Machine learning of time-series single-cell drug screening to elucidate HIV latency control mechanisms

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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.