Organization and integration of large-scale datasets for designing a metabolic model and re-annotating the genome of mycoplasma pneumoniae

Mycoplasma pneumoniae, one of the smallest known self-replicating organisms, is a promising model organism in systems biology when aiming to assess understanding of an entire living cell. One of the key steps towards this goal is the design of mathematical models that describe the cellular processes by connecting the involved components and that allow to unravel underlying mechanisms. For Mycoplasma pneumoniae, a wealth of genome-wide datasets on genomics, transcriptomics, proteomics, and the metabolism has been produced in a collaborative research project during the past years. However, a proper system facilitating easy exchange of the generated information and mathematical models to integrate the different datasets and to fill remaining knowledge gaps were lacking. In addition, the analysis of the metabolome was somewhat lagging behind and different in vivo observations of metabolic behavior remained unexplained. This thesis presents a combinatorial approach to design a metabolic model for Mycoplasma pneumoniae. First, we developed a database, MyMpn, in order to provide access to structured and organized data, a prerequisite for successful modeling of biological systems. Second, we built a predictive, genome-scale, constraint-based metabolic model and, in parallel, we explored the metabolome in vivo reaching unprecedented coverage of the in silico predicted metabolome. We defined the biomass composition of a Mycoplasma pneumoniae cell, corrected the metabolic wiring diagram, showed that Mycoplasma pneumoniae dedicates a large proportion of its energy to cellular homeostasis, and analyzed the metabolic behavior under different growth conditions and in deleterious mutants. Finally, as suggested from the correction of the functional annotation of metabolic key enzymes and the in silico knock-out predictions, we manually reannotated the genome of Mycoplasma pneumoniae. The database, despite not yet being released to the public, is internally already used for data analysis, integration and visualization, as well as for the design of different mathematical models. The definition of the biomass composition of Mycoplasma pneumoniae provides the basis for in silico growth simulations of wall-less bacteria. Unraveling the principles governing energy metabolism and adaptive capabilities upon gene deletion facilitates the development of engineering tools and dynamic models for metabolic sub-systems. Our results highlight the impact of the reductive genome evolution on the metabolism and especially the cellular energy balancing in a minimal bacterium which reflects the high degree of adaptation to a relatively unchanging niche, the human lung. Furthermore, we revealed that the degree of complexity in which the genome of Mycoplasma pneumoniae is organized far exceeds what has been considered possible so far. We showed that different genes can be transcribed from the same genomic region and identified numerous small RNAs. The 32 newly identified genes together with the corrections of previously annotated genes allowed us to correct in total a 10% error rate in the annotation.