Reduction of Probabilistic Chemical Reaction Networks
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Computer Science > Machine Learning
Title:Reduction of Probabilistic Chemical Reaction Networks
Abstract: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.
| Comments: | Accepted to ICML 2026 |
| Subjects: | Machine Learning (cs.LG); Category Theory (math.CT) |
| Cite as: | arXiv:2606.27737 [cs.LG] |
| (or arXiv:2606.27737v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27737
arXiv-issued DOI via DataCite (pending registration)
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