corneto.methods.signaling.CellNOptDAG#

class corneto.methods.signaling.CellNOptDAG(lambda_reg=0.001, max_flow=None, epsilon=1.0, backend=None)#

Bases: FlowMethod

Infer a shared acyclic Boolean model from multiple conditions.

The method selects reactions globally and evaluates their Boolean truth in every condition. A single nonnegative flow has exactly the selected reaction dependencies as its internal support. Conservation, positive support, and acyclicity therefore require every selected dependency to lie on a path from a controlled input or inhibitor to a measured output.

Dummy vertices named AND<number> are compiled into one reaction per product. All operands of such a reaction share one selection variable and are evaluated conjunctively.

Parameters:

lambda_reg (float) – Penalty for every selected reaction.
max_flow (Optional[float]) – Upper bound for structural flow. By default, the number of compiled dependency edges is used.
epsilon (float) – Minimum flow on every selected dependency edge.
backend (Optional[Backend]) – Optimization backend.

__init__(lambda_reg=0.001, max_flow=None, epsilon=1.0, backend=None)#

Parameters:

lambda_reg (float)
max_flow (float | None)
epsilon (float)
backend (Backend | None)

Methods

`__init__`([lambda_reg, max_flow, epsilon, ...])
`build`(pkn, *, inputs, measurements[, inhibitors])	Build a single-condition CellNOpt problem.
`build_from_data`(graph[, data])	Build the complete optimization problem.
`build_many`(pkn, *, inputs, measurements[, ...])	Build a problem for multiple named experimental conditions.
`create_flow_based_problem`(flow_problem, ...)	Add vectorized Boolean propagation and shared-flow selection.
`create_problem`(graph, data)	Create the optimization problem using a flow-based formulation.
`description`()	Return a short method description.
`get_flow_bounds`(graph, data)	Return bounds for the single shared structural flow.
`name`()	Return the method name.
`preprocess`(graph, data)	Compile reactions, validate condition data, and add flow boundaries.
`references`()	Returns citation keys for this method.
`show_bibtex`()	Display raw BibTeX entries in a formatted block for easy copying.
`show_references`()	Display formatted citations in a Jupyter notebook.

Attributes

backend

Return the optimization backend being used.

build(pkn, *, inputs, measurements, inhibitors=None)#

Build a single-condition CellNOpt problem.

Parameters:: pkn (BaseGraph)
Return type:: ProblemDef

build_many(pkn, *, inputs, measurements, inhibitors=None)#

Build a problem for multiple named experimental conditions.

Parameters:: pkn (BaseGraph)
Return type:: ProblemDef

preprocess(graph, data)#

Compile reactions, validate condition data, and add flow boundaries.

Parameters:

graph (BaseGraph)
data (Data)

get_flow_bounds(graph, data)#

Return bounds for the single shared structural flow.

Parameters:

graph (BaseGraph)
data (Data)

create_flow_based_problem(flow_problem, graph, data)#

Add vectorized Boolean propagation and shared-flow selection.

Parameters:

flow_problem (ProblemDef)
graph (BaseGraph)
data (Data)

Return type:

ProblemDef

static name()#

Return the method name.

Return type:: str

static description()#

Return a short method description.

Return type:: str