BNMPy.steady_state

class BNMPy.steady_state.SteadyStateCalculator(network)

Steady-state calculation for Boolean and Probabilistic Boolean Network Supports both Two-State Markov Chain (TSMC) and Monte Carlo methods Reference: pbnStationary_TS.m from optPBN (Trairatphisan et al. 2014)

Methods

compute_stationary_deterministic([max_steps])	Find deterministic steady state (attractors) for Boolean networks TODO: need more discussion on this
compute_stationary_mc([n_runs, n_steps, p_noise])	Monte Carlo steady-state calculation, handle input nodes and constant nodes
compute_stationary_tsmc([epsilon, r, s, ...])	Two-State Markov Chain steady-state calculation In addition to the original function, handle input nodes and constant nodes
compute_steady_state([method])	steady-state calculation
get_convergence_info()	Get information about convergence properties
reset_network_conditions()	Reset network to original state
set_experimental_conditions([stimuli, ...])	Set experimental conditions

compute_stationary_deterministic(max_steps: int = 1000) → ndarray | None

Find deterministic steady state (attractors) for Boolean networks TODO: need more discussion on this

compute_stationary_mc(n_runs: int = 10, n_steps: int = 1000, p_noise: float = 0) → ndarray

Monte Carlo steady-state calculation, handle input nodes and constant nodes

compute_stationary_tsmc(epsilon: float = 0.001, r: float = 0.025, s: float = 0.95, p_noise: float = 0, p_mir: float = 0.001, initial_nsteps: int = 100, max_iterations: int = 500, freeze_self_loop: bool = False) → ndarray

Two-State Markov Chain steady-state calculation In addition to the original function, handle input nodes and constant nodes

Ref: pbnStationary_TS.m Approach 1

compute_steady_state(method: str = 'tsmc', **kwargs) → ndarray

steady-state calculation

get_convergence_info() → Dict[str, Any]

Get information about convergence properties

reset_network_conditions()

Reset network to original state

set_experimental_conditions(stimuli: List[str] | None = None, stimuli_efficacy: List[float] | None = None, inhibitors: List[str] | None = None, inhibitors_efficacy: List[float] | None = None, node_dict: Dict[str, int] | None = None)

Set experimental conditions