(6il) Data Science and Omics Approaches for Network Biology

Biological cells are complex dynamic systems that exhibit
both extreme robustness and fragility. Robustness can be characterized as a
cell's ability to isolate perturbations locally, which is a useful feature in
the context of drug discovery, as treatments need to be efficacious with
minimal adverse side effects. On the other hand, cells also possess fragile
points that can stimulate apoptoic (cell death)
pathways, providing intuitive targets for anti-cancer therapy.  My overall goal is to develop a strong
engineering research platform to study these two system properties at a
fundamental level and ultimately facilitate the discovery of novel drug targets
in diseased mammalian cells. My research involves the integration of large-scale
omics data with metabolic and signaling networks to quantify cellular dynamics
in response to various stimuli. In particular, I plan to develop
state-of-the-art experimental workflows for efficient metabolomics and
proteomics data collection using LC/MS-MS (liquid chromatography-mass
spectrometry). I also have a strong interest in applying ?Big Data Analytics'
and parallel cloud computing for systems biology applications to ensure that the
algorithms we develop are scalable with the increasing complexity of biological
networks. As a primary application of my work, I plan to explore novel
treatments for liver disease, specifically nonalcoholic fatty liver disease
(NAFLD) and hepatocellular carcinoma (liver cancer).