发表机构
Institute of Industrial Science, The University of Tokyo（东京大学工业科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文为指数增长随机化学反应网络建立大偏差理论，通过分解体积与组成，证明组成涨落满足大偏差原理，并提供零模型解释单细胞异质性。
AI 中文摘要
增长的生化系统本质上是有噪声的，其中一个重要的随机性来源是底层生化网络中的离散反应事件。由于增长系统通常不具有平稳的丰度分布，因此如何将这种内在的化学噪声与群体和单细胞数据中的其他变异性来源区分开来尚不清楚。在此，我们通过将丰度分解为体积和组成，为指数增长随机化学反应网络发展了一种大偏差理论。在这些坐标下，平衡增长对应于稳定的组成以及指数增长的体积。我们证明组成涨落满足一个大偏差原理，其速率等于增长系统的体积，相应的准势由接触哈密顿-雅可比方程的解选择。我们还推导了累积可观测量的涨落理论，并表明它们的协方差随累积体积衰减，而不是随物理时间衰减。对最小自催化网络和粗粒化细胞生长模型的应用展示了该理论如何预测组成准势、增长率涨落以及可观测量之间的相关性。因此，该框架为底层化学反应网络内在随机性产生的单细胞异质性提供了一个随机零模型。
英文摘要
Growing biochemical systems are intrinsically noisy, with one important source of stochasticity arising from the discrete reaction events of the underlying biochemical network. Because growing systems do not generally admit stationary abundance distributions, it is unclear how to separate this intrinsic chemical noise from other sources of variability in population and single-cell data. Here we develop a large-deviation theory for exponentially growing stochastic chemical reaction networks by decomposing abundance into volume and composition. In these coordinates, balanced growth corresponds to a stable composition together with exponentially increasing volume. We show that composition fluctuations satisfy a large-deviation principle with speed equal to the volume of the growing system, and the corresponding quasipotential is selected by a solution of the contact Hamilton--Jacobi equation. We also derive a fluctuation theory for accumulated observables and show that their covariances decay with accumulated-volume, rather than with physical time. Applications to a minimal autocatalytic network and a coarse-grained cellular growth model demonstrate how the theory predicts composition quasipotentials, growth-rate fluctuations, and correlations between observables. The framework therefore provides a stochastic null model for single-cell heterogeneity generated by the intrinsic stochasticity of the underlying chemical reaction network.
Comments13 pages, 6 figures
