Getting started with INDRA
Importing INDRA and its modules
INDRA can be imported and used in a Python script or interactively in a Python shell. Note that similar to some other packages (e.g scipy), INDRA doesn't automatically import all its submodules, so import indra is not enough to access its submodules. Rather, one has to explicitly import each submodule that is needed. For example to access the BEL API, one has to
from indra.sources import bel
Similarly, each model output assembler has its own submodule under indra.assemblers with the assembler class accessible at the submodule level, so they can be imported as, for instance,
from indra.assemblers.pysb import PysbAssembler
To get a detailed overview of INDRA's submodule structure, take a look at the :ref:`indra_modules_ref`.
Basic usage examples
Here we show some basic usage examples of the submodules of INDRA. More complex usage examples are shown in the Tutorials section.
Reading a sentence with TRIPS
In this example, we read a sentence via INDRA's TRIPS submodule to produce an INDRA Statement.
from indra.sources import trips sentence = 'MAP2K1 phosphorylates MAPK3 at Thr-202 and Tyr-204' trips_processor = trips.process_text(sentence)
The trips_processor object has a statements attribute which contains a list of INDRA Statements extracted from the sentence.
Reading a PubMed Central article with REACH
from indra.sources import reach reach_processor = reach.process_pmc('3717945')
The reach_processor object has a statements attribute which contains a list of INDRA Statements extracted from the paper.
Getting the neighborhood of proteins from the BEL Large Corpus
In this example, we search the neighborhood of the KRAS and BRAF proteins in the BEL Large Corpus.
from indra.sources import bel bel_processor = bel.process_pybel_neighborhood(['KRAS', 'BRAF'])
The bel_processor object has a statements attribute which contains a list of INDRA Statements extracted from the queried neighborhood.
Getting paths between two proteins from PathwayCommons (BioPAX)
In this example, we search for paths between the BRAF and MAPK3 proteins in the PathwayCommons databases using INDRA's BioPAX API. Note that this example will only work if all dependencies of the indra.sources.biopax module are installed.
See the Installation instructions for more details.
from indra.sources import biopax proteins = ['BRAF', 'MAPK3'] limit = 2 biopax_processor = biopax.process_pc_pathsbetween(proteins, limit)
We passed the second argument limit = 2, which defines the upper limit on the length of the paths that are searched. By default the limit is 1. The biopax_processor object has a statements attribute which contains a list of INDRA Statements extracted from the queried paths.
Constructing INDRA Statements manually
It is possible to construct INDRA Statements manually or in scripts. The following is a basic example in which we instantiate a Phosphorylation Statement between BRAF and MAP2K1.
from indra.statements import Phosphorylation, Agent braf = Agent('BRAF') map2k1 = Agent('MAP2K1') stmt = Phosphorylation(braf, map2k1)
Assembling a PySB model and exporting to SBML
In this example, assume that we have already collected a list of INDRA Statements from any of the input sources and that this list is called stmts. We will instantiate a PysbAssembler, which produces a PySB model from INDRA Statements.
from indra.assemblers.pysb import PysbAssembler pa = PysbAssembler() pa.add_statements(stmts) model = pa.make_model()
Here the model variable is a PySB Model object representing a rule-based executable model, which can be further manipulated, simulated, saved and exported to other formats.
For instance, exporting the model to SBML format can be done as
sbml_model = pa.export_model('sbml')
which gives an SBML model string in the sbml_model variable, or as
which writes the SBML model into the model.sbml file. Other formats for export that are supported include BNGL, Kappa and Matlab. For a full list, see the PySB export module.