Fixed rate laws in rna degradation and transcription

tests/rand_gen/test_rna_degradation.py

@@ -95,17 +95,17 @@ def test(self):
             rnas[0].get_len() - 1)
 
         # check rate laws
-        '''for rna, rxn in zip(rnas, submodel.reactions):
+        deg_rnase = model.observables.get_one(id='deg_rnase_obs')
+        deg_rnase = deg_rnase.species[0].species.species_type
+        deg_avg_conc = 5000/scipy.constants.Avogadro / cytosol.initial_volume
+        for rna, rxn in zip(rnas, submodel.reactions):
             self.assertEqual(len(rxn.rate_laws), 1)
             rl = rxn.rate_laws[0]
             self.assertEqual(rl.direction.name, 'forward')
             self.assertEqual(rl.equation.expression,
-                             'k_cat * {0}[c] / (k_m + {0}[c])'.format(rna.id))
-            self.assertEqual(rl.equation.modifiers, [
-                             rxn.participants[0].species])
+                '{0}[c] * (((k_cat * {1}[c]) / (k_m + {1}[c])) + {2})'.format(rna.id, deg_rnase.id, '0.1'))
+            self.assertEqual(rl.equation.modifiers, [deg_rnase.species.get_one(compartment=cytosol), rxn.participants[0].species])
             self.assertEqual(rl.equation.parameters, [])
-            self.assertEqual(rl.k_m, rna.concentration)
+            self.assertEqual(rl.k_m,deg_avg_conc)
+            self.assertEqual(rl.k_cat, 2 * numpy.log(2) / rna.half_life)
 
-        k_cats = [rxn.rate_laws[0].k_cat for rxn in submodel.reactions]
-        numpy.testing.assert_almost_equal(numpy.mean(
-            k_cats), 2. * numpy.log(2.) * (1. / 120.), decimal=2)'''

tests/rand_gen/test_transcription.py

@@ -98,16 +98,16 @@ def test(self):
             + submodel.reactions[0].participants.get_one(species=h).coefficient,
             -(rnas[0].get_len() - 1))
 
+        rna_poly = model.observables.get_one(
+            id='rna_poly_obs')
+        rna_poly = rna_poly.species[0].species
+        poly_avg_conc = 3000/scipy.constants.Avogadro / cytosol.initial_volume #http://bionumbers.hms.harvard.edu/bionumber.aspx?s=n&v=2&id=106199
         # check rate laws
         for rxn in submodel.reactions:
             self.assertEqual(len(rxn.rate_laws), 1)
             rl = rxn.rate_laws[0]
             self.assertEqual(rl.direction.name, 'forward')
-            self.assertEqual(rl.equation.expression, 'k_cat')
-            self.assertEqual(rl.equation.modifiers, [])
+            self.assertEqual(rl.equation.expression, '(((k_cat * rna_poly[c]) / (k_m + rna_poly[c])))')
+            self.assertEqual(rl.equation.modifiers, [rna_poly])
             self.assertEqual(rl.equation.parameters, [])
-            numpy.testing.assert_equal(rl.k_m, float('nan'))
-
-        k_cats = [rxn.rate_laws[0].k_cat for rxn in submodel.reactions]
-        numpy.testing.assert_almost_equal(numpy.mean(
-            k_cats), 10. * numpy.log(2.) * (1. / 120.), decimal=2)
+            numpy.testing.assert_equal(rl.k_m, poly_avg_conc)

wc_model_gen/rand_gen/transcription.py

@@ -130,26 +130,25 @@ def gen_reactions(self):
 
     def gen_rate_laws(self):
         """ Generate rate laws associated with submodel """
+        model = self.model
         cell = self.knowledge_base.cell
+        cytosol = model.compartments.get_one(id='c')
 
         mean_volume = cell.properties.get_one(id='mean_volume').value
         mean_doubling_time = cell.properties.get_one(id='mean_doubling_time').value
-
-        equation = None
-        for rxn in self.model.get_reactions():
-            for rl in rxn.rate_laws:
-                if rl.equation.expression == 'k_cat':
-                    equation = rl.equation
-                    break
-            if equation is not None:
-                break
-        if equation is None:
-            equation = wc_lang.RateLawEquation(expression='k_cat')
-
+        poly_avg_conc = 3000/scipy.constants.Avogadro / cytosol.initial_volume #http://bionumbers.hms.harvard.edu/bionumber.aspx?s=n&v=2&id=106199
+        rna_poly = self.model.observables.get_one(
+            id='rna_poly_obs')
+        rna_poly = rna_poly.species[0].species.species_type
+        exp='(((k_cat * {}[c]) / (k_m + {}[c])))'.format(rna_poly.id, rna_poly.id)
+        equation = wc_lang.RateLawEquation(expression = exp)
+
         rnas = cell.species_types.get(__type=wc_kb.RnaSpeciesType)
         for rna, rxn in zip(rnas, self.submodel.reactions):
             rl = rxn.rate_laws.create()
-            rl.direction = wc_lang.RateLawDirection.forward
             rl.equation = equation
-            rl.k_cat = rna.concentration * scipy.constants.Avogadro * mean_volume * numpy.log(2) * (1 / rna.half_life)
-            rl.k_m = float('nan')
+            rl.direction = wc_lang.RateLawDirection.forward
+            rl.k_cat = 2 * (numpy.log(2) / rna.half_life + numpy.log(2) / mean_doubling_time)
+            rl.k_m = poly_avg_conc
+            rl.equation.modifiers.append(
+                rna_poly.species.get_one(compartment=cytosol))