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 don't apply. By embedding CRNs into factor graphs in a structure- and dynamics-preserving manner, we leverage recent factor-graph reduction results to obtain significantly smaller CRNs, a gain we demonstrate numerically.