Perturbation and sensitivity analysis of Boolean networks

In this tutorial, we study how Boolean networks respond to perturbations. Rather than implementing perturbations manually, we leverage BoolForge’s built-in robustness and sensitivity measures.

What you will learn

You will learn how to:

• quantify robustness and fragility of Boolean networks under synchronous update,

• interpret basin-level and attractor-level robustness measures,

• perform exact and approximate robustness computations, and

• compute Derrida values as a measure of dynamical sensitivity.

Together, these tools allow us to assess dynamical stability and resilience of Boolean network models in a principled and computationally efficient way.

Setup

import boolforge as bf
import pandas as pd

We reuse the small Boolean network from the previous tutorial as a running example.

string = """
x = y
y = x OR z
z = y
"""

bn = bf.BooleanNetwork.from_string(string, separator="=")

print("Variables:", bn.variables)
print("Number of nodes:", bn.N)

Variables: ['x' 'y' 'z']
Number of nodes: 3

Exact attractors and robustness measures

BoolForge provides a single method that computes:

• all attractors,

• basin sizes,

• overall network coherence and fragility,

• basin-level coherence and fragility, and

• attractor-level coherence and fragility.

These quantities are defined via systematic single-bit perturbations in the Boolean hypercube and can be computed exactly for small networks.

results_exact = bn.get_attractors_and_robustness_synchronous_exact()
for key in results_exact.keys():
    print(key)

Attractors
NumberOfAttractors
BasinSizes
AttractorID
Coherence
Fragility
BasinCoherences
BasinFragilities
AttractorCoherences
AttractorFragilities

For convenience, information about the dynamics (attractors, basin sizes, etc), described in detail in the previous tutorial, is also returned by this method.

print("Number of attractors:", results_exact["NumberOfAttractors"])
print("Attractors (decimal states):", results_exact["Attractors"])
print("Eventual attractor of each state:", results_exact["AttractorID"])

print("Basin sizes:", results_exact["BasinSizes"])

Number of attractors: 3
Attractors (decimal states): [[0], [2, 5], [7]]
Eventual attractor of each state: [0 1 1 2 1 1 2 2]
Basin sizes: [0.125 0.5   0.375]

Network-, basin- and attractor-level robustness

Robustness can be resolved at different structural levels. Network-level metrics report the average robustness of any network state when subjected to perturbation.

print("Overall coherence:", results_exact["Coherence"])
print("Overall fragility:", results_exact["Fragility"])

Overall coherence: 0.3333333333333333
Overall fragility: 0.3333333333333333

The same robustness metrics, coherence and fragility, can also be averaged across a smaller set of states, e.g., all states in one basin of attraction, or an even smaller set of states, e.g., all states that form an attractor.

df_basins = pd.DataFrame({
    "BasinSizes": results_exact["BasinSizes"],
    "BasinCoherences": results_exact["BasinCoherences"],
    "BasinFragilities": results_exact["BasinFragilities"],
})

df_attractors = pd.DataFrame({
    "AttractorCoherences": results_exact["AttractorCoherences"],
    "AttractorFragilities": results_exact["AttractorFragilities"],
})

print("Basin-level robustness:")
print(df_basins)

print("Attractor-level robustness:")
print(df_attractors)

Basin-level robustness:
   BasinSizes  BasinCoherences  BasinFragilities
0       0.125         0.000000          0.500000
1       0.500         0.333333          0.333333
2       0.375         0.444444          0.277778
Attractor-level robustness:
   AttractorCoherences  AttractorFragilities
0             0.000000              0.500000
1             0.333333              0.333333
2             0.666667              0.166667

Interpretation:

• Coherence measures the fraction of single-bit perturbations that do not change the final attractor.

• Fragility measures how much the attractor state changes.

The robustness metrics considered thus far describe how a single perturbation affects the network dynamics in the long-term, i.e., at the attractor. These metrics are very meaningful biologically because attractors typically correspond to cell types of phenotypes.

It turns out that attractors in biological networks are often less stable than their basins, a phenomenon explored in detail in Tutorial 10.

Approximate robustness for larger networks

For larger networks, exact enumeration of all \(2^N\) states is infeasible. BoolForge therefore provides a Monte Carlo approximation that samples random initial conditions and perturbations.

results_approx = bn.get_attractors_and_robustness_synchronous(n_simulations=500)

print("Number of attractors (lower bound):", results_approx["NumberOfAttractorsLowerBound"])
print("Approximate coherence:", results_approx["CoherenceApproximation"])
print("Approximate fragility:", results_approx["FragilityApproximation"])

Number of attractors (lower bound): 3
Approximate coherence: 0.352
Approximate fragility: 0.324

Even when only using 500 random initial states, the approximate values closely match the exact ones. For larger networks, these approximations are often the only feasible option.

Derrida value: dynamical sensitivity

An older and very popular robustness metric, the Derrida value, measures how perturbations propagate after one synchronous update. It is defined as the expected Hamming distance between updated states that initially differed in exactly one bit.

BoolForge includes routines for the exact calculation and estimation of Derrida values. For networks with low degree, the exact calculation is strongly preferable. It is faster and more accurate.

derrida_exact = bn.get_derrida_value(exact=True)
derrida_approx = bn.get_derrida_value(n_simulations=2000)

print("Exact Derrida value:", derrida_exact)
print("Approximate Derrida value:", derrida_approx)

Exact Derrida value: 1.0
Approximate Derrida value: 1.0

Interpretation:

• Small Derrida values indicate ordered, stable dynamics.

• Large Derrida values indicate sensitive or chaotic dynamics.

Derrida values are closely related to average sensitivity of the update functions, and provide a complementary notion of robustness.

Summary

In this tutorial you learned how to:

• compute exact robustness measures for small Boolean networks,

• interpret coherence and fragility at network, basin, and attractor levels,

• approximate robustness measures for larger networks, and

• assess dynamical sensitivity using the Derrida value.

In Tutorial 9, we will finally analyze biological Boolean network models and design ensemble experiments.