Build Status Documentation Status

INDRA

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

General purpose causal relation reading systems:

Reader Reference
Eidos https://github.com/clulab/eidos
TRIPS/CWMS http://trips.ihmc.us/parser/cgi/cwmsreader
Hume https://github.com/BBN-E/Hume
Sofia https://sofia.worldmodelers.com/ui/

Biology-oriented reading systems:

Reader Reference
TRIPS/DRUM http://trips.ihmc.us/parser/cgi/drum
REACH https://github.com/clulab/reach
Sparser https://github.com/ddmcdonald/sparser
TEES https://github.com/jbjorne/TEES
MedScan https://doi.org/10.1093/bioinformatics/btg207
RLIMS-P https://research.bioinformatics.udel.edu/rlimsp
ISI/AMR https://github.com/sgarg87/big_mech_isi_gg
Geneways https://www.ncbi.nlm.nih.gov/pubmed/15016385

Biological pathway databases:

Database / Exchange format Reference
PathwayCommons / BioPax http://pathwaycommons.org/
http://www.biopax.org/
Large Corpus / BEL https://github.com/pybel/pybel
https://github.com/OpenBEL
Signor https://signor.uniroma2.it/
BioGRID https://thebiogrid.org/
Target Affinity Spectrum https://doi.org/10.1101/358978
LINCS small molecules http://lincs.hms.harvard.edu/db/sm/

Custom knowledge bases:

Database / Exchange format Reference
NDEx / CX http://ndexbio.org
INDRA DB / INDRA Statements https://github.com/indralab/indra_db

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 indra.tools.assemble_corpus 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.

Citation

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

Documentation

Documentation is available at http://indra.readthedocs.io.

Installation

For detailed installation instructions, see the documentation.

INDRA works with both Python 2 and 3 (tested with 2.7 and 3.5). Note: release 1.11 will drop Python 2 compatibility.

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

$ pip install git+https://github.com/sorgerlab/indra.git

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 pip during setup. For certain modules and use cases, other “extra” dependencies may be needed, which are described in detail in the documentation.

Using INDRA

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.pysb 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
pa.add_statements(trips_processor.statements)
# 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 for a neighborhood of a given list of proteins using their HGNC gene names. This example performs the query via PyBEL.

from indra.sources import bel
# Process the neighborhood of BRAF and MAP2K1
bel_processor = bel.process_pybel_neighborhood(['BRAF', 'MAP2K1'])

At this point, bel_processor.statements contains a list of INDRA statements extracted from the neighborhood 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 pyjnius, 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.

INDRA REST API

A REST API for INDRA is available at http://api.indra.bio:8000 with documentation at http://www.indra.bio/rest_api/docs. Note that the REST API is ideal for prototyping and for building light-weight web apps, but should not be used for large reading and assembly workflows.

INDRA Docker

INDRA is available as a Docker image on Dockerhub and can be pulled as

docker pull labsyspharm/indra

You can run the INDRA REST API using the container as

docker run -id -p 8080:8080 --entrypoint python labsyspharm/indra /sw/indra/rest_api/api.py

To build the image locally, there are currently two Dockerfiles for INDRA and its dependencies. They are available in the following repositories: