29.04.2024

Algorithm for detecting and solving energy-generating cycles

In his master’s thesis, Tobias Fehrenbach used the metabolism of S. sanguinis as an example to develop a new method for automatically improving genome-scale metabolic models.

Streptococcus sanguinis is a facultative anaerobic member of the Viridians Streptococcus group. The commensal is usually found in the human nasopharynx, where it is seen as an antagonist for pathogens. However, it can also colonize the gut and is considered one of the most common pathogens for infective endocarditis when it enters the bloodstream. Therefore, a deeper understanding of the metabolism of S. sanguinis can help to develop new control methods. Genome-scale metabolic models are a valuable tool for this. Based on the annotated genome sequence of an organism and the reactions and metabolites based on it, they offer the possibility of simulating the entire metabolism in the computer. In this way, such models can support experimental biomedical research. However, creating such models is a complex process that cannot yet be fully automated. For example, automatically generated metabolic models often contain energy-generating cycles contradicting introductory thermodynamics. In this work, Tobias Fehrenbach developed a tool that detects and removes such cycles. By applying this method, he created a strain-specific model of the opportunistic pathogenic bacterium Streptococcus sanguinis SK1 that meets the current systems biology standards.