A noisy linear map underlies oscillations in cell size and gene expression in bacteria.


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.





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Publication Info

Tanouchi, Yu, Anand Pai, Heungwon Park, Shuqiang Huang, Rumen Stamatov, Nicolas E Buchler and Lingchong You (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 https://hdl.handle.net/10161/10801.

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Lingchong You

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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