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docs/src/index.md

@@ -29,7 +29,7 @@ etc).
 - Models can be [coupled with events](@ref constraint_equations_events) that affect the system and its state during simulations.
 - By leveraging ModelingToolkit, users have a variety of options for generating optimized system representations to use in solvers. These include construction of [dense or sparse Jacobians](@ref ode_simulation_performance_sparse_jacobian), [multithreading or parallelization of generated derivative functions](@ref ode_simulation_performance_parallelisation), [automatic classification of reactions into optimized jump types for Gillespie type simulations](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#jump_types), [automatic construction of dependency graphs for jump systems](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#Jump-Aggregators-Requiring-Dependency-Graphs), and more.
 - [Symbolics.jl](/~https://github.com/JuliaSymbolics/Symbolics.jl) symbolic expressions and Julia `Expr`s can be obtained for all rate laws and functions determining the deterministic and stochastic terms within resulting ODE, SDE, or jump models.
-- [Steady states](@ref homotopy_continuation) (and their [stabilities](@ref steady_state_stability)) can be computed for model ODE representations.
+- [Steady states](@ref homotopy_continuation) can be computed for model ODE representations.
 
 #### [Features of Catalyst composing with other packages](@id doc_index_features_composed)
 - [OrdinaryDiffEq.jl](/~https://github.com/SciML/OrdinaryDiffEq.jl) Can be used to numerically solve generated reaction rate equation ODE models.