up

Author: TorkelE

docs/pages.jl

@@ -12,7 +12,7 @@ pages = Any[
         #"model_creation/compositional_modeling.md",
         #"model_creation/constraint_equations.md",
         # Events.
-        #"model_creation/parametric_stoichiometry.md",# Distributed parameters, rates, and initial conditions.
+        "model_creation/parametric_stoichiometry.md",# Distributed parameters, rates, and initial conditions.
         # Loading and writing models to files.
         "model_creation/model_visualisation.md",
         #"model_creation/network_analysis.md",

docs/src/model_creation/parametric_stoichiometry.md

@@ -7,7 +7,7 @@ use symbolic stoichiometries, and discuss several caveats to be aware of.
 Let's first consider a simple reversible reaction where the number of reactants
 is a parameter, and the number of products is the product of two parameters.
 ```@example s1
-using Catalyst, Latexify, DifferentialEquations, ModelingToolkit, Plots
+using Catalyst, Latexify, OrdinaryDiffEq, ModelingToolkit, Plots
 revsys = @reaction_network revsys begin
     k₊, m*A --> (m*n)*B
     k₋, B --> A
@@ -141,6 +141,7 @@ The parameter `b` does not need to be explicitly declared in the
 
 We next convert our network to a jump process representation
 ```@example s1
+using JumpProcesses
 jsys = convert(JumpSystem, burstyrn; combinatoric_ratelaws = false)
 jsys = complete(jsys)
 equations(jsys)