BNS tutorial for fixed points and synchronous attractors

The bns package provides functions for the identification of fixed points or of all synchronous attractors. These methods call the BNS tool uses a SAT solver to compute attractors of synchronous models.

import bns
from colomoto_jupyter import tabulate

tabulate(bns.fixpoints("cellcycle.cnet"))

	0	1	2	3	4	5	6	7	8	9
0	0	1	0	0	0	1	0	1	0	0

tabulate(bns.attractors("cellcycle.cnet"))

		0	1	2	3	4	5	6	7	8	9
0	0	0	1	0	0	0	1	0	1	0	0
1	0	1	0	0	0	0	0	1	1	1	0
	5	1	0	0	0	1	0	0	0	1	1
	6	1	0	0	0	1	0	1	0	1	1
	4	1	0	0	1	1	0	0	0	0	0
	1	1	0	1	0	0	0	0	1	1	0
	2	1	0	1	1	0	0	0	1	0	0
	3	1	0	1	1	1	0	0	1	0	0

The search can be restricted to attractors of a specific length. Note that an attractor on length k is also an attractor of length n*k.

This restriction is in particular used to identify only fixed points, wich are found for any length.

tabulate(bns.attractors("cellcycle.cnet", length=3))

	0	1	2	3	4	5	6	7	8	9
0	0	1	0	0	0	1	0	1	0	0

Import from GINsim or bioLQM

Models can also be imported from biolqm and ginsim objects. In that case, the names of the nodes will be used instead of a number.

import ginsim

lrg = ginsim.load("http://ginsim.org/sites/default/files/boolean_cell_cycle.zginml")

Downloading http://ginsim.org/sites/default/files/boolean_cell_cycle.zginml

tabulate(bns.attractors(lrg))

		CycD	Rb	E2F	CycE	CycA	p27	Cdc20	cdh1	UbcH10	CycB
0	0	0	1	0	0	0	1	0	1	0	0
1	0	1	0	0	0	0	0	1	1	1	0
	5	1	0	0	0	1	0	0	0	1	1
	6	1	0	0	0	1	0	1	0	1	1
	4	1	0	0	1	1	0	0	0	0	0
	1	1	0	1	0	0	0	0	1	1	0
	2	1	0	1	1	0	0	0	1	0	0
	3	1	0	1	1	1	0	0	1	0	0