New Method Reduces Probabilistic Chemical Reaction Networks

Programming adaptive behaviors at the cellular level using biochemical systems, often modeled as Chemical Reaction Networks (CRNs), is a challenging goal. While CRNs can realize complex probabilistic models like hidden Markov models and factor graphs, their implementation typically requires prohibitively large networks. Existing CRN reduction techniques are often not directly applicable to these probabilistic systems. This new research addresses the issue by recovering the underlying factor graph structure encoded in Napp-Adams-compiled CRNs. By transporting recent factor-graph reduction results to their chemical implementations, the method achieves significantly smaller CRNs. Crucially, this reduction preserves the belief-propagation fixed points on the surviving variables, making the realization of probabilistic computation in biochemical systems more practical and scalable.