We are interested in biological sensing and decision making. We study how bacteria sense and explore their environment, how flies smell, and how T cells decide to mount or not an immune response. We use experiments and mathematical modeling to study the dynamical properties of biological systems and uncover the molecular origin of behavior. Our lab employs a mixture of biologists, physicists, computer scientists and mathematicians.

	Digital assays and multiscale agent-based modeling: Behavior is dynamics. Recent advances in high-throughput laboratory investigations provide unprecedented information about the molecular components that mediate cellular mechanisms. A major challenge now is to integrate and transform this data into a dynamical understanding of biological systems, connecting molecules to behavior. The goal of this project is to build an integrative and user-friendly computational framework that will give researchers the capability to incrementally assemble whole-cell and multi-cell models of complex biological systems to explore the connection between molecular mechanisms and desirable biological behaviors.
	Dynamical encoding of odors by the fly: We are interested in the role of time in the encoding of odor identity and intensity in the primary layer of the fly olfactory system. In collaboration with John Carlson (Yale MCDB) we perform in vivo electrophysiological recordings to assay the dynamical properties of olfactory receptor neurons. In collaboration with Steve Zucker (Yale Applied Mathematics) we are analyzing the geometry of the odor space.
	The role of phenotypic variability in bacterial sensing: The question is how molecular noise might control a biological function at the population level by tuning the distribution of single cell behaviors. As model system we use a canonical sensory system in biology, bacterial chemotaxis in E. coli. For this project, we use direct comparison between in vivo and in silico experiments.
	Spatial localization in signal processing: We are interested in the effect of spatial localization on signaling pathway and in understanding the interplay between physical forces, enzymatic reactions and spatial control in bacteria. We have developed MicrobeTracker, an automated cell detection and lineage analysis software that enable us to quantify the spatio-temporal localization of fluorescently labeled proteins inside single cells within a genealogy. In collaboration with the Jacobs-Wagner lab we are looking at the localization of mRNA in bacteria and we study the relationship between cell shape and bacterial cell wall synthesis. (see MicrobeTracker Software).
	Modeling the dynamical interaction between T regulatory cells and T effector cells during an immune response: Together with the Altan-Bonnet lab at the Memorial Sloan-Kettering Cancer Center we are studying the highly dynamical interactions between effector and regulatory T cells and the role of this dynamics in decision making.