Merge pull request #328 from nanglo123/sbml_qual_support

Add support for sbml qual models

docs/source/sources.rst

@@ -41,6 +41,13 @@ SBML extraction (:py:mod:`mira.sources.sbml`)
  :members:
  :show-inheritance:
 
+SBML Qual extraction (:py:mod:`mira.sources.sbml.qual_api`)
+-----------------------------------------------------------
+.. automodule:: mira.sources.sbml.qual_api
+ :members:
+ :show-inheritance:
+
+
 BioModels client (:py:mod:`mira.sources.biomodels`)
 ---------------------------------------------------
 .. automodule:: mira.sources.biomodels

mira/modeling/amr/regnet.py

@@ -5,7 +5,6 @@
 __all__ = ["AMRRegNetModel", "ModelSpecification",
  "template_model_to_regnet_json"]
 
-
 import json
 import logging
 from copy import deepcopy
@@ -47,7 +46,7 @@ def __init__(self, model: Model):
  model.template_model.annotations.name else "Model"
  self.model_description = model.template_model.annotations.description \
  if model.template_model.annotations and \
- model.template_model.annotations.description else self.model_name
+  model.template_model.annotations.description else self.model_name
 
  self.rates = []
  self.observables = []
@@ -84,7 +83,7 @@ def __init__(self, model: Model):
  self.states.append(state_data)
  self._states_by_id[name] = state_data
 
- idx = 0
+ edge_id = 1
  # It's possible that something is naturally degraded/replicated
  # by multiple transitions so we need to collect and aggregate rates
  intrinsic_by_var = defaultdict(list)
@@ -132,47 +131,70 @@ def __init__(self, model: Model):
  # a regular transition in the regnet framework
  # Possibilities are:
  # - ControlledReplication / GroupedControlledProduction
- # - ControlledDegradation
+ # - ControlledDegradation / GroupedControlledDegradation
+ # - ControlledProduction if the control and produced concept are not the same
  else:
- tid = f"t{idx + 1}"
- transition_dict = {'id': tid}
  # If we have multiple controls then the thing that replicates
  # is both a control and a produced variable.
  if len(transition.control) > 1:
- indep_ctrl = {c.key for c in transition.control} - \
- {transition.produced[0].key}
- # There is one corner case where both controllers are also
- # the same as the produced variable, in which case.
- if not indep_ctrl:
- indep_ctrl = {transition.produced[0].key}
- transition_dict['source'] = vmap[sorted(indep_ctrl)[0]]
+ # GroupedControlledProduction
+ if is_production(transition.template) or is_replication(
+ transition.template
+ ):
+ indep_ctrl = {c.key for c in transition.control} - {
+ transition.produced[0].key
+ }
+ # There is one corner case where both controllers are also
+ # the same as the produced variable, in which case.
+ if not indep_ctrl:
+ indep_ctrl = {transition.produced[0].key}
+
+ for index, controller in enumerate(indep_ctrl):
+ self.create_edge(
+ transition,
+ vmap[controller],
+ vmap[transition.produced[0].key],
+ edge_id,
+ False if index == 0 else True
+ )
+
+ edge_id += 1
+ else:
+ # GroupedControlledDegradation
+ indep_ctrl = {c.key for c in transition.control} - {
+ transition.consumed[0].key
+ }
+ # There is one corner case where both controllers are also
+ # the same as the consumed variable, in which case.
+ if not indep_ctrl:
+ indep_ctrl = {transition.consumed[0].key}
+
+ for index, controller in enumerate(indep_ctrl):
+ self.create_edge(
+ transition,
+ vmap[controller],
+ vmap[transition.consumed[0].key],
+ edge_id,
+ False if index == 0 else True
+ )
+
+ edge_id += 1
  else:
- transition_dict['source'] = vmap[transition.control[0].key]
- transition_dict['target'] = \
- vmap[transition.consumed[0].key if
- transition.consumed else transition.produced[0].key]
- transition_dict['sign'] = (is_production(transition.template) or
- is_replication(transition.template))
-
- # Include rate law
- if transition.template.rate_law:
- rate_law = transition.template.rate_law.args[0]
- pnames = transition.template.get_parameter_names()
- # We just choose an arbitrary one deterministically
- rate_const = sorted(pnames)[0] if pnames else None
-
- transition_dict['properties'] = {
- 'name': tid,
- 'rate_constant': rate_const,
- }
- self.rates.append({
- 'target': tid,
- 'expression': str(rate_law),
- 'expression_mathml': expression_to_mathml(rate_law)
- })
-
- self.transitions.append(transition_dict)
- idx += 1
+ # ControlledProduction if produced and controller are not the same
+ # ControlledDegradation
+ target = vmap[
+ transition.consumed[0].key
+ if transition.consumed
+ else transition.produced[0].key
+ ]
+ self.create_edge(
+ transition,
+ vmap[transition.control[0].key],
+ target,
+ edge_id,
+ False
+ )
+ edge_id += 1
 
  for var, rates in intrinsic_by_var.items():
  rate_law = sum(rates)
@@ -224,6 +246,55 @@ def __init__(self, model: Model):
  self.time = None
  add_metadata_annotations(self.metadata, model)
 
+ def create_edge(self, transition, source, target, edge_id, duplicate):
+ """Create and append a transition dictionary to the list of transitions
+
+ Parameters
+ ----------
+ transition : Transition
+ The Transition object
+ source : str
+ The name of the source of the transition
+ target : str
+ The name of the target of the transition
+ edge_id : int
+ The id to assign to the transition
+ duplicate : bool
+ A boolean that tells us whether the transition we are processing has already been
+ processed at least once. This is for the purpose of not adding duplicate rate laws
+ to the output amr.
+ """
+ tid = f"t{edge_id}"
+ transition_dict = {"id": tid}
+ transition_dict["source"] = source
+ transition_dict["target"] = target
+ transition_dict["sign"] = is_production(
+ transition.template
+ ) or is_replication(transition.template)
+
+ #
+ if transition.template.rate_law:
+ # If we are processing a duplicate rate, set the rate to 0
+ rate_law = transition.template.rate_law.args[0] if not duplicate \
+ else safe_parse_expr('0')
+ pnames = transition.template.get_parameter_names()
+ # We just choose an arbitrary one deterministically
+ rate_const = sorted(pnames)[0] if pnames else None
+
+ transition_dict["properties"] = {
+ "name": tid,
+ "rate_constant": rate_const,
+ }
+ self.rates.append(
+ {
+ "target": tid,
+ "expression": str(rate_law),
+ "expression_mathml": expression_to_mathml(rate_law),
+ }
+ )
+
+ self.transitions.append(transition_dict)
+
  def to_json(
  self,
  name: str = None,
@@ -461,4 +532,3 @@ class ModelSpecification(BaseModel):
  model: RegNetModel
  semantics: Optional[Ode]
  metadata: Optional[Dict]
-

mira/sources/sbml/__init__.py

@@ -1 +1,2 @@
 from .api import *
+from .qual_api import *