forked from bgyori/erk_invitro
/
run_mapk_mcmc.py
348 lines (305 loc) · 11 KB
/
run_mapk_mcmc.py
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import sys
import csv
import numpy
import matplotlib.pyplot as plt
import pickle
import emcee
from pysb.integrate import Solver
from pysb import *
import mapk_model
folder_path = '/Users/aditya/erk_invitro'
use_mpi = False
class MapkExperiment(object):
def __init__(self):
self.ts = []
self.ERK = []
self.ERKpY = []
self.ERKpT = []
self.ERKpTpY = []
self.ERK_std = []
self.ERKpY_std = []
self.ERKpT_std = []
self.ERKpTpY_std = []
self.ERKtot = 0
self.MEKtot = 0
self.MKPtot = 0
def process_data(self):
self.ERKpTpY = numpy.array(self.ERKpTpY)
self.ERKpT = numpy.array(self.ERKpT)
self.ERKpY = numpy.array(self.ERKpY)
self.ERK = numpy.array(self.ERK)
sigma_min = 0.05
self.ERK_std = numpy.array([d if d > sigma_min else
sigma_min for d in self.ERK_std])
self.ERKpY_std = numpy.array([d if d > sigma_min else
sigma_min for d in self.ERKpY_std])
self.ERKpT_std = numpy.array([d if d > sigma_min else
sigma_min for d in self.ERKpT_std])
self.ERKpTpY_std = numpy.array([d if d > sigma_min else
sigma_min for d in self.ERKpTpY_std])
def getfloat(s):
if s == '':
return numpy.nan
else:
return float(s)
def read_data():
nexp = 9
nt = 23
ns = 4
nrep = 9
initial_conditions = [[930, 0, 0], [930, 0, 0], [930, 0, 93],
[930, 93, 93], [930, 186, 93], [930, 465, 93],
[930, 93, 93], [930, 93, 0], [465, 93, 93]]
data = []
with open(folder_path + '/combined_data_9.csv', 'r') as fh:
lines = fh.readlines()
blocksize = 1 + nt
for i in range(nexp):
lines_exp = lines[i*blocksize : (i+1)*blocksize]
csv_reader = csv.DictReader(lines_exp)
exp = MapkExperiment()
for line in csv_reader:
erk_data = numpy.array([getfloat(line['A%d' % (r + 1)]) for
r in range(nrep)])
erk_mean = numpy.nanmean(erk_data)
# Assumption: all values will be nan so it's enough to test ERK
if not numpy.isnan(erk_mean):
exp.ERK.append(erk_mean)
exp.ERK_std.append(numpy.nanstd(erk_data))
erkpy_data = numpy.array([getfloat(line['B%d' % (r + 1)])
for r in range(nrep)])
erkpt_data = numpy.array([getfloat(line['C%d' % (r + 1)])
for r in range(nrep)])
erkptpy_data = numpy.array([getfloat(line['D%d' % (r + 1)])
for r in range(nrep)])
erkptpy_data -= erkpt_data
erkpt_data -= erkpy_data
erkpy_data -= erk_data
exp.ERKpY.append(numpy.nanmean(erkpy_data))
exp.ERKpY_std.append(numpy.nanstd(erkpy_data))
exp.ERKpT.append(numpy.nanmean(erkpt_data))
exp.ERKpT_std.append(numpy.nanstd(erkpt_data))
exp.ERKpTpY.append(numpy.nanmean(erkptpy_data))
exp.ERKpTpY_std.append(numpy.nanstd(erkptpy_data))
exp.ts.append(getfloat(line['R%d' % (i + 1)]) * 60.0)
exp.ERKtot = initial_conditions[i][0]
exp.MEKtot = initial_conditions[i][1]
exp.MKPtot = initial_conditions[i][2]
exp.process_data()
data.append(exp)
return data
def sim_experiment(model, exp, pd=None):
if pd is None:
pd = {}
pd['ERK_0'] = exp.ERKtot * exp.ERK[0]
pd['ERKpT_0'] = exp.ERKtot * exp.ERKpT[0]
pd['ERKpY_0'] = exp.ERKtot * exp.ERKpY[0]
pd['ERKpTpY_0'] = exp.ERKtot * exp.ERKpTpY[0]
pd['MEK_0'] = exp.MEKtot
pd['MKP_0'] = exp.MKPtot
solver = Solver(model, exp.ts, use_analytic_jacobian=True)
solver.run(pd)
return solver.yobs
def gauss_lh(x, mu, sigma):
return -numpy.sum((x-mu)**2 / sigma**2)
def parameter_dict(model, p):
pd = {}
pe = [pp for pp in model.parameters if pp.name[0]=='k']
for pnew, pold in zip(p, pe):
pd[pold.name] = numpy.power(10.0, pnew)
return pd
def plot_fit(model, data, pd=None):
fig, axs = plt.subplots(nrows=3, ncols=3, sharex=True)
for i, exp in enumerate(data):
if i == 1:
continue
ax = axs[int(numpy.floor(i/3)), i%3]
yobs = sim_experiment(model, exp, pd)
erkpt = yobs['ERKpT'] / exp.ERKtot
erkpy = yobs['ERKpY'] / exp.ERKtot
erkptpy = yobs['ERKpTpY'] / exp.ERKtot
erk0 = exp.ERKtot * exp.ERK[0]
erkpt0 = exp.ERKtot * exp.ERKpT[0]
erkpy0 = exp.ERKtot * exp.ERKpY[0]
erkptpy0 = exp.ERKtot * exp.ERKpTpY[0]
mek0 = exp.MEKtot
mkp0 = exp.MKPtot
ax.set_title('R%d: [(%d, %d, %d, %d), %d, %d]' %
(i+1, erk0, erkpt0, erkpy0, erkptpy0, mek0, mkp0))
ax.set_xlim([0, 19800])
ax.set_ylim([0, 1])
ax.errorbar(exp.ts, exp.ERKpY, yerr=exp.ERKpY_std,
fmt='bo', label='ERKpY')
ax.errorbar(exp.ts, exp.ERKpT, yerr=exp.ERKpT_std,
fmt='go', label='ERKpT')
ax.errorbar(exp.ts, exp.ERKpTpY, yerr=exp.ERKpTpY_std,
fmt='ro', label='ERKpTpY')
ax.plot(exp.ts, erkpy, 'b')
ax.plot(exp.ts, erkpt, 'g')
ax.plot(exp.ts, erkptpy, 'r')
fig.show()
def plot_best(model, data, sampler):
idx = numpy.argmax(sampler.flatlnprobability)
p = sampler.flatchain[idx]
pd = parameter_dict(model, p)
plot_fit(model, data, pd)
def likelihood(p, model, data, plot=False):
pd = parameter_dict(model, p)
lh = 0
for i, exp in enumerate(data):
if i == 1:
continue
yobs = sim_experiment(model, exp, pd)
erk = yobs['ERKu'] / exp.ERKtot
erkpt = yobs['ERKpT'] / exp.ERKtot
erkpy = yobs['ERKpY'] / exp.ERKtot
lh += gauss_lh(erk[1:], exp.ERK[1:], exp.ERK_std[1:])
lh += gauss_lh(erkpt[1:], exp.ERKpT[1:], exp.ERKpT_std[1:])
lh += gauss_lh(erkpy[1:], exp.ERKpY[1:], exp.ERKpY_std[1:])
print lh
print lh
if numpy.isnan(lh):
return -numpy.inf
else:
return lh
def prior(p, model):
pd = parameter_dict(model, p)
lp = 0
for pn, pv in pd.iteritems():
lp += -(numpy.log10(pv) - numpy.log10(model.parameters[pn].value))**2 / 4.0
print lp
return lp
def posterior(p, model, data):
lpri = prior(p, model)
llh = likelihood(p, model, data)
lp = lpri + llh
print lpri, llh
return lp
def build_markevich_2step():
Model()
mapk_model.mapk_monomers()
mapk_model.mek_phos_erk_2_step_specific()
mapk_model.mkp_dephos_erk_2_step_specific()
mapk_model.mapk_initials()
mapk_model.mapk_observables()
return model
def build_erk_autophos_any():
Model()
mapk_model.mapk_monomers()
mapk_model.mek_phos_erk_2_step_specific()
mapk_model.mkp_dephos_erk_2_step_specific()
mapk_model.erk_dimerize_any()
mapk_model.erk_autophos()
mapk_model.mapk_initials()
mapk_model.mapk_observables()
return model
def build_erk_autophos_uT():
Model()
mapk_model.mapk_monomers()
mapk_model.mek_phos_erk_2_step_specific()
mapk_model.mkp_dephos_erk_2_step_specific()
mapk_model.erk_dimerize_uT()
mapk_model.erk_autophos()
mapk_model.mapk_initials()
mapk_model.mapk_observables()
return model
def build_erk_autophos_phos():
Model()
mapk_model.mapk_monomers()
mapk_model.mek_phos_erk_2_step_specific()
mapk_model.mkp_dephos_erk_2_step_specific()
mapk_model.erk_dimerize_uT()
mapk_model.erk_autophos()
mapk_model.mapk_initials()
mapk_model.mapk_observables()
return model
def build_erk_activate_mkp():
Model()
mapk_model.mapk_monomers()
mapk_model.mek_phos_erk_2_step_specific()
mapk_model.mkp_dephos_erk_2_step_specific()
mapk_model.erk_dimerize_uT()
mapk_model.erk_autophos()
mapk_model.erk_activate_mkp()
mapk_model.mapk_initials()
mapk_model.mapk_observables()
return model
def run_one_model(model, model_number, data, ns, pool=None):
# Vector of nominal parameters
p = numpy.log10(numpy.array([pp.value for pp in model.parameters
if pp.name[0]=='k']))
print posterior(p, model, data)
# Number of temperatures, dimensions and walkers
ntemps = 20
ndim = len(p)
blocksize = 4
nblocks = int(numpy.ceil((2*ndim+1)/(1.0*blocksize)))
nwalkers = blocksize * nblocks
print 'Running %d walkers at %d temperatures for %d steps.' %\
(nwalkers, ntemps, ns)
sampler = emcee.PTSampler(ntemps, nwalkers, ndim, likelihood, prior,
threads=1, pool=pool, betas=None, a=2.0, Tmax=None,
loglargs=[model, data], logpargs=[model],
loglkwargs={}, logpkwargs={})
# Random initial parameters for walkers
p0 = numpy.ones((ntemps, nwalkers, ndim))
for i in range(ntemps):
for j in range(nwalkers):
p0[i, j, :] = p + 1.0*(numpy.random.rand(ndim)-0.5)
# Run sampler
fname = folder_path + 'chain_%d.dat' % model_number
step = 0
for result in sampler.sample(p0, iterations=ns, storechain=True):
print '---'
position = result[0]
with open(fname, 'a') as fh:
for w in range(nwalkers):
for t in range(ntemps):
pos_str = '\t'.join(['%f' % p for p in position[t][w]])
fh.write('%d\t%d\t%d\t%s\n' % (step, w, t, pos_str))
step += 1
return sampler
if __name__ == '__main__':
if use_mpi:
pool = emcee.mpi_pool.MPIPool()
if not pool.is_master():
pool.wait()
sys.exit(0)
else:
pool = None
if len(sys.argv) > 1:
model_number = int(sys.argv[1])
else:
print 'Using default model'
model_number = 1
if len(sys.argv) > 2:
ns = int(sys.argv[2])
else:
print 'Using default step number'
ns = 100
print 'Running model %d for %d steps' % (model_number, ns)
# Read experimental data
print 'Reading data...'
data = read_data()
# Build model of interest
print 'Building model...'
if model_number == 1:
model = build_markevich_2step()
elif model_number == 2:
model = build_erk_autophos_phos()
elif model_number == 3:
model = build_erk_autophos_uT()
elif model_number == 4:
model = build_erk_activate_mkp()
print 'Starting sampler...'
sampler = run_one_model(model, model_number, data, ns, pool)
if use_mpi:
pool.close()
sampler.pool = []
sampler.loglargs = []
sampler.logparges = []
sampler.logl = None
sampler.logp = None
print 'Saving results...'
with open(folder_path + 'model_%d.pkl' % model_number, 'wb') as fh:
pickle.dump(sampler, fh)