通过因子图压缩技术，大幅缩小生化反应网络规模，同时保持推理准确性。
Reduction of Probabilistic Chemical Reaction Networks
- 利用因子图结构优化生化反应网络，实现高效压缩。
- 压缩后网络规模显著减小，但保留原始推理结果的固定点。
- 适合研究生物计算与系统生物学中的简化模型设计。
在细胞层面编程自适应行为是长期目标，关键在于如何在生化系统中实现概率计算。化学反应网络（CRNs）为此提供了基础，已成功实现隐马尔可夫模型和因子图等概率模型，其动态可模拟贝叶斯推断与信念传播。然而，传统编码方式通常需要极大规模的反应网络，且经典缩减方法不适用。本文通过恢复Napp--Adams-compiled CRNs中的因子图结构，将近期因子图缩减成果迁移至其化学实现，得到显著更小的CRNs，同时在剩余变量上保持信念传播的固定点。
原文摘要 · Abstract (English)
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.
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