booldog.continuous.semi_quantitative

Attributes

logger

Classes

ContinuousMixin

Class for Continuous simulations functions.

Module Contents

booldog.continuous.semi_quantitative.logger
class booldog.continuous.semi_quantitative.ContinuousMixin

Class for Continuous simulations functions.

This class is not intended to be used directly, but rather as a mixin.

transform_bool_to_continuous(transform='normalisedhillcube', **kwargs)

Generate an ODE from RegulatoryNetwork/Boolean graph.

Note that the Network object is kept in memory as the primes of the Boolean network. This means that importing the graph may take a while, depending on the size of the network.

Parameters:

  • transform (str) – One of accepted transforms. See booldog.ode.transforms for accepted options.

  • **kwargs – Additional keyword arguments passed to booldog.ODE().

continuous_simulation(node_events=None, edge_events=None, t_min=0, t_max=30, initial_state=0, ode_system=None, solver=solve_ivp, **kwargs)

Run continuous semi-quantitative simulation.

Parameters:

  • node_events (None or list of dict, optional) – List of node events with a dictionary defining each event. See Notes for description of event definitions.

  • edge_events (None or list of dict, optional) – Disrupt connections #TODO not implemented

  • t_min (float, optional) – Interval of integration, simulation starts with t=t_min and integrates until it reaches t=t_max.

  • t_max (float, optional) – Interval of integration, simulation starts with t=t_min and integrates until it reaches t=t_max.

  • initial_state (float or int or array or dict, optional) – Initial state of nodes. See Notes for description of format.

  • ode_system (None or booldog.ODE(), optional) – If none, the ODE is created with transform_bool_to_continuous()

  • **kwargs –

    If ode_system is None, additional keyword arguments are passed to transform_bool_to_continuous(). For description of these arguments see booldog.ODE().

    If plot=True , additional keyword arguments are passed to plot_simulation().

Returns:

  • r (object #TODO)

  • t (ndarray, shape (n_time_points,)) – Time-points.

  • y (ndarray, shape (n_time_points, n_nodes)) – Values of the solution at t.

Notes

Format of the node_events parameter

The node events are passed as a list of dictionaries defining each event. Dictionary keys are:

  • time: time at which the event occurs

  • node: name of node which is perturbed

  • value: value to which the node is set

  • duration: (optional) duration for which the node is fixed if longer than 0, (i.e. not a point perturbation)

Example - at timepoint 10, node X is set to 0.25 for 5 time-steps. and at timepoint 12, node Y and Z are set to 1 for 0 timesteps:

node_events = [
    {'time':10, 'node':'X', 'value':.25, 'duration':5},
    {'time':12, 'node':'Y', 'value':1},
    {'time':12, 'node':'X', 'value':1}
]
Format of the initial_state parameter

If the initial state is an int or float, the value is assigned for all variables. Otherwise the parameter argument should be a dict with keys as node names and values for their initial state. In this case, if the initial state is not defined for all nodes, a default key with the default value should also be present in the dict.