Merge pull request #463 from biosustain/fixed_dgf_bug

Fixed dgf bug

.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

@@ -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):

pyproject.toml

@@ -21,10 +21,10 @@ classifiers = [
 license = {text = "GNU General Public License version 3"}
 description = "Bayesian statistical models of metabolic networks"
 readme = "README.rst"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dependencies = [
     "pip >= 20",
-    "arviz >= 0.12.1",
+    "arviz >= 0.18.0",
     "importlib_resources >= 3.2",
     "numpy",
     "scipy",
@@ -76,6 +76,7 @@ packages = [
     "maud.data.example_inputs.example_ode",
     "maud.data.example_inputs.linear",
     "maud.data.example_inputs.methionine",
+    "maud.data.example_inputs.linear_multidgf",
     "maud.data.example_outputs",
     "maud.data.example_outputs.linear",
     "maud.data.example_outputs.linear.user_input",
@@ -85,7 +86,6 @@ packages = [
 
 [tool.black]
 line-length = 80
-python-version = ['py38', 'py39']
 exclude = '''
 (
     __init__.py
@@ -117,23 +117,23 @@ markers = ["raises"]
 [tool.ruff]
 # Enable the following rules:
 # pycodestyle (`E`), Pyflakes (`F`), pycodestyle (`W`), flake8-bugbear (`B`)
-select = ["E", "F", "B", "W", "D"]
-ignore = [
+lint.select = ["E", "F", "B", "W", "D"]
+lint.ignore = [
     "B905",  # Use zip() without a `strict` parameter.
     "D107",  # Init methods can be undocumented.
     "D203",  # Class docstrints don't need blank lines before them.
     "D213",  # https://beta.ruff.rs/docs/rules/multi-line-summary-second-line/
     "D104",  # __init__.py can be empty.
 ]
-fixable = ["ALL"]
-unfixable = []
+lint.fixable = ["ALL"]
+lint.unfixable = []
 line-length = 80
 target-version = "py310"
 
 [tool.tox]
 legacy_tox_ini = """
     [tox]
-    envlist = isort, black, ruff, safety, py3{9,10}
+    envlist = isort, black, ruff, safety, py3{10,11}
 
     [testenv]
     deps =
@@ -143,11 +143,11 @@ legacy_tox_ini = """
     commands =
         pytest --cov=maud --cov-report=term --ignore=tests/test_integration {posargs}
 
-    [testenv:py38]
+    [testenv:py310]
     passenv =
         CMDSTAN
 
-    [testenv:py39]
+    [testenv:py311]
     passenv =
         CMDSTAN
 
@@ -171,7 +171,7 @@ legacy_tox_ini = """
     deps=
         ruff
     commands=
-        ruff .
+        ruff check .
 
     [testenv:safety]
     deps=

tests/test_unit/test_load_maud_input.py

@@ -6,7 +6,7 @@
 import numpy as np
 from numpy.testing import assert_equal
 
-from maud.data.example_inputs import linear
+from maud.data.example_inputs import linear, linear_multidgf
 from maud.loading_maud_inputs import load_maud_input
 
 
@@ -22,7 +22,7 @@ def test_load_maud_input():
         "dissociation_constant": [["r1_M2_c_activation", "r2_M1_c_inhibition"]],
     }
     linear_files = importlib_resources.files(linear)
-    mi = load_maud_input(data_path=linear_files._paths[0])  # path 0 is package
+    mi = load_maud_input(data_path=str(linear_files))  # path 0 is package
     r1 = next(r for r in mi.kinetic_model.reactions if r.id == "r1")
     assert r1.stoichiometry == {"M1_e": -1, "M1_c": 1}
     assert "r1_r1" in mi.parameters.kcat.ids[0]
@@ -36,3 +36,10 @@ def test_load_maud_input():
         actual = v.tolist() if isinstance(v, np.ndarray) else v
         expected = expected_stan_input[k]
         assert_equal(actual, expected, err_msg=f"{k} different from expected.")
+
+
+def test_load_multidgf():
+    """Test that the multidgf input loads correctly."""
+    files = importlib_resources.files(linear_multidgf)
+    mi = load_maud_input(data_path=str(files))  # path 0 is package
+    assert mi.inits_dict["dgf_free"] == [-10.0, -32.0]