Indiana University-Purdue University Indianapolis (IUPUI) / Metabolic flux analyses and their more comprehensive forms, genome-scale metabolic networks (GSMNs), have gained tremendous attention in industrial and medical research. Saccharomyces cerevisiae (S. cerevisiae) is one of the organisms that has had its GSMN subjected to multiple frequent updates. The objective of this study is to develop a visualization tool for the GSMN of S. cerevisiae for educational and research purposes. This visualization tool is called the Master Metabolic Map of Saccharomyces cerevisiae (MMMSC). In this study, a metabolic database of S. cerevisiae developed by us was transferred to Cytoscape, a useful and efficient bioinformatics software platform for visualizing molecular networks. After the MMMSC was created, nodes, representing metabolites and enzymes, and edges, representing the chemical reactions that connect the nodes, were curated manually to develop a metabolic visualization map of the whole metabolic system of S. cerevisiae (Figure 4). In the discussion, examples are provided regarding possible applications of MMMSC to predict possible effects of the manipulation of the S. cerevisiae metabolome for industrial and medical purposes. Ultimately, it is concluded that further work is needed to complete the metabolic database of S. cerevisiae and the related MMMSC. In future studies, these tools may be integrated with other omics and other approaches, especially the directed-evolution approach, to increase cost and time efficiency of future research and to find solutions for complex and, thus far, poorly managed environmental and health problems.

Cyanobakterien sind meist photoautotroph lebende Prokaryoten, welche nahezu alle Biotope der Welt besiedeln. Sie gehören zu den wichtigsten Produzenten der weltweiten Nahrungskette. Um sich auf den täglichen Wechsel von Tag und Nacht einzustellen, besitzen Cyanobakterien eine innere Uhr, bestehend aus den Proteinen KaiA, KaiB und KaiC, deren biochemische Interaktionen zu einem 24-stündigen Rhythmus von Phosphorylierung und Dephosphorylierung führen. Die circadiane Genexpression im Modellorganismus Synechocystis sp. PCC 6803 habe ich mittels drei verschiedener Zeitserienexperimente untersucht, wobei ich einen genauen Zeitplan der Genaktivierung in einer Tag-Nacht-Umgebung, aber keine selbsterhaltenden Rhythmen entdecken konnte. Allerdings beobachtete ich einen überaus starken Anstieg der ribosomalen RNA in der Dunkelheit. Aufgrund ihrer hohen Wachstumsraten und der geringen Anforderungen an die Umwelt bilden Cyanobakterien eine gute Grundlage für die nachhaltige Erzeugung von Biokraftstoffen, für einen industriellen Einsatz sind aber weitere Optimierung und ein verbessertes Verständnis des Metabolismus von Nöten. Hierfür habe ich die Orthologie von verschiedenen Cyanobakterien sowie die Konservierung von Genen und Stoffwechselwegen untersucht. Mit einer neu entwickelten Methode konnte ich gemeinsam vorkommende Gene identifizieren und zeigen, dass diese Gene häufig an einem gemeinsamen biologischen Prozess beteiligt sind, und damit bisher unbekannte Beziehungen aufdecken. Zusätzlich zu den diskutierten Modulen habe ich den SimilarityViewer entwickelt, ein grafisches Computerprogramm für die Identifizierung von gemeinsam vorkommenden Partnern für jedes beliebige Gen. Des Weiteren habe ich für alle Organismen automatische Rekonstruktionen des Stoffwechsels erstellt und konnte zeigen, dass diese die Synthese von gewünschten Stoffen gut vorhersagen, was hilfreich für zukünftige Forschung am Metabolismus von Cyanobakterien sein wird. / Cyanobacteria are photoautotrophic prokaryotes populating virtually all habitats on the surface of the earth. They are one of the prime producers for the global food chain. To cope with the daily alternation of light and darkness, cyanobacteria harbor a circadian clock consisting of the three proteins KaiA, KaiB, and KaiC, whose biochemical interactions result in a phosphorylation cycle with a period of approximately 24 hours. I conducted three time-series experiments in the model organism Synechocystis sp. PCC 6803, which revealed a tight diurnal schedule of gene activation. However, I could not identify any self-sustained oscillations. On the contrary, I observed strong diurnal accumulation of ribosomal RNAs during dark periods, which challenges common assumptions on the amount of ribosomal RNAs. Due to their high growth rates and low demand on their environment, cyanobacteria emerged as a viable option for sustainable production of biofuels. For an industrialized production, however, optimization of growth and comprehensive knowledge of the cyanobacterial metabolism is inevitable. To address this issue, I analyzed the orthology of multiple cyanobacteria and studied the conservation of genes and metabolic pathways. Systematic analysis of genes shared by similar subsets of organisms indicates high rates of functional relationship in such co-occurring genes. I designed a novel approach to identify modules of co-occurring genes, which exhibit a high degree of functional coherence and reveal unknown functional relationships between genes. Complementing the precomputed modules, I developed the SimilarityViewer, a graphical toolbox that facilitates further analysis of co-occurrence with respect to specific cyanobacterial genes of interest. Simulations of automatically generated metabolic reconstructions revealed the biosynthetic capacities of individual cyanobacterial strains, which will assist future research addressing metabolic engineering of cyanobacteria.