Organisms maintain the status quo, holding key physiological variables constant to within an acceptable tolerance, and yet adapt with precision and plasticity to dynamic changes in externalities. What organizational principles ensure such exquisite yet robust control of systems-level “state variables” in complex systems with an extraordinary number of moving parts and fluctuating variables? Here, we focus on these issues in the specific context of intra- and intergenerational life histories of individual bacterial cells, whose biographies are precisely charted via high-precision dynamic experiments using the SChemostat technology. We highlight intra- and intergenerational scaling laws and other “emergent simplicities” revealed by these high-precision data. In turn, these facilitate a principled route to dimensional reduction of the problem and serve as essential building blocks for phenomenological and mechanistic theory. Parameter-free data-theory matches for multiple organisms validate theory frameworks and explicate the systems physics of stochastic homeostasis and adaptation.

中文翻译：

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单个细胞生活历史中的新兴简化性


生物体维持现状，将关键生理变量保持在可接受的容忍范围内，同时又能精确和可塑地适应外部因素的动态变化。在具有大量活动部件和波动变量的复杂系统中，什么样的组织原则确保了对系统级“状态变量”的如此精细而稳健的控制？在这里，我们在单个细菌细胞的代内和代际生活史的特定背景下关注这些问题，其传记通过使用 SChemostat 技术的高精度动态实验精确绘制。我们强调了这些高精度数据揭示的代内和代际缩放定律和其他“新兴的简单性”。反过来，这些促进了问题降维的原则性路线，并成为现象学和机械论的重要组成部分。多种生物体的无参数数据理论匹配验证了理论框架并阐明了随机稳态和适应的系统物理学。