removed core model from tests that use OSQP

docs/src/notebooks/2_finding_balance.jl

@@ -155,6 +155,8 @@ dict_soln = parsimonious_flux_balance_analysis_dict(
     modifications = [
         silence, # silence the optimizer (OSQP is very verbose by default)
         change_constraint("R_EX_glc__D_e"; lb = -12, ub = -12),
+        change_optimizer_attribute("polish", true),
+        change_optimizer_attribute("max-iter", 10_000),
     ],
 )
 
@@ -176,6 +178,7 @@ vec_soln = parsimonious_flux_balance_analysis_vec(
     qp_modifications = [
         change_optimizer(OSQP.Optimizer), # now switch to OSQP (Tulip wouldn't be able to finish the computation)
         change_optimizer_attribute("polish", true), # get an accurate solution, see OSQP's documentation
+        change_optimizer_attribute("max-iter", 10_000),
         silence, # and make it quiet.
     ],
 )
@@ -188,7 +191,11 @@ vec_soln = parsimonious_flux_balance_analysis_vec(
 reference_fluxes = parsimonious_flux_balance_analysis_dict( # reference distribution
     model,
     OSQP.Optimizer;
-    modifications = [silence, change_optimizer_attribute("polish", true)],
+    modifications = [
+        silence,
+        change_optimizer_attribute("polish", true),
+        change_optimizer_attribute("max-iter", 10_000),
+    ],
 )
 
 moma = minimize_metabolic_adjustment_analysis_dict(
@@ -198,6 +205,7 @@ moma = minimize_metabolic_adjustment_analysis_dict(
     modifications = [
         silence,
         change_optimizer_attribute("polish", true),
+        change_optimizer_attribute("max-iter", 10_000),
         change_constraint("R_CYTBD"; lb = 0.0, ub = 0.0), # find flux distribution closest to the CYTBD knockout
     ],
 )
@@ -248,6 +256,7 @@ moment_moma = minimize_metabolic_adjustment_analysis_dict(
     modifications = [
         silence,
         change_optimizer_attribute("polish", true),
+        change_optimizer_attribute("max-iter", 10_000),
         change_constraint("EX_glc__D_e", lb = -1000),
         change_constraint("CYTBD"; lb = 0, ub = 0),
         add_moment_constraints(ksas, protein_mass_fraction;),

test/analysis/minimize_metabolic_adjustment.jl

 @testset "MOMA" begin
-    model = load_model(StandardModel, model_paths["e_coli_core.json"])
+    model = test_toyModel()
 
-    sol = parsimonious_flux_balance_analysis_dict(
-        model,
-        OSQP.Optimizer;
-        modifications = [silence, change_optimizer_attribute("polish", true)],
-    )
+    sol = [looks_like_biomass_reaction(rid) ? 0.5 : 0.0 for rid in reactions(model)]
 
     moma = minimize_metabolic_adjustment_analysis_dict(
         model,
@@ -14,13 +10,13 @@
         modifications = [
             silence,
             change_optimizer_attribute("polish", true),
-            change_constraint("CYTBD"; lb = 0.0, ub = 0.0),
+            change_optimizer_attribute("max-iter", 100_000),
         ],
     )
 
     @test isapprox(
-        moma["BIOMASS_Ecoli_core_w_GAM"],
-        0.06214149238730545,
+        moma["biomass1"],
+        0.07692307692307691,
         atol = QP_TEST_TOLERANCE,
     )
 end

test/analysis/parsimonious_flux_balance_analysis.jl

 @testset "Parsimonious flux balance analysis with StandardModel" begin
-    model = load_model(StandardModel, model_paths["e_coli_core.json"])
+    model = test_toyModel()
+
     d = parsimonious_flux_balance_analysis_dict(
         model,
         Tulip.Optimizer;
         modifications = [
-            change_constraint("EX_glc__D_e"; lb = -12, ub = -12),
+            change_constraint("EX_m1(e)", lb=-10.0),
             change_optimizer_attribute("IPM_IterationsLimit", 500),
         ],
         qp_modifications = [
@@ -17,5 +18,5 @@
 
     # The used optimizer doesn't really converge to the same answer everytime
     # here, we therefore tolerate a wide range of results.
-    @test isapprox(d["PGM"], -17.606459419216442, atol = QP_TEST_TOLERANCE)
+    @test isapprox(d["biomass1"], 10.0, atol = QP_TEST_TOLERANCE)
 end