UC Santa Cruz

Regulation of gene expression is critical for all life processes. RNAseq allows for the investigation of the expression state and the inference of gene regulatory net- works (GRNs). The GRN reveals mechanisms of disease and development both at an organismal and cellular level. Constructing GRNs from expression data is done by de- tecting statistical relationships over a series of samples. However, these relationships are noisy and do not take into account many important variables. To explore one such limitation, I implemented dual RNAseq to investigate the regulatory mechanisms that occur between both host and virus transcriptomes in SARS-CoV-2 infected samples (Chapter 1). To address the temporal aspect of GRNs, I developed a statistical frame- work for detecting relationships between transcription factors and their targets from inferred trajectory progressions (”pseudotime”) in single-cells (Chapter 2). Finally, I developed an interpretable, time-aware deep learning transformer for modeling disease progression and demonstrate its ability to identify gene programs that promote breast cancer resistance post-treatment (Chapter 3).