Emergent Simplicities in Stochastic Intergenerational Homeostasis

How do complex systems maintain key emergent "state variables" at desired target values to within specified tolerances? This question was first posed in the context of homeostasis in living systems over a century ago, and yet the precise quantitative rules governing this phenomenon have remained fiercely debated. We herein present a direct solution through a synthesis of high-precision experiments and first principles-based physics theory. After introducing a general approach that incorporates the inherently stochastic and dynamic nature of organismal homeostasis, we provide direct experimental evidence that stochastic intergenerational homeostasis is indeed maintained. Next, we identify a series of emergent simplicities hidden in these data. Remarkably, the dynamics of intergenerational homeostasis of organismal sizes are Markovian, or history-independent. The precision data reveal an intergenerational scaling law that fully determines, with no fine-tuning parameters, the exact stochastic map governing homeostasis, as borne out by compelling data- theory matches. These emergent simplicities in turn yield the necessary and sufficient condition for stochastic homeostasis, with surprising implications for the architecture of the underlying control system. Validation across different growth conditions, cell morphologies, experimental modalities, and organisms comprehensively establishes the universality of the results presented here.