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docs/src/introduction_to_catalyst/introduction_to_catalyst.md

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`oprob` and `oprob2` are functionally equivalent, each representing the same
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underlying problem.
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!!! note
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Above we have used `odesys = complete(odesys)` to mark the `ODESystem` as *complete*, indicating to Catalyst and ModelingToolkit that these models are finalized. This must be done before any system is given as input to a `convert` call or some problem type. `ReactionSystem` models created through the `@reaction_network` DSL, like `rn` above, are always marked as complete when generated. Hence `complete` does not need to be called on them. Symbolically generated `ReactionSystem`s, `ReactionSystem`s generated via the `@network_component` macro, and any ModelingToolkit system generated by `convert` always needs to be manually marked as `complete` as we do for `odesys` above. An expanded description on *completeness* can be found [here](@ref completeness_note).
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At this point we are all set to solve the ODEs. We can now use any ODE solver
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from within the
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[OrdinaryDiffEq.jl](https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/)
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choices of ODE solvers, see the [OrdinaryDiffEq.jl
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documentation](https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/).
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!!! note
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In the above example we used `odesys = complete(odesys)` to mark the `ODESystem` as *complete*, indicating to Catalyst and ModelingToolkit that these models are finalized. This must be done before any system is given as input to a `convert` call or some problem type. `ReactionSystem` models created through the `@reaction_network` DSL, like `rn` above, are always marked as complete when generated. Hence `complete` does not need to be called on them. Symbolically generated `ReactionSystem`s, `ReactionSystem`s generated via the `@network_component` macro, and any ModelingToolkit system generated by `convert` always needs to be manually marked as `complete` as we do for `odesys` above. An expanded description on *completeness* can be found [here](@ref completeness_note).
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## Stochastic simulation algorithms (SSAs) for stochastic chemical kinetics

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