The question of how a cell determines which cell fate it will adopt is at the heart of developmental biology. In most cases, morphogens control cell fates by directing cells to activate or repress downstream gene expression programs containing a specific subset of genes required for the desired cell function and morphology. Over the past few decades, this question has been dissected in great detail in several model organisms. One of the best-studied examples is the early embryo of the fruit fly, Drosophila melanogaster, whose cascade of gene expression network has been revealed by decades of hard work. Indeed, the fruit fly is one of the first organisms whose morphogens were identified using mutant screens pioneered by Christiane Nüsslein-Volhard and Eric Wieschaus. The identified morphogens revealed how the initial gradients of morphogens instruct cells at different positions along the embryo body axis to adopt different fates, such as those corresponding to legs or antennae, by activating differential transcriptional programs.

For the past decade, this knowledge of classic developmental biology, which revealed great details about the underlying gene regulatory networks, has been under quantitative dissection via the development of a wide array of new experimental techniques. The accumulation of these quantitative data demands quantitative models to understand the underlying principles behind gene regulation. Ultimately, one of the dreams of developmental biologists is to have a predictive understanding of gene expression patterns solely from the patterns of input morphogens and the regulatory DNA sequences.

I believe that the field of developmental biology is at an exciting new phase where quantitative measurements meet theoretical modeling to unveil the molecular underpinnings of transcriptional and translational regulation. In my view, the dream of predictive developmental biology can only be achieved by an active dialogue between theoretical modeling that generates experimentally testable hypotheses and quantitative measurements to test these hypotheses.

My dissertation is an attempt to contribute to this new phase of quantitative data meeting theoretical modeling by developing and characterizing molecular tools for quantitative measurements of gene expression following the central dogma (transcription and translation), and theoretical frameworks to understand and predict transcriptional regulation and protein pattern formation.