def main():
print "time_evol module is the main code."
## to import a network of 3-node example
EDGE_FILE = 'C:\Boolean_Delay_in_Economics\Manny\EDGE_FILE.dat'
NODE_FILE = 'C:\Boolean_Delay_in_Economics\Manny\NODE_FILE.dat'
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
'''
## to obtain time series data for all possible initial conditions for 3-node example network
timeSeriesData = ensemble_time_series(net, nodes_list, 2, 10)#, Nbr_States=2, MAX_TimeStep=20)
initState = 1
biStates = decimal_to_binary(nodes_list, initState)
print 'initial state', biStates
## to print time series data for each node: a, b, c starting particualr decimal inital condition 1
print 'a', timeSeriesData['a'][1]
print 'b', timeSeriesData['b'][1]
print 'c', timeSeriesData['c'][1]
'''
## to obtain and visulaize transition map in the network state space
decStateTransMap = net_state_transition(net, nodes_list)
nx.write_graphml(decStateTransMap,'C:\Boolean_Delay_in_Economics\Manny\Results\BDE.graphml')
'''
def main():
print "time_evol module is the main code."
EDGE_FILE = '../data/example/example-net-edges.dat'
NODE_FILE = '../data/example/example-net-nodes.dat'
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
timeSeriesData = ensemble_time_series(net, nodes_list, 2, 10)#, Nbr_States=2, MAX_TimeStep=20)
initState = 1
biStates = decimal_to_binary(nodes_list, initState)
print 'initial state', biStates
print 'CycD', timeSeriesData['CycD'][1]
print 'Rb', timeSeriesData['Rb'][1]
print 'E2F', timeSeriesData['E2F'][1]
print 'CycE', timeSeriesData['CycE'][1]
print 'CycA', timeSeriesData['CycA'][1]
print 'P27', timeSeriesData['P27'][1]
print 'Cdc20', timeSeriesData['Cdc20'][1]
print 'UbcH10', timeSeriesData['UbcH10'][1]
print 'Cdh1', timeSeriesData['Cdh1'][1]
print 'CycB', timeSeriesData['CycB'][1]
decStateTransMap = net_state_transition(net, nodes_list)
#nx.draw(decStateTransMap)
plt.show()
nx.write_graphml(decStateTransMap, '/home/tessa/Documents/cyclegraph.graphml')
attractors = find_attractor(decStateTransMap)
print attractors
def main():
print "updating_rule module is the main code."
EDGE_FILE = '../data/example/example-net-edges.dat'
NODE_FILE = '../data/example/example-net-nodes.dat'
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
#prevState = {'CycD':0.0, 'b':0.0, 'c':1.0}
prevState = {}
prevState['CycD'] = float(sys.argv[1])
prevState['Rb'] = float(sys.argv[2])
prevState['E2F'] = float(sys.argv[3])
prevState['CycE'] = float(sys.argv[4])
prevState['CycA'] = float(sys.argv[5])
prevState['P27'] = float(sys.argv[6])
prevState['Cdc20'] = float(sys.argv[7])
prevState['Cdh1'] = float(sys.argv[8])
prevState['UbcH10'] = float(sys.argv[9])
prevState['CycB'] = float(sys.argv[10])
#print "network state @ previous step", OrderedDict(sorted(prevState.items(), key=lambda t: t[0]))
for v in nodes_list:
print prevState[v],
currState = sigmoid_updating(net, prevState)
print '\n'
#print "network state @ current step", OrderedDict(sorted(currState.items(), key=lambda t: t[0]))
for v in nodes_list:
print currState[v],
def main():
outpath = 'test.csv'
print "updating_rule module is the main code."
EDGE_FILE = '../data/example/example-net-edges.dat'
NODE_FILE = '../data/example/example-net-nodes.dat'
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
#prevState = {'CycD':0.0, 'b':0.0, 'c':1.0}
prevState = {}
prevState['CycD'] = 1
prevState['Rb'] = 0
prevState['E2F'] = 0
prevState['CycE'] = 0
prevState['CycA'] = 1
prevState['P27'] = 0
prevState['Cdc20'] = 1
prevState['Cdh1'] = 0
prevState['UbcH10'] = 1
prevState['CycB'] = 1
#print "network state @ previous step", OrderedDict(sorted(prevState.items(), key=lambda t: t[0]))
with open(outpath, "w") as f:
writer = csv.writer(f)
writer.writerow(nodes_list)
#for v in nodes_list:
prevList = []
for v in nodes_list:
prevList.append(prevState[v])
with open(outpath, "a") as f:
writer = csv.writer(f)
writer.writerow(prevList)
currState = cell_updating(net, prevState)
currList = []
for v in nodes_list:
currList.append(currState[v])
with open(outpath, "a") as f:
writer = csv.writer(f)
writer.writerow(currList)
repeat = 0
while(repeat < 6):
prevState = currState.copy()
currState = cell_updating(net, prevState)
currList = []
for v in nodes_list:
currList.append(currState[v])
with open(outpath, "a") as f:
writer = csv.writer(f)
writer.writerow(currList)
repeat += 1
def main(): EDGE_FILE = '../data/fission-net/fission-net-edges.txt' NODE_FILE = '../data/fission-net/fission-net-nodes.txt' BIO_INIT_FILE = '../data/fission-net/fission-net-bioSeq-initial.txt' net = inet.read_network_from_file(EDGE_FILE, NODE_FILE) nodes_list = inet.build_nodes_list(NODE_FILE) bio_initStates = inet.read_init_from_file(BIO_INIT_FILE) decStateTransMap = net_state_transition(net, nodes_list) attractors = find_attractor(decStateTransMap) print attractors
def main(): ''' print "time_evol module is the main code." ## to import a network of 3-node example EDGE_FILE = '../data/example/example-net-edges.dat' NODE_FILE = '../data/example/example-net-nodes.dat' net = inet.read_network_from_file(EDGE_FILE, NODE_FILE) nodes_list = inet.build_nodes_list(NODE_FILE) ## to obtain time series data for all possible initial conditions for 3-node example network timeSeriesData = ensemble_time_series(net, nodes_list, 2, 10)#, Nbr_States=2, MAX_TimeStep=20) initState = 1 biStates = decimal_to_binary(nodes_list, initState) print 'initial state', biStates ## to print time series data for each node: a, b, c starting particualr decimal inital condition 1 print 'a', timeSeriesData['a'][1] print 'b', timeSeriesData['b'][1] print 'c', timeSeriesData['c'][1] ## to obtain and visulaize transition map in the network state space decStateTransMap = net_state_transition(net, nodes_list) nx.draw(decStateTransMap) plt.show() ## to find fixed point attractors and limited cycle attractors with given transition map. attractors = find_attractor(decStateTransMap) print attractors ''' ## to obtain biological sequence for the Fission Yeast Cell-Cycle Net starting from biological inital state EDGE_FILE = '../data/fission-net/fission-net-edges.txt' NODE_FILE = '../data/fission-net/fission-net-nodes.txt' #BIO_INIT_FILE = '../data/fission-net/fission-net-bioSeq-initial.txt' BIO_INIT_FILE = '../data/fission-net/fission-net-bioSeq-initial.txt' net = inet.read_network_from_file(EDGE_FILE, NODE_FILE) nodes_list = inet.build_nodes_list(NODE_FILE) bio_initStates = inet.read_init_from_file(BIO_INIT_FILE) outputFile = '../results/fission-net/fission-net-bioSeq.txt' bioSeq = biological_sequence(net, nodes_list, bio_initStates, outputFile)
def main(args):
## to obtain biological sequence for the Fission Yeast Cell-Cycle Net starting from biological inital state
EDGE_FILE = '../data/fission-net/fission-net-edges.txt'
NODE_FILE = '../data/fission-net/fission-net-nodes.txt'
BIO_INIT_FILE = '../data/fission-net/fission-net-bioSeq-initial.txt'
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
#input_file_name1 = 'time-series/%s-step%d-trans0.dat'%(network_index, maxStep)
#input_file1 = open( input_file_name1, 'r')
Nbr_Initial_States = np.power(2,len(nodes_list))
maxStep = 20
Nbr_States = 2
historyLength = 5
result_ai = open('../results/fission-net/ai-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
result_te = open('../results/fission-net/te-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
timeSeries = tev.time_series(net, nodes_list, Nbr_Initial_States, Nbr_States, MAX_TimeStep=20)
print 'AI'
AI = {}
for n in nodes_list:
AI[n] = info.compute_AI(timeSeries[n], historyLength, Nbr_Initial_States, Nbr_States)
result_ai.write('%s\t%f\n'%(n, AI[n]))
print n, AI[n]
print 'done AI'
print 'TE'
TE = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE[(v, n)] = info.compute_TE(timeSeries[v], timeSeries[n], historyLength, Nbr_Initial_States, Nbr_States)
result_te.write('%s\t%s\t%f\n'%(v, n,TE[(v, n)] ))
print v, n, TE[(v, n)]
print 'done TE'
def main(args):
EDGE_FILE = "net-edges.dat"
NODE_FILE = "net-nodes.dat"
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE, node_delete="none", edge_delete="none")
nodes_list = inet.build_nodes_list(NODE_FILE, node_delete="none")
nbrRanNet = 10 # the number of random networks
# generate global random networks (ER nets)#
for i in range(0, nbrRanNet):
result_file = open("p53-gr%d.dat" % (i), "w")
gr_net = global_random_network(net)
for u, v in gr_net.edges_iter():
result_file.write("%s\t%s\t%d\n" % (u, v, gr_net[u][v]["weight"]))
# generate local random networks (SF nets)#
for i in range(0, nbrRanNet):
result_file = open("p53-lr%d.dat" % (i), "w")
lr_net = local_random_network(net)
for u, v in lr_net.edges_iter():
result_file.write("%s\t%s\t%d\n" % (u, v, lr_net[u][v]["weight"]))
def main(args):
EDGE_FILE = '../data/fission-net/fission-net-edges.txt'
NODE_FILE = '../data/fission-net/fission-net-nodes.txt'
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
nbrRanNet = 10 # the number of random networks
#generate global random networks (ER nets)#
for i in range(0, nbrRanNet):
result_file = open('../random-samples/fission-gr%d.dat'%(i), 'w')
gr_net = global_random_network(net)
for u, v in gr_net.edges_iter():
result_file.write('%s\t%s\t%d\n'%(u, v, gr_net[u][v]['weight']))
#generate local random networks (SF nets)#
for i in range(0, nbrRanNet):
result_file = open('../random-samples/fission-lr%d.dat'%(i), 'w')
lr_net = local_random_network(net)
for u, v in lr_net.edges_iter():
result_file.write('%s\t%s\t%d\n'%(u, v, lr_net[u][v]['weight']))
def main(args):
maxStep = int(raw_input("Type in the length of time steps (maxStep): ")) # the length of time steps starting from each initial state
Nbr_States = 2 # the number of all possible states of a node
historyLength = int(raw_input("Type in the length of history states (h): ")) # the length of history states
## 1. Network Information from Data File ##
random_network = str(raw_input("Type l for local random, g for global random: "))
NODE_FILE = 'net-nodes.dat'
# Asks user if network has been damaged.
damage = raw_input("Type in 1 for DNA damage and 0 for no damage: ")
# Asks user if there has been a node deletion.
node_delete = str(raw_input("Type in node for deletion or none for no deletion: "))
# Asks user if there has been a single edge link deletion.
u = str(raw_input("Type in upstream node of edge for deletion or none for no deletion: "))
if u != 'none':
v = str(raw_input("Type in downstream node of edge for deletion: "))
edge_delete = u,v
else:
edge_delete = u
for i in range(10):
ran_network_filename = i
if random_network == 'g':
EDGE_FILE = 'p53-gr%s.dat'%(ran_network_filename)
elif random_network == 'l':
EDGE_FILE = 'p53-lr%s.dat'%(ran_network_filename)
## 2. To Build net and nodes_list: Module 'input_net' is required ##
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE, node_delete, edge_delete)
nodes_list = inet.build_nodes_list(NODE_FILE, node_delete)
## 5. To generate time series data from 1, 2 and 3: Modules 'time_evol' and 'updating_rule' are required ##
timeSeries_Type = ['all_initial'] # 'all_initial', 'primary_attractor', 'one_trajectory'
if 'all_initial' in timeSeries_Type:
## 5-1. time series for all possible initial network states.
Nbr_All_Initial_States = np.power(Nbr_States, len(nodes_list)) # the number of initial states of the network
timeSeriesAll = tev.time_series_all(net, nodes_list, damage, Nbr_All_Initial_States, Nbr_States, maxStep) # To generate time series data over all possible initial states
if 'primary_attractor' in timeSeries_Type:
## 5-2. time series for initial network states that converge to primary (or biological) attractor
decStateTransMap = tev.net_state_transition(net, nodes_list, Nbr_States) # To build transition map between network states over network state space
attractors = tev.find_attractor(decStateTransMap) # To find attractors ==> attractors = {attractor state1: {'type': 'fixed', 'basin-size': 2, 'basin': [network state a, network state b]}, attractor state2: {}, ... }
primary_attractor = attractors.keys()[0] #attractors is ordered with basin size from the function "find_attractor" and hence primary attractor is the first one.
Initial_States_List = list(attractors[primary_attractor]['basin']) # To assign all initial states in the basin of primary attractor to the list of initail states
timeSeriesPa = tev.time_series_pa(net, nodes_list, Initial_States_List, Nbr_States, maxStep) # To generate time series data over all initial states that converge to the primary attractor
if 'one_trajectory' in timeSeries_Type:
## 5-3. time sereis for one initial network state
InitBiState = { 'SK': 1, 'Cdc2_Cdc13': 0, 'Ste9': 1, 'Rum1': 1, 'Slp1': 0, 'Cdc2_Cdc13_active': 0, 'Wee1_Mik1': 1, 'Cdc25': 0, 'PP': 0 } # initial state for fission yeast
#InitBiState = {'Cln3': 1, 'MBF':0, 'SBF': 0, 'Cln1_2':0, 'Cdh1':1, 'Swi5':0, 'Cdc20_Cdc14':0, 'Clb5_6':0, 'Sic1': 1, 'Clb1_2':0, 'Mcm1_SFF':0} # initial state for budding yeast
timeSeriesOne = tev.time_series_one(net, nodes_list, InitBiState, Nbr_States, maxStep) # To generate time series data for one particular initial state
'''
The format for timeSeries
==>
timeSeries = { node1: {1st initial_state: [0,1,1, ......, 0], ...... , Nth initial_state: [1,0,1, ......, 1]},
node 2: {1st initial_state: [1,1,1, ......, 0], ...... , Nth initial_state: [1,0,0, ......, 0]}, .......}
==>
timeSeries[node2][initial_state 1] = [1,1,1, ......, 0]
'''
## 4. Output files ##
#result_ai = open('../results/fission-net/ai-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
#result_te = open('../results/fission-net/te-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
## 6. To measure active information (AI) and transfer entropy (TE): using 'compute_AI' and 'compute_TE' from Module 'info_dyn" ##
## 6-1. For all possible initial network states
if 'all_initial' in timeSeries_Type:
## 6-1-a. To compute AI
if random_network == 'n':
result_ai_all = open('ai-all-step%d-h%d-%s.dat'%(maxStep, historyLength,damage),'w')
elif random_network == 'g':
result_ai_all = open('ai-all-step%d-h%d-gr%s-%s.dat'%(maxStep, historyLength, ran_network_filename,damage),'w')
elif random_network == 'l':
result_ai_all = open('ai-all-step%d-h%d-lr%s-%s.dat'%(maxStep, historyLength, ran_network_filename,damage),'w')
AI_all = {}
for n in nodes_list:
AI_all[n] = info.compute_AI(timeSeriesAll[n], historyLength, Nbr_All_Initial_States, Nbr_States)
result_ai_all.write('%s\t%f\n'%(n, AI_all[n]))
print n, AI_all[n]
## 6-1-b. To compute TE
if random_network == 'n':
result_te_all = open('te-all-step%d-h%d-%s.dat'%(maxStep, historyLength,damage),'w')
elif random_network == 'g':
result_te_all = open('te-all-step%d-h%d-gr%s-%s.dat'%(maxStep, historyLength, ran_network_filename,damage),'w')
elif random_network == 'l':
result_te_all = open('te-all-step%d-h%d-lr%s-%s.dat'%(maxStep, historyLength, ran_network_filename,damage),'w')
TE_all = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE_all[(v, n)] = info.compute_TE(timeSeriesAll[v], timeSeriesAll[n], historyLength, Nbr_All_Initial_States, Nbr_States)
result_te_all.write('%s\t%s\t%f\n'%(v, n,TE_all[(v, n)] ))
print v, n,TE_all[(v, n)]
result_file_name = 'ai-all-scale-step%d-h%d-%sr%s-%s.dat'%(maxStep, historyLength, random_network, ran_network_filename,damage)
viz_file_name = 'ai-all-scale-step%d-h%d-%sr%s-%s.pdf'%(maxStep, historyLength, random_network, ran_network_filename,damage)
draw_plots.plot_AI_scale(AI_all, result_file_name, viz_file_name)
result_file_name = 'te-all-scale-step%d-h%d-%sr%s-%s.dat'%(maxStep, historyLength, random_network, ran_network_filename,damage)
viz_file_name = 'te-all-scale-step%d-h%d-%sr%s-%s.pdf'%(maxStep, historyLength, random_network, ran_network_filename,damage)
draw_plots.plot_TE_scale(TE_all, result_file_name, viz_file_name)
## 6-2. For initial network states that converge to the primary attractor
if 'primary_attractor' in timeSeries_Type:
## 6-2-a. To compute AI
result_ai_pa = open('../results/fission-net/ai-pa-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
AI_pa = {}
for n in nodes_list:
AI_pa[n] = info.compute_AI(timeSeriesPa[n], historyLength, len(Initial_States_List), Nbr_States)
result_ai_pa.write('%s\t%f\n'%(n, AI_pa[n]))
## 6-2-b. To compute TE
result_te_pa = open('../results/fission-net/te-pa-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
TE_pa = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE_pa[(v, n)] = info.compute_TE(timeSeriesPa[v], timeSeriesPa[n], historyLength, len(Initial_States_List), Nbr_States)
result_te_pa.write('%s\t%s\t%f\n'%(v, n,TE_pa[(v, n)] ))
## 6-2-c. Scale behavior for AI (optional)
result_file_name = '../results/fission-net/ai-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/ai-pa-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_AI_scale(AI_pa, result_file_name, viz_file_name) ### plot and result file for AI scale
## 6-2-d. Scale behavior for TE (optional)
result_file_name = '../results/fission-net/te-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/te-pa-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_TE_scale(TE_pa, result_file_name, viz_file_name) ### plot and result file for TE scale
## 6-3. For a particular initial network state
if 'one_trajectory' in timeSeries_Type:
## 6-3-a. To compute AI
result_ai_one = open('../results/fission-net/ai-one-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
AI_one = {}
for n in nodes_list:
AI_one[n] = info.compute_AI(timeSeriesOne[n], historyLength, 1, Nbr_States)
result_ai_one.write('%s\t%f\n'%(n, AI_one[n]))
## 6-3-b. To compute TE
result_te_one = open('../results/fission-net/te-one-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
TE_one = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE_one[(v, n)] = info.compute_TE(timeSeriesOne[v], timeSeriesOne[n], historyLength, 1, Nbr_States)
result_te_one.write('%s\t%s\t%f\n'%(v, n,TE_one[(v, n)] ))
## 6-3-c. Scale behavior for AI (optional)
result_file_name = '../results/fission-net/ai-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/ai-one-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_AI_scale(AI_one, result_file_name, viz_file_name) ### plot and result file for AI scale
## 6-3-d. Scale behavior for TE (optional)
result_file_name = '../results/fission-net/te-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/te-one-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_TE_scale(TE_one, result_file_name, viz_file_name) ### plot and result file for TE scale
def edge_print(EDGE_FILE,NODE_FILE,node_delete,edge_delete,damage,random_network,i,node_number):
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE, node_delete,edge_delete)
nodes_list = inet.build_nodes_list(NODE_FILE,node_delete)
initState = 1
biStates = decimal_to_binary(nodes_list, initState)
#print 'initial state', biStates
decStateTransMap = net_state_transition(net, nodes_list, damage, Nbr_States=2)
u = edge_delete
#if u != 'none':
#v = edge_delete.pop([0])
# Exports network and attractor landscape for use in cytoscape.
if random_network == 'n':
if (u == 'none') and (node_delete == 'none'):
nx.write_graphml(net,"network_%s_p53_%s_normal.graphml"%(damage,node_number))
nx.write_graphml(decStateTransMap,"attractor_%s_p53_%s_normal.graphml"%(damage,node_number))
attractor_file = 'attractors_%s_%s_%s_%s_%s.txt'%(random_network,damage,node_number,node_delete,edge_delete)
elif (u == 'none') and (node_delete != 'none'):
nx.write_graphml(net,"network_%s.graphml"%(node_delete))
nx.write_graphml(decStateTransMap,"attractor_%s.graphml"%(node_delete))
attractor_file = 'attractors_%s_%s_%s_%s_%s.txt'%(random_network,damage,node_number,node_delete,edge_delete)
elif (u != 'none'):
nx.write_graphml(net,"network_%s_%s.graphml"%(u,v))
nx.write_graphml(decStateTransMap,"attractor_%s_%s.graphml"%(u,v))
attractor_file = 'attractors_%s_%s_%s_%s_%s.txt'%(random_network,damage,node_number,node_delete,edge_delete)
elif random_network == 'g':
nx.write_graphml(net,"network_p53_gr%s_%s.graphml"%(i,damage))
nx.write_graphml(decStateTransMap,"attractor_p53_gr%s_%s.graphml"%(i,damage))
attractor_file = 'attractors_%s_%s.txt'%(random_network,damage)
elif random_network == 'l':
nx.write_graphml(net,"network_p53_lr%s_%s.graphml"%(i,damage))
nx.write_graphml(decStateTransMap,"attractor_p53_lr%s_%s.graphml"%(i,damage))
attractor_file = 'attractors_%s_%s.txt'%(random_network,damage)
# Prints out all attractors
attractors = find_attractor_old(decStateTransMap)
print attractors
print '\n'
with open(attractor_file,'a') as f:
f.write('%s, %s'%(i,attractors))
f.write('\n')
# Prints out the fixed attractors for the network with values in a matrix format.
f.write('*** fixed ***\n')
for a in attractors['fixed']:
biState = decimal_to_binary(nodes_list,a)
b = []
c = []
keymaster = []
for key, values in biState.items():
b.append(values)
keymaster.append(key)
c = np.concatenate(b, axis=0)
size = len(a)
d = np.transpose(c.reshape((len(nodes_list),size)))
print a
print d, '\n'
print keymaster, '\n'
f.write('%s\n'%(a))
f.write('%s\n'%(keymaster))
f.write('%s\n'%(d))
f.write('\n')
f.write('\n')
# Prints out the cyclic attractors for the network with values in a matrix format.
f.write('*** cyclic ***\n')
for a in attractors['cycle']:
biState = decimal_to_binary(nodes_list,a)
b = []
c = []
keymaster = []
for key, values in biState.items():
b.append(values)
keymaster.append(key)
c = np.concatenate(b, axis = 0)
size = len(a)
d = np.transpose(c.reshape((len(nodes_list),size)))
print a
print d, '\n'
print keymaster, '\n'
f.write('%s\n'%(a))
f.write('%s\n'%(keymaster))
f.write('%s\n\n'%(d))
f.write('\n')
'''
# Writes to a file the time series data.
Nbr_Nodes = len(net.nodes())
Nbr_All_Initial_States = np.power(2, Nbr_Nodes)
timeSeriesData = time_series_all(net, nodes_list, damage, Nbr_All_Initial_States, Nbr_States=2, MAX_TimeStep=20)
if (u == 'none') and (node_delete == 'none'):
TIME_FILE = 'timeseries_data_%s_normal.txt' %(damage)
elif (u == 'none') and (node_delete != 'none'):
TIME_FILE = 'timeseries_data_%s_damaged.txt' %(node_delete)
elif (u != 'none'):
TIME_FILE = 'timeseries_data_%s_%s_damaged.txt' %(u,v)
with open(TIME_FILE,'a') as f:
f.writelines('{}:{}\n'.format(k,v) for k, v in timeSeriesData.items())
f.write('\n')
'''
'''
print 'ATM', timeSeriesData['ATM'][n]
print 'p53', timeSeriesData['p53'][n]
print 'Mdm2', timeSeriesData['Mdm2'][n]
print 'MdmX', timeSeriesData['MdmX'][n]
print 'Wip1', timeSeriesData['Wip1'][n]
print 'cyclinG', timeSeriesData['cyclinG'][n]
print 'PTEN', timeSeriesData['PTEN'][n]
print 'p21', timeSeriesData['p21'][n]
print 'AKT', timeSeriesData['AKT'][n]
print 'cyclinE', timeSeriesData['cyclinE'][n]
print 'Rb', timeSeriesData['Rb'][n]
print 'E2F1', timeSeriesData['E2F1'][n]
print 'p14ARF', timeSeriesData['p14ARF'][n]
print 'Bcl2', timeSeriesData['Bcl2'][n]
print 'Bax', timeSeriesData['Bax'][n]
print 'caspase', timeSeriesData['caspase'][n]
'''
return
## to obtain and visulaize transition map in the network state space
decStateTransMap = net_state_transition(net, nodes_list)
nx.draw(decStateTransMap)
plt.show()
## to find fixed point attractors and limited cycle attractors with given transition map.
attractors = find_attractor(decStateTransMap)
print attractors
'''
## to obtain biological sequence for the Fission Yeast Cell-Cycle Net starting from biological inital state
EDGE_FILE = '../data/fission-net/fission-net-edges.txt'
NODE_FILE = '../data/fission-net/fission-net-nodes.txt'
<<<<<<< HEAD
#BIO_INIT_FILE = '../data/fission-net/fission-net-bioSeq-initial.txt'
=======
BIO_INIT_FILE = '../data/fission-net/fission-net-bioSeq-initial.txt'
>>>>>>> origin/master
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
bio_initStates = inet.read_init_from_file(BIO_INIT_FILE)
outputFile = '../results/fission-net/fission-net-bioSeq.txt'
bioSeq = biological_sequence(net, nodes_list, bio_initStates, outputFile)
if __name__=='__main__':
main()
def main(args):
maxStep = 20 # the length of time steps starting from each initail state
Nbr_States = 2 # the number of all possible states of a node
historyLength = 5 # the length of history states
## 1. Network Information from Data File ##
EDGE_FILE = '../data/fission-net/fission-net-edges.txt'
NODE_FILE = '../data/fission-net/fission-net-nodes.txt'
#EDGE_FILE = '../data/budding-net/budding-net-edges.txt'
#NODE_FILE = '../data/budding-net/budding-net-nodes.txt'
## 2. To Build net and nodes_list: Module 'input_net' is required ##
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
## 5. To generate time series data from 1, 2 and 3: Modules 'time_evol' and 'updating_rule' are required ##
timeSeries_Type = ['all_initial'] # 'all_initial', 'primary_attractor', 'one_trajectory'
if 'all_initial' in timeSeries_Type:
## 5-1. time series for all possible initial network states.
Nbr_All_Initial_States = np.power(Nbr_States, len(nodes_list)) # the number of initial states of the network
timeSeriesAll = tev.time_series_all(net, nodes_list, Nbr_All_Initial_States, Nbr_States, maxStep) # To generate time series data over all possible initial states
if 'primary_attractor' in timeSeries_Type:
## 5-2. time series for initial network states that converge to primary (or biological) attractor
decStateTransMap = tev.net_state_transition(net, nodes_list, Nbr_States) # To build transition map between network states over network state space
attractors = tev.find_attractor(decStateTransMap) # To find attractors ==> attractors = {attractor state1: {'type': 'fixed', 'basin-size': 2, 'basin': [network state a, network state b]}, attractor state2: {}, ... }
primary_attractor = attractors.keys()[0] #attractors is ordered with basin size from the function "find_attractor" and hence primary attractor is the first one.
Initial_States_List = list(attractors[primary_attractor]['basin']) # To assign all initial states in the basin of primary attractor to the list of initail states
timeSeriesPa = tev.time_series_pa(net, nodes_list, Initial_States_List, Nbr_States, maxStep) # To generate time series data over all initial states that converge to the primary attractor
if 'one_trajectory' in timeSeries_Type:
## 5-3. time sereis for one initial network state
InitBiState = { 'SK': 1, 'Cdc2_Cdc13': 0, 'Ste9': 1, 'Rum1': 1, 'Slp1': 0, 'Cdc2_Cdc13_active': 0, 'Wee1_Mik1': 1, 'Cdc25': 0, 'PP': 0 } # initial state for fission yeast
#InitBiState = {'Cln3': 1, 'MBF':0, 'SBF': 0, 'Cln1_2':0, 'Cdh1':1, 'Swi5':0, 'Cdc20_Cdc14':0, 'Clb5_6':0, 'Sic1': 1, 'Clb1_2':0, 'Mcm1_SFF':0} # initial state for budding yeast
timeSeriesOne = tev.time_series_one(net, nodes_list, InitBiState, Nbr_States, maxStep) # To generate time series data for one particular initial state
'''
The format for timeSeries
==>
timeSeries = { node1: {1st initial_state: [0,1,1, ......, 0], ...... , Nth initial_state: [1,0,1, ......, 1]},
node 2: {1st initial_state: [1,1,1, ......, 0], ...... , Nth initial_state: [1,0,0, ......, 0]}, .......}
==>
timeSeries[node2][initial_state 1] = [1,1,1, ......, 0]
'''
## 4. Output files ##
#result_ai = open('../results/fission-net/ai-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
#result_te = open('../results/fission-net/te-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
## 6. To measure active information (AI) and transfer entropy (TE): using 'compute_AI' and 'compute_TE' from Module 'info_dyn" ##
## 6-1. For all possible initial network states
if 'all_initial' in timeSeries_Type:
## 6-1-a. To compute AI
result_ai_all = open('../results/fission-net/ai-all-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
AI_all = {}
for n in nodes_list:
AI_all[n] = info.compute_AI(timeSeriesAll[n], historyLength, Nbr_All_Initial_States, Nbr_States)
result_ai_all.write('%s\t%f\n'%(n, AI_all[n]))
print n, AI_all[n]
## 6-1-b. To compute TE
result_te_all = open('../results/fission-net/te-all-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
TE_all = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE_all[(v, n)] = info.compute_TE(timeSeriesAll[v], timeSeriesAll[n], historyLength, Nbr_All_Initial_States, Nbr_States)
result_te_all.write('%s\t%s\t%f\n'%(v, n,TE_all[(v, n)] ))
print v, n,TE_all[(v, n)]
## 6-1-c. Scale behavior for AI (optional)
result_file_name = '../results/fission-net/ai-all-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/ai-all-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_AI_scale(AI_all, result_file_name, viz_file_name) ### plot and result file for AI scale
## 6-1-d. Scale behavior for TE (optional)
result_file_name = '../results/fission-net/te-all-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/te-all-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_TE_scale(TE_all, result_file_name, viz_file_name) ### plot and result file for TE scale
## 6-2. For initial network states that converge to the primary attractor
if 'primary_attractor' in timeSeries_Type:
## 6-2-a. To compute AI
result_ai_pa = open('../results/fission-net/ai-pa-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
AI_pa = {}
for n in nodes_list:
AI_pa[n] = info.compute_AI(timeSeriesPa[n], historyLength, len(Initial_States_List), Nbr_States)
result_ai_pa.write('%s\t%f\n'%(n, AI_pa[n]))
## 6-2-b. To compute TE
result_te_pa = open('../results/fission-net/te-pa-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
TE_pa = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE_pa[(v, n)] = info.compute_TE(timeSeriesPa[v], timeSeriesPa[n], historyLength, len(Initial_States_List), Nbr_States)
result_te_pa.write('%s\t%s\t%f\n'%(v, n,TE_pa[(v, n)] ))
## 6-2-c. Scale behavior for AI (optional)
result_file_name = '../results/fission-net/ai-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/ai-pa-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_AI_scale(AI_pa, result_file_name, viz_file_name) ### plot and result file for AI scale
## 6-2-d. Scale behavior for TE (optional)
result_file_name = '../results/fission-net/te-pa-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/te-pa-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_TE_scale(TE_pa, result_file_name, viz_file_name) ### plot and result file for TE scale
## 6-3. For a particular initial network state
if 'one_trajectory' in timeSeries_Type:
## 6-3-a. To compute AI
result_ai_one = open('../results/fission-net/ai-one-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
AI_one = {}
for n in nodes_list:
AI_one[n] = info.compute_AI(timeSeriesOne[n], historyLength, 1, Nbr_States)
result_ai_one.write('%s\t%f\n'%(n, AI_one[n]))
## 6-3-b. To compute TE
result_te_one = open('../results/fission-net/te-one-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
TE_one = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE_one[(v, n)] = info.compute_TE(timeSeriesOne[v], timeSeriesOne[n], historyLength, 1, Nbr_States)
result_te_one.write('%s\t%s\t%f\n'%(v, n,TE_one[(v, n)] ))
## 6-3-c. Scale behavior for AI (optional)
result_file_name = '../results/fission-net/ai-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/ai-one-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_AI_scale(AI_one, result_file_name, viz_file_name) ### plot and result file for AI scale
## 6-3-d. Scale behavior for TE (optional)
result_file_name = '../results/fission-net/te-one-scale-step%d-trans0-h%d.dat'%(maxStep, historyLength)
viz_file_name = '../viz/fission-net/te-one-scale-step%d-trans0-h%d.pdf'%(maxStep, historyLength)
draw_plots.plot_TE_scale(TE_one, result_file_name, viz_file_name) ### plot and result file for TE scale
## to obtain biological sequence for the Fission Yeast Cell-Cycle Net starting from biological inital state
EDGE_FILE = '../data/fission-net/fission-net-edges.txt'
NODE_FILE = '../data/fission-net/fission-net-nodes.txt'
BIO_INIT_FILE = '../data/fission-net/fission-net-bioSeq-initial.txt'
net = inet.read_network_from_file(EDGE_FILE, NODE_FILE)
nodes_list = inet.build_nodes_list(NODE_FILE)
#input_file_name1 = 'time-series/%s-step%d-trans0.dat'%(network_index, maxStep)
#input_file1 = open( input_file_name1, 'r')
Nbr_Initial_States = np.power(2,len(nodes_list))
maxStep = 20
Nbr_States = 2
historyLength = 5
result_ai = open('../results/fission-net/ai-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
result_te = open('../results/fission-net/te-step%d-trans0-h%d.dat'%(maxStep, historyLength),'w')
timeSeries = tev.time_series_all(net, nodes_list, Nbr_Initial_States, Nbr_States, MAX_TimeStep=20)
print 'AI'
AI = {}
for n in nodes_list:
AI[n] = info.compute_AI(timeSeries[n], historyLength, Nbr_Initial_States, Nbr_States)
result_ai.write('%s\t%f\n'%(n, AI[n]))
print n, AI[n]
print 'done AI'
print 'TE'
TE = defaultdict(float)
for v in nodes_list:
for n in nodes_list:
TE[(v, n)] = info.compute_TE(timeSeries[v], timeSeries[n], historyLength, Nbr_Initial_States, Nbr_States)
result_te.write('%s\t%s\t%f\n'%(v, n,TE[(v, n)] ))
print v, n, TE[(v, n)]
print 'done TE'