def Output(exportData,model,seed,selections,result,noiseLevel,resultNoisy,folderPath, fileName, antStr,bioTap):
# export csv of results
data = {next_name:result[:,i] for i, next_name in enumerate(selections)}
df = pd.DataFrame.from_dict(data)
df.set_index('time', inplace=True)
df.to_csv(folderPath / (fileName + "Result_Clean.csv"))
if noiseLevel != 0:
data = {next_name:resultNoisy[:,i] for i, next_name in enumerate(selections)}
df = pd.DataFrame.from_dict(data)
df.set_index('time', inplace=True)
df.to_csv(folderPath / (fileName + ".csv"))
# export csv file for importing into Biotapestry
if bioTap != '':
f2 = open(folderPath / ("biotapestry.csv"), 'w')
f2.write(bioTap)
f2.close()
# export Antimony model text
if exportData[0]:
if os.path.exists(folderPath / ("antimony.txt")):
print('\nWarning: antimony.txt already exists! Preventing overwrite.' )
exportData[1] == False
else:
fh = open(folderPath / ("antimony.txt"), 'w')
fh.write(str(antStr))
fh.close()
# export SBML model text
if exportData[1]:
sbmlStr = model.getSBML()
te.saveToFile (folderPath / ("SBML.xml"), sbmlStr)
print('\nExport Data Saved!\n')
def Output(modelName, folderPath, rSeed, r, antStr, Regulations):
if rSeed != 0:
fh = open(folderPath + modelName + ' Seed.txt', 'wb')
fh.write('Random Seed = ' + str(rSeed))
sbmlStr = r.getSBML()
te.saveToFile(folderPath + modelName + ' Model.xml', sbmlStr)
fh = open(folderPath + modelName + ' Antimony.txt', 'wb')
fh.write(str(antStr))
fh = open(folderPath + modelName + ' Network Regulations.txt', 'wb')
fh.write(Regulations)
plt.close('all')
def Output(data, Name,r,rSeed,result,NoisyResult):
for i in range(len(data)):
if i == 1 and data[i] == 'y':
writecsvFile(fileName + Name + '_Results.csv',r,result)
elif data[i] == 'y':
writecsvFile(fileName + Name + '_Noisy_result.csv',r,NoisyResult)
if rSeed != 0:
fh = open(fileName + Name + '_Seed.txt', 'wb')
fh.write('Random Seed = ' + str(rSeed))
sbmlStr = r.getSBML()
te.saveToFile (fileName + Name + '_Model.xml', sbmlStr)
def Output(data, Name,model,seed,result,NoisyResult, fileName, antStr):
for i in range(len(data)):
if i == 0 and data[i] == 'y':
writecsvFile(fileName + Name + '_Results.csv',model,result)
elif data[i] == 'y':
writecsvFile(fileName + Name + '_Noisy_Result.csv',model,NoisyResult)
if seed != 0:
fh = open(fileName + Name + '_Seed.txt', 'wb')
fh.write('Random Seed = ' + str(seed))
if seed == 0:
fh = open(fileName + Name + '_Seed.txt', 'wb')
fh.write('Random Seed not chosen.')
sbmlStr = model.getSBML()
te.saveToFile (fileName + Name + '_Model.xml', sbmlStr)
fh = open(fileName + Name + '_Antimony.txt', 'wb')
fh.write(str(antStr))
# plt.close('all')
print('\nData Saved!\n')
def Output(exportData, model, seed, result, noiseLevel, resultNoisy, filesPath,
antStr, bioTap, selections, filename):
# export csv of results
# do not want to give students the clean data
#data = {next_name:result[:,i] for i,next_name in enumerate(selections)}
#df = pd.DataFrame.from_dict(data)
#df.set_index('time', inplace=True)
#df.to_csv(filesPath + "Results_Clean2.csv")
data = {
next_name: resultNoisy[:, i]
for i, next_name in enumerate(selections)
}
df = pd.DataFrame.from_dict(data)
df.set_index('time', inplace=True)
df.to_csv(filesPath + filename + ".csv")
# export csv file for importing into Biotapestry
if bioTap != '':
f2 = open(filesPath + "biotapestry.csv", 'w')
f2.write(bioTap)
f2.close()
# export SBML model text
if exportData == True:
sbmlStr = model.getSBML()
te.saveToFile(filesPath + 'OrigModel.xml', sbmlStr)
# export Antimony model text
if exportData == True:
if np.DataSource().exists(filesPath + 'OrigAntimony.txt'):
print('Warning: ' + filesPath +
'OrigAntimony.txt already exists! Preventing overwrite.')
else:
fh = open(filesPath + 'OrigAntimony.txt', 'w')
fh.write(str(antStr))
print('\nData Saved!\n')
r = te.loada ('''
MJ00: MH -> MS; MH*0.2
MJ02: MH -> MD; MH*0.01
MJ10: MS -> MH; MS*0.1
MJ12: MS -> MD; MS*0.3
FJ00: FH -> FS; FH*0.1
FJ02: FH -> FD; FH*0.01
FJ10: FS -> FH; FS*0.1
FJ12: FS -> FD; FS*0.3
MH = 50
MS = 0
MD = 0
FH = 50
FS = 0
FD = 0
''')
# This will create the SBML XML file
te.saveToFile ('Example3.xml', r.getSBML())
r.setIntegrator('gillespie')
r.integrator.variable_step_size = True
r.getIntegrator().setValue('seed', 0)
result = r.simulate(0,10)
r.plot (result)
sp3 = "S" + str(i[2])
sp4 = "S" + str(i[3])
if i[1] == 0 and i[3] == 0:
m.addReaction([sp1], [sp2], "k" + str(count) + "*" + sp1)
elif i[3] == 0:
m.addReaction([sp1, sp2], [sp3], "k" + str(count) + "*" + sp1
+ "*" + sp2)
elif i[1] == 0:
m.addReaction([sp1], [sp2, sp3], "k" + str(count) + "*" + sp1)
else:
m.addReaction([sp1, sp2], [sp3, sp4], "k" + str(count) + "*" + sp1
+ "*" + sp2)
te.saveToFile('Model.xml', str(m))
r = te.loadSBMLModel('Model.xml')
try:
if forbidZeros:
r.simulate()
for i in r.getSteadyStateValues():
if i < 1e-5:
raise ValueError
if limitBoundarySpecies:
if speciesType.count(1) > maxBoundarySpecies:
raise ValueError
steady = r.steadyState() #Determine if concentrations converge
if steady < 10e-6: # If they do, it's a successful model
print "Success!"
print ("Number of tries = " + str(zcount))
def getElementaryModesDouble(self):
def isAllPositive(v):
AllPositive = True
for i in v:
if i < 0:
AllPositive = False
break
return AllPositive
def isAllNegative(v):
AllNegative = True
for i in v:
if i > 0:
AllNegative = False
break
return AllNegative
mStr = ''
rxn_ids = self.getReactionIds()
flt_ids = self.getFloatingSpeciesIds()
bnd_ids = self.getBoundarySpeciesIds()
if bnd_ids == ():
return None
matx = self.getStoichiometryMatrix()
mStr += "-ENZREV" + "\n"
for i in range(len(rxn_ids)):
if self.isReactionReversible(i):
mStr += rxn_ids[i] + " "
mStr += "\n\n"+"-ENZIRREV" + "\n"
for i in range(len(rxn_ids)):
if not self.isReactionReversible(i):
mStr += rxn_ids[i] + " "
mStr += "\n\n"+"-METINT"+"\n"
for i in flt_ids:
mStr += str (i) + " "
mStr += "\n\n"+"-METEXT"+"\n"
mStr += bnd_ids[0] + " "
mStr += "\n\n"+"-CAT"+"\n"
for i in range(len(rxn_ids)):
react_list = []
col = matx[:,i]
mStr += rxn_ids[i] + " : "
if isAllPositive(col):
mStr += bnd_ids[0]
else:
for j in range(len(flt_ids)):
if matx[j,i] < 0:
stStr = ''
if abs(matx[j,i]) > 1:
stStr = str(abs(matx[j,i])) + ' '
react_list.append(stStr + flt_ids[j])
mStr += react_list[0]
for k in range(1,len(react_list)):
mStr += " + " + react_list[k]
mStr += " = "
react_list = []
if isAllNegative(col):
mStr += bnd_ids[0]
else:
for j in range(len(flt_ids)):
if matx[j,i] > 0:
react_list.append(flt_ids[j])
mStr += react_list[0]
for k in range(1,len(react_list)):
mStr += " + " + react_list[k]
mStr += " .\n"
f = tempfile.TemporaryFile (delete=False)
d = tempfile.gettempdir()
resultFile = d+"\\MetaToolResult.txt"
metatoolFile = f.name
f.write (mStr)
f.close()
te.saveToFile ('c:\\tmp\\m.txt', mStr)
pathToMetatool = site.getsitepackages()[1] + '\\structural\\' + 'metaToolDouble.exe'
exit_code = subprocess.call ([pathToMetatool, metatoolFile, resultFile])
if exit_code == 0:
line_array = []
with open(resultFile) as f:
for lines in f:
line_array.append(lines)
start_pt = line_array.index("ELEMENTARY MODES\n")
if line_array[start_pt+1] == ' \n':
row_num = int(list(line_array[start_pt+2].split()[2])[1])
# col_num = int(list(line_array[start_pt+2].split()[4])[1])
elementaryModeMatrix = []
for i in range(row_num):
elementaryModeMatrix.append(line_array[start_pt+3+i].split())
elementaryModeMatrix = np.array(elementaryModeMatrix, dtype=float)
f.close()
return elementaryModeMatrix
else:
return exit_code
mStr += " + " + react_list[k]
mStr += " .\n"
return mStr
mStr = converSBMLtoMetaToolString(r.getSBML())
f = tempfile.TemporaryFile (delete=False)
d = tempfile.gettempdir()
resultFile = d+"\\MetaToolResult.txt"
metatoolFile = f.name
f.write (mStr)
f.close()
te.saveToFile ('c:\\tmp\\m.txt', mStr)
pathToMetatool = site.getsitepackages()[1] + '\\structural\\' + 'metaToolDouble.exe'
exit_code = subprocess.call ([pathToMetatool, metatoolFile, resultFile])
#print te.readFromFile(resultFile)
def getElementaryModesDouble(resultFile):
line_array = []
with open(resultFile) as f:
for lines in f:
line_array.append(lines)
start_pt = line_array.index("ELEMENTARY MODES\n")
if line_array[start_pt+1] == ' \n':
linewidth=2.0,
lineStyle='-',
color='black',
alpha=0.8)
r.k1 = r.k1 + 0.2
# Turn the notices back on
te.noticesOn()
# #### File helpers for reading and writing
# In[3]:
# create tmp file
import tempfile
ftmp = tempfile.NamedTemporaryFile(suffix=".xml")
print(ftmp.name)
# load model
r = te.loada('S1 -> S2; k1*S1; k1 = 0.1; S1 = 10')
# save to file
te.saveToFile(ftmp.name, r.getMatlab())
# or easier via
r.exportToMatlab(ftmp.name)
# load file
sbmlstr = te.readFromFile(ftmp.name)
print(sbmlstr)
# In[4]:
str(i[0]) + ")\n")
elif i[3] == 0:
m.addReaction(["S" + str(i[0]), "S" + str(i[1])],
["S" + str(i[2])], "k" + str(count) + "*S" +
str(i[0]) + "*S" + str(i[1]))
elif i[1] == 0:
m.addReaction(["S" + str(i[0])], ["S" + str(i[1]),
"S" + str(i[2])], "k" + str(count) + "*S" +
str(i[0]))
else:
m.addReaction(["S" + str(i[0]), "S" + str(i[1])], ["S" + str(i[2]),
"S" + str(i[3])], "k" + str(count) + "*S" +
str(i[0]) + "*S" + str(i[1]))
te.saveToFile('ProteinModel.xml', str(m))
r = te.loadSBMLModel('ProteinModel.xml')
try:
if forbidZeros:
r.simulate()
for i in r.getSteadyStateValues():
if i < 1e-5:
raise ValueError
r.simulate(end=1000)
steady = r.steadyState()
if steady < 10e-6:
print "Success!"
print ("Number of tries = " + str(zcount))
zcount = 0
for i in data:
m.addReaction([], [sp3], "(" + s.join([k1, sp1]) + "+" +
s.join([k2, sp2]) + ")/(1+" + s.join([k1, sp1]) + "+" +
s.join([k2, sp2]) + "+" + s.join([k3, sp1, sp2]) + ")")
elif i[6] == 8:
m.addReaction([], [sp3], "(1+" + s.join([k1, sp1, sp2]) +
")/(1+" + s.join([k1, sp1]) + "+" + s.join([k2, sp2]) + "+"
+ s.join([k3, sp1, sp2]) + ")")
elif i[6] == 9:
m.addReaction([], [sp3], "(" + s.join([k1, sp1]) + ")/(1+" +
s.join([k1, sp1]) + "+" + s.join([k2, sp2]) + "+" +
s.join([k3, sp1, sp2]) + ")")
decayData = [] # Holds protein decay information
re.proteindecay()
te.saveToFile('GeneticModel.xml', str(m))
r = te.loadSBMLModel('GeneticModel.xml')
try:
if forbidZeros:
r.simulate()
for i in r.getSteadyStateValues():
if i < 1e-5:
raise ValueError
steady = r.steadyState() # Determine if final concentrations steady out
if steady < 10e-6: # If they do, then it's a successful model
print "Success!"
print ("Number of tries = " + str(zcount))
zcount = 0
for i in data:
zcount += 1
r.k1 = r.k1 + 0.2
# Turn the notices back on
te.noticesOn()
# #### File helpers for reading and writing
# In[3]:
# create tmp file
import tempfile
ftmp = tempfile.NamedTemporaryFile(suffix=".xml")
print(ftmp.name)
# load model
r = te.loada('S1 -> S2; k1*S1; k1 = 0.1; S1 = 10')
# save to file
te.saveToFile(ftmp.name, r.getMatlab())
# or easier via
r.exportToMatlab(ftmp.name)
# load file
sbmlstr = te.readFromFile(ftmp.name)
print(sbmlstr)
# In[4]:
r = te.loada ('''
# Reactions:
compartment cytoplasm = 1.5, mitochondria = 2.6
const S1 in mitochondria
var S2 in cytoplasm
var S3 in cytoplasm
var S4 in cytoplasm
S1 -> S2; k1*S1
S2 -> S3; k2*S2
S3 -> S4; k3*S3
# Species initializations:
S1 = 10; S2 = 0; S3 = 0; S4 = 0;
k1 = 1; k2 = 1; k3 = 1;
''')
te.saveToFile ('mySBMLModel.xml', r.getCurrentSBML())
#result = r.simulate(0, 10, 50, ['Time', '[S4]'])
# note that plot only shows var species not const
result = r.simulate(0, 10, 50)
print(result)
r.plot (result)
at (time > 3600): dectog = 1;
at (time % 1200 < 1): iprot = iprot/2, imRNA = imRNA / 2, gmRNA = gmRNA / 2, gprot = gprot / 2
iprot = 0;
imRNA = 0;
gprot = 0;
gmRNA = 0;
dectog = 0;
end
model productionValues()
proteinProduction(10, 10, 10, 10);
proteinProduction(10, 100, 10, 100);
proteinProduction(10, 1000, 10, 1000);
proteinProduction(20, 10, 20, 10);
proteinProduction(20, 100, 20, 100);
proteinProduction(20, 1000, 20, 1000);
#proteinProduction(30, 10, 30, 10);
#proteinProduction(30, 100, 30, 100);
#proteinProduction(30, 1000, 30, 1000);
end
'''
r = te.loadAntimonyModel(model1)
output = r.simulate (0,32400,32400)
te.plotArray(output)
s = re.sub(' *\[*\]*', '', str(output))
te.saveToFile('C:\Users\Jared\Documents\CSE\CSE486\HW2\P2data.csv', s)
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 11 14:04:56 2019
@author: nrhaw
"""
import tellurium as te
import roadrunner
r = te.loada("""
ABCDEFG -> B; k1*ABCDEFG;
B -> C + D; k1*B;
C -> ABCDEFG; k3*C;
C -> D + E; k4*C;
D + B -> F; k5*D+B;
k1 = 1; k2 = 1; k3 = 1; k4 = 1; k5 = 2
""")
te.saveToFile('c:\\tmp\\largerpathway.xml', r.getSBML())