UCLA

Quantifying the dependency between biomolecular composition and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual and combinations of biomolecules to a given phenotype. In this work, I analyzed complex relationships related to transcriptional activity, receptor abundance, apoptotic decisions, and Ca2+ signaling. In the first chapter, I used an information theory approach to analyze multimodal data of the expression of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca2+ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. In the second chapter, I connect apoptotic decision-making to transcriptional activity, receptor abundance, and caspase activity. In both approaches, I quantify relationships between cell state and phenotype from multimodal, high-dimensional data.