Update docs/src/model_simulation/finite_state_projection_simulation.md

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

Author: TorkelE

docs/src/model_simulation/finite_state_projection_simulation.md

@@ -48,7 +48,7 @@ One can study the dynamics of stochastic chemical kinetics models by simulating
  &\vdots\\
 \end{aligned}
 ```
-We note that, since (for almost all chemical reaction networks) there is a non-zero probability that $X$ is at any specific integer value, the CME have an infinite number of variables (corresponding to the infinite number of potential states). Hence, it cannot be solved practically. However, for high enough species values, the probability of the system attaining such values becomes negligibly small. Here, a truncated version of the CME can be solved practically. An approach for this is the *finite state projection*[^2]. Below we describe how to use the [FiniteStateProjection.jl](https://github.com/SciML/FiniteStateProjection.jl) package to solve the truncated CME (with the package's [documentation](https://docs.sciml.ai/FiniteStateProjection/dev/) providing a more extensive description). While the CEM approach can be very powerful, we note that even for systems with few species, the truncated CME typically have too many states to be feasibly solved.
+For chemical reaction networks in which the total population is bounded, the CME corresponds to a finite set of ODEs. In contrast, for networks in which the system can (in theory) become unbounded, such as networks that include zero order reactions like $\varnothing \to A$, the CME will correspond to an infinite set of ODEs. Even in the finite case, the number of ODEs corresponds to the number of possible state vectors, i.e. vectors with components representing the integer populations of each species in the network. Therefore, for even simple reaction networks there can be many more ODEs than can be represented in typical levels of computer memory, and it becomes infeasible to numerically solve the full system of ODEs that correspond to the CME. However, in many cases the probability of the system attaining species values outside some small range can become negligibly small. Here, a truncated, approximating, version of the CME can be solved practically. An approach for this is the *finite state projection method*[^2]. Below we describe how to use the [FiniteStateProjection.jl](https://github.com/SciML/FiniteStateProjection.jl) package to solve the truncated CME (with the package's [documentation](https://docs.sciml.ai/FiniteStateProjection/dev/) providing a more extensive description). While the CME approach can be very powerful, we note that even for systems with a few species, the truncated CME typically has too many states for it to be feasible to solve the full set of ODEs.
 
 ## [Finite state projection simulation of single-species model](@id state_projection_one_species)
 For this example, we will use a simple [birth-death model](@ref basic_CRN_library_bd), where a single species ($X$) is created and degraded at constant rates ($p$ and $d$, respectively).