The post-genome period promises better understanding of complex diseases, better molecular biomarkers, and a realization of personalized medicine. These aims drive modern biology; including basic biomedical and clinical sciences. Nonetheless, with respect to the massive amount of data daily churned out from high-throughput biomedical experiments, biology seems to plead for novel methods to appropriately understand its observed results. More so, as observed on a global scale of genomes, transcriptomes, proteomes including posttranslational modifications, metabolomes and interactomes, there is no longer a doubt that biological processes are best described within the concept of “multi-functionality”, as opposed to isolated or linear pathways of causality.

Dr. Aiyetan’s research lies at the intersection of these high content biomedical experiment data on the one end and, mathematics, statistical methods and computation on the other. Dr. Aiyetan seeks to draw on the utility of the later to understand biological processes with respect to normal and diseased states. In addition to optimizing experimental methods and developing analytical tools, Dr. Aiyetan’s research involves integrating and mining experimental outcomes across different data platforms to uncover molecular contributions to interesting phenotypes.

Dr. Aiyetan’s work currently centers on the Clinical Proteomics Tumor Analysis Consortium (CPTAC) – the National Cancer Institute’s (NCI) multidisciplinary (proteomics, genomics, bioinformatics, experimental design, statistics, cancer biology and oncology) and integrated consortium seeking to identify proteins that result from changes in cancer genomes and their related biological processes.