INDRA Statements (`indra.statements`)¶

Statements represent mechanistic relationships between biological agents.

Statement classes follow an inheritance hierarchy, with all Statement types inheriting from the parent class Statement. At the next level in the hierarchy are the following classes:

Open Domain

Event
Influence
Association

Biological Domain

Complex
Modification
SelfModification
RegulateActivity
RegulateAmount
ActiveForm
Translocation
Gef
Gap
Conversion

There are several types of Statements representing post-translational modifications that further inherit from Modification:

There are additional subtypes of SelfModification:

Autophosphorylation
Transphosphorylation

Interactions between proteins are often described simply in terms of their effect on a protein’s “activity”, e.g., “Active MEK activates ERK”, or “DUSP6 inactives ERK”. These types of relationships are indicated by the RegulateActivity abstract base class which has subtypes

Activation
Inhibition

while the RegulateAmount abstract base class has subtypes

IncreaseAmount
DecreaseAmount

Statements involve one or more Concepts, which, depending on the semantics of the Statement, are typically biological Agents, such as proteins, represented by the class Agent. (However, :py:class`Influence` statements involve two or more :py:class`Event` objects, each of which takes a :py:class`Concept` as an argument.)

Agents can have several types of context specified on them including

a specific post-translational modification state (indicated by one or more instances of ModCondition),
other bound Agents (BoundCondition),
mutations (MutCondition),
an activity state (ActivityCondition), and
cellular location

The active form of an agent (in terms of its post-translational modifications or bound state) is indicated by an instance of the class ActiveForm.

Agents also carry grounding information which links them to database entries. These database references are represented as a dictionary in the db_refs attribute of each Agent. The dictionary can have multiple entries. For instance, INDRA’s input Processors produce genes and proteins that carry both UniProt and HGNC IDs in db_refs, whenever possible. FamPlex provides a name space for protein families that are typically used in the literature. More information about FamPlex can be found here: https://github.com/sorgerlab/famplex

Type	Database	Example
Gene/Protein	HGNC	{‘HGNC’: ‘11998’}
Gene/Protein	UniProt	{‘UP’: ‘P04637’}
Protein chain	UniProt	{‘UPPRO’: ‘PRO_0000435839’}
Gene/Protein	Entrez	{‘EGID’: ‘5583’}
Gene/Protein family	FamPlex	{‘FPLX’: ‘ERK’}
Gene/Protein family	InterPro	{‘IP’: ‘IPR000308’}
Gene/Protein family	Pfam	{‘PF’: ‘PF00071’}
Gene/Protein family	NextProt family	{‘NXPFA’: ‘03114’}
Chemical	ChEBI	{‘CHEBI’: ‘CHEBI:63637’}
Chemical	PubChem	{‘PUBCHEM’: ‘42611257’}
Chemical	LINCS / HMS-LINCS	{‘LINCS’: ‘42611257’}
Metabolite	HMDB	{‘HMDB’: ‘HMDB00122’}
Process, location, etc.	GO	{‘GO’: ‘GO:0006915’}
Process, disease, etc.	MeSH	{‘MESH’: ‘D008113’}
Disease	Disease Ontology	{‘DOID’: ‘DOID:8659’}
Phenotypic abnormality	Human Pheno. Ont.	{‘HP’: ‘HP:0031296’}
Experimental variables	Exp. Factor Ont.	{‘EFO’: ‘0007820’}
General terms	NCIT	{‘NCIT’: ‘C28597’}
Raw text	TEXT	{‘TEXT’: ‘Nf-kappaB’}

The evidence for a given Statement, which could include relevant citations, database identifiers, and passages of text from the scientific literature, is contained in one or more Evidence objects associated with the Statement.

JSON serialization of INDRA Statements¶

Statements can be serialized into JSON and deserialized from JSON to allow their exchange in a platform-independent way. We also provide a JSON schema (see http://json-schema.org to learn about schemas) in https://raw.githubusercontent.com/sorgerlab/indra/master/indra/resources/statements_schema.json which can be used to validate INDRA Statements JSONs.

Some validation tools include:

jsonschema

a Python package to validate JSON content with respect to a schema
ajv-cli

Available at https://www.npmjs.com/package/ajv-cli Install with “npm install -g ajv-cli” and then validate with: ajv -s statements_schema.json -d file_to_validate.json. This tool provides more sophisticated and better interpretable output than jsonschema.
Web based tools

There are a variety of web-based tools for validation with JSON schemas, including https://www.jsonschemavalidator.net

class indra.statements.statements.BoundCondition(agent, is_bound=True)[source]¶

Bases: object

Identify Agents bound (or not bound) to a given Agent in a given context.

Parameters:	agent (`Agent`) – Instance of Agent. is_bound (bool) – Specifies whether the given Agent is bound or unbound in the current context. Default is True.

Examples

EGFR bound to EGF:

>>> egf = Agent('EGF')
>>> egfr = Agent('EGFR', bound_conditions=[BoundCondition(egf)])

BRAF not bound to a 14-3-3 protein (YWHAB):

>>> ywhab = Agent('YWHAB')
>>> braf = Agent('BRAF', bound_conditions=[BoundCondition(ywhab, False)])

class indra.statements.statements.MutCondition(position, residue_from, residue_to=None)[source]¶

Bases: object

Mutation state of an amino acid position of an Agent.

Parameters:	position (str) – Residue position of the mutation in the protein sequence. residue_from (str) – Wild-type (unmodified) amino acid residue at the given position. residue_to (str) – Amino acid at the position resulting from the mutation.

Examples

Represent EGFR with a L858R mutation:

>>> egfr_mutant = Agent('EGFR', mutations=[MutCondition('858', 'L', 'R')])

class indra.statements.statements.ModCondition(mod_type, residue=None, position=None, is_modified=True)[source]¶

Bases: object

Post-translational modification state at an amino acid position.

Parameters:

mod_type (str) – The type of post-translational modification, e.g., ‘phosphorylation’. Valid modification types currently include: ‘phosphorylation’, ‘ubiquitination’, ‘sumoylation’, ‘hydroxylation’, and ‘acetylation’. If an invalid modification type is passed an InvalidModTypeError is raised.
residue (str or None) – String indicating the modified amino acid, e.g., ‘Y’ or ‘tyrosine’. If None, indicates that the residue at the modification site is unknown or unspecified.
position (str or None) – String indicating the position of the modified amino acid, e.g., ‘202’. If None, indicates that the position is unknown or unspecified.
is_modified (bool) – Specifies whether the modification is present or absent. Setting the flag specifies that the Agent with the ModCondition is unmodified at the site.

Examples

Doubly-phosphorylated MEK (MAP2K1):

>>> phospho_mek = Agent('MAP2K1', mods=[
... ModCondition('phosphorylation', 'S', '202'),
... ModCondition('phosphorylation', 'S', '204')])

ERK (MAPK1) unphosphorylated at tyrosine 187:

>>> unphos_erk = Agent('MAPK1', mods=(
... ModCondition('phosphorylation', 'Y', '187', is_modified=False)))

class indra.statements.statements.ActivityCondition(activity_type, is_active)[source]¶

Bases: object

An active or inactive state of a protein.

Examples

Kinase-active MAP2K1:

>>> mek_active = Agent('MAP2K1',
...                    activity=ActivityCondition('kinase', True))

Transcriptionally inactive FOXO3:

>>> foxo_inactive = Agent('FOXO3',
...                     activity=ActivityCondition('transcription', False))

Parameters:	activity_type (str) – The type of activity, e.g. ‘kinase’. The basic, unspecified molecular activity is represented as ‘activity’. Examples of other activity types are ‘kinase’, ‘phosphatase’, ‘catalytic’, ‘transcription’, etc. is_active (bool) – Specifies whether the given activity type is present or absent.

class indra.statements.statements.Statement(evidence=None, supports=None, supported_by=None)[source]¶

Bases: object

The parent class of all statements.

Parameters:

evidence (None or Evidence or list of Evidence) – If a list of Evidence objects is passed to the constructor, the value is set to this list. If a bare Evidence object is passed, it is enclosed in a list. If no evidence is passed (the default), the value is set to an empty list.
supports (list of Statement) – Statements that this Statement supports.
supported_by (list of Statement) – Statements supported by this statement.

agent_list(deep_sorted=False)[source]¶: Get the canonicalized agent list.

flip_polarity(agent_idx=None)[source]¶: If applicable, flip the polarity of the statement

get_hash(shallow=True, refresh=False, matches_fun=None)[source]¶

Get a hash for this Statement.

There are two types of hash, “shallow” and “full”. A shallow hash is as unique as the information carried by the statement, i.e. it is a hash of the matches_key. This means that differences in source, evidence, and so on are not included. As such, it is a shorter hash (14 nibbles). The odds of a collision among all the statements we expect to encounter (well under 10^8) is ~10^-9 (1 in a billion). Checks for collisions can be done by using the matches keys.

A full hash includes, in addition to the matches key, information from the evidence of the statement. These hashes will be equal if the two Statements came from the same sentences, extracted by the same reader, from the same source. These hashes are correspondingly longer (16 nibbles). The odds of a collision for an expected less than 10^10 extractions is ~10^-9 (1 in a billion).

Note that a hash of the Python object will also include the uuid, so it will always be unique for every object.

Parameters:	shallow (bool) – Choose between the shallow and full hashes described above. Default is true (e.g. a shallow hash). refresh (bool) – Used to get a new copy of the hash. Default is false, so the hash, if it has been already created, will be read from the attribute. This is primarily used for speed testing. matches_fun (Optional[function]) – A function which takes a Statement as argument and returns a string matches key which is then hashed. If not provided the Statement’s built-in matches_key method is used.
Returns:	hash – A long integer hash.
Return type:	int

make_generic_copy(deeply=False)[source]¶

Make a new matching Statement with no provenance.

All agents and other attributes besides evidence, belief, supports, and supported_by will be copied over, and a new uuid will be assigned. Thus, the new Statement will satisfy new_stmt.matches(old_stmt).

If deeply is set to True, all the attributes will be deep-copied, which is comparatively slow. Otherwise, attributes of this statement may be altered by changes to the new matching statement.

to_graph()[source]¶: Return Statement as a networkx graph.

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Modification(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

Generic statement representing the modification of a protein.

Parameters:

enz (indra.statement.Agent) – The enzyme involved in the modification.
sub (indra.statement.Agent) – The substrate of the modification.
residue (str or None) – The amino acid residue being modified, or None if it is unknown or unspecified.
position (str or None) – The position of the modified amino acid, or None if it is unknown or unspecified.
evidence (None or Evidence or list of Evidence) – Evidence objects in support of the modification.

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.AddModification(enz, sub, residue=None, position=None, evidence=None)[source]¶: Bases: indra.statements.statements.Modification

class indra.statements.statements.RemoveModification(enz, sub, residue=None, position=None, evidence=None)[source]¶: Bases: indra.statements.statements.Modification

class indra.statements.statements.SelfModification(enz, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

Generic statement representing the self-modification of a protein.

Parameters:

enz (indra.statement.Agent) – The enzyme involved in the modification, which is also the substrate.
residue (str or None) – The amino acid residue being modified, or None if it is unknown or unspecified.
position (str or None) – The position of the modified amino acid, or None if it is unknown or unspecified.
evidence (None or Evidence or list of Evidence) – Evidence objects in support of the modification.

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Phosphorylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Phosphorylation modification.

Examples

MEK (MAP2K1) phosphorylates ERK (MAPK1) at threonine 185:

>>> mek = Agent('MAP2K1')
>>> erk = Agent('MAPK1')
>>> phos = Phosphorylation(mek, erk, 'T', '185')

class indra.statements.statements.Autophosphorylation(enz, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.SelfModification

Intramolecular autophosphorylation, i.e., in cis.

Examples

p38 bound to TAB1 cis-autophosphorylates itself (see PMID:19155529).

>>> tab1 = Agent('TAB1')
>>> p38_tab1 = Agent('P38', bound_conditions=[BoundCondition(tab1)])
>>> autophos = Autophosphorylation(p38_tab1)

class indra.statements.statements.Transphosphorylation(enz, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.SelfModification

Autophosphorylation in trans.

Transphosphorylation assumes that a kinase is already bound to a substrate (usually of the same molecular species), and phosphorylates it in an intra-molecular fashion. The enz property of the statement must have exactly one bound_conditions entry, and we assume that enz phosphorylates this molecule. The bound_neg property is ignored here.

class indra.statements.statements.Dephosphorylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Dephosphorylation modification.

Examples

DUSP6 dephosphorylates ERK (MAPK1) at T185:

>>> dusp6 = Agent('DUSP6')
>>> erk = Agent('MAPK1')
>>> dephos = Dephosphorylation(dusp6, erk, 'T', '185')

class indra.statements.statements.Hydroxylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Hydroxylation modification.

class indra.statements.statements.Dehydroxylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Dehydroxylation modification.

class indra.statements.statements.Sumoylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Sumoylation modification.

class indra.statements.statements.Desumoylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Desumoylation modification.

class indra.statements.statements.Acetylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Acetylation modification.

class indra.statements.statements.Deacetylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Deacetylation modification.

class indra.statements.statements.Glycosylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Glycosylation modification.

class indra.statements.statements.Deglycosylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Deglycosylation modification.

class indra.statements.statements.Ribosylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Ribosylation modification.

class indra.statements.statements.Deribosylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Deribosylation modification.

class indra.statements.statements.Ubiquitination(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Ubiquitination modification.

class indra.statements.statements.Deubiquitination(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Deubiquitination modification.

class indra.statements.statements.Farnesylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Farnesylation modification.

class indra.statements.statements.Defarnesylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Defarnesylation modification.

class indra.statements.statements.Geranylgeranylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Geranylgeranylation modification.

class indra.statements.statements.Degeranylgeranylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Degeranylgeranylation modification.

class indra.statements.statements.Palmitoylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Palmitoylation modification.

class indra.statements.statements.Depalmitoylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Depalmitoylation modification.

class indra.statements.statements.Myristoylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Myristoylation modification.

class indra.statements.statements.Demyristoylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Demyristoylation modification.

class indra.statements.statements.Methylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.AddModification

Methylation modification.

class indra.statements.statements.Demethylation(enz, sub, residue=None, position=None, evidence=None)[source]¶

Bases: indra.statements.statements.RemoveModification

Demethylation modification.

class indra.statements.statements.RegulateActivity[source]¶

Bases: indra.statements.statements.Statement

Regulation of activity.

This class implements shared functionality of Activation and Inhibition statements and it should not be instantiated directly.

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Inhibition(subj, obj, obj_activity='activity', evidence=None)[source]¶

Bases: indra.statements.statements.RegulateActivity

Indicates that a protein inhibits or deactivates another protein.

This statement is intended to be used for physical interactions where the mechanism of inhibition is not explicitly specified, which is often the case for descriptions of mechanisms extracted from the literature.

Parameters:

subj (Agent) – The agent responsible for the change in activity, i.e., the “upstream” node.
obj (Agent) – The agent whose activity is influenced by the subject, i.e., the “downstream” node.
obj_activity (Optional[str]) – The activity of the obj Agent that is affected, e.g., its “kinase” activity.
evidence (None or Evidence or list of Evidence) – Evidence objects in support of the modification.

class indra.statements.statements.Activation(subj, obj, obj_activity='activity', evidence=None)[source]¶

Bases: indra.statements.statements.RegulateActivity

Indicates that a protein activates another protein.

This statement is intended to be used for physical interactions where the mechanism of activation is not explicitly specified, which is often the case for descriptions of mechanisms extracted from the literature.

Parameters:

subj (Agent) – The agent responsible for the change in activity, i.e., the “upstream” node.
obj (Agent) – The agent whose activity is influenced by the subject, i.e., the “downstream” node.
obj_activity (Optional[str]) – The activity of the obj Agent that is affected, e.g., its “kinase” activity.
evidence (None or Evidence or list of Evidence) – Evidence objects in support of the modification.

Examples

MEK (MAP2K1) activates the kinase activity of ERK (MAPK1):

>>> mek = Agent('MAP2K1')
>>> erk = Agent('MAPK1')
>>> act = Activation(mek, erk, 'kinase')

class indra.statements.statements.GtpActivation(subj, obj, obj_activity='activity', evidence=None)[source]¶: Bases: indra.statements.statements.Activation

class indra.statements.statements.ActiveForm(agent, activity, is_active, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

Specifies conditions causing an Agent to be active or inactive.

Types of conditions influencing a specific type of biochemical activity can include modifications, bound Agents, and mutations.

Parameters:

agent (Agent) – The Agent in a particular active or inactive state. The sets of ModConditions, BoundConditions, and MutConditions on the given Agent instance indicate the relevant conditions.
activity (str) – The type of activity influenced by the given set of conditions, e.g., “kinase”.
is_active (bool) – Whether the conditions are activating (True) or inactivating (False).

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.HasActivity(agent, activity, has_activity, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

States that an Agent has or doesn’t have a given activity type.

With this Statement, one cane express that a given protein is a kinase, or, for instance, that it is a transcription factor. It is also possible to construct negative statements with which one epxresses, for instance, that a given protein is not a kinase.

Parameters:	agent (`Agent`) – The Agent that that statement is about. Note that the detailed state of the Agent is not relevant for this type of statement. activity (str) – The type of activity, e.g., “kinase”. has_activity (bool) – Whether the given Agent has the given activity (True) or not (False).

class indra.statements.statements.Gef(gef, ras, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

Exchange of GTP for GDP on a small GTPase protein mediated by a GEF.

Represents the generic process by which a guanosine exchange factor (GEF) catalyzes nucleotide exchange on a GTPase protein.

Parameters:	gef (`Agent`) – The guanosine exchange factor. ras (`Agent`) – The GTPase protein.

Examples

SOS1 catalyzes nucleotide exchange on KRAS:

>>> sos = Agent('SOS1')
>>> kras = Agent('KRAS')
>>> gef = Gef(sos, kras)

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Gap(gap, ras, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

Acceleration of a GTPase protein’s GTP hydrolysis rate by a GAP.

Represents the generic process by which a GTPase activating protein (GAP) catalyzes GTP hydrolysis by a particular small GTPase protein.

Parameters:	gap (`Agent`) – The GTPase activating protein. ras (`Agent`) – The GTPase protein.

Examples

RASA1 catalyzes GTP hydrolysis on KRAS:

>>> rasa1 = Agent('RASA1')
>>> kras = Agent('KRAS')
>>> gap = Gap(rasa1, kras)

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Complex(members, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

A set of proteins observed to be in a complex.

Parameters:	members (list of `Agent`) – The set of proteins in the complex.

Examples

BRAF is observed to be in a complex with RAF1:

>>> braf = Agent('BRAF')
>>> raf1 = Agent('RAF1')
>>> cplx = Complex([braf, raf1])

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Translocation(agent, from_location=None, to_location=None, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

The translocation of a molecular agent from one location to another.

Parameters:	agent (`Agent`) – The agent which translocates. from_location (Optional[str]) – The location from which the agent translocates. This must be a valid GO cellular component name (e.g. “cytoplasm”) or ID (e.g. “GO:0005737”). to_location (Optional[str]) – The location to which the agent translocates. This must be a valid GO cellular component name or ID.

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.RegulateAmount(subj, obj, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

Superclass handling operations on directed, two-element interactions.

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.DecreaseAmount(subj, obj, evidence=None)[source]¶

Bases: indra.statements.statements.RegulateAmount

Degradation of a protein, possibly mediated by another protein.

Note that this statement can also be used to represent inhibitors of synthesis (e.g., cycloheximide).

Parameters:	subj (`indra.statement.Agent`) – The protein mediating the degradation. obj (`indra.statement.Agent`) – The protein that is degraded. evidence (None or `Evidence` or list of `Evidence`) – Evidence objects in support of the degradation statement.

class indra.statements.statements.IncreaseAmount(subj, obj, evidence=None)[source]¶

Bases: indra.statements.statements.RegulateAmount

Synthesis of a protein, possibly mediated by another protein.

Parameters:	subj (`indra.statement.Agent`) – The protein mediating the synthesis. obj (`indra.statement.Agent`) – The protein that is synthesized. evidence (None or `Evidence` or list of `Evidence`) – Evidence objects in support of the synthesis statement.

class indra.statements.statements.Influence(subj, obj, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

An influence on the quantity of a concept of interest.

Parameters:	subj (`indra.statement.Event`) – The event which acts as the influencer. obj (`indra.statement.Event`) – The event which acts as the influencee. evidence (None or `Evidence` or list of `Evidence`) – Evidence objects in support of the statement.

agent_list(deep_sorted=False)[source]¶: Get the canonicalized agent list.

flip_polarity(agent_idx)[source]¶: If applicable, flip the polarity of the statement

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Conversion(subj, obj_from=None, obj_to=None, evidence=None)[source]¶

Bases: indra.statements.statements.Statement

Conversion of molecular species mediated by a controller protein.

Parameters:

subj (indra.statement.Agent) – The protein mediating the conversion.
obj_from (list of indra.statement.Agent) – The list of molecular species being consumed by the conversion.
obj_to (list of indra.statement.Agent) – The list of molecular species being created by the conversion.
evidence (None or Evidence or list of Evidence) – Evidence objects in support of the synthesis statement.

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Unresolved(uuid_str=None, shallow_hash=None, full_hash=None)[source]¶

Bases: indra.statements.statements.Statement

A special statement type used in support when a uuid can’t be resolved.

When using the stmts_from_json method, it is sometimes not possible to resolve the uuid found in support and supported_by in the json representation of an indra statement. When this happens, this class is used as a place-holder, carrying only the uuid of the statement.

class indra.statements.statements.Association(members, evidence=None)[source]¶

Bases: indra.statements.statements.Complex

A set of events associated with each other without causal relationship.

Parameters:	members (list of :py:class:Event) – A list of events associated with each other. evidence (None or `Evidence` or list of `Evidence`) – Evidence objects in support of the modification.

agent_list(deep_sorted=False)[source]¶: Get the canonicalized agent list.

flip_polarity(agent_idx)[source]¶: If applicable, flip the polarity of the statement

to_json(use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Event(concept, delta=None, context=None, evidence=None, supports=None, supported_by=None)[source]¶

Bases: indra.statements.statements.Statement

An event representing the change of a Concept.

concept¶

The concept over which the event is defined.

Type:	indra.statements.concept.Concept

delta¶

Represents a change in the concept, with a polarity and an adjectives entry.

Type:	indra.statements.delta.Delta

context¶

The context associated with the event.

Type:	indra.statements.context.Context

flip_polarity(agent_idx=None)[source]¶: If applicable, flip the polarity of the statement

to_json(with_evidence=True, use_sbo=False, matches_fun=None)[source]¶

Return serialized Statement as a JSON dict.

Parameters:	use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – The JSON-serialized INDRA Statement.
Return type:	dict

class indra.statements.statements.Migration(concept, delta=None, context=None, evidence=None, supports=None, supported_by=None)[source]¶

Bases: indra.statements.statements.Event

A special class of Event representing Migration.

exception indra.statements.statements.InputError[source]¶: Bases: Exception

exception indra.statements.statements.UnresolvedUuidError[source]¶: Bases: Exception

exception indra.statements.statements.InvalidLocationError(name)[source]¶

Bases: ValueError

Invalid cellular component name.

exception indra.statements.statements.InvalidResidueError(name)[source]¶

Bases: ValueError

Invalid residue (amino acid) name.

exception indra.statements.statements.NotAStatementName[source]¶: Bases: Exception

class indra.statements.statements.Concept(name, db_refs=None)[source]¶

Bases: object

A concept/entity of interest that is the argument of a Statement

Parameters:	name (str) – The name of the concept, possibly a canonicalized name. db_refs (dict) – Dictionary of database identifiers associated with this concept.

class indra.statements.statements.Agent(name, mods=None, activity=None, bound_conditions=None, mutations=None, location=None, db_refs=None)[source]¶

Bases: indra.statements.concept.Concept

A molecular entity, e.g., a protein.

Parameters:

name (str) – The name of the agent, preferably a canonicalized name such as an HGNC gene name.
mods (list of ModCondition) – Modification state of the agent.
bound_conditions (list of BoundCondition) – Other agents bound to the agent in this context.
mutations (list of MutCondition) – Amino acid mutations of the agent.
activity (ActivityCondition) – Activity of the agent.
location (str) – Cellular location of the agent. Must be a valid name (e.g. “nucleus”) or identifier (e.g. “GO:0005634”)for a GO cellular compartment.
db_refs (dict) – Dictionary of database identifiers associated with this agent.

entity_matches_key()[source]¶

Return a key to identify the identity of the Agent not its state.

The key is based on the preferred grounding for the Agent, or if not available, the name of the Agent is used.

Returns:	The key used to identify the Agent.
Return type:	str

get_grounding(ns_order=None)[source]¶

Return a tuple of a preferred grounding namespace and ID.

Returns:	A tuple whose first element is a grounding namespace (HGNC, CHEBI, etc.) and the second element is an identifier in the namespace. If no preferred grounding is available, a tuple of Nones is returned.
Return type:	tuple

matches_key()[source]¶: Return a key to identify the identity and state of the Agent.

state_matches_key()[source]¶: Return a key to identify the state of the Agent.

class indra.statements.statements.Evidence(source_api=None, source_id=None, pmid=None, text=None, annotations=None, epistemics=None, context=None, text_refs=None)[source]¶

Bases: object

Container for evidence supporting a given statement.

Parameters:

source_api (str or None) – String identifying the INDRA API used to capture the statement, e.g., ‘trips’, ‘biopax’, ‘bel’.
source_id (str or None) – For statements drawn from databases, ID of the database entity corresponding to the statement.
pmid (str or None) – String indicating the Pubmed ID of the source of the statement.
text (str) – Natural language text supporting the statement.
annotations (dict) – Dictionary containing additional information on the context of the statement, e.g., species, cell line, tissue type, etc. The entries may vary depending on the source of the information.
epistemics (dict) – A dictionary describing various forms of epistemic certainty associated with the statement.
text_refs (dict) – A dictionary of various reference ids to the source text, e.g. DOI, PMID, URL, etc.

There are some attributes which are not set by the parameters above:

source_hash : int: A hash calculated from the evidence text, source api, and pmid and/or source_id if available. This is generated automatcially when the object is instantiated.
stmt_tag : int: This is a hash calculated by a Statement to which this evidence refers, and is set by said Statement. It is useful for tracing ownership of an Evidence object.

get_source_hash(refresh=False)[source]¶

Get a hash based off of the source of this statement.

The resulting value is stored in the source_hash attribute of the class and is preserved in the json dictionary.

to_json()[source]¶: Convert the evidence object into a JSON dict.

class indra.statements.statements.QualitativeDelta(polarity=None, adjectives=None)[source]¶

Bases: indra.statements.delta.Delta

Qualitative delta defining an Event.

Parameters:	polarity (1, -1 or None) – Polarity of an Event. adjectives (list[str]) – Adjectives describing an Event.

class indra.statements.statements.QuantitativeState(entity=None, value=None, unit=None, modifier=None, text=None, polarity=None)[source]¶

Bases: indra.statements.delta.Delta

An object representing numerical value of something.

Parameters:

entity (str) – An entity to capture the quantity of.
value (float or int) – Quantity of a unit (or range?)
unit (str) – Measurement unit of value (e.g. absolute, daily, percentage, etc.)
modifier (str) – Modifier to value (e.g. more than, at least, approximately, etc.)
text (str) – Natural language text describing quantitative state.
polarity (1, -1 or None) – Polarity of an Event.

static convert_unit(source_unit, target_unit, source_value, source_period=None, target_period=None)[source]¶: Convert value per unit from source to target unit. If a unit is absolute, total timedelta period has to be provided. If a unit is a month or a year, it is recommended to pass timedelta period object directly, if not provided, the approximation will be used.

static from_seconds(value_per_second, period)[source]¶: Get total value per given period given timedelta period object and value per second.

static value_per_second(value, period)[source]¶: Get value per second given total value per period and a timedelta period object.

class indra.statements.statements.BioContext(location=None, cell_line=None, cell_type=None, organ=None, disease=None, species=None)[source]¶

Bases: indra.statements.context.Context

An object representing the context of a Statement in biology.

Parameters:

location (Optional[RefContext]) – Cellular location, typically a sub-cellular compartment.
cell_line (Optional[RefContext]) – Cell line context, e.g., a specific cell line, like BT20.
cell_type (Optional[RefContext]) – Cell type context, broader than a cell line, like macrophage.
organ (Optional[RefContext]) – Organ context.
disease (Optional[RefContext]) – Disease context.
species (Optional[RefContext]) – Species context.

class indra.statements.statements.WorldContext(time=None, geo_location=None)[source]¶

Bases: indra.statements.context.Context

An object representing the context of a Statement in time and space.

Parameters:	time (Optional[TimeContext]) – A TimeContext object representing the temporal context of the Statement. geo_location (Optional[RefContext]) – The geographical location context represented as a RefContext

class indra.statements.statements.TimeContext(text=None, start=None, end=None, duration=None)[source]¶

Bases: object

An object representing the time context of a Statement

Parameters:	text (Optional[str]) – A string representation of the time constraint, typically as seen in text. start (Optional[datetime]) – A datetime object representing the start time end (Optional[datetime]) – A datetime object representing the end time duration (int) – The duration of the time constraint in seconds

class indra.statements.statements.RefContext(name=None, db_refs=None)[source]¶

Bases: object

An object representing a context with a name and references.

Parameters:	name (Optional[str]) – The name of the given context. In some cases a text name will not be available so this is an optional parameter with the default being None. db_refs (Optional[dict]) – A dictionary where each key is a namespace and each value is an identifier in that namespace, similar to the db_refs associated with Concepts/Agents.

class indra.statements.statements.Context[source]¶

Bases: object

An abstract class for Contexts.

class indra.statements.statements.MovementContext(locations=None, time=None)[source]¶

Bases: indra.statements.context.Context

An object representing the context of a movement between start and end points in time.

Parameters:	locations (Optional[list[dict]) – A list of dictionaries each containing a RefContext object representing geographical location context and its role (e.g. ‘origin’, ‘destination’, etc.) time (Optional[TimeContext]) – A TimeContext object representing the temporal context of the Statement.

indra.statements.statements.stmts_from_json(json_in, on_missing_support='handle')[source]¶

Get a list of Statements from Statement jsons.

In the case of pre-assembled Statements which have supports and supported_by lists, the uuids will be replaced with references to Statement objects from the json, where possible. The method of handling missing support is controled by the on_missing_support key-word argument.

Parameters:	json_in (iterable[dict]) – A json list containing json dict representations of INDRA Statements, as produced by the to_json methods of subclasses of Statement, or equivalently by stmts_to_json. on_missing_support (Optional[str]) – Handles the behavior when a uuid reference in supports or supported_by attribute cannot be resolved. This happens because uuids can only be linked to Statements contained in the json_in list, and some may be missing if only some of all the Statements from pre- assembly are contained in the list. Options: ’handle’ : (default) convert unresolved uuids into Unresolved Statement objects. ’ignore’ : Simply omit any uuids that cannot be linked to any Statements in the list. ’error’ : Raise an error upon hitting an un-linkable uuid.
Returns:	stmts – A list of INDRA Statements.
Return type:	list[`Statement`]

indra.statements.statements.get_unresolved_support_uuids(stmts)[source]¶: Get uuids unresolved in support from stmts from stmts_from_json.

indra.statements.statements.stmts_to_json(stmts_in, use_sbo=False, matches_fun=None)[source]¶

Return the JSON-serialized form of one or more INDRA Statements.

Parameters:	stmts_in (Statement or list[Statement]) – A Statement or list of Statement objects to serialize into JSON. use_sbo (Optional[bool]) – If True, SBO annotations are added to each applicable element of the JSON. Default: False matches_fun (Optional[function]) – A custom function which, if provided, is used to construct the matches key which is then hashed and put into the return value. Default: None
Returns:	json_dict – JSON-serialized INDRA Statements.
Return type:	dict

indra.statements.statements.stmts_from_json_file(fname, format='json')[source]¶

Return a list of statements loaded from a JSON file.

Parameters:	fname (str) – Path to the JSON file to load statements from. format (Optional[str]) – One of ‘json’ to assume regular JSON formatting or ‘jsonl’ assuming each statement is on a new line.
Returns:	The list of INDRA Statements loaded from the JSOn file.
Return type:	list[indra.statements.Statement]

indra.statements.statements.stmts_to_json_file(stmts, fname, format='json', **kwargs)[source]¶

Serialize a list of INDRA Statements into a JSON file.

Parameters:	stmts (list[indra.statement.Statements]) – The list of INDRA Statements to serialize into the JSON file. fname (str) – Path to the JSON file to serialize Statements into. format (Optional[str]) – One of ‘json’ to use regular JSON with indent=1 formatting or ‘jsonl’ to put each statement on a new line without indents.

indra.statements.statements.get_valid_residue(residue)[source]¶: Check if the given string represents a valid amino acid residue.

indra.statements.statements.draw_stmt_graph(stmts)[source]¶

Render the attributes of a list of Statements as directed graphs.

The layout works well for a single Statement or a few Statements at a time. This function displays the plot of the graph using plt.show().

Parameters:	stmts (list[indra.statements.Statement]) – A list of one or more INDRA Statements whose attribute graph should be drawn.

indra.statements.statements.get_all_descendants(parent)[source]¶: Get all the descendants of a parent class, recursively.

indra.statements.statements.make_statement_camel(stmt_name)[source]¶: Makes a statement name match the case of the corresponding statement.

indra.statements.statements.get_statement_by_name(stmt_name)[source]¶: Get a statement class given the name of the statement class.

indra.statements.statements.make_hash(s, n_bytes)[source]¶: Make the hash from a matches key.

indra.statements.statements.stmt_type(obj, mk=True)[source]¶

Return standardized, backwards compatible object type String.

This is a temporary solution to make sure type comparisons and matches keys of Statements and related classes are backwards compatible.

INDRA Statements (indra.statements)¶

JSON serialization of INDRA Statements¶

INDRA Statements (`indra.statements`)¶