A noisy linear map underlies oscillations in cell size and gene expression in bacteria.
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During bacterial growth, a cell approximately doubles in size before division, after which it splits into two daughter cells. This process is subjected to the inherent perturbations of cellular noise and thus requires regulation for cell-size homeostasis. The mechanisms underlying the control and dynamics of cell size remain poorly understood owing to the difficulty in sizing individual bacteria over long periods of time in a high-throughput manner. Here we measure and analyse long-term, single-cell growth and division across different Escherichia coli strains and growth conditions. We show that a subset of cells in a population exhibit transient oscillations in cell size with periods that stretch across several (more than ten) generations. Our analysis reveals that a simple law governing cell-size control-a noisy linear map-explains the origins of these cell-size oscillations across all strains. This noisy linear map implements a negative feedback on cell-size control: a cell with a larger initial size tends to divide earlier, whereas one with a smaller initial size tends to divide later. Combining simulations of cell growth and division with experimental data, we demonstrate that this noisy linear map generates transient oscillations, not just in cell size, but also in constitutive gene expression. Our work provides new insights into the dynamics of bacterial cell-size regulation with implications for the physiological processes involved.
Gene Expression Regulation, Bacterial
Published Version (Please cite this version)10.1038/nature14562
Publication InfoBuchler, NE; Huang, S; Pai, Anand; Park, H; Stamatov, R; Tanouchi, Y; & You, L (2015). A noisy linear map underlies oscillations in cell size and gene expression in bacteria. Nature, 523(7560). pp. 357-360. 10.1038/nature14562. Retrieved from http://hdl.handle.net/10161/10801.
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Professor of Biomedical Engineering
Dr. You's research interest focus on computational systems biology & synthetic biology, including mathematical modeling of cellular networks; mechanisms of information processing by gene networks; design, modeling and construction of robust gene networks for applications in engineering and medicine.