The Laub Lab | MIT | Department of Biology | Cambridge, MA 02139 | t: 617.253.3677 | e: laub at mit dot edu 


Our lab studies the molecular mechanisms and evolution of information processing at the cellular level. A defining and essential characteristic of cells is an ability to regulate their own behavior, modulating gene expression, cellular structures, motility, or metabolic state to ensure survival and proliferation. This regulatory capacity stems in large part from the coordinated action of signal transduction pathways that process information from the environment or internal cellular state. Using bacteria as model organisms, my lab aims to (i) elucidate the detailed molecular mechanisms responsible for the remarkable information-processing capability of cells, and (ii) understand the selective pressures and mechanisms that drive the evolution of signaling pathways. Our work is rooted in a desire to develop a deeper, fundamental understanding of how cells function and evolve, but it also has important medical implications, as many signaling pathways in pathogenic bacteria are needed for virulence.


Specific projects in our lab fall into four general areas: (1) We examine the mechanisms through which bacterial cells evolve new signaling pathways. These studies include experimental investigations of protein evolution both in vitro and in vivo, ancestral protein reconstruction, directed evolution studies, and the design of synthetic signaling circuits. We focus in particular on two-component signaling systems. (2) We investigate toxin-antitoxin systems in bacteria, exploring their induction, mechanisms of action, specificity, and evolution. (3) We study the regulatory circuits that control cell cycle progression and cellular asymmetry in Caulobacter crescentus. We use genetics, biochemistry, and computational methods along with new fluorescence microscopy and deep-sequencing methods to dissect cell cycle control. (4) We are probing the origins and consequences of chromosome structure and organization in bacteria, using microscopy, Hi-C, genetics, and computational/evolutionary analyses.


Read more here.