Chemical reaction networks (CRN) comprise an important class of models to understand biological functions such as cellular information processing, the robustness and control of metabolic pathways, circadian rhythms, and many more. However, any CRN describing a certain function does not act in isolation but is a part of a much larger network and as such is constantly subject to external changes. In [Shinar, Alon, and Feinberg. "Sensitivity and robustness in chemical reaction networks." SIAM J App Math (2009): 977-998.], the responses of CRN to changes in the linear conserved quantities, called sensitivities, were studied in and the question of how to construct absolute, i.e., basis-independent, sensitivities was raised. In this article, by applying information geometric methods, such a construction is provided. The idea is to track how concentration changes in a particular chemical propagate to changes of all the other chemicals within a steady state. This is encoded in the matrix of absolute sensitivites. A linear algebraic characterization of the matrix of absolute sensitivities for quasi-thermostatic CRN is derived via a Cramer-Rao bound for CRN, which is based on the the analogy between quasi-thermostatic steady states and the exponential family of probability distributions.

翻译：化学反应网络（CRN）是一类重要的模型，用于理解细胞信息处理、代谢通路的稳健性与调控、昼夜节律等生物学功能。然而，任何描述特定功能的CRN并非孤立运作，而是更大网络的一部分，因此不断受到外部变化的影响。在文献[Shinar, Alon, and Feinberg. "Sensitivity and robustness in chemical reaction networks." SIAM J App Math (2009): 977-998]中，研究了CRN对线性守恒量变化的响应（称为灵敏度），并提出了如何构造绝对（即基无关）灵敏度的问题。本文通过应用信息几何方法，给出了这样一种构造。其核心思想是追踪特定化学物质浓度变化如何传播至稳态下所有其他化学物质的变化，这一关系由绝对灵敏度矩阵编码。基于准恒温稳态与指数族概率分布之间的类比，通过化学反应网络的克拉美-劳界，推导出准恒温CRN的绝对灵敏度矩阵的线性代数刻画。