Single cell growth and gene expression dynamics in model organisms.

摘要

The developing discipline of synthetic biology attempts to recreate in artificial systems the emergent properties found in natural biology. Progress in this field requires a thorough understanding of the basic cellular functions that underly complex biological networks. Here, we present several studies that use existing and novel methods to probe the dynamic behavior of the model organisms Saccharomyces cerevisiae and Escherichia coli at the single cell level. First, we develop a microfluidic chemostat for monitoring single-cell gene expression within large populations of S. cerevisiae over many cellular generations. Second, we investigate the sources of extrinsic variability in eukaryotic gene expression using a combination of computational modeling and fluorescence data generated from multiple promoter-gene inserts in S. cerevisiae. Third, we use an enhanced version of the microfluidic chemostat to subject a large population of S. cerevisiae to a periodically varying carbon source, uncovering a novel regulatory property of a well-characterized metabolic network. Fourth, we use fluorescence mix-croscopy to acquire long-term volume trajectories for a large population of S. cerevisiae cells and reveal cell cycle dependent variations in protein concentration. Finally, we design and construct a synthetic signaling network in E. coli to investigate the coupling effect of "waiting lines" for enzymatic processing and discover correlated signaling through coupled protein degradation. Together, these studies illustrate the need for new approaches to studying fundamental cellular processes, in order to ultimately advance the goals of synthetic biology.