OPRA
OPTIMALITY PRINCIPLES IN RESPONSES TO ANTIBIOTICS
| Coordinatore | Institute of Science and Technology Austria
Organization address
address: Am Campus 1 contact info |
| Nazionalità Coordinatore | Austria [AT] |
| Totale costo | 100˙000 € |
| EC contributo | 100˙000 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2011-CIG |
| Funding Scheme | MC-CIG |
| Anno di inizio | 2013 |
| Periodo (anno-mese-giorno) | 2013-02-01 - 2017-01-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
Institute of Science and Technology Austria
Organization address
address: Am Campus 1 contact info |
AT (Klosterneuburg) | coordinator | 100˙000.00 |
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Obiettivo del progetto (Objective)
'Antibiotics kill bacteria or inhibit their growth by targeting essential cellular processes. In response to antibiotic exposure, bacteria activate gene regulation programs that are specific to the action of the antibiotic. These responses to individual antibiotics are often well-mapped, but how do bacteria respond dynamically to drugs? And is this regulation optimized for survival and growth in the presence of the drugs? We propose an interdisciplinary experimental-theoretical approach to measure, model, and synthetically manipulate the regulatory response to antibiotics. Specifically, we will (1) use an automated robotic system, an Escherichia coli library of fluorescent transcriptional reporters, and RNA-seq to measure changes in growth, physiology, and global gene expression in response to antibiotics; (2) develop theoretical models of gene regulation and predict ‘re-wirings’ of the gene regulation network that would worsen or improve growth and survival under antibiotic stress; (3) use a synthetic biology approach to test these predictions and to quantify the extent of optimization in bacterial gene regulation. We will develop this approach using the powerful model system Escherichia coli and then apply our key findings to Staphylococcus aureus, a clinically more relevant pathogen. Our work will lead to the first quantitative genome-wide characterization of the extent to which microbial stress responses are optimized for responding to drugs. We anticipate that this knowledge can be exploited to improve drug treatments. The systematic fundamental research proposed here will reveal exploitable weaknesses in cellular responses to drugs. It will thus contribute to the alleviation of one of the most serious public health concerns of our time: the rapid spread of drug-resistant bacterial pathogens, including methicillin-resistant S. aureus (MRSA), which coincides with a dramatic decline in the rate at which new efficient antibiotics are discovered.'