title: To grow is not enough: impact of noise on cell environmental response and fitness.
abstract: Quantitative single cell measurements have shown that cell cycle duration (the time between cell divisions) for diverse cell types is a noisy variable. The underlying distribution is mean scalable with a universal shape for many cell types in a variety of environments. Here we explore through both experiment and theory the response of these distributions to large environmental perturbations. In particular, we discuss how the stochasticity of the ensemble may be related to the response. Our findings show that slow growing, noisy populations are more adaptive than those which are fast growing. We suggest that even non-cooperative cells in exponential growth phase may not optimize fitness through growth rate alone, but also optimize adaptability to changing conditions. In this work, we wish to emphasize that in a manner similar to genetic evolution, noise in biochemical processes may be important to allow for cells to adapt to rapid to environmental changes.
authors.affiliation: Department of Chemical and Biomolecular Engineering, Johns Hopkins University, USA.
authors.forename: Nash
authors.initials: N
authors.lastname: Rochman
authors.affiliation: Department of Mechanical Engineering, Johns Hopkins University, USA.
authors.forename: Fangwei
authors.initials: F
authors.lastname: Si
authors.affiliation: Department of Mechanical Engineering, Johns Hopkins University, USA and Department of Biomedical Engineering, Johns Hopkins University, USA.
authors.forename: Sean X
authors.initials: SX
authors.lastname: Sun
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D002453:Cell Cycle / Q000502:physiology*; D000066670:Cell Plasticity / Q000502:physiology; D049109:Cell Proliferation / Q000502:physiology*; D002470:Cell Survival / Q000502:physiology; D003198:Computer Simulation; D017753:Ecosystem; D004926:Escherichia coli / Q000166:cytology / Q000254:growth & development; D056084:Genetic Fitness / Q000502:physiology; D008954:Models, Biological; D015233:Models, Statistical