Robot Planning Graphs as Metabolic Pathways for Drug Target Identification in Biological Organisms
Jonathan Mulhern (SUNY Albany)
Through a simple mosquito bite, the Zika Virus has infiltrated its’ way to infect thousands of people throughout the world, and without a definite vaccination, it has been harder to eradicate medicinally. According to the World Health Organization, the Zika virus or (ZIKV) is an emerging mosquito-borne Flavivirus related to dengue, yellow fever, Japanese encephalitis, and West Nile viruses, and is transmitted by Aedes spp. mosquitoes. In Professor Ekenna’s laboratory at the University at Albany, our objective is to identify which enzymatic and compound reactions in the Kyoto Encyclopedia of Genes and Genomes (KEGG) that correspond to metabolic pathways of the Zika Virus and subsequently represent drug targets of the Zika Virus. We intend to redefine our algorithmic data into discrete steps that models a smaller complexity as seen in motion planning for robotics by using machine learning and data mining techniques. By narrowing down the potential drug targets, for a potentially large metabolic pathway, we can represent our data using a bipartite graph, in which biomedical engineers and worldwide health professors can utilize in order to determine a potential cure and aid victims.