Scalability of MPBNs on random instances¶

In this notebook, we apply the mpbn Python implementation of Most Permissive Boolean Networks (MPBNs) on very large scale Boolean networks (BNs) instances.

The instances have been generated from scale-free random network structure, on which has been applied the inhibitor-dominant rule to devise the logical functions. The generated networks range from 1,000 to 100,000 nodes, with in-degrees ranging up to 1400. See https://doi.org/10.5281/zenodo.3714876 for more information.

The following computations are measured:

• computation of 1 attractor;
• enumeration of (at most) 1000 attractors;
• enumeration of (at most) 1000 attractors reachable from random initial configurations.

The computation times exclude the time for parsing the input text file.

In [1]:
!uname -p

Intel(R) Xeon(R) E-2124 CPU @ 3.30GHz

In [2]:
import time

import numpy.random
import matplotlib.pyplot as plt

import mpbn

In [3]:
def attractors(mbn,limit):
return len(list(mbn.attractors(limit=limit)))
def reachable_attractors(mbn, init):
return len(list(mbn.attractors(reachable_from=init, limit=1000)))

def time_call(func, *args):
t1 = time.perf_counter()
func(*args)
t2 = time.perf_counter()
return t2-t1

def computations(mbn, d):
n = len(mbn)
d["a1"].append((n,time_call(attractors, mbn, 1)))
d["a1000"].append((n,time_call(attractors, mbn, 1000)))
# compute reachable attractors from 3 random initial configurations
for _ in range(3):
init = dict(zip(mbn, numpy.random.choice([0,1],len(mbn))))
d["reach_a"].append((n,time_call(reachable_attractors, mbn, init)))

In [4]:
networks = [
'random-1000-1.bnet',
'random-1000-2.bnet',
'random-1000-3.bnet',
'random-1000-4.bnet',
'random-2000-1.bnet',
'random-2000-2.bnet',
'random-2000-3.bnet',
'random-2000-4.bnet',
'random-5000-1.bnet',
'random-5000-2.bnet',
'random-5000-3.bnet',
'random-5000-4.bnet',
'random-10000-1.bnet',
'random-10000-2.bnet',
'random-10000-3.bnet',
'random-10000-4.bnet',
'random-20000-1.bnet',
'random-20000-2.bnet',
'random-20000-3.bnet',
'random-20000-4.bnet',
'random-50000-1.bnet',
'random-50000-2.bnet',
'random-50000-3.bnet',
'random-50000-4.bnet',
'random-100000-1.bnet',
'random-100000-2.bnet',
'random-100000-3.bnet',
'random-100000-4.bnet']

In [5]:
d = {"a1": [], "a1000": [], "reach_a": []}
for filename in networks:
computations(mbn, d)

In [6]:
legend ={
"a1": "1 attractor",
"a1000": "attractor list (<=1000)",
"reach_a": "reachable attractors",
}
marker = {
"a1": "x",
"a1000": "x",
"reach_a": "*",
}
plt.figure(figsize=(12,10))
plt.rcParams.update({'font.size': 14})
for k, v in d.items():
x,y = zip(*v)
plt.plot(x, y, linestyle='none', marker=marker[k], label=legend[k])
plt.xscale("log")
plt.yscale("log")
plt.legend()
plt.grid(which='both',color='0.95')
plt.ylabel("computation time (s)")
plt.xlabel("number of components")
plt.savefig("generated/scalability-random.pdf");

In [ ]: