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INDRA (Integrated Network and Dynamical Reasoning Assembler) is an automated model assembly system, originally developed for molecular systems biology and currently being generalized to other domains. INDRA draws on natural language processing systems and structured databases to collect mechanistic and causal assertions, represents them in a standardized form (INDRA Statements), and assembles them into various modeling formalisms including causal graphs and dynamical models. INDRA also provides knowledge assembly procedures that operate on INDRA Statements and correct certain errors, find and resolve redundancies, infer missing information, filter to a scope of interest and assess belief.

Knowledge sources

INDRA is currently integrated with the following natural language processing systems:

and can collect information from these databases:

These input modules (available in indra.sources) all produce INDRA Statements.

Output model assemblers

INDRA also provides several model output assemblers that take INDRA Statements as input. INDRA can assemble into the following modeling formalisms

Internal knowledge assembly

The internal assembly steps of INDRA are exposed in the submodule. This submodule contains functions that take Statements as input and produce processed Statements as output. They can be composed to form an assembly pipeline connecting knowledge collected from sources with an output model.

INDRA also contains utility modules to access literature content (e.g. PubMed), ontological information (e.g. UniProt, HGNC), and other resources.


From word models to executable models of signal transduction using automated assembly, Molecular Systems Biology (2017)


Documentation is available at


For detailed installation instructions, see the documentation.

INDRA works with both Python 2 and 3 (tested with 2.7 and 3.5).

The preferred way to install INDRA is by pointing pip to the source repository as

$ pip install git+

or by cloning the repository and then using pip to install the package as

$ git clone
$ cd indra
$ pip install .

You can also install INDRA by cloning this repository and running as

$ git clone
$ cd indra
$ python install

Releases of INDRA are also available on PyPI, you can install the latest release as

$ pip install indra

However, releases will usually be behind the latest code available in this repository.

INDRA depends on a few standard Python packages. These packages are installed by either setup method (using pip or running install). For certain modules and use cases, other dependencies may be needed, which are described in detail in the documentation.


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

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

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

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

Next we look at an example of reading the 10 most recent PubMed abstracts on BRAF and collecting the results in INDRA statements.

from indra.sources import reach
from indra.literature import pubmed_client
# Search for 10 most recent abstracts in PubMed on 'BRAF'
pmids = pubmed_client.get_ids('BRAF', retmax=10)
all_statements = []
for pmid in pmids:
    abs = pubmed_client.get_abstract(pmid)
    if abs is not None:
        reach_processor = reach.process_text(abs)
        if reach_processor is not None:
            all_statements += reach_processor.statements
# At this point, the all_statements list contains all the statements
# extracted from the 10 abstracts.

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

from indra.sources import bel
# Process the neighborhood of BRAF and MAP2K1
bel_processor = bel.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. Note: see installation notes above for installing jnius, which is required for using the BioPAX API of INDRA.

from indra.sources import biopax
# Process the neighborhood of BRAF and MAP2K1
biopax_processor = biopax.process_pc_pathsfromto(['BRAF', 'RAF1'], ['MAP2K1', 'MAP2K2'])
# At this point, biopax_processor.statements contains a list of INDRA 
# Statements extracted from the paths-from-to query.