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

@@ -152,7 +152,7 @@ plot(plt_1, plt_2, size=(1200,400))
 ```
 We note that the copy numbers $Pₐ$ are highly stochastic. Meanwhile, $M$ exists in so large copy numbers that its trajectory can be considered approximately deterministic. For models like this one, jump simulations are required to capture the stochastic behaviour of the promoter. However, the large number of jumps generated by $M$ makes such simulations excessively expensive. Here, *hybrid simulations* can be used. Hybrid simulations employ different simulation strategies for their different components. In our example, the state of the promoter could be simulated using a jump simulation, while $M$ could be simulated using an ODE.
 
-Hybrid simulations for Catalyst models are currently not supported. However, it is a work in progress. If you require such simulations, please [raise an issue](https://github.com/SciML/Catalyst.jl/issues) and we can notify you of the current state of our implementation. Alternatively, other CRN modelling tools that support hybrid simulations exist.
+Hybrid simulations for Catalyst models are currently not supported, though they are supported by the lower-level solvers in JumpProcesses and DifferentialEquations. It is work in progress to interface Catalyst to such lower-level solvers, and to provide better optimized hybrid methods. If you require such simulations, please [raise an issue](https://github.com/SciML/Catalyst.jl/issues) and we can notify you of the current state of our implementation, or give suggestions on how one can directly interface with the lower-level hybrid solver interface.
 
 
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