Many biological systems can be modeled by a chemical reaction network with unknown parameters through the chemical master equation. Data available to identify these parameters are often in the form of a stationary distribution, such as obtained from single cell measurements in a cell population. In this work, we introduce a framework for analyzing the identifiability of the reaction rate coefficients of stochastic chemical reaction networks from stationary distribution data. Working with the linear noise approximation, which is a diffusive approximation to the chemical master equation, we give a computational procedure to certify global identifiability based on Hilbert's Nullstellensatz. We present a variety of examples that show the applicability of our method to chemical reaction networks of interest in systems and synthetic biology.

中文翻译：

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具有固定分布的内在和外在噪声的化学反应网络的可识别性

许多生物系统可以通过化学主方程由未知参数的化学反应网络建模。可用于识别这些参数的数据通常采用平稳分布的形式，例如从细胞群中的单细胞测量中获得。在这项工作中，我们引入了一个框架，用于从平稳分布数据中分析随机化学反应网络的反应速率系数的可识别性。使用线性噪声近似，它是化学主方程的扩散近似，我们给出了一个计算程序来证明基于 Hilbert 的 Nullstellensatz 的全局可识别性。我们提供了各种示例，展示了我们的方法对系统和合成生物学中感兴趣的化学反应网络的适用性。