DYNAMICAL MODELING AND SYSTEMS ANALYSIS OF BIOLOGICAL NETWORKS
	Thierry Emonet Assistant Professor of Molecular, Cellular and Developmental Biology Room: KBT 1048 Phone: 203-432-3516 Email: thierry.emonet@yale.edu Lab: http://emonet.biology.yale.edu Dipl. Phys. ETHZürich (M.S.) , 1992; Ph.D., University of La Laguna, Spain, 1998

We study how live cells process information and make decisions using wet lab experiments and computational modeling.

Despite spectacular advances in our understanding of molecular processes in living cells, predicting cellular behavior remains difficult. In biology as in communication systems, cellular behavior emerges from interactions between multiple components, but unlike in computers, an element of chance contributes to the operation of each cell and population of cells. To make predictions in biology we need to understand how chance influences the behavior of individual cells and populations of cells. The effect of chance on behavior is clearly noticeable in clonal populations of bacteria where genetically identical cells exhibit diverse behaviors. Our long term goal is to contribute significantly to the nascent computational theory of biology.

We focus on the relationship between signal transduction, information processing and cell-to-cell variability and ask how the molecular mechanisms in single cells are optimized and controlled to generate multicellular behavior. We want to build a systems level understanding of information processing in biology that relates cellular behavior to key aspects of the molecular environment, such as gene position on the chromosome, protein network architecture, and noise in gene expression and signal transduction.

As model systems we use various pathways in prokaryotes and eukaryotes, such as the sensory apparatus used by bacteria to navigate complex environments, the olfactory receptor system in Drosophila, and the cytoskeleton and cell cycle control systems in the aquatic bacteria Caulobacter crescentus. We analyze these systems using a combination of experimental and computational techniques such as time-lapse fluorescent microscopy, electrophysiological recordings and computational and analytical modeling. We collaborate closely with the Carlson, Gerstein and Jacobs-Wagner labs.

We have several projects available for graduate and undergraduate students that combine experiments and modeling.

1080 digital E. coli swimming in a 3D medium with a vertical gradient of aspartate (10-8 M/m). 540 cells are sensitive to aspartate (red) and 540 cells are not sensitive (green). To illustrate the complicated trajectory of cells, the trace of two typical cells is shown. In this model, each bacterium is an agent equipped with its own chemotaxis network and motors.

Selected Publications

Korobkova E*, Emonet T*, Vilar JM, Shimizu TS, Cluzel P. (2004) From molecular noise to behavioural variability in a single bacterium. Nature 428:574-8. *Contributed equally.

Emonet T, Macal CM, North MJ, Wickersham CE, Cluzel P. (2005) AgentCell: a digital single-cell assay for bacterial chemotaxis. Bioinformatics. 21:2714-21

Le TT, Harlepp S, Guet CC, Dittmar K, Emonet T, Pan T, Cluzel P. (2005) Real-time RNA profiling within a single bacterium. Proc Natl Acad Sci USA. 102:9160-4.

Korobkova EA, Emonet T, Park H, Cluzel P. (2006) Hidden stochastic nature of a single bacterial motor. Phys Rev Lett 96:058105.

Le TT, Emonet T., Harlepp S., Guet CC, Cluzel P. (2006) Dynamical determinants of inducible gene expression in a single bacterium. Biophysical J 90:3315-21.

Emonet T, Cluzel P. (2008) Relationship between cellular response and behavioral variability in bacterial chemotaxis. Proc Natl Acad Sci U S A. 105:3304-9.

top