现象学细胞大小稳态模型中的粗粒化与随机振荡
Coarse-graining and stochastic oscillations in a phenomenological model of cell-size homeostasis
AI总结：
该研究探讨了细胞大小稳态模型中粗粒化与随机振荡的机制，通过比较不同反馈结构模型揭示了大小控制策略的动态特性。
摘要：
在连续时间、随机模型的单细胞大小稳态研究中，我们探讨了从大小到生长速率和细胞周期进程的反馈结构如何塑造整体大小动态，包括细胞周期内和跨细胞周期。我们关注一个模型，其中细胞大小对这些其他过程的反馈仅通过大小偏差发生，定义为绝对大小与细胞周期进展的差值。在该模型的线性区域内，动态减少为由随机力驱动的简单谐振子，从而得到母子大小相关性的闭合表达式。我们将这些结果与更高阶回归系数进行比较，这些系数测量了在多个世代中大小记忆的强度。我们的分析揭示了细胞周期时间和内在波动之间的相互作用如何塑造看似粗粒化的大小控制策略，并特别指出粗粒化相关性可能不反映机制反馈结构。我们将此模型与更常用的方法进行比较，后者将粗粒化动态硬编码到模型中；因此，一阶自回归模型对大小动态的描述是完美的，因此更准确地反映了反馈结构。
英文摘要：
Within a continuous-time, stochastic model of single-cell size homeostasis, we study how the structure of feedback from size to growth rates and cell-cycle progression shapes overall size dynamics, both within and across cell cycles. We focus on a model in which the feedback from cell size to these other processes occurs only through the size deviations, defined as the difference between the absolute size and the progression through the cell cycle. In a linear regime of this model, the dynamics reduce to a stochastically forced simple harmonic oscillator, yielding closed-form expressions for mother-daughter size correlations. We compare these to the higher order regression coefficients that measure the size memory over many generations. Our analysis reveals how the interplay between cell-cycle timing and intrinsic fluctuations shapes the apparent coarse-grained size control strategy, and in-particular, that coarse-grained correlations may not reflect the mechanistic feedback structure. We compare this model to a more commonly used approach where the coarse-grained dynamics are hard-coded into the model; hence, the first order autoregressive model for sizes is a perfect description of the size dynamics and therefore more accurately reflects the feedback structure.
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