Using AMICI’s Python interface
In the following we will give a detailed overview how to specify models in Python and how to call the generated simulation files.
Model definition
This document provides an overview of different interfaces to import models in AMICI. Further examples are available in the AMICI repository in the python/examples directory.
SBML import
AMICI can import SBML models via the
amici.sbml_import.SbmlImporter()
class.
Status of SBML support in Python-AMICI
Python-AMICI currently passes 1215 out of the 1821 (~67%) test cases from the semantic SBML Test Suite (current status).
The following SBML test suite tags are currently supported (i.e., at least one test case with the respective test passes; tag descriptions):
Component tags:
AlgebraicRule
AssignmentRule
comp
Compartment
CSymbolAvogadro
CSymbolTime
Deletion
EventNoDelay
ExternalModelDefinition
FunctionDefinition
InitialAssignment
ModelDefinition
Parameter
Port
RateRule
Reaction
ReplacedBy
ReplacedElement
SBaseRef
Species
Submodel
Test tags:
0D-Compartment
Amount
AssignedConstantStoichiometry
AssignedVariableStoichiometry
BoolNumericSwap
BoundaryCondition
comp
Concentration
ConstantSpecies
ConversionFactor
ConversionFactors
DefaultValue
EventT0Firing
ExtentConversionFactor
HasOnlySubstanceUnits
InitialValueReassigned
L3v2MathML
LocalParameters
MultiCompartment
NoMathML
NonConstantCompartment
NonConstantParameter
NonUnityCompartment
NonUnityStoichiometry
ReversibleReaction
SpeciesReferenceInMath
SubmodelOutput
TimeConversionFactor
UncommonMathML
VolumeConcentrationRates
Additional support may be added in the future. However, the following features are unlikely to be supported:
factorial(), ceil(), floor(), due to incompatibility with symbolic sensitivity computations
delay() due to missing SUNDIALS solver support
events with delays, events with non-persistent triggers
Tutorials
A basic tutorial on how to import and simulate SBML models is available in the Getting Started notebook, while a more detailed example including customized import and sensitivity computation is available in the Example Steadystate notebook.
PySB import
AMICI can import PySB models via
amici.pysb_import.pysb2amici()
.
BNGL import
AMICI can import BNGL models via
amici.bngl_import.bngl2amici()
.
PEtab import
AMICI can import PEtab-based model definitions and run simulations for the specified simulations conditions. For usage, see python/examples/example_petab/petab.ipynb.
Importing plain ODEs
The AMICI Python interface does not currently support direct import of ODEs. However, it is straightforward to encode them as RateRules in an SBML model. The yaml2sbml package may come in handy, as it facilitates generating SBML models from a YAML-based specification of an ODE model. Besides the SBML model it can also create PEtab files.
SED-ML import
We also plan to implement support for the Simulation Experiment Description Markup Language (SED-ML).
Environment variables affecting model import
In addition to the environment variables listed here, the following environment variables control various behaviours during model import and compilation:
Variable |
Purpose |
Example |
---|---|---|
|
Extract common subexpressions. May significantly reduce file size and compile time for large models, but makes the generated code less readable. Disabled by default. |
|
|
Number of processes to be used for model import. Defaults to 1. Speeds up import of large models. Will slow down import of small models, benchmarking recommended. |
|
|
Compute conservation laws for non-constant species. SBML-import only.
See |
Miscellaneous
OpenMP support for parallelized simulation for multiple experimental conditions
AMICI can be built with OpenMP support, which allows to parallelize model simulations for multiple experimental conditions.
On Linux and OSX this is enabled by default. This can be verified using:
import amici
amici.compiledWithOpenMP()
If not already enabled by default, you can enable OpenMP support by setting
the environment variables AMICI_CXXFLAGS
and AMICI_LDFLAGS
to the
correct OpenMP flags of your compiler and linker, respectively. This has to be
done for both AMICI package installation and model compilation. When using
gcc
on Linux, this would be:
# on your shell:
AMICI_CXXFLAGS=-fopenmp AMICI_LDFLAGS=-fopenmp pip3 install amici
# in python, before model compilation:
import os
os.environ['AMICI_CXXFLAGS'] = '-fopenmp'
os.environ['AMICI_LDFLAGS'] = '-fopenmp'