[Illinois] Coping with the Unknown: A Guide for Bacteria and Bacteriologists
Our findings further show that the adaptive mechanisms through which regulatory mutations reshape the cellular fitness landscape reach throughout the cell's regulatory and metabolic networks, underscoring the need for systems-level data in attempting to understand adaptation to new conditions. Existing approaches for directly measuring the regulatory state of cells, however, must target a single transcription factor at a time, requiring a combinatorially increasing number of experiments to obtain measurements on multiple factors under multiple environmental conditions. We have recently developed a method enabling the simultaneous measurement of the complete regulatory state of bacterial cells, by physically separating protein-DNA complexes from bulk genomic DNA, sequencing the footprinted complexes, and then applying a statistical mechanics-based model to identify protein occupancies. The newly developed method is applicable to any bacterium without the need for prior knowledge of its regulatory network, and will allow the rapid dissection of bacterial responses to environmental stresses.
I apply a combination of computational and experimental approaches in order to understand how cells sense and respond to their environment, with foci ranging from the molecular details of perception to the evolutionary basis for existing regulatory architectures. In the process, I make heavy use of microbial population genetics and systems biology tools, bioinformatic analysis, and molecular and atomistic-level simulations. Some of my current projects include:
Investigation of the phenotypic and evolutionary consequences of mutations to the bacterial transcription termination factor Rho Identification of evolved instances of anticipatory regulation in bacterial populations and their link to the corresponding native habitat Development of more efficient methods for measuring changes in cellular transcriptional regulatory state -Taken from Dr. Freddolino's profile page for the Tavazoie Lab
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