(untitled) (Communications in Computational Physics, 2020)
Based on Transition Path Theory (TPT) for Markov jump processes [1, 2], we develop a general approach for identifying and calculating Transition States (TS) of stochastic chemical reacting networks. We first extend the concept of probability current, originally defined on edges connecting different nodes in the configuration space [2], to each sub-network. To locate sub-networks with maximal probability current on the separatrix between reactive and non-reactive events, which will give the Transition States of the reaction, constraint optimization is conducted. We further introduce an alternative scheme to compute the transition pathways by topological sorting, which is shown to be highly efficient through analysis.