Reduction of Probabilistic Chemical Reaction Networks
This work provides a method to reduce the size of CRNs for probabilistic computation, which is important for cellular-level programming but is incremental as it adapts existing graph reduction techniques.
The authors address the problem of large size in chemical reaction networks (CRNs) that implement probabilistic models. They apply factor-graph reduction techniques to Napp–Adams-compiled CRNs, achieving significantly smaller networks while preserving belief-propagation fixed points on remaining variables.
Programming adaptive behaviors at the cellular level is a long-standing goal that raises the question of how probabilistic computation can be implemented in biochemical systems. Chemical reaction networks (CRNs) provide such a substrate and have been shown to realize probabilistic models, including hidden Markov models and factor graphs, with dynamics reproducing Bayesian inference and belief propagation. However, encoding these algorithms typically requires prohibitively large reaction networks, and classical CRN reduction techniques do not directly apply. By recovering the factor graph structure encoded in Napp--Adams-compiled CRNs, we transport recent factor-graph reduction results to their chemical implementations, obtaining significantly smaller CRNs while preserving the belief-propagation fixed points on surviving variables.