Scientists at Tufts University School of Medicine and the Graduate School of Biomedical Sciences (GSBS) have developed a genome-scale metabolic model or “subway map” of key metabolic activities of the bacterium that causes Lyme disease. Using this map, they have successfully identified two compounds that selectively target routes only used by Lyme disease to infect a host. Their research was published October 19 in the journal mSystems.
While neither medication is a viable treatment for Lyme because they have numerous side effects, the successful use of the computational “subway map” to predict drug targets and possible existing treatments demonstrates that it may be possible to develop micro-substances that only block Lyme disease while leaving other helpful bacteria untouched.
Genome-scale metabolic models (GEMs) collect all known metabolic information on a biological system, including the genes, enzymes, metabolites, and other information. These models use big data and machine learning to help scientists understand molecular mechanisms, make predictions, and identify new processes that might be previously unknown and even counter-intuitive to known biological processes.