temporarily prevent comment issues

docs/src/examples/07a-srba.jl

@@ -516,14 +516,10 @@ res = screen(mus, workers = [1]) do mu
 end
 
 # finally, we can plot the data, to see if we can recapitulate known phenomena
-
 #= TODO
-
 using CairoMakie
-
 # load measured ribosome protein mass fractions
 ribosome_measurements = CSV.File(joinpath(data_root, "ecoli_ribosomes.tsv"))
-
 # First, show that the predicted ribosome density matches experimental
 # observations, and also show that overflow metabolism occurs (latter is due to
 # the membrane bound).
@@ -537,12 +533,10 @@ scatter!(
 )
 lines!(ax, mus, [r.ribosome_mass / r.total_mass for r in res], label = "Model predictions")
 axislegend(ax, position = :lt)
-
 ax2 = Axis(fig[2, 1], xlabel = "Growth rate, 1/h", ylabel = "Metabolite flux")
 lines!(ax2, mus, [r.ac_flux for r in res], label = "Acetate")
 lines!(ax2, mus, [abs(r.glc_flux) for r in res], label = "Glucose")
 lines!(ax2, mus, [abs(r.o2_flux) for r in res], label = "Oxygen")
 axislegend(ax2, position = :lt)
 fig
-
 =#

docs/src/examples/07b-community-ecfba.jl

@@ -439,18 +439,15 @@ wt = simplified_enzyme_constrained_flux_balance_constraints(
 open_bounds_ecfbc!(wt, :EX_glc__D_e)
 fix_bounds_ecfbc!(wt)
 
-eccfba_res = screen(specs. workers = [1]) do spec
+eccfba_res = screen(specs, workers = [1]) do spec
     auxotrophe_fba(wt, spec)
 end
 
 #=
-
 # ## Plot cFBA vs. ec-cFBA vs. experimental data
 # Now that we have simulated the models, we need to compare them to data. The
 # data from the paper is processed below, and then plotted.
-
 using CairoMakie
-
 # data from Mee et al., 2014
 observed_abundances = [ # these pairings were simulated
     (:metA, :thrC, 17.0 / (17.0 + 80.8)),
@@ -464,18 +461,15 @@ observed_abundances = [ # these pairings were simulated
     (:ilvA, :metA, 71.3 / (71.3 + 17.3)),
 ]
 observed_abundances = last.(observed_abundances) # in order of aa_pairs
-
 # get maximum growth for each pairing in both simulations
 cfba_abs = zeros(9) # optimal abundance using cFBA
 eccfba_abs = zeros(9) # optimal abundance using ec-cFBA
 for (i, (ko1, ko2)) in enumerate(aa_pairs)
     x = argmax(x -> x.mu, filter(x -> haskey(x, ko1) && haskey(x, ko2), cfba_res))
     cfba_abs[i] = x[ko1] / (x[ko1] + x[ko2])
-
     x = argmax(x -> x.mu, filter(x -> haskey(x, ko1) && haskey(x, ko2), eccfba_res))
     eccfba_abs[i] = x[ko1] / (x[ko1] + x[ko2])
 end
-
 # The figure below shows that using enzyme constraints dramatically improves the
 # predictive validity of community simulations
 fig = Figure();
@@ -498,7 +492,6 @@ stem!(
 )
 xlims!(ax, 0, 1)
 ylims!(ax, 0, 1)
-
 ax2 = Axis(fig[1, 2], xlabel = "Observed composition", title = "ec-cFBA")
 stem!(
     ax2,
@@ -514,5 +507,4 @@ stem!(
 xlims!(ax2, 0, 1)
 ylims!(ax2, 0, 1)
 fig
-
 =#