个体细胞生命史中的涌现简单性
Emergent Simplicities in the Living Histories of Individual Cells
AI总结：
本研究利用SChemostat技术精确记录细菌细胞代内与代间生命史，揭示了标度律等涌现简单性，为降维和构建现象学及机制理论提供基础，并通过无参数数据-理论匹配验证了随机稳态与适应的系统物理学。
摘要：
生物体维持现状，将关键生理变量保持在可接受的容差范围内，同时以精确性和可塑性适应外部环境的动态变化。在具有大量运动部件和波动变量的复杂系统中，什么组织原则能确保对系统级“状态变量”进行如此精妙而稳健的控制？在此，我们聚焦于个体细菌细胞代内与代间生命史的特定背景，这些细胞的传记通过使用SChemostat技术的高精度动态实验被精确记录。我们强调了这些高精度数据所揭示的代内与代间标度律及其他“涌现简单性”。这些规律为问题的降维提供了有原则的途径，并作为现象学与机制理论的基本构建块。针对多种生物体的无参数数据-理论匹配验证了理论框架，并阐明了随机稳态与适应的系统物理学。
英文摘要：
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.
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