Merge branch 'main' of https://github.com/biosustain/Maud

.github/workflows/run_tox.yml

@@ -23,15 +23,15 @@ jobs:
 
     - uses: actions/checkout@v2
 
-    - name: Set up Python 3.9
+    - name: Set up Python 3.10
       uses: actions/setup-python@v2
       with:
-        python-version: '3.9'
+        python-version: '3.10'
 
-    - name: Set up Python 3.10
+    - name: Set up Python 3.11
       uses: actions/setup-python@v2
       with:
-        python-version: '3.10'
+        python-version: '3.11'
 
     - name: Install build dependencies
       run: |

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
     -   id: check-merge-conflict
     -   id: detect-private-key
 -   repo: https://github.com/psf/black
-    rev: 22.10.0
+    rev: 24.3.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort
@@ -19,6 +19,6 @@ repos:
         name: isort (python)
 - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
-  rev: v0.0.278
+  rev: v0.3.4
   hooks:
     - id: ruff

docs/getting_started.md

@@ -4,7 +4,7 @@ This page explains how to install Maud and use its command line interface.
 
 ## Installing Maud
 
-Maud is compatible with Python versions 3.9 and above
+Maud is compatible with Python versions 3.10 and above.
 
 We recommend using a fresh virtual environment to install Maud. To make one and
 then activate it, run the following commands:

maud/data/example_inputs/linear_multidgf/config.toml

@@ -0,0 +1,24 @@
+name = "linear_multidgf"
+kinetic_model_file = "kinetic_model.toml"
+priors_file = "priors.toml"
+experiments_file = "experiments.toml"
+likelihood = true
+steady_state_threshold_abs = 1e-6
+
+[cmdstanpy_config]
+refresh = 1
+iter_warmup = 300
+iter_sampling = 300
+chains = 4
+save_warmup = true
+seed = 1234
+
+[ode_solver_config]
+abs_tol = 1e-4
+rel_tol = 1e-6
+max_num_steps = 1e6
+
+[algebra_solver_config]
+abs_tol = 1e-4
+rel_tol = 1e-6
+max_num_steps = 1e6

maud/data/example_inputs/linear_multidgf/experiments.toml

@@ -0,0 +1,32 @@
+[[experiment]]
+id = "condition1"
+is_train = true
+is_test = true
+temperature = 299.0
+measurements = [
+  {target_type = "mic", metabolite = "M1", compartment = "c", value = 0.59, error_scale = 0.1},
+  {target_type = "mic", metabolite = "M1", compartment = "e", value = 1.09, error_scale = 0.05},
+  {target_type = "mic", metabolite = "M2", compartment = "e", value = 1.05, error_scale = 0.05},
+  {target_type = "flux", reaction = "r3", value = 0.19, error_scale = 0.1},
+  {target_type = "enzyme", enzyme = "r1", value = 1.5, error_scale = 0.1},
+  {target_type = "enzyme", enzyme = "r2", value = 1.5, error_scale = 0.1},
+  {target_type = "enzyme", enzyme = "r3", value = 1.5, error_scale = 0.1},
+]
+initial_state = [
+  {metabolite = "M2", compartment = "c", value = 0.38},
+]
+
+[[experiment]]
+id = "condition2"
+is_train = true
+is_test = true
+measurements = [
+  {target_type = "mic", metabolite = "M1", compartment = "c", value = 0.54, error_scale = 0.1},
+  {target_type = "mic", metabolite = "M2", compartment = "c", value = 0.38, error_scale = 0.1},
+  {target_type = "mic", metabolite = "M1", compartment = "e", value = 1.12, error_scale = 0.05},
+  {target_type = "mic", metabolite = "M2", compartment = "e", value = 1.14, error_scale = 0.05},
+  {target_type = "flux", reaction = "r3", value = 0.39, error_scale = 0.1},
+  {target_type = "enzyme", enzyme = "r1", value = 1.5, error_scale = 0.1},
+  {target_type = "enzyme", enzyme = "r2", value = 1.5, error_scale = 0.1},
+  {target_type = "enzyme", enzyme = "r3", value = 1.5, error_scale = 0.1},
+]

maud/data/example_inputs/linear_multidgf/kinetic_model.toml

@@ -0,0 +1,66 @@
+name = "Example kinetic model"
+
+compartment = [
+  {id = 'c', name = 'cytosol', volume = 1},
+  {id = 'e', name = 'external', volume = 1},
+]
+
+metabolite = [
+  {id = "M1", name = "Metabolite number 1"},
+  {id = "M2", name = "Metabolite number 2"},
+]
+
+enzyme = [
+  {id = "r1", name = "r1ase", subunits = 1},
+  {id = "r2", name = "r2ase", subunits = 1},
+  {id = "r3", name = "r3ase", subunits = 1},
+]
+
+metabolite_in_compartment = [
+  {metabolite_id = "M1", compartment_id = "e", balanced = false},
+  {metabolite_id = "M1", compartment_id = "c", balanced = true},
+  {metabolite_id = "M2", compartment_id = "c", balanced = true},
+  {metabolite_id = "M2", compartment_id = "e", balanced = false},
+]
+enzyme_reaction = [
+  {enzyme_id = "r1", reaction_id = "r1"},
+  {enzyme_id = "r2", reaction_id = "r2"},
+  {enzyme_id = "r3", reaction_id = "r3"},
+]
+
+[[reaction]]
+id = "r1"
+name = "Reaction number 1"
+mechanism = "reversible_michaelis_menten"
+stoichiometry = { M1_e = -1, M1_c = 1}
+
+[[reaction]]
+id = "r2"
+name = "Reaction number 2"
+mechanism = "irreversible_michaelis_menten"
+stoichiometry = { M1_c = -1, M2_c = 1}
+
+[[reaction]]
+id = "r3"
+name = "Reaction number 3"
+mechanism = "reversible_michaelis_menten"
+stoichiometry = { M2_c = -1, M2_e = 1}
+transported_charge = 1
+
+[[allostery]]
+enzyme_id = "r1"
+metabolite_id = "M2"
+compartment_id = "c"
+modification_type = "activation"
+
+[[allostery]]
+enzyme_id = "r2"
+metabolite_id = "M1"
+compartment_id = "c"
+modification_type = "inhibition"
+
+[[competitive_inhibition]]
+enzyme_id = "r2"
+reaction_id = "r2"
+metabolite_id = "M1"
+compartment_id = "c"

maud/data/example_inputs/linear_multidgf/parameters.toml

@@ -0,0 +1,37 @@
+kcat = [
+  {exploc = 1, scale = 0.6, reaction = "r1", enzyme = "r1"},
+  {exploc = 1, scale = 0.6, reaction = "r2", enzyme = "r2"},
+  {exploc = 1, scale = 0.6, reaction = "r3", enzyme = "r3"},
+]
+
+km = [
+  {exploc = 1, scale = 0.6, metabolite = "M1", compartment = "e", enzyme = "r1"},
+  {exploc = 1, scale = 0.6, metabolite = "M1", compartment = "c", enzyme = "r1"},
+  {exploc = 1, scale = 0.6, metabolite = "M1", compartment = "c", enzyme = "r2"},
+  {exploc = 1, scale = 0.6, metabolite = "M2", compartment = "c", enzyme = "r3"},
+  {exploc = 1, scale = 0.6, metabolite = "M2", compartment = "e", enzyme = "r3"},
+]
+
+transfer_constant = [
+  {exploc = 1, scale = 0.6, enzyme = "r1"},
+  {exploc = 1, scale = 0.6, enzyme = "r2"},
+]
+
+dissociation_constant = [
+  {exploc = 1, scale = 0.6, enzyme = "r1", metabolite = "M2", compartment = "c", modification_type = "activation"},
+  {exploc = 1, scale = 0.6, enzyme = "r2", metabolite = "M1", compartment = "c", modification_type = "inhibition"},
+]
+
+psi = [
+  {location = -0.95, scale = 0.2, experiment = "condition1"},
+  {location = -0.95, scale = 0.2, experiment = "condition2"},
+]
+
+ki = [
+  {exploc = 1, scale = 0.6, enzyme = "r2", reaction = "r2", metabolite = "M1", compartment = "c"},
+]
+
+[dgf]
+ids = [ "M1", "M2" ]
+mean_vector = [-1, -2]
+covariance_matrix = [ [0.5, 0.5], [0.5, 0.5] ]

maud/data/example_inputs/linear_multidgf/priors.toml

@@ -0,0 +1,37 @@
+kcat = [
+  {exploc = 1, scale = 0.6, reaction = "r1", enzyme = "r1"},
+  {exploc = 1, scale = 0.6, reaction = "r2", enzyme = "r2"},
+  {exploc = 1, scale = 0.6, reaction = "r3", enzyme = "r3"},
+]
+
+km = [
+  {exploc = 1, scale = 0.6, metabolite = "M1", compartment = "e", enzyme = "r1"},
+  {exploc = 1, scale = 0.6, metabolite = "M1", compartment = "c", enzyme = "r1"},
+  {exploc = 1, scale = 0.6, metabolite = "M1", compartment = "c", enzyme = "r2"},
+  {exploc = 1, scale = 0.6, metabolite = "M2", compartment = "c", enzyme = "r3"},
+  {exploc = 1, scale = 0.6, metabolite = "M2", compartment = "e", enzyme = "r3"},
+]
+
+transfer_constant = [
+  {exploc = 1, scale = 0.6, enzyme = "r1"},
+  {exploc = 1, scale = 0.6, enzyme = "r2"},
+]
+
+dissociation_constant = [
+  {exploc = 1, scale = 0.6, enzyme = "r1", metabolite = "M2", compartment = "c", modification_type = "activation"},
+  {exploc = 1, scale = 0.6, enzyme = "r2", metabolite = "M1", compartment = "c", modification_type = "inhibition"},
+]
+
+psi = [
+  {location = -0.95, scale = 0.2, experiment = "condition1"},
+  {location = -0.95, scale = 0.2, experiment = "condition2"},
+]
+
+ki = [
+  {exploc = 1, scale = 0.6, enzyme = "r2", reaction = "r2", metabolite = "M1", compartment = "c"},
+]
+
+[dgf]
+ids = [ "M1", "M2" ]
+mean_vector = [ -10, -32]
+covariance_matrix = [ [5,2], [2,4] ]

maud/data_model/maud_input.py

@@ -70,9 +70,9 @@ def inits_dict(self) -> Dict:
             inits_dict[param.name] = param.inits.inits_unscaled
             if param.inits.inits_scaled is not None:
                 scaled_pref = "log_" if param.non_negative else ""
-                inits_dict[
-                    scaled_pref + param.name + "_z"
-                ] = param.inits.inits_scaled
+                inits_dict[scaled_pref + param.name + "_z"] = (
+                    param.inits.inits_scaled
+                )
             if param.fixed_ids is not None:
                 met_to_init = dict(
                     zip(param.inits.ids[0], param.inits.inits_unscaled)
@@ -82,4 +82,6 @@ def inits_dict(self) -> Dict:
                     for met, init in met_to_init.items()
                     if met not in param.fixed_ids[0]
                 ]
+            elif param.fixable:
+                inits_dict[param.name + "_free"] = param.inits.inits_unscaled
         return inits_dict

maud/data_model/maud_parameter.py

@@ -31,12 +31,13 @@ class MaudParameter(BaseModel):
     init_input: Optional[List[InitAtomInput]]
     ids: List[List[str]]
     split_ids: List[List[List[str]]]
+    fixable: bool = False
     measurements: Optional[List[Measurement]] = None
 
     @computed_field
     def fixed_ids(self) -> Optional[List[List[str]]]:
         """Set the fixed_ids field."""
-        if self.name != "dgf":
+        if not self.fixable:
             return None
         elif self.user_input is None:
             return None
@@ -60,7 +61,7 @@ def fixed_ids(self) -> Optional[List[List[str]]]:
     @computed_field
     def fixed_values(self) -> Optional[List[List[float]]]:
         """Set the fixed_values field."""
-        if self.name != "dgf":
+        if not self.fixable:
             return None
         elif self.user_input is None:
             return None
@@ -244,6 +245,7 @@ class Dgf(MaudParameter):
     default_scale: float = 10
     prior_in_test_model: bool = False
     prior_in_train_model: bool = True
+    fixable: bool = True
 
 
 class DissociationConstant(MaudParameter):

maud/getting_idatas.py

@@ -1,4 +1,5 @@
 """Functions for creating InferenceData objects from Maud outputs."""
+
 from typing import List
 
 import arviz as az
@@ -36,31 +37,39 @@ def get_idata(csvs: List[str], mi: MaudInput, mode: str) -> az.InferenceData:
         "metabolites": [m.id for m in mi.kinetic_model.metabolites],
         "mics": [m.id for m in mi.kinetic_model.mics],
         "edges": [e.id for e in mi.kinetic_model.edges],
+        "edges1": [e.id for e in mi.kinetic_model.edges],
         "unbalanced_mics": [
             m.id for m in mi.kinetic_model.mics if not m.balanced
         ],
         "balanced_mics": [m.id for m in mi.kinetic_model.mics if m.balanced],
-        "phosphorylations": [p.id for p in mi.kinetic_model.phosphorylations]
-        if mi.kinetic_model.phosphorylations is not None
-        else [],
-        "phosphorylation_modifying_enzymes": [
-            pme.id for pme in mi.kinetic_model.phosphorylation_modifying_enzymes
-        ]
-        if mi.kinetic_model.phosphorylation_modifying_enzymes is not None
-        else [],
-        "allosteries": [p.id for p in mi.kinetic_model.allosteries]
-        if mi.kinetic_model.allosteries is not None
-        else [],
-        "allosteric_enzymes": [
-            e.id for e in mi.kinetic_model.allosteric_enzymes
-        ]
-        if mi.kinetic_model.allosteric_enzymes is not None
-        else [],
-        "competitive_inhibitions": [
-            p.id for p in mi.kinetic_model.competitive_inhibitions
-        ]
-        if mi.kinetic_model.competitive_inhibitions is not None
-        else [],
+        "phosphorylations": (
+            [p.id for p in mi.kinetic_model.phosphorylations]
+            if mi.kinetic_model.phosphorylations is not None
+            else []
+        ),
+        "phosphorylation_modifying_enzymes": (
+            [
+                pme.id
+                for pme in mi.kinetic_model.phosphorylation_modifying_enzymes
+            ]
+            if mi.kinetic_model.phosphorylation_modifying_enzymes is not None
+            else []
+        ),
+        "allosteries": (
+            [p.id for p in mi.kinetic_model.allosteries]
+            if mi.kinetic_model.allosteries is not None
+            else []
+        ),
+        "allosteric_enzymes": (
+            [e.id for e in mi.kinetic_model.allosteric_enzymes]
+            if mi.kinetic_model.allosteric_enzymes is not None
+            else []
+        ),
+        "competitive_inhibitions": (
+            [p.id for p in mi.kinetic_model.competitive_inhibitions]
+            if mi.kinetic_model.competitive_inhibitions is not None
+            else []
+        ),
         "kms": mi.parameters.km.ids[0],
         "kis": mi.parameters.ki.ids[0],
         "dissociation_constants": (mi.parameters.dissociation_constant.ids[0]),
@@ -103,7 +112,7 @@ def get_idata(csvs: List[str], mi: MaudInput, mode: str) -> az.InferenceData:
             "balanced_mics",
             "edges",
         ],
-        "flux_control_matrix": ["experiments", "edges", "edges"],
+        "flux_control_matrix": ["experiments", "edges", "edges1"],
         "flux_response_coefficient": ["experiments", "edges", "enzymes"],
         "concentration_response_coefficient": [
             "experiments",