Faculty and Research

Lawrence David, PhD
Assistant Professor  
Duke Institute for Genome Sciences and Policy

John Rawls, PhD

2171 CIEMAS
Box 3382 DUMC
Durham, N.C. 27710


Phone: 919.668.5388
Fax: 919.668.4777
Email: lawrence.david@duke.edu

research  biography • lab members  publications lab website

Research:

Human epithelial surfaces are colonized by hundreds of trillions of commensal microbes representing thousands of unique bacterial species (the human microbiota). Our lab seeks to understand, predict, and manipulate how human microbiota behave over time. We are particularly interested in how these human-associated microbial communities resist and respond to perturbation. Below are several research projects we are pursuing.

Predicting microbiome susceptibility to infection
We are actively collaborating with Regina LaRocque (Massachusetts General Hospital) and Firdausi Qadri (the International Center for Diarrheal Disease and Research, Bangladesh) to longitudinally study cholera infections among the residents of Dhaka, Bangladesh. Using a cohort of families at high risk of cholera transmission, we are studying the microbiota factors affecting individual susceptibility to cholera. We are also developing similar predictive models from nasal microbiomes following viral challenge in collaboration with Geoff Ginsburg’s group at Duke.

Post-infection microbial successions
Through our work on cholera transmission, we have observed that waves of distinct bacterial species colonize human guts in the weeks following diarrheal infection. We are now using a combination of next-generation sequencing and bioreactor design to study these successions and uncover their mechanisms. This work is being performed in collaboration with Marc Deshusses from the Pratt School of Engineering at Duke. Of particular interest are efforts in the lab to reproduce microbial successions in vitro, using simplified microbial communities.

Microbiome network inference and rational probiotic design
We have collected some of the world’s densest time-series of human behavior and commensal microbiota, having tracked these variables on daily time scales for weeks and even months. Now, we are using computational techniques to build dynamical models of how human microbiota behave over time. We are particularly interested in learning predictive models, and ultimately verifying them using experiments in vivo. A long-term goal of this work is the rational design of probiotic therapies for manipulating human microbiota.