Programming stress-induced altruistic death in engineered bacteria.
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Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is 'altruistic': the killing of some cells can benefit the survivors through release of 'public goods'. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, we determined such conditions by engineering tunable, stress-induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. We further predicted that altruistic death could generate the 'Eagle effect', a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, we experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. Our findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment.
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Published Version (Please cite this version)10.1038/msb.2012.57
Publication InfoTanouchi, Yu; Pai, Anand; Buchler, Nicolas E; & You, Lingchong (2012). Programming stress-induced altruistic death in engineered bacteria. Mol Syst Biol, 8. pp. 626. 10.1038/msb.2012.57. Retrieved from https://hdl.handle.net/10161/9352.
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Assistant Professor of Biology
Our lab is interested in the systems biology and evolution of epigenetic switches (bistability) and clocks (oscillators) in gene regulatory networks, two functions that are essential for patterning, cell proliferation, and differentiation in biological systems. We also study biochemical oscillators such as the cell cycle, metabolic rhythms, and circadian clocks, which co-exist in the same cells and interact with one another through shared resources.
Professor of Biomedical Engineering
The You lab uses a combination of mathematical modeling, machine learning, and quantitative experiments to elucidate principles underlying the dynamics of microbial communities in time and space and to control these dynamics for applications in computation, engineering, and medicine.
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