Update docs/src/catalyst_applications/jump_simulation_performance.md

Co-authored-by: Sam Isaacson <[email protected]>

Author: TorkelE

docs/src/catalyst_applications/jump_simulation_performance.md

@@ -1,5 +1,5 @@
 # [Advice for performant jump simulations](@id jump_simulation_performance)
-We have previously described how to perform *jump simulations* of *chemical reaction network* (CRN) models using e.g. Gillespie's algorithm. These simulations can, however, be highly computationally intensive. Fortunately, there are several ways to increase their performance (thus reducing runtime). Here, we describe various considerations for performant jump simulations. Furthermore, all jump simulations of Catalyst models are performed using the JumpProcesses.jl package, [which documentation](https://github.com/SciML/JumpProcesses.jl) provides a more extensive description of these.
+We have previously described how to perform simulations of stochastic chemical kinetics *chemical reaction network* (CRN) jump process models using e.g. Gillespie's algorithm. These simulations can, however, be highly computationally intensive. Fortunately, there are several ways to increase their performance (thus reducing runtime). Here, we describe various considerations for performant stochastic chemical kinetics simulations, which we will subsequently refer to as jump process simulations. All jump process simulations arising from stochastic chemical kinetics representations of Catalyst models are performed using stochastic simulation algorithms (SSAs) from JumpProcesses.jl. Please see the [JumpProcesses documentation](https://github.com/SciML/JumpProcesses.jl) for a more extensive introduction to the package and the available solvers.
 
 #### Brief (and optional) introduction to jump simulations
 Jump simulations are continuous time, discrete space, simulations (where the system's state, throughout the simulation, are the discrete copy numbers of each species). The system's state changes at discrete time points (in so-called jumps). For CRNs, these jumps correspond to the occurrence of individual reactions. Here, *stochastic chemical kinetics* describes how the reactions are distributed across time. Typically, the frequency of each reaction depends on its *propensity* (which in turn depends on its *rate* and *substrates*). These depend on the state of the system, and must thus be re-computed whenever the system's state changes. Hence, jump simulations' runtimes are heavily dependent on how frequently these propensities must be recomputed.