PROJECT SUMMARY
Gene expression dynamics yield a wealth of insight into the underlying structure-function relationships
of gene circuitry and the blueprints of disease. This is especially true for viruses whose decision-making
is highly dependent on their gene expression dynamics. Time-series gene expression perturbation data
elucidates biological causality and is essential to identify biological mechanisms, perform chemical
biology analyses, and for the discovery of novel drugs. However, a pipeline to comprehensively and
effectively analyze such time-series perturbation data does not exist. In this work, we propose to apply
machine learning to unravel the hidden (latent) structure of human immunodeficiency virus (HIV) time-
series gene expression when affected by chemical perturbations. This highly interdisciplinary effort
includes scientific areas relevant to the mission of the NIH such as biological, clinical, physical,
chemical, computational, engineering, and mathematical sciences. The proposed areas of research
combine machine learning, virology, systems biology, chemical sciences, single-cell biophysics, and
pharmaceutical sciences. The research will train and support two faculty members and two graduate
research assistants for the two-year term.