Boolean model for regulatory networks

bool.py

#########################################
## Boolean model for regulatory networks
#########################################

def update_node(G, states, i):
    ''' Return new state of node i given states vector '''


def update(G, states):
    ''' Return the next states vector - apply transition function to all nodes '''



def run(G, init, track = False):
    ''' Start with init vector, and run until steady state
        Does not detect loops for the moment
        Return the fixed point vector '''




#############################################
### Input network - toy example from lecture
print("Toy example:")
nodes = ['A','B','C','D','E','F'] 

init = [1, 0, 1, 0, 0, 0]

G = [
    [ 0, 0, 0, 1, 0, 0],
    [-1, 0, 0,-1,-1, 0],
    [-1, 0, 0, 0, 0, 0],
    [ 1, 0, 0, 0, 1, 0],
    [ 0, 0, 0, 0, 0, 1],
    [ 0, 0, 0,-1, 1,-1]
    ]

print(nodes)
run(G, init, True)


#############################################
### Input network - yeast cell-cycle
### "The Yeast Cell-Cycle Network is Robustly Designed", Li et.al, PNAS, 2004

##print("Yeast cell cycle:")
##nodes = ['Cln3','MBF','SBF','Cln1,2','Cdh1','Swi5','Cdc20/Cdc14','Clb5,6','Sic1','Clb1,2','Mcm1/SFF'] 
##
##init = [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0] #simulation of this is in the paper
##
##G = [
##    [-1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
##    [ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
##    [ 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
##    [ 0, 0, 0,-1,-1, 0, 0, 0,-1, 0, 0],
##    [ 0, 0, 0, 0, 0, 0, 0, 0, 0,-1, 0],
##    [ 0, 0, 0, 0, 0,-1, 0, 0, 1, 0, 0],
##    [ 0, 0, 0, 0, 1, 1,-1,-1, 1,-1, 0],
##    [ 0, 0, 0, 0,-1, 0, 0, 0,-1, 1, 1],
##    [ 0, 0, 0, 0, 0, 0, 0,-1, 0,-1, 0],
##    [ 0,-1,-1, 0,-1,-1, 1, 0,-1, 0, 1],
##    [ 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,-1]
##    ]
##
##print(nodes)
##run(G, init, True)
##
##final = [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0] #according to paper, 1764 initial vectors end here
##print(attractor_size(G, final), "initial vectors end at", final)




























##def update_node(G, states, i):
##    ''' Return new state of node i given states vector '''
##    n = len(states)
##    s = 0
##
##    for j in range(n):
##        s += G[j][i] * states[j]
##    if s > 0:
##        new = 1
##    elif s < 0:
##        new = 0
##    else:
##        new = states[i]
##
##    return new
##
##def update(G, states):
##    ''' Return the next states vector - apply transition function to all nodes '''
##    n = len(states)
##    next_states = [-1]*n
##
##    for i in range(n):
##        next_states[i] = update_node(G, states,i)
##    return next_states
##
##
##def run(G, init, track = False):
##    ''' Start with init vector, and run until steady state
##        Does not detect loops for the moment
##        Return the fixed point vector '''
##    if track:
##        print(init)
##
##    states = init
##    next_states = update(G, states)
##
##    while next_states != states:
##        if track:
##            print(next_states)
##        states = next_states
##        next_states = update(G, states)
##    return states #steady state (attractor) reached



##import itertools
##
##def attractor_size(G, attractor):
##    ''' count how many initial vectors end up in the attractor vector '''
##    cnt=0
##    n = len(G)
##    for states in itertools.product([0,1], repeat=n): #cartesian product
##        states = list(states) #convert tuple-->list
##        final = run(G, states)
##        if final == attractor:
##            cnt+=1
##    return cnt