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Model Enumerations

This module defines enumerations throughout Cellnition, primarily in the construction of regulatory network models.

CouplingType

Bases: Enum

In both continuous (ProbabilityNet) and Boolean (BooleanNet) regulatory network models, the CouplingType supplies a heuristic for the case where multiple nodes regulate the activity of a single downstream node. The different coupling types are:

  • additive: when multiple nodes act on a downstream node, their influences combine additively ("OR" function), regardless of whether they are activators or inhibitors.
  • multiplicative: when multiple nodes act on a downstream node, their influences combine multiplicatively ("AND" function), regardless of whether they are activators or inhibitors.
  • mix1: when multiple nodes act on a downstream node, activators combine additively ("OR" function), while inhibitors combine multiplicatively ("AND" function).
  • mix2: when multiple nodes act on a downstream node, inhibitors combine additively ("OR" function), while activators combine multiplicatively ("AND" function).
Source code in cellnition/science/network_models/network_enums.py 
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class CouplingType(Enum):
    '''
    In both continuous ([`ProbabilityNet`][cellnition.science.network_models.probability_networks.ProbabilityNet])
    and Boolean ([`BooleanNet`][cellnition.science.network_models.boolean_networks.BooleanNet]) regulatory
    network models, the CouplingType supplies a heuristic for the case where multiple nodes regulate the activity
    of a single downstream node. The different coupling types are:

    - `additive`: when multiple nodes act on a downstream node, their influences combine additively ("OR" function),
    regardless of whether they are activators or inhibitors.
    - `multiplicative`: when multiple nodes act on a downstream node, their influences combine multiplicatively ("AND" function),
    regardless of whether they are activators or inhibitors.
    - `mix1`: when multiple nodes act on a downstream node, activators combine additively ("OR" function), while
     inhibitors combine multiplicatively ("AND" function).
    - `mix2`: when multiple nodes act on a downstream node, inhibitors combine additively ("OR" function), while
     activators combine multiplicatively ("AND" function).

    '''
    additive = 'additive'
    multiplicative = 'multiplicative'
    mix1 = 'mix1' # activators "OR", inhibitors "AND"
    specified = 'specified'
    mix2 = 'mix2' # Activators "AND", inhibitors "OR"

EdgeType

Bases: Enum

Specify whether the directed edge of the regulatory network has an activating effect on the level of the target node (EdgeType.A), an inhibiting effect on the level of the target node (EdgeType.I). In non-regulatory network models, an additional neutral directed edge EdgeType.N can be specified.

Source code in cellnition/science/network_models/network_enums.py 
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class EdgeType(Enum):
    '''
    Specify whether the directed edge of the regulatory network
    has an activating effect on the level of the target node (`EdgeType.A`),
    an inhibiting effect on the level of the target node (`EdgeType.I`). In
    non-regulatory network models, an additional neutral directed
    edge `EdgeType.N` can be specified.

    '''
    A = 'Activator'
    I = 'Inhibitor'
    N = 'Neutral'
    As = 'Multiplicative Activation'
    Is = 'Multiplicative Inhibition'

EquilibriumType

Bases: Enum

When treating the regulatory network as a dynamic system, the identified equilibrium states have a particular dynamic character, which Cellnition marks using EquilibriumType:

  • attractor: an asymptotically-stable point attractor that moves monotonically to the attractor.
  • attractor_limit_cycle: an asymptotically-stable attractor that moves with diminishing-amplitude oscillations to the attractor.
  • limit_cycle: an attractor that perpetually repeats cyclic oscillations about a central point.
  • saddle: a metastable saddle-type attractor that leaves the attractor with small perturbations.
  • undetermined: an equilibrium with undetermined characteristics.
  • hidden: an attractor that was only found in time-series or pseudo-time series investigations.
Source code in cellnition/science/network_models/network_enums.py 
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class EquilibriumType(Enum):
    '''
    When treating the regulatory network as a dynamic system, the
    identified equilibrium states have a particular dynamic character,
    which Cellnition marks using EquilibriumType:

    - `attractor`: an asymptotically-stable point attractor that moves monotonically to the attractor.
    - `attractor_limit_cycle`: an asymptotically-stable attractor that moves with diminishing-amplitude oscillations to the attractor.
    - `limit_cycle`: an attractor that perpetually repeats cyclic oscillations about a central point.
    - `saddle`: a metastable saddle-type attractor that leaves the attractor with small perturbations.
    - `undetermined`: an equilibrium with undetermined characteristics.
    - `hidden`: an attractor that was only found in time-series or pseudo-time series investigations.

    '''
    attractor = 0
    attractor_limit_cycle = 1
    limit_cycle = 2
    saddle = 3
    repellor = 4
    repellor_limit_cycle = 5
    undetermined = 6
    hidden = 7 # corresponds to a hidden attractor

GraphType

Bases: Enum

When creating procedural graphs, the GraphType.scale_free tag can be used to generate graphs with scale-free degree distributions, whereas the GraphType.random can be used to generate random networks with binomial degree distributions.

Source code in cellnition/science/network_models/network_enums.py 
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class GraphType(Enum):
    '''
    When creating procedural graphs, the `GraphType.scale_free`
    tag can be used to generate graphs with scale-free degree
    distributions, whereas the `GraphType.random` can be used to
    generate random networks with binomial degree distributions.

    '''
    scale_free = 'Scale Free'
    random = 'Random'
    user = 'User Defined'

InterFuncType

Bases: Enum

In continuous models (see ProbabilityNet), this enumeration specifies the type of interaction function used when the level of one node acts to regulate the level of another node. InterFuncType.logistic specifies logistic function interactions (see f_acti_logi_s and f_inhi_logi_s), whereas InterFuncType.hill specifies hill function interactions (see f_acti_hill_s and f_inhi_hill_s).

Source code in cellnition/science/network_models/network_enums.py 
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class InterFuncType(Enum):
    '''
    In continuous models (see
    [`ProbabilityNet`][cellnition.science.network_models.probability_networks.ProbabilityNet]),
    this enumeration specifies the type of interaction function used when the level of one node
    acts to regulate the level of another node.
    InterFuncType.logistic specifies logistic function interactions
    (see [`f_acti_logi_s`][cellnition.science.network_models.interaction_functions.f_acti_logi_s] and
    [`f_inhi_logi_s`][cellnition.science.network_models.interaction_functions.f_inhi_logi_s]), whereas
    InterFuncType.hill specifies hill function interactions (see
    [`f_acti_hill_s`][cellnition.science.network_models.interaction_functions.f_acti_hill_s] and
    [`f_inhi_hill_s`][cellnition.science.network_models.interaction_functions.f_inhi_hill_s]).

    '''
    logistic = 'Logistic'
    hill = 'Hill'

NodeType

Bases: Enum

Specify the node type of the model. Typically, nodes are NodeType.gene by default. Other node types are ascribed for tagging nodes in networks (e.g. NodeType.Cycle could be used to color nodes present in network cycles), but generally NodeTypes other than NodeType.gene are not presently utilized in Cellnition and are planned for future work.

Source code in cellnition/science/network_models/network_enums.py 
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class NodeType(Enum):
    '''
    Specify the node type of the model. Typically, nodes are
    `NodeType.gene` by default. Other node types are ascribed for
    tagging nodes in networks (e.g. `NodeType.Cycle` could be
    used to color nodes present in network cycles), but generally
    NodeTypes other than `NodeType.gene` are not presently utilized
    in Cellnition and are planned for future work.

    '''
    gene = 'Gene'
    signal = 'Signal'
    process = 'Process'
    sensor = 'Sensor'
    effector = 'Effector'
    core = 'Hub Core'
    factor = 'Factor'
    cycle = 'Cycle'