Yale > Molecular Cellular and Developmental Biology

We study how living cells process information and make decisions using biochemical networks. For example, we try to understand how biological networks control the timing and localization of events within biological systems. To address this kind of questions we focus on relating the architecture of biological networks, to their biological function, and to cell behavior. In connecting architecture, function and behavior our goal is: (i) to gain a systems level understanding of specific biological systems and (ii) to unveil design principles of biological networks.

We analyze biological processes using a combination of computational modeling, systems analysis and direct comparison with experimental data. Dynamical models help focusing biological questions and targeting experiments, which in turn impose constraints on the models. The combination of numerical modeling together with high throughput and single cell experimental methods that probe the real-time dynamics of biological processes is bringing new predictive power to biology. To foster direct interaction between modeling and experiments our lab is located within wet lab space in the MCDB department where we are starting several collaborations with the surrounding wet labs. We consider applications from graduate students and postdocs of diverse backgrounds (biology, physics, computer science, mathematics, engineering, etc.). To apply, please contact me by email.

Existing biological simulations often concentrate on one level of interest: molecular, cellular or the population level. Our emphasis is to seek a multi-scale understanding of biological systems. We want to relate stochastic events inside single cells to the behavior of these cells as they interact with the environment and with each other. We are building a computational framework to digitally assay the relationship between network architecture and biological function. This approach (http://www.agentcell.org), already helped clarify the relationship between network architecture, behavioral variability and robustness of adaptation in the study of a canonical signal transduction network, the chemotaxis system in Escherichia coli. We are extending this project to study how the distribution of single cell behaviors within a bacterial population affects the fitness of that population to a given environment. Another area of research in the lab is the development of new methods that combine data-mining algorithms with dynamical systems analysis for the study of biological networks. We are also starting efforts to study the propagation of signals and molecular noise in biological systems where cell-to-cell communication plays a prominent role (e.g. quorum sensing in prokaryotes, development in multicellular organisms).

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

Emonet, T., Cluzel, P. Relationship between cellular response and behavioral variability in bacterial chemotaxis, submitted <http://arxiv.org/abs/0705.4635>.

Le TT, Emonet T., Harlepp S., Guet CC, Cluzel P. Dynamical determinants of inducible gene expression in a single bacterium, Biophysical J., 90(9):3315-21 (2006).

Korobkova E., Emonet T., Park H, Cluzel P. Hidden stochastic nature of a single bacterial motor, Phys. Rev. Letters, 96, 58105 (2006).

Le TT, Harlepp S., Guet CC, Dittmar K, Emonet T., Pan T., Cluzel P. Real-time RNA profiling within a single bacterium, Proc. Natl. Acad. Sci. U S A., 28; 102 (26):9160-4 (2005).

Emonet T., Macal CM, North MJ, Wickersham CE, Cluzel P. AgentCell: a digital single-cell assay for bacterial chemotaxis, Bioinformatics, 1; 21(11):2714-21 (2005).

Korobkova E.*, Emonet T.*, Vilar J., Shimizu T. & Cluzel P. From molecular noise to behavioral variability in a single bacterium, Nature, 428, 574-578 (2004).
* These authors contributed equally to this work

top

Faculty

Emeritus Faculty, Research Scientists and Lecturers

Research Faculty

Departmental Staff

Laboratory Staff

Graduate Students

Emergency Contacts