamydomonas reinhardtii is sensitive to rapamycin, establish this unicellular green alga as a useful model system to investigate TOR signaling in photosynthetic eukaryotes. The observation that rapamycin does not fully arrest Chlamydomonas growth, which is different from observations made in other organisms, prompted us to investigate the regulatory function of TOR in Chlamydomonas in context of the cell cycle. Therefore, a growth system that allowed synchronously growth under widely unperturbed cultivation in a fermenter system was set up and the synchronized cells were characterized in detail. In a highly resolved kinetic study, the synchronized cells were analyzed for their changes in cytological parameters as cell number and size distribution and their starch content. Furthermore, we applied mass spectrometric analysis for profiling of primary and lipid metabolism. This system was then used to analyze the response dynamics of the Chlamydomonas metabolome and lipidome to TOR-inhibition by rapamycin The results show that TOR inhibition reduces cell growth, delays cell division and daughter cell release and results in a 50\% reduced cell number at the end of the cell cycle. Consistent with the growth phenotype we observed strong changes in carbon and nitrogen partitioning in the direction of rapid conversion into carbon and nitrogen storage through an accumulation of starch, triacylglycerol and arginine. Interestingly, it seems that the conversion of carbon into triacylglycerol occurred faster than into starch after TOR inhibition, which may indicate a more dominant role of TOR in the regulation of TAG biosynthesis than in the regulation of starch. This study clearly shows, for the first time, a complex picture of metabolic and lipidomic dynamically changes during the cell cycle of Chlamydomonas reinhardtii and furthermore reveals a complex regulation and adjustment of metabolite pools and lipid composition in response to TOR inhibition.},
  language  = {en}
}