I designed a machine learning algorithm to identify novel, short coding regions of the human genome based on integrating evolutionary conservation data with structural information. Due to their lack of functional domains, these small protein products embeded in the putative non-coding genome (microproteins), are hypothesized to exert regulatory control over other classical protein complexes. I am using a systems biology approach to build an understanding of the molecular function of these novel microproteins in cancer cell lines and in vivo models.
Perturbed cell metabolism and energy regulation is a well established hallmark of cancer and has been studied since the middle of the nineteenth century. Cancer cells have been shown to exhibit greater activity of metabolic pathways including the glycolytic and glutaminolysis pathways and a reduced dependence on oxidative phosphorylation. However the mechanism by which cancer cells switch from traditional metabolic programs to their characteristic alternate programs remains poorly understood.
Such reprogramming events involve dynamic regulation of many genes and gene networks. My hypothesis is that these regulatory events are mediated through epigenetic means including chromatin modifications and functional long-noncoding RNAs. I am testing this hypothesis in the context of T-cell acute leukemia.
Here you can find links to some of my (wet-lab) protocols.
“Satisfaction of one's curiosity is one of the greatest sources of happiness in life.Linus Pauling