BNMPy.experiment_data¶

The experiment_data module provides tools for loading and managing experimental data for PBN optimization.

class BNMPy.experiment_data.ExperimentData[source]¶

Bases: object

Handle experimental data from CSV files

Methods

`get_experiment_summary`(experiments)	Get summary of experiments for debugging/inspection
`load_from_csv`(csv_file)	Load experiments from CSV file
`validate_experiments`(experiments, node_dict)	Validate experiments against PBN structure

static load_from_csv(csv_file)[source]¶

Load experiments from CSV file

Only required columns are used. Extra columns in the CSV are ignored.

CSV Format: Experiments,Stimuli,Stimuli_efficacy,Inhibitors,Inhibitors_efficacy,Measured_nodes,Measured_values 1,TGFa,1,TNFa,1,”NFkB,ERK,C8,Akt”,”0.7,0.88,0,1” 2,TNFa,1,TGFa,1,”NFkB,ERK,C8,Akt”,”0.3,0.12,1,0” 3,”TGFa,TNFa”,”1,1”,,,”NFkB,ERK,C8,Akt”,”1,1,1,1” 4,”TGFa,TNFa”,”1,1”,PI3K,0.7,”NFkB,ERK,C8,Akt”,”0.3,0.12,1,0”

Note: - The measured values are normalized to be between 0 and 1 if not already. - Simply divide the measured values by the maximum value of the measured values. - Stimuli_efficacy and Inhibitors_efficacy are optional columns. - If efficacy is not specified, defaults to 1.0 (full efficacy). - Efficacy < 1 means the probability of achieving the target state is reduced.

static validate_experiments(experiments, node_dict)[source]¶: Validate experiments against PBN structure

static get_experiment_summary(experiments)[source]¶: Get summary of experiments for debugging/inspection

BNMPy.experiment_data.extract_experiment_nodes(csv_file)[source]¶: Extract measured and perturbed nodes from experimental CSV file.

BNMPy.experiment_data.generate_experiments(pbn, experiment_csv: str, config: dict | None = None, output_csv: str | None = None, round_to: int = 4) → DataFrame[source]¶

Generate hypothesized experimental values using the current PBN parameters.

This function simulates the experiments defined in the CSV file using the current PBN parameters and generates predicted values for the measured nodes.

ExperimentData Class¶

class BNMPy.experiment_data.ExperimentData[source]¶

Bases: object

Handle experimental data from CSV files

Methods

`get_experiment_summary`(experiments)	Get summary of experiments for debugging/inspection
`load_from_csv`(csv_file)	Load experiments from CSV file
`validate_experiments`(experiments, node_dict)	Validate experiments against PBN structure

static load_from_csv(csv_file)[source]¶

Load experiments from CSV file

Only required columns are used. Extra columns in the CSV are ignored.

static validate_experiments(experiments, node_dict)[source]¶: Validate experiments against PBN structure

static get_experiment_summary(experiments)[source]¶: Get summary of experiments for debugging/inspection

Overview¶

The ExperimentData class loads and validates experimental data from CSV files. It handles stimuli, inhibitors, and measured values for model optimization.

Data Format¶

CSV file format:

Experiments,Stimuli,Stimuli_efficacy,Inhibitors,Inhibitors_efficacy,Measured_nodes,Measured_values
1,TGFa,1,TNFa,1,"NFkB,ERK,C8,Akt","0.7,0.88,0,1"
2,TNFa,1,TGFa,1,"NFkB,ERK,C8,Akt","0.3,0.12,1,0"
3,"TGFa,TNFa","1,1",,,"NFkB,ERK,C8,Akt","1,1,1,1"
4,"TGFa,TNFa","1,1",PI3K,0.7,"NFkB,ERK,C8,Akt","0.3,0.12,1,0"

Column Descriptions¶

Experiments: Experiment identifier
Stimuli: Nodes fixed to 1 (comma-separated)
Stimuli_efficacy: Efficacy values 0-1 (optional, defaults to 1.0)
Inhibitors: Nodes fixed to 0 (comma-separated)
Inhibitors_efficacy: Efficacy values 0-1 (optional, defaults to 1.0)
Measured_nodes: Nodes with experimental measurements
Measured_values: Corresponding values 0-1 (normalized)

Efficacy Values¶

1.0 (default): Full efficacy - node completely knocked out/stimulated
< 1.0: Partial efficacy - creates probabilistic perturbation
- For inhibitors (target=0): P(node=0) = efficacy, P(node=1) = 1-efficacy
- For stimuli (target=1): P(node=1) = efficacy, P(node=0) = 1-efficacy
Example: PI3K,0.7 means PI3K inhibition has 70% probability of setting PI3K=0, 30% of PI3K=1

Basic Usage¶

import BNMPy

# Load experimental data
exp_data = BNMPy.ExperimentData("experiments.csv")

# Access experiment information
print(f"Number of experiments: {len(exp_data.experiments)}")
print(f"Measured nodes: {exp_data.get_measured_nodes()}")
print(f"Perturbed nodes: {exp_data.get_perturbed_nodes()}")

# Iterate through experiments
for exp in exp_data.experiments:
    print(f"Experiment {exp['id']}:")
    print(f"  Stimuli: {exp['stimuli']}")
    print(f"  Inhibitors: {exp['inhibitors']}")
    print(f"  Measurements: {exp['measurements']}")

Utility Functions¶

extract_experiment_nodes¶

Extract measured and perturbed nodes from experimental data:

measured_nodes, perturbed_nodes = BNMPy.extract_experiment_nodes("experiments.csv")
print(f"Measured: {measured_nodes}")
print(f"Perturbed: {perturbed_nodes}")

This is useful for model compression:

# Extract nodes from experiments
measured_nodes, perturbed_nodes = BNMPy.extract_experiment_nodes("experiments.csv")

# Compress model based on experimental information
compressed_network, info = BNMPy.compress_model(
    network,
    measured_nodes=measured_nodes,
    perturbed_nodes=perturbed_nodes
)

generate_experiments¶

Generate hypothesized experimental values using the current PBN parameters:

# Generate predictions for experiments
results_df = BNMPy.generate_experiments(
    pbn,
    experiment_csv="experiments.csv",
    output_csv="predicted_experiments.csv",
    config={'steady_state': {'method': 'monte_carlo'}}
)

# The output DataFrame contains:
# - Original experiment conditions in experiment_csv
# - Predicted values based on current PBN parameters

This is useful for:

Validating model predictions before optimization
Comparing different parameter sets
Generating synthetic data for testing