Gene-Wei Li
Assistant Professor of Biology
My laboratory aims to elucidate how cells optimize their genetic information to establish desired physiology. We use ‘simple’ bacterial cells to discover quantitative principles governing gene expression. We invent new tools that provide key missing observables, coupled with the development of analytical frameworks enabled by these new observables. Ultimately, we want to understand how cells work as an optimization process through evolution. To achieve this objective, my lab is developing methods to directly answer the following questions: 1) How do cells fine-tune protein synthesis rates? My lab is developing several high-resolution methods to define the post-transcriptional processes that determine the rates of protein synthesis. We are establishing a single-molecule approach to profile the kinetics of ribosome initiation in living cells. We are also creating a new RNA-seq approach for mapping mRNA processing sites and quantifying isoform abundance in bacteria. Our goal is to build a predictive model for these key determinants that will help advance studies in both basic and synthetic biology. 2) What determines the optimal protein level? Cells tightly regulate their protein expression, but we have no means of rationalizing the observed levels for most proteins. Traditional studies of cellular pathways and processes have largely omitted information on absolute protein abundance, even though it is one of the key outcomes of gene regulation. My lab is developing theoretical and experimental frameworks for understanding the balancing act between the need for protein activity and the cost of protein synthesis, with an initial focus on amino acid biosynthetic enzymes. From a systems perspective, we are also trying to determine the extent to which genetic regulatory networks have evolved to buffer against fluctuations in protein levels. Highlighted in this proposed study is our drive to reveal the whole complement of backup circuits built in to bacterial genomes. Our goal is to provide a conceptual framework for rationalizing the quantitative composition of a proteome that is shaped by evolution.