At the mesoscale, reaction networks are described in terms of stochastic processes. In well-stirred solutions, the time evolution is ruled by the chemical master equation for the probability distribution of the random numbers of molecules. The entropy production is obtained for these reactive processes in the framework of stochastic thermodynamics. The entropy production can be decomposed using the Hill–Schnakenberg cycle decomposition in terms of the affinities and the reaction rates of the stoichiometric cycles of the reaction network. The multivariate fluctuation relation is established for the reactive currents. The results are applied to several examples of reaction networks, in particular, describing autocatalytic bistability, noisy chemical clocks, enzymatic kinetics, and copolymerization processes.

At the mesoscale, the fluctuating phenomena are described using the theory of stochastic processes. Depending on the random variables, different stochastic processes can be defined. The properties of stationarity, reversibility, and Markovianity are defined and discussed. The classes of discrete- and continuous-state Markov processes are presented including their master equation, their spectral theory, and their reversibility condition. For discrete-state Markov processes, the entropy production is deduced and the network theory is developed, allowing us to obtain the affinities on the basis of the Hill–Schnakenberg cycle decomposition. Continuous-state Markov processes are described by their master equation, as well as stochastic differential equations. The spectral theory is also considered in the weak-noise limit. Furthermore, Langevin stochastic processes are presented in particular for Brownian motion and their deduction is carried out from the underlying microscopic dynamics.