# Information geometric bound on general chemical reaction networks

arXiv: https://arxiv.org/abs/2309.10334 · HTML: https://mlsanity.com/paper/2309.10334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [Accelerated Information Gradient flow](https://mlsanity.com/paper/1909.02102/) (2019)

## Abstract (arXiv)

We investigate the dynamics of chemical reaction networks (CRNs) with the goal of deriving an upper bound on their reaction rates. This task is challenging due to the nonlinear nature and discrete structure inherent in CRNs. To address this, we employ an information geometric approach, using the natural gradient, to develop a nonlinear system that yields an upper bound for CRN dynamics. We validate our approach through numerical simulations, demonstrating faster convergence in a specific class of CRNs. This class is characterized by the number of chemicals, the maximum value of stoichiometric coefficients of the chemical reactions, and the number of reactions. We also compare our method to a conventional approach, showing that the latter cannot provide an upper bound on reaction rates of CRNs. While our study focuses on CRNs, the ubiquity of hypergraphs in fields from natural sciences to engineering suggests that our method may find broader applications, including in information science.