INDRA (Integrated Dynamical Reasoning Assembler) generates executable models of pathway dynamics from natural language (using the TRIPS parser), BioPAX and BEL sources (including the PathwayCommons database and NDEx).

INDRA works with Python 2.7.x (tested with 2.7.3 and higher). Python 3 is currently not supported. When installing INDRA and its dependencies, make sure that the correct version of Python is used.

You can install INDRA by cloning this repository and running setup.py from the terminal as

$ git clone https://github.com/sorgerlab/indra.git
$ cd indra
$ python setup.py install

INDRA depends on a few standard Python packages (e.g. rdflib, requests) and also PySB (for more information on PySB, see https://github.com/pysb/pysb). These packages are installed by setup.py.

INDRA also uses a package called pyjnius (https://github.com/kivy/pyjnius) to allow using Java classes from Python. This is used only in the BioPAX API and the rest of INDRA will work without pyjnius. Pyjnius requires cython and needs JRE and JDK 1.8 to be installed.

In this example INDRA assembles a PySB model from the natural language description of a mechanism via the TRIPS parser web service.

from indra.pysb_assembler import PysbAssembler
from indra.trips import trips_api
pa = PysbAssembler()
# Process a natural language description of a mechanism
trips_processor = trips_api.process_text('MEK2 phosphorylates ERK1 at Thr-202 and Tyr-204')
# Collect extracted mechanisms in PysbAssembler
pa.add_statements(trips_processor.statements)
# Assemble the model
model = pa.make_model(policies='two_step')

The next example shows querying the BEL large corpus network thorugh NDEx for a neighborhood of a given list of proteins using their HGNC gene names.

from indra.bel import bel_api
# Process the neighborhood of BRAF and MAP2K1
bel_processor = bel_api.process_ndex_neighborhood(['BRAF', 'MAP2K1'])
# At this point, bel_processor.statements contains a list of INDRA statements
# extracted from the neihborhood query.

Next, we look at an example of querying the Pathway Commons database for paths between two lists of proteins.

from indra.biopax import biopax_api
# Process the neighborhood of BRAF and MAP2K1
biopax_processor = biopax_api.process_pc_pathsfromto(['BRAF', 'RAF1'], ['MAP2K1', 'MAP2K2'])
# Query the resulting BioPAX object model for phosphorylation
biopax_processor.get_phosphorylation()
# At this point, biopax_processor.statements contains a list of INDRA 
# Phosphorylation statements extracted from the paths-from-to query.

INDRA also provides an interface for the REACH natural language parser. In this example, a full paper from PubMed Central is processed. The paper's PMC ID is PMC3717945.

from indra.reach import reach_api
# Process the neighborhood of BRAF and MAP2K1
reach_processor = reach_api.process_pmc('3717945')
# At this point, reach_processor.statements contains a list of INDRA statements
# extracted from the PMC paper.