细胞命运特化过程中的动态景观与生长统计极限
Dynamic landscapes and statistical limits on growth during cell fate specification
AI总结：
本研究针对Waddington细胞分化势景观隐喻的数学精确性问题，证明生长最大化调控策略可由含时势景观描述，给出种群生长时间边界，搭建非平衡热力学与发育细胞决策的概念桥梁。
摘要：
多细胞生物中基因调控网络的复杂性使得可解释的低维模型极具研究价值。一种归功于Waddington的颇具吸引力的几何图景，将细胞分化为多种功能类型的过程可视化为动态势景观上的梯度流。然而，目前尚不清楚这一隐喻在何种生物学约束下具备数学上的精确性。本研究表明，在某些通用的生长-调控权衡条件下，引导单个细胞发育为目标细胞类型分布的生长最大化调控策略，可由含时势景观描述。我们的分析得出了种群生长至特定规模的目标分布所需时间的严格边界。我们在一个生长与分化的示例模型中，展示了如何利用该框架计算调控策略与生长曲线。该理论为非平衡热力学与发育过程中的细胞决策建立了概念层面的联系。
英文摘要：
The complexity of gene regulatory networks in multicellular organisms makes interpretable low-dimensional models highly desirable. An attractive geometric picture, attributed to Waddington, visualizes the differentiation of a cell into diverse functional types as gradient flow on a dynamic potential landscape. However, it is unclear under what biological constraints this metaphor is mathematically precise. Here, we show that growth-maximizing regulatory strategies that guide a single cell to a target distribution of cell types are described by time-dependent potential landscapes under certain generic growth-control tradeoffs. Our analysis leads to a sharp bound on the time it takes for a population to grow to a target distribution of a certain size. We show how the framework can be used to compute regulatory strategies and growth curves in an illustrative model of growth and differentiation. The theory suggests a conceptual link between nonequilibrium thermodynamics and cellular decision-making during development.
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