In [2]:
import pandas as pd
import re
import os
import numpy as np
import simulation_utils
from scipy.interpolate import interp1d
In [3]:
experimentdata = pd.read_table(
'../processeddata/platereader/measured_yfprates_for_initiation_simulations.tsv',
sep='\t',
index_col=0)
'''
# Uncomment this region if run_simulations_whole_cell_parameter_sweep.ipynb
# was run to generate new simulation data
simulationdata = simulation_utils.get_simulation_data(runnumber=2)
simulationdata.drop(
['files'], axis=1).to_csv(
'../rawdata/simulations/run2_data.tsv', sep='\t', index_label='index')
'''
simulationdata = pd.read_table(
'../rawdata/simulations/run2_data.tsv', index_col=0)
pretermtypes = ['5primepreterm', 'selpreterm']
for pretermtype in pretermtypes:
pretermrates = np.unique(simulationdata[pretermtype])
for pretermrate in pretermrates:
fitresults = dict()
if pretermtype == 'selpreterm' and pretermrate == 0:
continue
for mutant in experimentdata.index:
subset = simulationdata[(simulationdata[pretermtype] == pretermrate
) & (simulationdata['mutant'] == mutant.
lower())]
# if pretermrate is 0, make sure all other preterm rates are also 0
if pretermrate == 0:
for innerpretermtype in pretermtypes:
if innerpretermtype == pretermtype:
continue
subset = subset[(subset[innerpretermtype] == 0)]
# to avoid parameter ranges that do not have any effect at pause
# sites
subset = subset[subset['ps_ratio'] < 0.9].sort_values(
by=['stallstrength'])
fit = interp1d(subset['ps_ratio'], subset['stallstrength'])
temp = fit(experimentdata.ix[mutant]['measuredRateNormalized'])
fitresults[mutant] = {'stallstrength': temp}
if pretermrate == 0:
title = 'trafficjam'
else:
title = pretermtype
fitresults = pd.DataFrame.from_dict(fitresults, orient='index')
fitresults.to_csv(
'../processeddata/simulations/run2_fit_stallstrength_for_initiation_'
+ title + '.tsv',
sep='\t',
index_label='mutant')
In [7]:
experimentdata = pd.read_table(
'../processeddata/platereader/measured_yfprates_for_double_simulations.tsv',
sep='\t',
index_col=0)
'''
# Uncomment this region if run_simulations_whole_cell_parameter_sweep.ipynb
# was run to generate new simulation data
simulationdata = simulation_utils.get_simulation_data(runnumber=2)
'''
simulationdata = pd.read_table(
'../rawdata/simulations/run2_data.tsv', index_col=0)
pretermtypes = ['5primepreterm', 'selpreterm']
for pretermtype in pretermtypes:
pretermrates = np.unique(simulationdata[pretermtype])
for pretermrate in pretermrates:
fitresults = dict()
if pretermtype == 'selpreterm' and pretermrate == 0:
continue
for mutant in experimentdata.index:
subset = simulationdata[(simulationdata[pretermtype] == pretermrate
) & (simulationdata['mutant'] == mutant)]
# if pretermrate is 0, make sure all other preterm rates are also 0
if pretermrate == 0:
for innerpretermtype in pretermtypes:
if innerpretermtype == pretermtype:
continue
subset = subset[(subset[innerpretermtype] == 0)]
# to avoid parameter ranges that do not have any effect at pause
# sites
subset = subset[subset['ps_ratio'] < 0.9].sort_values(
by=['stallstrength'])
fit = interp1d(subset['ps_ratio'], subset['stallstrength'])
temp = fit(experimentdata.ix[mutant]['measuredRateNormalized'])
fitresults[mutant] = {'stallstrength': temp}
if pretermrate == 0:
title = 'trafficjam'
else:
title = pretermtype
fitresults = pd.DataFrame.from_dict(fitresults, orient='index')
fitresults.to_csv(
'../processeddata/simulations/run2_fit_stallstrength_for_double_' +
title + '.tsv',
sep='\t',
index_label='mutant')
In [10]:
experimentdata = pd.read_table(
'../processeddata/platereader/measured_yfprates_for_distance_simulations.tsv',
sep='\t',
index_col=0)
'''
# Uncomment this region if run_simulations_whole_cell_parameter_sweep.ipynb
# was run to generate new simulation data
simulationdata = simulation_utils.get_simulation_data(runnumber=2)
'''
simulationdata = pd.read_table(
'../rawdata/simulations/run2_data.tsv', index_col=0)
pretermtypes = ['5primepreterm', 'selpreterm']
for pretermtype in pretermtypes:
pretermrates = np.unique(simulationdata[pretermtype])
for pretermrate in pretermrates:
fitresults = dict()
if pretermtype == 'selpreterm' and pretermrate == 0:
continue
for mutant in experimentdata.index:
subset = simulationdata[(simulationdata[pretermtype] == pretermrate
) & (simulationdata['mutant'] == mutant.
lower())]
# if pretermrate is 0, make sure all other preterm rates are also 0
if pretermrate == 0:
for innerpretermtype in pretermtypes:
if innerpretermtype == pretermtype:
continue
subset = subset[(subset[innerpretermtype] == 0)]
# to avoid parameter ranges that do not have any effect at pause
# sites
subset = subset[subset['ps_ratio'] < 0.9].sort_values(
by=['stallstrength'])
fit = interp1d(subset['ps_ratio'], subset['stallstrength'])
temp = fit(experimentdata.ix[mutant]['measuredRateNormalized'])
fitresults[mutant] = {'stallstrength': temp}
if pretermrate == 0:
title = 'trafficjam'
else:
title = pretermtype
fitresults = pd.DataFrame.from_dict(fitresults, orient='index')
fitresults.to_csv(
'../processeddata/simulations/run2_fit_stallstrength_for_ctc_distance_'
+ title + '.tsv',
sep='\t',
index_label='mutant')
In [11]:
experimentdata = pd.read_table(
'../processeddata/platereader/measured_yfprates_for_cta_distance_simulations.tsv',
sep='\t',
index_col=0)
'''
# Uncomment this region if run_simulations_whole_cell_parameter_sweep.ipynb
# was run to generate new simulation data
simulationdata = simulation_utils.get_simulation_data(runnumber=2)
'''
simulationdata = pd.read_table(
'../rawdata/simulations/run2_data.tsv', index_col=0)
pretermtypes = ['5primepreterm', 'selpreterm']
for pretermtype in pretermtypes:
pretermrates = np.unique(simulationdata[pretermtype])
for pretermrate in pretermrates:
fitresults = dict()
if pretermtype == 'selpreterm' and pretermrate == 0:
continue
for mutant in experimentdata.index:
subset = simulationdata[(simulationdata[pretermtype] == pretermrate
) & (simulationdata['mutant'] == mutant.
lower())]
# if pretermrate is 0, make sure all other preterm rates are also 0
if pretermrate == 0:
for innerpretermtype in pretermtypes:
if innerpretermtype == pretermtype:
continue
subset = subset[(subset[innerpretermtype] == 0)]
# to avoid parameter ranges that do not have any effect at pause
# sites
subset = subset[subset['ps_ratio'] < 0.9].sort_values(
by=['stallstrength'])
fit = interp1d(subset['ps_ratio'], subset['stallstrength'])
temp = fit(experimentdata.ix[mutant]['measuredRateNormalized'])
fitresults[mutant] = {'stallstrength': temp}
if pretermrate == 0:
title = 'trafficjam'
else:
title = pretermtype
fitresults = pd.DataFrame.from_dict(fitresults, orient='index')
fitresults.to_csv(
'../processeddata/simulations/run2_fit_stallstrength_for_cta_distance_'
+ title + '.tsv',
sep='\t',
index_label='mutant')
In [12]:
experimentdata = pd.read_table(
'../processeddata/platereader/measured_yfprates_for_serine_initiation_simulations.tsv',
sep='\t',
index_col=0)
'''
# Uncomment this region if run_simulations_whole_cell_parameter_sweep.ipynb
# was run to generate new simulation data
simulationdata = simulation_utils.get_simulation_data(runnumber=13)
simulationdata.drop(
['files'], axis=1).to_csv(
'../rawdata/simulations/run13_data.tsv', sep='\t', index_label='index')
'''
simulationdata = pd.read_table(
'../rawdata/simulations/run13_data.tsv', index_col=0)
pretermtypes = ['5primepreterm', 'selpreterm']
for pretermtype in pretermtypes:
pretermrates = np.unique(simulationdata[pretermtype])
for pretermrate in pretermrates:
fitresults = dict()
if pretermtype == 'selpreterm' and pretermrate == 0:
continue
for mutant in experimentdata.index:
subset = simulationdata[(simulationdata[pretermtype] == pretermrate
) & (simulationdata['mutant'] == mutant.
lower())]
# if pretermrate is 0, make sure all other preterm rates are also 0
if pretermrate == 0:
for innerpretermtype in pretermtypes:
if innerpretermtype == pretermtype:
continue
subset = subset[(subset[innerpretermtype] == 0)]
# to avoid parameter ranges that do not have any effect at pause
# sites
subset = subset[subset['ps_ratio'] < 0.9].sort_values(
by=['stallstrength'])
fit = interp1d(subset['ps_ratio'], subset['stallstrength'])
temp = fit(experimentdata.ix[mutant]['measuredRateNormalized'])
fitresults[mutant] = {'stallstrength': temp}
if pretermrate == 0:
title = 'trafficjam'
else:
title = pretermtype
fitresults = pd.DataFrame.from_dict(fitresults, orient='index')
fitresults.to_csv(
'../processeddata/simulations/run13_serine_fit_stallstrength_for_initiation_'
+ title + '.tsv',
sep='\t',
index_label='mutant')
In [19]:
experimentdata = pd.read_table(
'../processeddata/platereader/measured_yfprates_for_serine_double_simulations.tsv',
sep='\t',
index_col=0)
'''
# Uncomment this region if run_simulations_whole_cell_parameter_sweep.ipynb
# was run to generate new simulation data
simulationdata = simulation_utils.get_simulation_data(runnumber=13)
'''
simulationdata = pd.read_table(
'../rawdata/simulations/run13_data.tsv', index_col=0)
pretermtypes = ['5primepreterm', 'selpreterm']
for pretermtype in pretermtypes:
pretermrates = np.unique(simulationdata[pretermtype])
for pretermrate in pretermrates:
fitresults = dict()
if pretermtype == 'selpreterm' and pretermrate == 0:
continue
for mutant in experimentdata.index:
if mutant == 'tcg8': # I did not use TCG8 for double mutants
continue
subset = simulationdata[(simulationdata[pretermtype] == pretermrate
) & (simulationdata['mutant'] == mutant.
lower())]
# if pretermrate is 0, make sure all other preterm rates are also 0
if pretermrate == 0:
for innerpretermtype in pretermtypes:
if innerpretermtype == pretermtype:
continue
subset = subset[(subset[innerpretermtype] == 0)]
# to avoid parameter ranges that do not have any effect at pause
# sites
subset = subset[subset['ps_ratio'] < 0.9].sort_values(
by=['stallstrength'])
fit = interp1d(subset['ps_ratio'], subset['stallstrength'])
temp = fit(experimentdata.ix[mutant]['measuredRateNormalized'])
fitresults[mutant] = {'stallstrength': temp}
if pretermrate == 0:
title = 'trafficjam'
else:
title = pretermtype
fitresults = pd.DataFrame.from_dict(fitresults, orient='index')
fitresults.to_csv(
'../processeddata/simulations/run13_serine_fit_stallstrength_for_double_'
+ title + '.tsv',
sep='\t',
index_label='mutant')
In [16]:
experimentdata = pd.read_table(
'../processeddata/platereader/measured_yfprates_for_leucine_multiple_simulations.tsv',
sep='\t',
index_col=0)
'''
# Uncomment this region if run_simulations_whole_cell_parameter_sweep.ipynb
# was run to generate new simulation data
simulationdata = simulation_utils.get_simulation_data(runnumber=2)
'''
simulationdata = pd.read_table(
'../rawdata/simulations/run2_data.tsv', index_col=0)
pretermtypes = ['5primepreterm', 'selpreterm']
for pretermtype in pretermtypes:
pretermrates = np.unique(simulationdata[pretermtype])
for pretermrate in pretermrates:
fitresults = dict()
if pretermtype == 'selpreterm' and pretermrate == 0:
continue
for mutant in experimentdata.index:
subset = simulationdata[(simulationdata[pretermtype] == pretermrate
) & (simulationdata['mutant'] == mutant)]
# if pretermrate is 0, make sure all other preterm rates are also 0
if pretermrate == 0:
for innerpretermtype in pretermtypes:
if innerpretermtype == pretermtype:
continue
subset = subset[(subset[innerpretermtype] == 0)]
# to avoid parameter ranges that do not have any effect at pause
# sites
subset = subset[subset['ps_ratio'] < 0.9].sort_values(
by=['stallstrength'])
fit = interp1d(subset['ps_ratio'], subset['stallstrength'])
temp = fit(experimentdata.ix[mutant]['measuredRateNormalized'])
fitresults[mutant] = {'stallstrength': temp}
if pretermrate == 0:
title = 'trafficjam'
else:
title = pretermtype
fitresults = pd.DataFrame.from_dict(fitresults, orient='index')
fitresults.to_csv(
'../processeddata/simulations/run2_fit_stallstrength_for_leucine_multiple_'
+ title + '.tsv',
sep='\t',
index_label='mutant')