├── .gitignore ├── KineticLearning ├── __init__.py ├── helper.py └── plot.py ├── License.pdf ├── README.md ├── data ├── isopentenol_data.csv ├── limonene_data.csv ├── time_series_metabolomics.csv └── time_series_proteomics.csv ├── notebooks ├── Biological Intuition Plots.ipynb ├── Biological_Intuition.ipynb ├── Create Data Set.ipynb ├── LearnLimoneneDynamics.ipynb ├── ManuscriptNotebook.ipynb ├── README.md └── __init__.py └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | 49 | # Translations 50 | *.mo 51 | *.pot 52 | 53 | # Django stuff: 54 | *.log 55 | local_settings.py 56 | 57 | # Flask stuff: 58 | instance/ 59 | .webassets-cache 60 | 61 | # Scrapy stuff: 62 | .scrapy 63 | 64 | # Sphinx documentation 65 | docs/_build/ 66 | 67 | # PyBuilder 68 | target/ 69 | 70 | # Jupyter Notebook 71 | .ipynb_checkpoints 72 | 73 | # pyenv 74 | .python-version 75 | 76 | # celery beat schedule file 77 | celerybeat-schedule 78 | 79 | # SageMath parsed files 80 | *.sage.py 81 | 82 | # dotenv 83 | .env 84 | 85 | # virtualenv 86 | .venv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | .spyproject 93 | 94 | # Rope project settings 95 | .ropeproject 96 | 97 | # mkdocs documentation 98 | /site 99 | 100 | # mypy 101 | .mypy_cache/ 102 | 103 | # Project Specific 104 | .DS_Store 105 | -------------------------------------------------------------------------------- /KineticLearning/__init__.py: -------------------------------------------------------------------------------- 1 | # # A Simplified Version of the Kinetic Learning Algorithm 2 | # 3 | # There was a bunch of kruft in the old kinetic learning source code. I boiled it down into its key components and simplified the data structures. Now the code is more managable, understandable, and extensible. 4 | # 5 | # **Todo:** 6 | # 1. Add Smoothing to Data Augmentation as an Option. 7 | # 2. Add a Random Seed Input 8 | # 3. Remove Warning From Import! 9 | 10 | import pandas as pd 11 | from IPython.display import display 12 | from scipy.signal import savgol_filter 13 | import numpy as np 14 | from tpot import TPOTRegressor 15 | from scipy.interpolate import interp1d 16 | from scipy.integrate import odeint,ode 17 | import matplotlib.pyplot as plt 18 | import seaborn as sns 19 | 20 | 21 | #Decorators 22 | def evenly_space(fun,times): 23 | '''Decorate Functions that require even spacing.''' 24 | pass 25 | 26 | 27 | def read_timeseries_data(csv_path,states,controls,impute=True, 28 | time='Time',strain='Strain',augment=None, 29 | est_derivative=True,smooth=False,n=None): 30 | '''Put DataFrame into the TSDF format. 31 | 32 | The input csv or dataframe should have a 33 | column for time and ever state and control 34 | variable input for that time. Optional Columns are 35 | "Replicate" and "Strain". 36 | 37 | ''' 38 | 39 | #Load Raw Data 40 | df = pd.read_csv(csv_path) 41 | 42 | #Remove Unused Columns 43 | df = df[[strain,time] + states+controls] 44 | 45 | #Set Time Column to Float 46 | 47 | #Standardize Index Names to Strain & Time 48 | df.columns = ['Strain','Time']+states+controls 49 | 50 | #MultiIndex Columns 51 | df = df.set_index(['Strain','Time']) 52 | columns = [('states',state) for state in states] + [('controls',control) for control in controls] 53 | df.columns = pd.MultiIndex.from_tuples(columns) 54 | 55 | #Sample num_strains Without Replacement 56 | if n is not None: 57 | strains = np.random.choice(df.reset_index()['Strain'].unique(),size=n) 58 | df = df.loc[df.index.get_level_values(0).isin(strains)] 59 | 60 | #Impute NaN Values using Interpolation 61 | if impute: 62 | df = df.groupby('Strain').apply(lambda group: group.interpolate()) 63 | 64 | #Augment the data using an interpolation scheme 65 | if augment is not None: 66 | df = augment_data(df,n=augment) 67 | 68 | #Estimate the Derivative 69 | if est_derivative: 70 | df = estimate_state_derivative(df) 71 | 72 | return df 73 | 74 | 75 | def augment_data(tsdf,n=200): 76 | '''Augment the time series data for improved fitting. 77 | 78 | The time series data points are interpolated to create 79 | smooth curves for each time series and fill in blank 80 | values. 81 | ''' 82 | 83 | def augment(df): 84 | #Find New Times 85 | times = df.index.get_level_values(1) 86 | new_times = np.linspace(min(times),max(times),n) 87 | 88 | #Build New Indecies 89 | strain_name = set(df.index.get_level_values(0)) 90 | new_indecies = pd.MultiIndex.from_product([strain_name,new_times]) 91 | 92 | #Reindex the Data Frame & Interpolate New Values 93 | df = df.reindex(df.index.union(new_indecies)) 94 | df.index.names = ['Strain','Time'] 95 | df = df.interpolate() 96 | 97 | #Remove Old Indecies 98 | df.index = df.index.droplevel(0) 99 | times_to_remove = set(times) - (set(times) & set(new_times)) 100 | df = df.loc[~df.index.isin(times_to_remove)] 101 | return df 102 | 103 | tsdf = tsdf.groupby('Strain').apply(augment) 104 | return tsdf 105 | 106 | 107 | def estimate_state_derivative(tsdf): 108 | '''Estimate the Derivative of the State Variables''' 109 | 110 | #Check if a vector is evenly spaced 111 | evenly_spaced = lambda x: max(set(np.diff(x))) - min(set(np.diff(x))) < 10**-5 112 | 113 | #Find the difference between elements of evenly spaced vectors 114 | delta = lambda x: np.diff(x)[0] 115 | 116 | 117 | 118 | def estimate_derivative(tsdf): 119 | state_df = tsdf['states'] 120 | times = state_df.index.get_level_values(1) 121 | diff = delta(times) 122 | 123 | #Find Derivative of evenly spaced data using the savgol filter 124 | savgol = lambda x: savgol_filter(x,7,2,deriv=1,delta=diff) 125 | 126 | if evenly_spaced(times): 127 | state_df = state_df.apply(savgol) 128 | else: 129 | state_df = state_df.apply(savgol_uneven) 130 | 131 | #Add Multicolumn 132 | state_df.columns = pd.MultiIndex.from_product([['derivatives'],state_df.columns]) 133 | 134 | #Merge Derivatives Back 135 | tsdf = pd.merge(tsdf, state_df,left_index=True, right_index=True,how='left') 136 | 137 | return tsdf 138 | 139 | 140 | tsdf = tsdf.groupby('Strain').apply(estimate_derivative) 141 | return tsdf 142 | 143 | 144 | #Reconstruct the curve using the derivative (Check that derivative Estimates are Close...) 145 | def check_derivative(tsdf): 146 | '''Check the Derivative Estimates to Make sure they are good.''' 147 | 148 | #First Integrate The Derivative of Each Curve Starting at the initial condition 149 | 150 | for name,strain_df in tsdf.groupby('Strain'): 151 | #display(strain_df['derivatives'].tail) 152 | times = strain_df.index.get_level_values(1) 153 | dx_df = strain_df['derivatives'].apply(lambda y: interp1d(times,y,fill_value='extrapolate')) 154 | dx = lambda y,t: dx_df.apply(lambda x: x(t)).values 155 | x0 = strain_df['states'].iloc[0].values 156 | 157 | #Solve Differential Equation 158 | result = odeint(dx,x0,times) 159 | trajectory_df = pd.DataFrame(result,columns=strain_df['states'].columns) 160 | trajectory_df['Time'] = times 161 | trajectory_df = trajectory_df.set_index('Time') 162 | 163 | for column in strain_df['states'].columns: 164 | plt.figure() 165 | ax = plt.gca() 166 | strain_df['states'].reset_index().plot(x='Time',y=column,ax=ax) 167 | trajectory_df.plot(y=column,ax=ax) 168 | plt.show() 169 | 170 | 171 | 172 | class dynamic_model(object): 173 | '''A MultiOutput Dynamic Model created from TPOT''' 174 | 175 | def __init__(self,tsdf): 176 | self.tsdf = tsdf 177 | 178 | 179 | def search(self,generations=50,population_size=30,verbose=False): 180 | '''Find the best model that fits the data with TPOT.''' 181 | 182 | X = self.tsdf[['states','controls']].values 183 | 184 | if verbose: 185 | verbosity = 2 186 | else: 187 | verbosity = 0 188 | 189 | def fit_single_output(row): 190 | tpot = TPOTRegressor(generations=generations, population_size=population_size, verbosity=verbosity,n_jobs=1) 191 | fit_model = tpot.fit(X,row).fitted_pipeline_ 192 | return fit_model 193 | 194 | self.model_df = self.tsdf['derivatives'].apply(fit_single_output).to_frame() 195 | display(self.model_df) 196 | 197 | def fit(self,tsdf): 198 | '''Fit the Dynamical System Model. 199 | 200 | Fit the dynamical system model and 201 | return the map f. 202 | ''' 203 | 204 | #update the data frame 205 | self.tsdf = tsdf 206 | X = self.tsdf[['states','controls']].values 207 | 208 | #Fit the dataframe data to existing models 209 | #self.model_df.apply(lambda model: print(model),axis=1) 210 | self.model_df = self.model_df.apply(lambda model: model[0].fit(X,self.tsdf['derivatives'][model.name]),axis=1) 211 | 212 | 213 | def predict(self,X): 214 | '''Return a Prediction''' 215 | y = self.model_df.apply(lambda model: model[0].predict(X.reshape(1,-1)),axis=1).values.reshape(-1,) 216 | return y 217 | 218 | 219 | def fit_report(self): 220 | '''Report the Quality of the Fit in Plots''' 221 | #Calculate The Error Distribution, Broken down by Fit 222 | 223 | 224 | pass 225 | 226 | 227 | 228 | # New Stiff Integrator 229 | def odeintz(fun,y0,times,tolerance=1e-4,verbose=False): 230 | '''Stiff Integrator for Integrating over Machine Learning Models''' 231 | maxDelta = 10 232 | 233 | f = lambda t,x: fun(x,t) 234 | r = ode(f).set_integrator('dop853',nsteps=1000,atol=1e-4) 235 | r.set_initial_value(y0,times[0]) 236 | 237 | #progress bar 238 | #f = FloatProgress(min=0, max=max(times)) 239 | #display(f) 240 | 241 | #Perform Integration 242 | x = [y0,] 243 | curTime = times[0] 244 | for nextTime in times[1:]: 245 | #print(r.t) 246 | #while r.successful() and r.t < nextTime: 247 | while r.t < nextTime: 248 | if nextTime-curTime < maxDelta: 249 | dt = nextTime-curTime 250 | else: 251 | dt = maxDelta 252 | 253 | value = r.integrate(r.t + dt) 254 | curTime = r.t 255 | if verbose: 256 | print(curTime, end='\r') 257 | sleep(0.001) 258 | f.value = curTime 259 | x.append(value) 260 | return x 261 | 262 | 263 | def learn_dynamics(df,generations=50,population_size=30,verbose=False): 264 | '''Find system dynamics Time Series Data. 265 | 266 | Take in a Data Frame containing time series data 267 | and use that to find the dynamics x_dot = f(x,u). 268 | ''' 269 | 270 | #Fit Model 271 | model = dynamic_model(df) 272 | model.search(generations=generations,population_size=population_size,verbose=False) 273 | 274 | return model 275 | 276 | 277 | def simulate_dynamics(model,strain_df,time_points=None,tolerance=1e-4,verbose=False): 278 | '''Use Learned Dynamics to Generate a Simulated Trajectory in the State Space''' 279 | display(strain_df) 280 | times = strain_df.index.get_level_values(1) 281 | 282 | #Get Controls as a Function of Time Using Interpolations 283 | u_df = strain_df['controls'].apply(lambda y: interp1d(times,y,fill_value='extrapolate')) 284 | u = lambda t: u_df.apply(lambda x: x(t)).values 285 | 286 | #Get Initial Conditions from the Strain Data Frame 287 | x0 = strain_df['states'].iloc[0].values 288 | 289 | #Solve Differential Equation For Same Time Points 290 | #f = lambda x,t: (model.predict(np.concatenate([x, u(t)])),print(t))[0] 291 | f = lambda x,t: model.predict(np.concatenate([x, u(t)])) 292 | 293 | #Return DataFrame with Predicted Trajectories (Use Integrator with Sufficiently Low tolerances...) 294 | sol = odeintz(f,x0,times,tolerance=tolerance) 295 | #sol = odeint(f,x0,times,atol=5*10**-4,rtol=10**-6) 296 | trajectory_df = pd.DataFrame(sol,columns=strain_df['states'].columns) 297 | trajectory_df['Time'] = times 298 | #display(trajectory_df) 299 | 300 | return trajectory_df -------------------------------------------------------------------------------- /KineticLearning/helper.py: -------------------------------------------------------------------------------- 1 | import random 2 | import pandas as pd 3 | import numpy as np 4 | import math 5 | from scipy.integrate import ode 6 | from scipy.interpolate import interp1d 7 | from scipy.signal import savgol_filter 8 | from sklearn.model_selection import learning_curve 9 | import matplotlib.pyplot as plt 10 | 11 | 12 | #====================# 13 | # Helper Functions # 14 | #--------------------# 15 | 16 | def extractNamedColumns(data,extract_names,col_names): 17 | #Get Columns associated with names 18 | column_numbers = [col_names.index(name) for name in extract_names] 19 | print(column_numbers) 20 | #Extract Columns from Array 21 | data_extract = [[row[col] for col in column_numbers] for row in data] 22 | return data_extract 23 | 24 | #Removes Nan Vaulues in a Series and caps the ends 25 | def remove_NaN(x,y): 26 | x_sanitized = [] 27 | y_sanitized = [] 28 | for i,val in enumerate(y): 29 | if math.isnan(val): 30 | if i == len(y)-1: 31 | y_sanitized.append(y_sanitized[-1]) 32 | x_sanitized.append(x[i]) 33 | elif i==0: 34 | for j in range(len(y)): 35 | if ~math.isnan(y[j]): 36 | y_sanitized.append(y[j]) 37 | x_sanitized.append(x[i]) 38 | else: 39 | x_sanitized.append(x[i]) 40 | y_sanitized.append(y[i]) 41 | return x_sanitized,y_sanitized 42 | 43 | 44 | def mlode(modelDict, df, targets, specific_features,time_index='Time (h)'): 45 | #Create Interpolation functions for each feature 46 | interpFun = {} 47 | for feature in df.columns: 48 | 49 | if feature not in targets: 50 | #print(feature) 51 | X,y = remove_NaN(df.reset_index()[time_index].tolist(),df[feature].tolist()) 52 | if isinstance(feature,tuple): 53 | feature = feature[1] 54 | interpFun[feature] = interp1d(X,y) 55 | 56 | #print(targets) 57 | 58 | #Define the function to integrate 59 | def f(x,t): 60 | x_dot = [] 61 | #Generate Derivatives for Each Target 62 | for target in targets: 63 | x_pred = [] 64 | for feature in df.columns: 65 | 66 | #If the Feature is dynamically changing, use the Dynamic Value 67 | if feature in targets: 68 | x_pred= np.append(x_pred, x[targets.index(feature)]) 69 | 70 | #Otherwise use a value parameterized by time 71 | else: 72 | x_pred= np.append(x_pred, interpFun[feature](t)) 73 | 74 | #Append the Predicted Derivative to the output vector 75 | x_dot = np.append(x_dot,modelDict[target].predict([x_pred])) 76 | 77 | #Make sure state doesn't go negative 78 | #eps = 10**-4 79 | #c = 5 80 | #for i,val in enumerate(x): 81 | # if val < 0: 82 | # x_dot[i] = -val 83 | 84 | return x_dot 85 | 86 | return f 87 | 88 | # New Stiff Integrator 89 | def odeintz(fun,y0,times): 90 | maxDelta = 10 91 | 92 | f = lambda t,x: fun(x,t) 93 | r = ode(f).set_integrator('dopri5',nsteps=1000,atol=1e-4) 94 | r.set_initial_value(y0,times[0]) 95 | 96 | #progress bar 97 | #f = FloatProgress(min=0, max=max(times)) 98 | #display(f) 99 | 100 | 101 | #Perform Integration 102 | x = [y0,] 103 | curTime = times[0] 104 | for nextTime in times[1:]: 105 | #while r.successful() and r.t < nextTime: 106 | while r.t < nextTime: 107 | if nextTime-curTime < maxDelta: 108 | dt = nextTime-curTime 109 | else: 110 | dt = maxDelta 111 | 112 | value = r.integrate(r.t + dt) 113 | curTime = r.t 114 | print(curTime, end='\r') 115 | #sleep(0.001) 116 | f.value = curTime 117 | x.append(value) 118 | return x 119 | 120 | def generateTSDataSet(dataframe,features,targets,n_points=100): 121 | 122 | strains = tuple(dataframe.index.get_level_values(0).unique()) 123 | numSamples = len(strains) 124 | print( 'Total Time Series in Data Set: ', numSamples ) 125 | 126 | ml_df = pd.DataFrame() 127 | for strain in strains: 128 | strain_series = {} 129 | strain_df = dataframe.loc[(strain,slice(None)),:] 130 | strain_df.index = strain_df.index.get_level_values(1) 131 | 132 | #Interpolate & Smooth Each Feature & Target Then Add To Series 133 | for measurement in features + targets: 134 | #Extract Measurement 135 | measurement_series = strain_df[measurement].dropna() 136 | #print(measurement_series.index.tolist(), 137 | # measurement_series.tolist()) 138 | 139 | #Generate n_points interpolated points 140 | times = measurement_series.index.tolist() 141 | deltaT = (max(times) - min(times))/n_points 142 | 143 | measurement_fun = interp1d(times, 144 | measurement_series.tolist(),kind='linear') 145 | interpolated_measurement = measurement_fun(np.linspace(min(times),max(times),n_points)) 146 | 147 | #Smooth Points 148 | smoothed_measurement = savgol_filter(interpolated_measurement,7,2) 149 | 150 | #If feature write out points 151 | if measurement in features: 152 | strain_series[('feature',measurement)]=smoothed_measurement 153 | 154 | #if target calculate derivative + write out points and derivative 155 | if measurement in targets: 156 | strain_series[('feature',measurement)]=smoothed_measurement 157 | strain_series[('target',measurement)]=np.gradient([point/deltaT for point in smoothed_measurement]) 158 | 159 | #Make this more readable by breaking up into multiple lines... 160 | strain_df = pd.DataFrame(strain_series, 161 | index=pd.MultiIndex.from_product([[strain],np.linspace(min(times),max(times),n_points)], 162 | names=['Strain', 'Time (h)'])) 163 | ml_df = pd.concat([ml_df,strain_df]) 164 | #display(ml_df) 165 | return ml_df 166 | 167 | #====================# 168 | # Plotting Functions # 169 | #--------------------# 170 | 171 | def plot_species_curves(modelDict, title, df, targets, specific_features, ylim=None, cv=None, 172 | n_jobs=1, train_sizes=np.linspace(.1, 1.0, 3),training_sets=5): 173 | """ 174 | Generate a simple plot of the test and training learning curve. Returns Metrics for each predicted curve 175 | 176 | Parameters 177 | ---------- 178 | estimator : object type that implements the "fit" and "predict" methods 179 | An object of that type which is cloned for each validation. 180 | 181 | title : string 182 | Title for the chart. 183 | 184 | X : array-like, shape (n_samples, n_features) 185 | Training vector, where n_samples is the number of samples and 186 | n_features is the number of features. 187 | 188 | y : array-like, shape (n_samples) or (n_samples, n_features), optional 189 | Target relative to X for classification or regression; 190 | None for unsupervised learning. 191 | 192 | ylim : tuple, shape (ymin, ymax), optional 193 | Defines minimum and maximum yvalues plotted. 194 | 195 | cv : int, cross-validation generator or an iterable, optional 196 | Determines the cross-validation splitting strategy. 197 | Possible inputs for cv are: 198 | - None, to use the default 3-fold cross-validation, 199 | - integer, to specify the number of folds. 200 | - An object to be used as a cross-validation generator. 201 | - An iterable yielding train/test splits. 202 | 203 | For integer/None inputs, if ``y`` is binary or multiclass, 204 | :class:`StratifiedKFold` used. If the estimator is not a classifier 205 | or if ``y`` is neither binary nor multiclass, :class:`KFold` is used. 206 | 207 | Refer :ref:`User Guide ` for the various 208 | cross-validators that can be used here. 209 | 210 | n_jobs : integer, optional 211 | Number of jobs to run in parallel (default 1). 212 | """ 213 | 214 | #Set Random Seed For training 215 | seed = 103 216 | random.seed(seed) 217 | 218 | #Create figure / plots 219 | fig = plt.figure(figsize=(12,16)) 220 | #fig.set_title(title) 221 | #if ylim is not None: 222 | # plt.ylim(*ylim) 223 | 224 | #Create subplots for each target 225 | ax = {} 226 | for i,target in enumerate(targets): 227 | ax[target] = plt.subplot(int(len(targets)/2)+1, 2, i+1) 228 | 229 | #Get Randomized List of all Strains 230 | strains = df.index.get_level_values(0).unique() 231 | strains = list(strains.values) 232 | #print(strains) 233 | strains = random.sample(strains, len(strains)) 234 | 235 | #Pick test strain 236 | test_df = df.loc[(slice(strains[0],strains[0]),slice(None)),:] 237 | strains = strains[1:] 238 | 239 | #Create Interpolation functions for each feature in the test strain 240 | interpFun = {} 241 | #display(test_df.reset_index()) 242 | for feature in df.columns: 243 | X,y = remove_NaN(test_df.reset_index()['Time (h)'].tolist(),test_df[feature].tolist()) 244 | if isinstance(feature,tuple): 245 | if feature[0] == 'feature': 246 | feature = feature[1] 247 | else: 248 | continue 249 | 250 | interpFun[feature] = interp1d(X,y) 251 | 252 | train_sizes = [int(len(strains)*size/training_sets)-1 for size in train_sizes] 253 | for i,size in enumerate(train_sizes): 254 | if size < 2: 255 | train_sizes[i] = 2 256 | 257 | #Create Fits for each training set 258 | fits = {} 259 | for training_set in range(training_sets): 260 | fits[training_set] = {} 261 | 262 | #Generate training strain data for this training set 263 | training_strains = strains[0:(train_sizes[-1] + 1)] 264 | #print(training_strains) 265 | strains = strains[train_sizes[-1]:] 266 | endSamples = train_sizes 267 | #print('Strains:',strains) 268 | #print('End Samples',endSamples) 269 | sample_sets = [df.loc[(training_strains[0:endSample],slice(None)),:] for endSample in endSamples] 270 | 271 | #For each set size in the training set fit the model and store it 272 | for j,sample_set in enumerate(sample_sets): 273 | 274 | #print('Sample Set:',sample_set.index.get_level_values(0).unique().values) 275 | 276 | # Train Model 277 | print('Training Models for Training Set',training_set,'In Sample set',j) 278 | for target in targets: 279 | feature_indecies = [('feature', feature) for feature in specific_features[target]] 280 | X = sample_set[feature_indecies].values.tolist() 281 | 282 | #print(feature_indecies) 283 | #display(sample_set[feature_indecies]) 284 | target_index = ('target',target) 285 | y = sample_set[target_index].values.tolist() 286 | modelDict[target].fit(X,y) 287 | 288 | print('Integrating ODEs!') 289 | # Integrate Given Model Test Case 290 | g = mlode(modelDict, test_df, targets, specific_features) 291 | times = test_df.reset_index()['Time (h)'].tolist() 292 | 293 | #Set Y0 initial condition 294 | appended_targets = [('feature',target) for target in targets] 295 | #display(test_df) 296 | #display(test_df[appended_targets].iloc[0]) 297 | y0 = test_df[appended_targets].iloc[0].tolist() 298 | 299 | #print('times:',times) 300 | fit = odeintz(g,y0,times) 301 | fitT = list(map(list, zip(*fit))) 302 | fits[training_set][train_sizes[j]] = fitT 303 | 304 | 305 | #Perform Statistics on Fits and generate plots 306 | colors = ['b','g','k','y','m'] 307 | predictions = {} 308 | lines =[] 309 | labels = [] 310 | for k,target in enumerate(targets): 311 | actual_data = [interpFun[target](t) for t in times] 312 | predictions[target] = {'actual':actual_data} 313 | predictions['Time'] = times 314 | if k == 0: 315 | lines.append(ax[target].plot(times,actual_data,'--', color='r')[0]) 316 | labels.append('Actual Dynamics') 317 | else: 318 | ax[target].plot(times,actual_data,'--', color='r') 319 | ax[target].set_title(target) 320 | 321 | for j in range(len(sample_sets)): 322 | upper = [] 323 | lower = [] 324 | aves = [] 325 | 326 | predictions[target][train_sizes[j]] = [] 327 | for training_set in range(training_sets): 328 | predictions[target][train_sizes[j]].append(fits[training_set][train_sizes[j]][k]) 329 | 330 | for i,time in enumerate(times): 331 | 332 | values = [] 333 | for training_set in range(training_sets): 334 | #print(training_set,train_sizes[j],i) 335 | values.append(fits[training_set][train_sizes[j]][k][i]) 336 | 337 | #Compute Statistics of Values 338 | #print(values) 339 | ave = statistics.mean(values) 340 | std = statistics.stdev(values) 341 | aves += [ave,] 342 | upper += [ave + std,] 343 | lower += [ave - std,] 344 | 345 | #print(upper) 346 | #print(times) 347 | 348 | #Compute upper and lower bounds for shading 349 | ax[target].fill_between(times, lower,upper, alpha=0.1, color=colors[j]) 350 | if k == 0: 351 | lines.append(ax[target].plot(times,aves,colors[j])[0]) 352 | labels.append(str(train_sizes[j]) + ' Strain Prediction') 353 | else: 354 | ax[target].plot(times,aves,colors[j]) 355 | print(colors[j],train_sizes[j]) 356 | plt.figlegend( lines, labels, loc = 'lower center', ncol=5, labelspacing=0. ) 357 | 358 | return predictions -------------------------------------------------------------------------------- /KineticLearning/plot.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import matplotlib.colors as colors 3 | from mpl_toolkits.axes_grid1 import AxesGrid 4 | from mpl_toolkits.axes_grid1 import make_axes_locatable 5 | from sklearn.decomposition import PCA 6 | from sklearn.model_selection import learning_curve 7 | import seaborn as sns 8 | import numpy as np 9 | 10 | 11 | def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'): 12 | ''' 13 | Function to offset the "center" of a colormap. Useful for 14 | data with a negative min and positive max and you want the 15 | middle of the colormap's dynamic range to be at zero 16 | 17 | Input 18 | ----- 19 | cmap : The matplotlib colormap to be altered 20 | start : Offset from lowest point in the colormap's range. 21 | Defaults to 0.0 (no lower ofset). Should be between 22 | 0.0 and `midpoint`. 23 | midpoint : The new center of the colormap. Defaults to 24 | 0.5 (no shift). Should be between 0.0 and 1.0. In 25 | general, this should be 1 - vmax/(vmax + abs(vmin)) 26 | For example if your data range from -15.0 to +5.0 and 27 | you want the center of the colormap at 0.0, `midpoint` 28 | should be set to 1 - 5/(5 + 15)) or 0.75 29 | stop : Offset from highets point in the colormap's range. 30 | Defaults to 1.0 (no upper ofset). Should be between 31 | `midpoint` and 1.0. 32 | ''' 33 | cdict = { 34 | 'red': [], 35 | 'green': [], 36 | 'blue': [], 37 | 'alpha': [] 38 | } 39 | 40 | # regular index to compute the colors 41 | reg_index = np.linspace(start, stop, 257) 42 | 43 | # shifted index to match the data 44 | shift_index = np.hstack([ 45 | np.linspace(0.0, midpoint, 128, endpoint=False), 46 | np.linspace(midpoint, 1.0, 129, endpoint=True) 47 | ]) 48 | 49 | for ri, si in zip(reg_index, shift_index): 50 | r, g, b, a = cmap(ri) 51 | 52 | cdict['red'].append((si, r, r)) 53 | cdict['green'].append((si, g, g)) 54 | cdict['blue'].append((si, b, b)) 55 | cdict['alpha'].append((si, a, a)) 56 | 57 | newcmap = colors.LinearSegmentedColormap(name, cdict) 58 | plt.register_cmap(cmap=newcmap) 59 | 60 | return newcmap 61 | 62 | 63 | def plot_classifier(model,data,targets,midpoint=0.5,pcs=None,title=None,zlabel=None,ax=None): 64 | '''Plots a 2d projection of the model onto the principal components. 65 | The data is overlayed onto the model for visualization. 66 | ''' 67 | 68 | #Create Principal Compoenents for Visualiztion of High Dimentional Space 69 | pca = PCA(n_components=2) 70 | if pcs is not None: 71 | pca.fit(pcs) 72 | data_transformed = pca.transform(data) 73 | else: 74 | data_transformed = pca.fit_transform(data) 75 | 76 | #Get Data Range 77 | xmin = np.amin(data_transformed[:,0]) 78 | xmax = np.amax(data_transformed[:,0]) 79 | ymin = np.amin(data_transformed[:,1]) 80 | ymax = np.amax(data_transformed[:,1]) 81 | 82 | #Scale Plot Range 83 | scaling_factor = 0.5 84 | xmin = xmin - (xmax - xmin)*scaling_factor/2 85 | xmax = xmax + (xmax - xmin)*scaling_factor/2 86 | ymin = ymin - (ymax - ymin)*scaling_factor/2 87 | ymax = ymax + (ymax - ymin)*scaling_factor/2 88 | 89 | #Generate Points in transformed Space 90 | points = 1000 91 | x = np.linspace(xmin,xmax,num=points) 92 | y = np.linspace(ymin,ymax,num=points) 93 | xv, yv = np.meshgrid(x,y) 94 | 95 | #reshape data for inverse transform 96 | xyt = np.concatenate((xv.reshape([xv.size,1]),yv.reshape([yv.size,1])),axis=1) 97 | xy = pca.inverse_transform(xyt) 98 | 99 | #predict z values for plot 100 | z = model.predict(xy).reshape([points,points]) 101 | minpoint = min([min(p) for p in z]) 102 | maxpoint = max([max(p) for p in z]) 103 | 104 | #Plot Contour from Model 105 | if ax is None: 106 | fig = plt.figure() 107 | ax = plt.gca() 108 | 109 | scaled_targets = [target/max(targets)*200 for target in targets] 110 | 111 | #Overlay Scatter Plot With Training Data 112 | 113 | #Plot Each Catagory with different Marker on Scatter Plot 114 | ax.scatter(data_transformed[targets==1,0], 115 | [1*value for value in data_transformed[targets==1,1]], 116 | c='k', 117 | cmap=plt.cm.bwr, 118 | marker='+', 119 | s=scaled_targets, 120 | linewidths=1.5 121 | ) 122 | 123 | ax.scatter(data_transformed[targets==0,0], 124 | [1*value for value in data_transformed[targets==0,1]], 125 | c='k', 126 | cmap=plt.cm.bwr, 127 | marker='.', 128 | s=scaled_targets, 129 | linewidths=1.5 130 | ) 131 | 132 | ax.grid(b=False) 133 | 134 | midpercent = (midpoint-minpoint)/(maxpoint-minpoint) 135 | centered_cmap = shiftedColorMap(plt.cm.bwr, midpoint=midpercent) 136 | cmap = centered_cmap 137 | 138 | if midpercent > 1: 139 | midpercent = 1 140 | cmap = plt.cm.Blues_r 141 | elif midpercent < 0: 142 | midpercent = 0 143 | cmap = plt.cm.Reds 144 | 145 | z = [row for row in reversed(z)] 146 | im = ax.imshow(z,extent=[xmin,xmax,ymin,ymax],cmap=cmap,aspect='auto') 147 | ax.set_aspect('auto') 148 | 149 | if title is not None: 150 | ax.set_title(title) 151 | 152 | ax.set_xlabel('Principal Component 1') 153 | ax.set_ylabel('Principal Component 2') 154 | 155 | 156 | # create an axes on the right side of ax. The width of cax will be 5% 157 | # of ax and the padding between cax and ax will be fixed at 0.05 inch. 158 | divider = make_axes_locatable(ax) 159 | cax = divider.append_axes("right", size="4%", pad=0.05) 160 | if zlabel is not None: 161 | plt.colorbar(im, cax=cax,label=zlabel) 162 | else: 163 | plt.colorbar(im, cax=cax) 164 | 165 | 166 | def plot_model_fit(name,predicted,actual,log=False): 167 | if log: 168 | predicted = [math.log(x) for x in predicted] 169 | actual = [math.log(y) for y in actual] 170 | 171 | #mu,sigma = calculate_moments(actual,predicted) 172 | #r2,pval = pearsonr(predicted,actual) 173 | #mse = mean_squared_error(predicted,actual) 174 | #print(name,'R^2: ', r2,'p-val: ',pval,'MSE: ',mse) 175 | 176 | plt.scatter(predicted,actual) 177 | plt.title(name + ' Predicted vs. Actual') 178 | ax = plt.gca() 179 | ax.plot([-120,120], [-120,120], ls="--", c=".3") 180 | 181 | #Plot Correct Ranges 182 | padding_y = (max(actual) - min(actual))*0.1 183 | plt.ylim(min(actual)-padding_y,max(actual)+padding_y) 184 | 185 | padding_x = (max(predicted) - min(predicted))*0.1 186 | plt.xlim(min(predicted)-padding_x,max(predicted)+padding_x) 187 | 188 | plt.xlabel('Predicted ' + name) 189 | plt.ylabel('Actual ' + name) 190 | 191 | #plt.show() 192 | 193 | 194 | def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, 195 | n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)): 196 | """ 197 | Generate a simple plot of the test and training learning curve. 198 | 199 | Parameters 200 | ---------- 201 | estimator : object type that implements the "fit" and "predict" methods 202 | An object of that type which is cloned for each validation. 203 | 204 | title : string 205 | Title for the chart. 206 | 207 | X : array-like, shape (n_samples, n_features) 208 | Training vector, where n_samples is the number of samples and 209 | n_features is the number of features. 210 | 211 | y : array-like, shape (n_samples) or (n_samples, n_features), optional 212 | Target relative to X for classification or regression; 213 | None for unsupervised learning. 214 | 215 | ylim : tuple, shape (ymin, ymax), optional 216 | Defines minimum and maximum yvalues plotted. 217 | 218 | cv : int, cross-validation generator or an iterable, optional 219 | Determines the cross-validation splitting strategy. 220 | Possible inputs for cv are: 221 | - None, to use the default 3-fold cross-validation, 222 | - integer, to specify the number of folds. 223 | - An object to be used as a cross-validation generator. 224 | - An iterable yielding train/test splits. 225 | 226 | For integer/None inputs, if ``y`` is binary or multiclass, 227 | :class:`StratifiedKFold` used. If the estimator is not a classifier 228 | or if ``y`` is neither binary nor multiclass, :class:`KFold` is used. 229 | 230 | Refer :ref:`User Guide ` for the various 231 | cross-validators that can be used here. 232 | 233 | n_jobs : integer, optional 234 | Number of jobs to run in parallel (default 1). 235 | """ 236 | plt.figure() 237 | plt.title(title) 238 | if ylim is not None: 239 | plt.ylim(*ylim) 240 | plt.xlabel("Training examples") 241 | plt.ylabel("Score") 242 | train_sizes, train_scores, test_scores = learning_curve( 243 | estimator, X, y, cv=cv, n_jobs=n_jobs) 244 | train_scores_mean = np.mean(train_scores, axis=1) 245 | train_scores_std = np.std(train_scores, axis=1) 246 | test_scores_mean = np.mean(test_scores, axis=1) 247 | test_scores_std = np.std(test_scores, axis=1) 248 | plt.grid() 249 | 250 | plt.fill_between(train_sizes, train_scores_mean - train_scores_std, 251 | train_scores_mean + train_scores_std, alpha=0.1, 252 | color="r") 253 | plt.fill_between(train_sizes, test_scores_mean - test_scores_std, 254 | test_scores_mean + test_scores_std, alpha=0.1, color="g") 255 | plt.plot(train_sizes, train_scores_mean, 'o-', color="r", 256 | label="Training score") 257 | plt.plot(train_sizes, test_scores_mean, 'o-', color="g", 258 | label="Cross-validation score") 259 | 260 | plt.legend(loc="best") 261 | return plt -------------------------------------------------------------------------------- /License.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JBEI/KineticLearning/c34249105c43dc4b7889bee7ebb82531f3e89fb5/License.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Kinetic Learning 2 | 3 | This Repository is a set of tools used to learn dynamics from time series data. This tool was applied so as to learn kinetic models from Time Series Proteomics and Metabolomics Data. 4 | 5 | ## How to Use This Repository 6 | 7 | There are two main files in this repository: 'Create Data Set.ipynb' and 'KineticLearning.ipynb'. The first creates simulated data sets, and the second builds and analyzes models generated from proteomic and metabolomic data. 8 | 9 | Install the correct dependencies for the project, open the notebook of interest. Set the parameters at the top of the file, and run! 10 | 11 | 12 | # Installation 13 | 14 | 15 | *This code distributed under the license specified in the License.pdf file and is Patent Pending. The objective of the license is to make it freely available for academic and personal use.* 16 | -------------------------------------------------------------------------------- /data/isopentenol_data.csv: -------------------------------------------------------------------------------- 1 | Strain,Hour,AtoB,GPPS,HMGR,HMGS,Idi,Limonene Synthase,MK,NudB,PMD,PMK,Acetyl-CoA,HMG-CoA,Mevalonate,Mev-P,IPP/DMAPP,Limonene,OD600,GPP,NAD,NADP,Acetate,Pyruvate,citrate,Isopentenol 2 | I1,0.0,3709.5,1839.0,35334.5,4410.75,1363.0,978.0,37907.5,45592.5,194542.5,35904.0,0.196875955,0.0,0.00348469,0.016535125,2.64120095,,1.659609467,0.0,0.479328672,0.060779232999999995,0.4622,0.049283772,0.051544614,0.0 3 | I1,2.0,,,,,,,,,,,,,,,,,,,,,0.940422,0.005115802,, 4 | I1,4.0,1137211.5,1881.0,35435.5,88653.0,1623.0,805.5,57317.5,3479817.5,8494498.0,60748.5,0.492312608,0.068923427,0.275342622,0.9386684940000001,18.24779706,,2.24,0.0,1.829253002,0.221245876,1.10673,0.003930587,0.076327931,0.059590433 5 | I1,6.0,,,,,,,,,,,,,,,,,,,,,1.20436,0.009759218,, 6 | I1,8.0,1284640.5,1532.5,23406.0,100101.5,2203.0,810.0,65056.0,3979808.5,9318453.0,63854.0,,,0.595143213,1.215890787,17.16390824,,2.69,0.0,,,1.30293,0.06334545700000001,0.07523677,0.10985059900000001 7 | I1,10.0,,,,,,,,,,,,,,,,,,,,,1.40601,0.07693351,, 8 | I1,12.0,1462050.5,1701.5,31620.75,116497.25,3601.0,1978.0,76151.5,4467660.0,11100000.0,67435.0,0.449105391,0.053513555,0.523027072,2.646921943,32.72875677,,2.8160000000000003,0.004491421,1.9310338630000001,0.264771718,1.53421,0.076668366,0.047335613,0.114827408 9 | I1,16.0,,,,,,,,,,,,,,,,,,,,,1.60723,0.07349797200000001,, 10 | I1,18.0,1324483.5,2185.5,27946.0,96875.25,1585.0,809.0,61235.5,4147478.5,9293262.0,65696.0,0.34337364600000003,0.09140322699999999,0.6733195809999999,1.7592458309999999,24.57809789,,2.865,0.004081293,1.697476454,0.18235572,1.74705,0.06804779400000001,0.050817641,0.123506941 11 | I1,20.0,,,,,,,,,,,,,,,,,,,,,1.8170400000000002,0.056594489000000005,, 12 | I1,24.0,1400528.0,1571.0,27008.5,108538.25,2755.5,1280.0,61628.5,4204528.5,9933837.0,69801.5,0.274808025,0.102695403,0.7070405359999999,1.240129513,21.01883882,,2.763,0.00295011,1.5268132140000001,0.160628844,1.9683700000000002,0.051310195999999995,0.049991694,0.134869712 13 | I1,36.0,1493030.5,2349.5,26590.25,114327.0,2969.5,410.5,69561.0,4351987.0,11200000.0,69599.5,,,0.63578018,0.722271744,17.70245353,,3.0,0.0,,,2.25325,0.040606545,,0.140889465 14 | I1,48.0,1496977.0,1999.0,30424.0,118851.25,3025.5,1075.0,68151.0,4767873.0,10900000.0,75768.5,0.126217537,0.130981353,0.645384424,0.7430925490000001,15.79063301,,3.0,0.0,1.0796933640000002,0.095280821,2.35948,0.038859315,0.037399636,0.153882384 15 | I1,72.0,1382228.5,2896.5,27130.25,115593.25,2876.0,1145.0,60403.5,4327006.5,11200000.0,67954.0,,,0.53614677,0.5284053000000001,8.836209271,,3.07,0.0,,,1.22482,0.021504861,,0.17350588 16 | I2,0.0,13637.5,2599.0,14419.0,13196.75,2744.5,1254.5,18005.5,63880.5,339204.0,11920.5,0.116582264,0.009832111999999999,0.012538565,0.307744008,10.08837077,,0.612,0.0,0.295064167,0.052145054,0.3227,0.10970029699999999,0.054740625999999994,0.0 17 | I2,2.0,,,,,,,,,,,,,,,,,,,,,0.7191649999999999,0.18003455899999998,, 18 | I2,4.0,3178236.0,1657.5,106486.75,664215.0,1664.0,1070.5,47259.5,3700243.5,8971152.0,56935.0,0.290864396,0.100557304,1.252410203,0.623555306,5.77835918,,2.2119999999999997,0.0016560589999999999,1.210413042,0.15912963800000002,0.8546360000000001,0.002888141,0.06892699,0.031285222 19 | I2,6.0,,,,,,,,,,,,,,,,,,,,,0.831759,0.0026592559999999996,, 20 | I2,8.0,3785349.5,2033.5,139108.75,797111.0,2343.0,1739.5,58830.0,5424475.5,10900000.0,73375.0,,,2.758794205,0.869716041,5.363780298,,2.932,0.0016479160000000001,,,0.766656,0.001775444,0.109409256,0.101174153 21 | I2,10.0,,,,,,,,,,,,,,,,,,,,,0.626666,0.001487638,, 22 | I2,12.0,4062592.0,2132.5,133428.25,891678.0,3167.5,874.0,81906.0,6713922.0,11100000.0,82222.0,0.27462615,0.129050576,3.675133727,1.283673581,7.188823922999999,,3.492,0.004505487,1.871280825,0.247255325,0.406988,0.000317379,0.143539526,0.1846021 23 | I2,16.0,,,,,,,,,,,,,,,,,,,,,0.16524,0.0,, 24 | I2,18.0,3617661.5,2084.5,75877.25,858975.5,3134.0,1114.0,89106.0,6071427.0,10000000.0,93638.5,0.215372237,0.13890286300000002,4.221687429,1.90867172,8.473734079,,3.6430000000000002,0.0016234860000000002,2.012239198,0.308033031,0.12598900000000002,0.0,0.271480214,0.473839155 25 | I2,20.0,,,,,,,,,,,,,,,,,,,,,0.127399,0.0,, 26 | I2,24.0,4611085.0,1780.5,71489.25,1062152.25,3135.5,1434.5,117581.0,7313743.5,11000000.0,98839.0,0.184016702,0.159128418,4.182626563,1.601773249,7.03810151,,3.838,0.002234236,2.223868359,0.302358775,0.149538,0.0,0.326388,0.728283462 27 | I2,36.0,4253490.0,922.0,62474.0,1012303.25,3484.5,743.5,118410.5,7109809.5,10300000.0,95213.5,,,4.1698736489999995,0.648013004,0.8637008829999999,,3.365,0.004067228,,,0.230769,0.0,,1.036197538 28 | I2,48.0,4871852.5,3212.5,72798.75,1250907.75,3715.5,1864.0,162667.0,8729384.0,12600000.0,100702.0,0.050051686,0.015027106,3.914136149,0.763137194,0.6959694890000001,,3.365,0.0,2.078671364,0.253960819,0.26933,0.0,0.576725458,1.116717825 29 | I2,72.0,5061202.0,1327.0,76135.0,1395125.75,3793.5,541.0,153608.5,9079535.0,13100000.0,98090.5,,,4.226167097,1.105771525,0.25406551,,3.16,0.0,,,0.271301,0.0,,1.136421117 30 | I3,0.0,8897.5,2124.0,5352.0,11167.75,1987.0,696.5,49623.0,45523.5,187399.5,38856.5,0.151420248,0.0,0.014374633999999999,0.307744008,0.33804911,,1.4726604980000002,0.0,0.052535768,0.063575429,0.51708,0.132314569,0.09332623699999999,0.0 31 | I3,2.0,,,,,,,,,,,,,,,,,,,,,0.902075,0.003860335,, 32 | I3,4.0,1331549.5,2650.0,51356.75,254491.75,3150.5,1681.5,99844.5,3868850.0,7706071.5,80511.0,0.279537377,0.00767629,1.10484463,0.623555306,11.55273333,,2.7310000000000003,0.0,1.77817708,0.209858877,1.08572,0.003749292,0.094792796,0.077045862 33 | I3,6.0,,,,,,,,,,,,,,,,,,,,,1.15697,0.003283817,, 34 | I3,8.0,1265187.5,2561.5,63412.5,277390.0,3111.5,1754.0,113500.0,4628502.0,9129930.0,88444.5,,,1.672044795,0.869716041,11.58330034,,3.4360000000000004,0.002313449,,,1.0751700000000002,0.002151631,0.108457797,0.23192486399999998 35 | I3,10.0,,,,,,,,,,,,,,,,,,,,,0.9774579999999999,0.002652458,, 36 | I3,12.0,1281132.0,1569.5,52396.75,237474.25,3575.0,494.0,109144.5,4913407.0,8476178.0,97058.0,0.353337577,0.008369372,1.764374424,1.283673581,12.83644077,,3.676,0.003872528,2.115673665,0.24434061,0.887249,0.0014740410000000002,0.139961502,0.336581552 37 | I3,16.0,,,,,,,,,,,,,,,,,,,,,0.7501180000000001,0.001639472,, 38 | I3,18.0,1651535.0,2977.0,67232.25,306558.0,4228.0,524.0,150376.0,7602226.0,10200000.0,125928.0,0.275772821,0.006214554000000001,1.820365525,1.90867172,11.32056215,,3.522,0.002260147,2.400439643,0.326855578,0.676712,0.000723253,0.19976839,0.644332741 39 | I3,20.0,,,,,,,,,,,,,,,,,,,,,0.617065,0.001177397,, 40 | I3,24.0,1645486.0,2013.5,68439.5,278242.5,3640.0,1303.5,156381.0,7837262.5,9668760.0,121908.5,0.314829327,0.008422429,2.130786859,1.601773249,11.04764205,,3.609,0.002204624,3.0059932419999997,0.358952877,0.634946,0.0,0.241985408,0.868716038 41 | I3,36.0,1592795.0,2352.0,60518.5,260284.25,4797.0,1441.5,153641.0,7497983.5,9963522.0,123403.0,,,2.08655147,0.648013004,0.80087708,,3.1430000000000002,0.001721946,,,0.517504,0.0,,1.232061366 42 | I3,48.0,1484506.0,1923.5,59436.25,254507.25,4165.5,485.5,152473.0,7880829.0,9890994.0,115158.0,0.129837475,0.022634511,1.986295041,0.763137194,0.24724255899999997,,3.1430000000000002,0.0,2.974407218,0.47023439100000003,0.20707399999999998,0.0,0.431172654,1.235809988 43 | I3,72.0,1504861.5,1659.0,60934.75,261730.25,4459.5,1167.0,176848.5,7896547.5,11300000.0,106709.5,,,2.204279305,1.105771525,0.065169513,,3.0410000000000004,0.0,,,0.00222315,0.0,,1.255362473 44 | -------------------------------------------------------------------------------- /data/limonene_data.csv: -------------------------------------------------------------------------------- 1 | Strain,Hour,AtoB,GPPS,HMGR,HMGS,Idi,Limonene Synthase,MK,NudB,PMD,PMK,Acetyl-CoA,HMG-CoA,Mevalonate,Mev-P,IPP/DMAPP,Limonene,OD600,GPP,NAD,NADP,Acetate,Pyruvate,citrate,Isopentenol 2 | 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L1,12.0,780345.5,602773.0,45376.0,118744.0,467269.0,6845767.5,26853.0,20080.5,346243.0,9772.5,0.488733408,0.23116012600000002,0.16815381100000001,0.804747598,1.5082187880000002,0.014845661999999999,3.285,0.08955858,2.895685168,0.23692705600000002,2.2549,0.145496975,0.064192815, 9 | L1,16.0,,,,,,,,,,,,,,,,,,,,,2.53372,0.022264033,, 10 | L1,18.0,968542.0,804810.5,54544.25,147543.25,616684.0,8071599.5,33214.0,29762.5,421278.5,9482.0,0.31720957699999996,0.19057414399999997,0.14282339800000002,2.333129585,2.774148752,0.018470844,3.449,0.16276105,2.24147818,0.212470574,2.81957,0.006631881,0.03340531, 11 | L1,20.0,,,,,,,,,,,,,,,,,,,,,2.8113099999999998,0.0071297619999999996,, 12 | L1,24.0,738690.5,639859.0,44253.0,120913.25,442807.5,6637907.5,26846.5,17515.5,334353.0,8015.5,0.238256017,0.189051103,0.180405249,2.2848245990000002,2.556154855,0.017365185,3.426,0.127268825,1.8412082109999999,0.227695394,3.0260700000000003,0.009000045,0.056511507999999995, 13 | L1,36.0,886284.5,635190.0,46218.0,125305.75,512574.5,7654993.0,27892.0,24815.5,398870.5,11565.5,,,0.08945183,0.7438046859999999,1.472126403,0.017365185,3.5839999999999996,0.053026943,,,3.5873699999999995,0.013104109,, 14 | L1,48.0,826610.5,566745.0,43875.25,116906.5,449925.5,6591686.0,27460.5,20198.5,359881.5,8218.0,0.07793903,0.10803095300000001,0.068481971,0.485417408,1.016424684,0.017302859,3.5839999999999996,0.046478254000000004,0.965385003,0.21132998,3.6588300000000005,0.013924468,0.041272128, 15 | L1,72.0,651061.0,468498.5,35763.75,112887.25,437765.0,6084939.5,25586.5,14725.5,355359.0,9285.5,,,0.069763402,0.179994978,0.402491717,0.016883268,3.87,0.017068348,,,3.80366,0.015812881,, 16 | L2,0.0,634025.0,119167.0,243825.75,453634.25,496354.5,508091.5,33322.0,6926.5,111043.0,19435.5,0.286359015,0.007628361,0.892762772,0.046703445,0.173320815,0.0,0.713,0.008239197,0.542593105,0.081441575,0.308866,0.039607157000000004,0.021560634, 17 | L2,2.0,,,,,,,,,,,,,,,,,,,,,0.562589,0.019868675,, 18 | L2,4.0,5847488.5,5867246.0,1388069.5,3218633.25,781230.0,7576393.0,428259.0,17584.5,560021.0,390328.0,0.34183784799999994,0.041512468999999996,2.65148138,0.083405523,0.182425151,0.002552426,2.213,0.051389770999999994,1.585056438,0.494896754,0.583792,0.04965316,0.023301079, 19 | L2,6.0,,,,,,,,,,,,,,,,,,,,,0.46014099999999997,0.001344868,, 20 | L2,8.0,10200000.0,9343760.0,2148291.75,5802429.25,1365395.5,12900000.0,797530.0,29471.0,1460266.0,615462.5,,,3.2870036369999998,0.102936345,0.27152086,0.007849873,3.196,0.121794023,,,0.327002,0.001417386,0.040125699, 21 | L2,10.0,,,,,,,,,,,,,,,,,,,,,0.24144000000000002,0.00014379,, 22 | L2,12.0,10200000.0,7564160.5,1986026.5,5502522.25,1141836.5,10900000.0,828093.0,25649.5,1507277.5,873647.5,0.23886614399999997,0.059232985999999994,4.087342683999999,0.129306488,0.416616508,0.015687963,3.617,0.08955858,1.7790635559999999,0.78782104,0.195258,4.41e-05,0.145917517, 23 | L2,16.0,,,,,,,,,,,,,,,,,,,,,0.169946,0.0,, 24 | L2,18.0,8862928.0,6771955.5,1705475.5,4671715.25,1042258.5,10400000.0,827407.5,20781.0,1656065.0,948688.0,0.185168042,0.039768952,3.3573186610000003,0.17174561600000002,0.476509225,0.02902455,3.767,0.16276105,1.763555611,0.652359182,0.185868,0.0,0.062458502, 25 | L2,20.0,,,,,,,,,,,,,,,,,,,,,0.19431500000000002,0.0,, 26 | L2,24.0,10000000.0,7585512.5,1631822.25,4706973.25,1255643.0,9793156.0,852007.0,26873.5,1709930.5,1106194.5,0.142601589,0.020488833,4.594334921000001,0.26471707,0.523531106,0.033795041,3.8489999999999998,0.127268825,1.524906176,0.389643832,0.212698,0.0,0.11177446, 27 | L2,36.0,9045450.0,7496528.5,1285192.0,3957601.75,1100444.0,9832824.0,776391.0,26256.0,1660417.5,1047256.0,,,5.166682559,0.49247558799999996,0.8826356890000001,0.045727937,3.63,0.053026943,,,0.1343,0.0,, 28 | L2,48.0,8528785.0,8563900.0,1162320.0,3401689.75,986714.0,10400000.0,747271.5,23933.5,1614838.5,1175150.0,0.081545473,0.006855693,4.371739139,0.389230334,0.462744751,0.056035836,3.63,0.046478254000000004,0.957042203,0.107615363,0.40573899999999996,0.005109004,0.326585684, 29 | L2,72.0,7680604.5,9218672.0,1036260.25,2579002.25,835403.0,10700000.0,644173.0,27036.0,1417794.0,1227048.5,,,5.4627205739999996,0.535649943,0.083261593,0.055729367,3.81,0.017068348,,,0.59813,0.116730007,, 30 | L3,0.0,25219.5,148706.0,30645.25,19078.5,162997.0,159975.0,13609.0,2063.0,46155.5,105963.5,0.246535416,0.003096465,0.036669797999999997,0.018218812,0.050344718,0.0,1.3779735709999998,0.014082151000000001,0.6284959379999999,0.056778601,0.568617,0.119580869,0.030870757000000002, 31 | L3,2.0,,,,,,,,,,,,,,,,,,,,,1.02332,0.004637637,, 32 | L3,4.0,621737.5,2021922.5,89467.25,198184.75,1389342.5,5840207.5,417048.5,15764.0,944875.5,294043.5,0.35692433,0.009279363,0.700391572,2.76900295,8.084991415,0.022377361000000002,2.8930000000000002,0.0,2.3523362569999997,0.169019875,1.14858,0.036814989,0.053163138, 33 | L3,6.0,,,,,,,,,,,,,,,,,,,,,1.30417,0.008440297,, 34 | L3,8.0,818790.0,1876792.0,110350.75,256255.75,1678839.5,6709012.5,555678.0,4974.0,1440895.5,375310.5,,,0.848759795,2.7900224639999998,6.970880644,0.052849195,3.4989999999999997,0.0,,,1.40529,0.011202778,0.040149961, 35 | L3,10.0,,,,,,,,,,,,,,,,,,,,,1.40432,0.005433068,, 36 | L3,12.0,875566.5,1578399.0,124484.25,260386.0,1961295.0,7443790.5,715362.5,23680.0,1671952.0,479840.5,0.375530774,0.014750006999999999,0.9451501940000001,1.4903895569999999,5.621033275,0.085682139,3.701,0.0,2.268526494,0.21963120600000002,1.3591,0.004619734,, 37 | L3,16.0,,,,,,,,,,,,,,,,,,,,,1.32132,0.003214472,, 38 | L3,18.0,794345.0,1355287.0,108874.0,241611.75,1981244.5,7639484.0,789934.0,22490.0,1917700.0,521896.0,0.42046279299999995,0.008977964,0.991804474,2.8964970169999997,7.230596048,0.186369591,3.727,0.0,2.808284923,0.303868977,1.27007,0.002582433,0.07042403, 39 | L3,20.0,,,,,,,,,,,,,,,,,,,,,1.2483899999999999,0.0024054429999999997,, 40 | L3,24.0,857162.0,1348690.5,113990.0,258031.75,2081970.0,7903789.0,936026.5,17828.5,2133435.0,583897.5,0.24674338899999998,0.009705154,1.498061352,1.327356096,5.95363388,0.235486355,3.8489999999999998,0.0,2.4874782509999998,0.222971124,1.31036,4.6299999999999994e-05,0.119009249, 41 | L3,36.0,825658.5,1445620.0,114476.25,269871.0,2026212.5,8171338.5,1028950.0,21471.5,2343362.5,654848.5,,,0.9318217529999999,2.192836867,7.7600871289999995,0.370228933,3.53,0.0,,,1.3818,7.35e-05,, 42 | L3,48.0,891883.5,1554353.0,115457.0,271541.75,2135828.5,8290409.5,1084421.0,21818.0,2464271.0,704475.5,0.16331372800000002,0.0,1.213345441,,,0.41078854299999995,3.53,,1.721404486,0.282932636,1.49557,0.0,0.059403724000000005, 43 | L3,72.0,825584.5,1430948.0,102777.25,245270.25,1874325.0,7801155.0,959653.0,17189.0,2391342.0,617310.5,,,1.369914628,9.433808688,10.87102981,0.503866581,3.63,0.0,,,1.6601700000000001,0.000417771,, 44 | -------------------------------------------------------------------------------- /data/time_series_metabolomics.csv: -------------------------------------------------------------------------------- 1 | Hour,Strain,Sample,OD600,Intracellular volume / sample,Glucose g/L,Pyruvate g/L,Succinate g/L,Lactate g/L,Formate g/L,Acetate g/L,Isopentenol g/L,Bisabolene g/L,Limonene g/L,Acetoacetyl-coA (uM),HMG-CoA (uM),Intracellular Mevalonate (uM),Mev-P (uM),IPP/DMAPP (uM),IP (uM),GPP (uM),FPP (uM),AMP (uM),ADP (uM),ATP (uM),NAD (uM),NADP (uM),Acetyl-CoA (uM),"Fru-1,6-BiP 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72,I1,118,3.07,7.68E-07,2.17034,0.021504861,0.107039,0.611864,0.0711682,1.22482,0.17350588,,,,,0.53614677,0.5284053,8.836209271,0.720525317,0,0,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,,,,,,,,,,,,,,,,,,,,,,4.259993911,93.1929463,6.095311695,34.84925859,0,2.754311583,0,3.532142722,0,11.08619764,2.381642169,0,0.553370819,0,1.585830929,0.502969092,0,0,0,0,0,0 16 | 0,I2,2,0.612,0.000000306,10.74,0.109700297,0.00975,0.06354,0.03559,0.3227,0,,,0,0.009832112,0.012538565,0.307744008,10.08837077,0.400207024,0,0,0.101614869,0.040938892,0.147754393,0.295064167,0.052145054,0.116582264,3.393868352,0.73322757,0.014875782,0.910365856,11.16553302,0.323459331,1.686081501,0,0.267767059,0.036279674,0.010054625,0.054740626,0,0.059709484,0.01034309,0.035352693,0,0,0.105369021,0.030637127,2.317385982,0.911173054,17.86485084,2.011192211,0.763009624,0.861009745,0,1.309822688,0.888640806,4.298423153,0.762151988,0.580118288,0.476074103,6.758590144,0.270229347,0.27254827,2.23633923,2.763453419,1.061038154,0.246816948,0,1.889276598,4.209292522,0,0.55161012,0,0,0,0,0,10.18624021,0,0,0,0,1.302694852,0,0,0,0,0,0,0 17 | 2,I2,11,,,10.16711,0.180034559,0.239089,0.225824,0.211999,0.719165,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 18 | 4,I2,20,2.212,0.000000553,9.20972,0.002888141,0.236801,0.317863,0.0789312,0.854636,0.031285222,,,0,0.100557304,1.252410203,0.623555306,5.77835918,3.994369964,0.001656059,0,0.232186128,0.124701314,0.263656879,1.210413042,0.159129638,0.290864396,5.028560498,1.621319501,0.024994128,0.208846948,57.2834047,0.440983176,5.300239928,0,0.254146969,0.028857112,0.044128307,0.06892699,0,0.409385491,0.083242778,0.30261583,0,0,0.623022234,0.064285477,3.41663539,1.536294264,0,4.629784088,0.824191211,0,0,1.577074984,0.638714329,0.392829608,0,0.258068516,0.615179033,10.56007377,0.19297529,0.407251876,3.16218197,5.075931478,1.16574256,0.363678966,0,2.630933702,57.69722021,1.152842178,30.54207793,0,0,0,2.113785161,0,11.11729321,0.780809113,0,0,0,1.546733008,0,0,0,0,0,0,0 19 | 6,I2,29,,,8.72653,0.002659256,0.238314,0.339829,0.105699,0.831759,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.167759537,68.40928784,1.03640735,55.24644852,0,0.784431574,0,4.106475363,0,11.61508135,1.117354776,,,,,,,,,,, 20 | 8,I2,38,2.932,0.000000733,7.70806,0.001775444,0.233845,0.393183,0.115864,0.766656,0.101174153,,,,,2.758794205,0.869716041,5.363780298,6.082446964,0.001647916,0,,,,,,,6.024064952,2.171693195,0.041417871,0.055675578,79.37726053,0.34848151,7.796172705,0,0.256736749,0.034221521,0.072001523,0.109409256,0.001771575,0.376203645,0.092407121,0.207136715,0,0,0.876020315,0.047398386,,,,,,,,,,,,,,,,,,,,,,1.45750685,91.441629,5.182998454,108.5552627,0,0.830173149,0,10.32048329,0,10.69702186,1.075658272,,,,,,,,,,, 21 | 10,I2,47,,,6.53259,0.001487638,0.235908,0.424935,0.15221,0.626666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.754549074,93.95510644,6.750540958,152.4785772,0,0.980867418,0,15.25823674,0,10.21996409,1.473546116,,,,,,,,,,, 22 | 12,I2,56,3.492,0.000000873,5.85184,0.000317379,0.232033,0.435388,0.160198,0.406988,0.1846021,,,0,0.129050576,3.675133727,1.283673581,7.188823923,12.00525513,0.004505487,0,0.462269946,0.193568247,0.386296185,1.871280825,0.247255325,0.27462615,3.72819861,1.689040396,0.031301895,0.093650763,76.42907997,0.273832861,11.10456625,0,0.235413656,0.033598935,0.071625447,0.143539526,0.004774019,0.199305155,0.090647301,0.127206258,0,0,2.92415724,0.031565988,0.363057425,0.910598691,0,0.500965955,0.320464039,0,0,1.231225043,0,0.514510286,0,0.275475079,0.385825131,0,0,0.161403465,2.106806513,3.595568186,0.637413443,0.193215746,0,0.516943397,88.1968338,8.269448738,194.8279322,0,1.527914364,0,19.18139324,1.231945485,10.64956411,1.955165873,0,0,0,2.005829359,0,0,0,0,0,0,0 23 | 16,I2,65,,,4.67455,0,0.227798,0.412805,0.171495,0.16524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.276433807,111.0944943,12.47545939,234.369999,0,2.194438133,0,29.01346423,2.589669646,12.75121698,2.252135935,,,,,,,,,,, 24 | 18,I2,74,3.643,9.11E-07,3.91219,0,0.232225,0.388971,0.173532,0.125989,0.473839155,,,0,0.138902863,4.221687429,1.90867172,8.473734079,10.79217989,0.001623486,0,0.371302815,0.193820103,0.374854686,2.012239198,0.308033031,0.215372237,3.15820717,1.541392309,0.046205563,0.080093721,61.99693466,0.248299848,8.539535718,0,0.235058048,0.033341197,0.087186032,0.271480214,0.006754703,0.177505039,0.099745236,0.070925077,0,0,3.688483497,0.017725604,0.398618036,0.646750295,0,0.408062253,0.31110563,0.062215475,0,1.303086027,0,0.587511139,0,0.335925796,0.603568059,0,0,0.218818215,2.234519698,3.881033315,0.767516415,0.18417786,0,0.369792277,110.4883726,13.40161429,239.7580251,0,2.548598816,0,28.14553143,2.60938414,8.981546264,3.057608737,0,0,0,1.483306836,0,0,0,0,0,0,0 25 | 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24,I2,92,3.838,9.60E-07,2.01273,0,0.250043,0.380873,0.181182,0.149538,0.728283462,,,0,0.159128418,4.182626563,1.601773249,7.03810151,11.20516648,0.002234236,0,0.43476391,0.195270702,0.380186242,2.223868359,0.302358775,0.184016702,1.578079925,1.612059709,0.062155787,0.111936749,70.71562603,0.244279848,8.101651232,0,0.222336706,0.020200555,0.121112215,0.326388,0.01195106,0.138329053,0.103928957,0.246783131,0,0,10.39137915,0.015206789,0.508175815,0.654620667,0,0.353778784,0.263208548,0.060827973,0,1.186150379,0,0.596914106,0,0.254371803,1.13643387,0,0,0.578603691,2.531951397,4.472459775,0.920979068,0.184069677,0,0.539811714,127.3229198,21.29404087,278.8820866,0,3.096031586,0,43.82522938,1.942200922,11.5900198,4.842566729,0,0,0,1.94661328,0,0,0,0,0,0,0 27 | 36,I2,101,3.365,8.41E-07,0.0492529,0,0.261576,0.267691,0.214109,0.230769,1.036197538,,,,,4.169873649,0.648013004,0.863700883,29.5171301,0.004067228,0,,,,,,,,,,,,,,,,,,,,,,0.047708684,0,0,10.23130535,0,,,,,,,,,,,,,,,,,,,,,,0.402484719,84.34174844,29.23383136,291.481146,0,4.733957225,0,30.05776855,2.395505999,13.1818165,3.391683658,,,,,,,,,,, 28 | 48,I2,110,3.365,8.41E-07,0,0,0.258078,0.103522,0.233858,0.26933,1.116717825,,,0,0.015027106,3.914136149,0.763137194,0.695969489,25.87216058,0,0,0.631740082,0.115715877,0.076320216,2.078671364,0.253960819,0.050051686,0,3.038914427,0.203500958,0.092534821,7.723065912,0.109338268,0.536524106,0,0.084244765,0.003867088,0.104724843,0.576725458,0.013424376,0.078673088,0.203674193,0.015440764,0,0,8.694062365,0,0.177654903,0.509018777,0,0.069497401,0.180730809,0,0,0.633100895,0.008149545,0.560365736,0,0,2.433290997,0,0,0.602140705,2.30322541,4.001707503,0.735549609,0.14719867,0,0.35807124,44.35268495,28.34243517,269.7907625,0,4.893259655,0,14.42150651,4.548213791,14.95267181,3.007942451,0,0,0,2.027674734,0,0,0,0,0,0,0 29 | 72,I2,119,3.16,0.00000079,0,0,0.233385,0.00303925,0.209544,0.271301,1.136421117,,,,,4.226167097,1.105771525,0.25406551,6.217285198,0,0,,,,,,,,,,,,,,,,,,,,,,0,0,0,6.779665676,0,,,,,,,,,,,,,,,,,,,,,,0.309184797,18.20004215,28.67068521,260.915082,0,4.956483343,0,4.751051539,6.149988198,31.43642993,3.542475443,0,0,0,2.024259554,0,0,0,0,0,0,0 30 | 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-------------------------------------------------------------------------------- /notebooks/Biological_Intuition.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Create Biological Intuition From a Model\n", 8 | "\n", 9 | "In response to reviewer concerns, we use data simulated data to create biological intuition. In this case we take a set of data and generate a dynamic model. We then use that dynamic model to simulate a held back set of strains to see final production. Final production is overlayed onto a PCA of the proteomics at the Final Time. In this way we can understand the proteomics profile that improves production purely from simulation." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "metadata": { 16 | "collapsed": true 17 | }, 18 | "outputs": [], 19 | "source": [ 20 | "%matplotlib inline\n", 21 | "from KineticLearning import learn_dynamics,read_timeseries_data,simulate_dynamics\n", 22 | "\n", 23 | "#Visualization Tools\n", 24 | "import seaborn as sns\n", 25 | "import matplotlib.pyplot as plt\n", 26 | "from sklearn.decomposition import PCA\n", 27 | "from sklearn.cross_decomposition import CCA\n", 28 | "\n", 29 | "from IPython.display import display\n", 30 | "import numpy as np" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "## Reading Data & Preparing the DataFrame\n", 38 | "\n", 39 | "Data is read in using the `read_timeseries_data` function. This also prepares it for processing by doing data imputation, data augmentation, and derivative estimation on the time series. In this case we are reading in a large data set so the number of strains that are read is limited to 200. This speeds up the import process dramatically. " 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": null, 45 | "metadata": { 46 | "scrolled": true 47 | }, 48 | "outputs": [], 49 | "source": [ 50 | "#Read In Raw DataFrame & Put it into Apropriate Format\n", 51 | "proteins = ['AtoB', 'HMGS', 'HMGR', 'MK', 'PMK', 'PMD', 'Idi','GPPS', 'LS']\n", 52 | "metabolites = ['GPPS', 'LS', 'Acetyl-CoA', 'Acetoacetyl-CoA', 'HMG-CoA', 'Mev', 'MevP','MevPP', 'IPP', 'DMAPP', 'GPP', 'Limonene']" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": null, 58 | "metadata": { 59 | "scrolled": true 60 | }, 61 | "outputs": [ 62 | { 63 | "data": { 64 | "text/html": [ 65 | "
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statescontrols
GPPSLSAcetyl-CoAAcetoacetyl-CoAHMG-CoAMevMevPMevPPIPPDMAPP...LimoneneAtoBHMGSHMGRMKPMKPMDIdiGPPSLS
StrainTime
108.546974e+007.102368e+000.2000000.2000000.2000000.2000000.2000000.2000000.2000000.2...0.0000009.600104e+006.536708e-015.869749e+003.399382e+003.886694e+005.129363e+005.984526e-018.546974e+007.102368e+00
13.488546e+066.205517e+060.2020570.2000500.1987390.2010520.1994430.1892910.2114760.2...0.0004462.858194e+064.728040e+068.770207e+062.289815e+064.126997e+065.624252e+066.243208e+063.488546e+066.205517e+06
23.857668e+067.334069e+060.2040790.2001360.1971910.2022440.1984760.1744890.2276010.2...0.0011633.467969e+066.188499e+069.203323e+062.787437e+065.100021e+066.354137e+066.755196e+063.857668e+067.334069e+06
33.998702e+067.807357e+060.2060890.2002330.1956110.2034200.1973810.1597190.2438690.2...0.0019523.733472e+066.898832e+069.357360e+063.005128e+065.535019e+066.641433e+066.945044e+063.998702e+067.807357e+06
44.073159e+068.067673e+060.2080940.2003370.1940260.2045760.1962280.1456310.2595380.2...0.0027753.882075e+067.318873e+069.436329e+063.127242e+065.781585e+066.795049e+067.044026e+064.073159e+068.067673e+06
54.119178e+068.232364e+060.2100940.2004440.1924430.2057140.1950470.1324330.2743640.2...0.0036153.977054e+067.596379e+069.484353e+063.205393e+065.940358e+066.890678e+067.104782e+064.119178e+068.232364e+06
64.150440e+068.345946e+060.2120900.2005550.1908670.2068330.1938500.1201940.2882570.2...0.0044664.042998e+067.793378e+069.516641e+063.259701e+066.051142e+066.955940e+067.145871e+064.150440e+068.345946e+06
74.173062e+068.429013e+060.2140850.2006680.1892990.2079340.1926470.1089250.3011950.2...0.0053234.091456e+067.940466e+069.539840e+063.299633e+066.132837e+067.003317e+067.175513e+064.173062e+068.429013e+06
84.190191e+068.492407e+060.2160770.2007840.1877400.2090180.1914430.0986100.3131890.2...0.0061844.128569e+068.054477e+069.557313e+063.330230e+066.195571e+067.039276e+067.197906e+064.190191e+068.492407e+06
94.203611e+068.542377e+060.2180680.2009010.1861930.2100850.1902410.0892110.3242700.2...0.0070474.157903e+068.145441e+069.570947e+063.354422e+066.245259e+067.067501e+067.215420e+064.203611e+068.542377e+06
104.214409e+068.582778e+060.2200560.2010200.1846560.2111350.1890440.0806840.3344820.2...0.0079104.181672e+068.219706e+069.581883e+063.374031e+066.285586e+067.090244e+067.229492e+064.214409e+068.582778e+06
114.223286e+068.616118e+060.2220430.2011400.1831300.2121680.1878520.0729760.3438740.2...0.0087734.201323e+068.281483e+069.590849e+063.390246e+066.318971e+067.108961e+067.241047e+064.223286e+068.616118e+06
124.230711e+068.644101e+060.2240290.2012620.1816150.2131850.1866680.0660310.3525000.2...0.0096354.217840e+068.333677e+069.598333e+063.403878e+066.347064e+067.124634e+067.250704e+064.230711e+068.644101e+06
134.237014e+068.667921e+060.2260130.2013860.1801130.2141860.1854920.0597930.3604160.2...0.0104964.231918e+068.378358e+069.604675e+063.415498e+066.371030e+067.137950e+067.258896e+064.237014e+068.667921e+06
144.242432e+068.688442e+060.2279960.2015100.1786210.2151710.1843250.0542060.3676770.2...0.0113554.244060e+068.417039e+069.610118e+063.425522e+066.391718e+067.149403e+067.265932e+064.242432e+068.688442e+06
154.247139e+068.706307e+060.2299770.2016360.1771420.2161410.1831680.0492120.3743380.2...0.0122134.254639e+068.450853e+069.614840e+063.434257e+066.409756e+067.159359e+067.272041e+064.247139e+068.706307e+06
164.251266e+068.721998e+060.2319580.2017640.1756740.2170940.1820210.0447590.3804520.2...0.0130674.263939e+068.480664e+069.618976e+063.441937e+066.425623e+067.168094e+067.277395e+064.251266e+068.721998e+06
174.254914e+068.735891e+060.2339370.2018920.1742180.2180330.1808840.0407960.3860700.2...0.0139204.272179e+068.507142e+069.622628e+063.448742e+066.439688e+067.175818e+067.282125e+064.254914e+068.735891e+06
184.258162e+068.748277e+060.2359150.2020220.1727730.2189560.1797570.0372750.3912390.2...0.0147704.279530e+068.530818e+069.625876e+063.454813e+066.452243e+067.182698e+067.286336e+064.258162e+068.748277e+06
194.261073e+068.759389e+060.2378910.2021530.1713410.2198640.1786410.0341510.3960030.2...0.0156174.286129e+068.552114e+069.628785e+063.460263e+066.463517e+067.188866e+067.290107e+064.261073e+068.759389e+06
204.263695e+068.769414e+060.2398670.2022850.1699200.2207570.1775360.0313830.4004040.2...0.0164614.292086e+068.571371e+069.631404e+063.465184e+066.473698e+067.194425e+067.293504e+064.263695e+068.769414e+06
214.266071e+068.778504e+060.2418410.2024180.1685100.2216350.1764420.0289330.4044800.2...0.0173024.297489e+068.588869e+069.633775e+063.469647e+066.482937e+067.199463e+067.296580e+064.266071e+068.778504e+06
224.268233e+068.786785e+060.2438140.2025530.1671120.2224980.1753590.0267660.4082640.2...0.0181414.302413e+068.604839e+069.635931e+063.473715e+066.491359e+067.204048e+067.299379e+064.268233e+068.786785e+06
234.270209e+068.794358e+060.2457870.2026880.1657260.2233470.1742860.0248510.4117890.2...0.0189764.306919e+068.619471e+069.637901e+063.477437e+066.499068e+067.208240e+067.301937e+064.270209e+068.794358e+06
244.272022e+068.801312e+060.2477580.2028250.1643510.2241820.1732250.0231600.4150830.2...0.0198084.311057e+068.632928e+069.639707e+063.480857e+066.506150e+067.212087e+067.304283e+064.272022e+068.801312e+06
254.273691e+068.807720e+060.2497280.2029620.1629870.2250020.1721750.0216660.4181700.2...0.0206374.314872e+068.645346e+069.641369e+063.484008e+066.512680e+067.215630e+067.306442e+064.273691e+068.807720e+06
264.275233e+068.813643e+060.2516960.2031010.1616350.2258080.1711360.0203460.4210750.2...0.0214624.318399e+068.656840e+069.642904e+063.486923e+066.518719e+067.218903e+067.308437e+064.275233e+068.813643e+06
274.276662e+068.819134e+060.2536640.2032410.1602930.2266000.1701070.0191810.4238180.2...0.0222854.321670e+068.667511e+069.644326e+063.489625e+066.524320e+067.221937e+067.310285e+064.276662e+068.819134e+06
284.277990e+068.824239e+060.2556310.2033810.1589640.2273780.1690900.0181520.4264170.2...0.0231044.324711e+068.677442e+069.645647e+063.492139e+066.529530e+067.224756e+067.312002e+064.277990e+068.824239e+06
294.279226e+068.828998e+060.2575970.2035230.1576450.2281420.1680840.0172420.4288880.2...0.0239204.327547e+068.686709e+069.646877e+063.494482e+066.534389e+067.227383e+067.313601e+064.279226e+068.828998e+06
.....................................................................
10000406.705666e+061.007370e+060.2767440.2075600.1665000.2119770.1617550.0184480.4413200.2...0.0040836.400325e+061.652353e+065.725171e+063.959854e+066.242538e+065.352033e+064.799660e+066.705666e+061.007370e+06
416.706647e+061.007499e+060.2786260.2077850.1656910.2122050.1609070.0179000.4432960.2...0.0041866.402311e+061.653266e+065.728297e+063.961012e+066.245594e+065.352716e+064.800098e+066.706647e+061.007499e+06
426.707581e+061.007622e+060.2805080.2080110.1648870.2124290.1600670.0173980.4452190.2...0.0042896.404203e+061.654137e+065.731278e+063.962115e+066.248507e+065.353366e+064.800515e+066.707581e+061.007622e+06
436.708472e+061.007739e+060.2823880.2082390.1640870.2126470.1592340.0169360.4470950.2...0.0043926.406009e+061.654968e+065.734123e+063.963168e+066.251288e+065.353986e+064.800913e+066.708472e+061.007739e+06
446.709322e+061.007851e+060.2842660.2084680.1632900.2128610.1584090.0165120.4489280.2...0.0044966.407733e+061.655762e+065.736842e+063.964173e+066.253944e+065.354578e+064.801293e+066.709322e+061.007851e+06
456.710135e+061.007957e+060.2861440.2086980.1624980.2130690.1575920.0161210.4507200.2...0.0045996.409382e+061.656522e+065.739442e+063.965135e+066.256485e+065.355143e+064.801656e+066.710135e+061.007957e+06
466.710913e+061.008060e+060.2880190.2089300.1617090.2132730.1567820.0157610.4524750.2...0.0047026.410960e+061.657249e+065.741931e+063.966054e+066.258917e+065.355685e+064.802004e+066.710913e+061.008060e+06
476.711658e+061.008157e+060.2898940.2091630.1609240.2134730.1559800.0154280.4541950.2...0.0048056.412471e+061.657946e+065.744316e+063.966936e+066.261247e+065.356203e+064.802336e+066.711658e+061.008157e+06
486.712372e+061.008251e+060.2917670.2093970.1601430.2136680.1551850.0151200.4558850.2...0.0049086.413920e+061.658614e+065.746604e+063.967780e+066.263482e+065.356700e+064.802655e+066.712372e+061.008251e+06
496.713057e+061.008341e+060.2936390.2096330.1593660.2138580.1543970.0148350.4575450.2...0.0050116.415310e+061.659255e+065.748800e+063.968591e+066.265627e+065.357176e+064.802961e+066.713057e+061.008341e+06
506.713714e+061.008428e+060.2955090.2098700.1585930.2140430.1536170.0145700.4591780.2...0.0051146.416646e+061.659872e+065.750909e+063.969369e+066.267687e+065.357634e+064.803255e+066.713714e+061.008428e+06
516.714346e+061.008511e+060.2973790.2101090.1578230.2142240.1528440.0143240.4607860.2...0.0052176.417929e+061.660464e+065.752938e+063.970117e+066.269668e+065.358074e+064.803537e+066.714346e+061.008511e+06
526.714954e+061.008590e+060.2992460.2103480.1570580.2144000.1520780.0140940.4623710.2...0.0053206.419164e+061.661034e+065.754890e+063.970837e+066.271574e+065.358497e+064.803808e+066.714954e+061.008590e+06
536.715539e+061.008667e+060.3011130.2105890.1562960.2145720.1513190.0138800.4639340.2...0.0054226.420353e+061.661584e+065.756769e+063.971530e+066.273410e+065.358904e+064.804069e+066.715539e+061.008667e+06
546.716103e+061.008741e+060.3029780.2108320.1555380.2147390.1505670.0136790.4654770.2...0.0055256.421498e+061.662113e+065.758580e+063.972197e+066.275178e+065.359296e+064.804321e+066.716103e+061.008741e+06
556.716646e+061.008813e+060.3048420.2110760.1547840.2149010.1498220.0134910.4670010.2...0.0056286.422601e+061.662623e+065.760326e+063.972841e+066.276883e+065.359674e+064.804563e+066.716646e+061.008813e+06
566.717170e+061.008881e+060.3067040.2113210.1540340.2150590.1490840.0133150.4685080.2...0.0057306.423666e+061.663115e+065.762011e+063.973461e+066.278528e+065.360038e+064.804797e+066.717170e+061.008881e+06
576.717675e+061.008948e+060.3085660.2115670.1532870.2152130.1483540.0131480.4699980.2...0.0058336.424694e+061.663590e+065.763638e+063.974060e+066.280116e+065.360390e+064.805023e+066.717675e+061.008948e+06
586.718163e+061.009012e+060.3104260.2118150.1525440.2153620.1476290.0129910.4714730.2...0.0059366.425686e+061.664049e+065.765209e+063.974639e+066.281650e+065.360729e+064.805240e+066.718163e+061.009012e+06
596.718635e+061.009074e+060.3122840.2120640.1518050.2155060.1469120.0128430.4729330.2...0.0060386.426645e+061.664493e+065.766728e+063.975198e+066.283132e+065.361057e+064.805451e+066.718635e+061.009074e+06
606.719091e+061.009134e+060.3141410.2123140.1510700.2156470.1462010.0127020.4743800.2...0.0061416.427573e+061.664922e+065.768197e+063.975738e+066.284566e+065.361374e+064.805654e+066.719091e+061.009134e+06
616.719532e+061.009192e+060.3159970.2125660.1503390.2157820.1454970.0125680.4758130.2...0.0062436.428470e+061.665338e+065.769619e+063.976261e+066.285954e+065.361681e+064.805851e+066.719532e+061.009192e+06
626.719959e+061.009248e+060.3178520.2128190.1496110.2159140.1448000.0124400.4772350.2...0.0063456.429338e+061.665740e+065.770995e+063.976767e+066.287297e+065.361978e+064.806042e+066.719959e+061.009248e+06
636.720372e+061.009302e+060.3197050.2130730.1488870.2160410.1441090.0123180.4786440.2...0.0064486.430180e+061.666129e+065.772329e+063.977258e+066.288599e+065.362266e+064.806226e+066.720372e+061.009302e+06
646.720772e+061.009355e+060.3215580.2133280.1481670.2161640.1434250.0122020.4800430.2...0.0065506.430995e+061.666507e+065.773621e+063.977733e+066.289860e+065.362544e+064.806405e+066.720772e+061.009355e+06
656.721161e+061.009405e+060.3234080.2135850.1474500.2162830.1427470.0120900.4814300.2...0.0066526.431785e+061.666873e+065.774874e+063.978193e+066.291083e+065.362814e+064.806578e+066.721161e+061.009405e+06
666.721537e+061.009455e+060.3252580.2138430.1467370.2163970.1420750.0119830.4828080.2...0.0067546.432552e+061.667228e+065.776090e+063.978640e+066.292269e+065.363076e+064.806746e+066.721537e+061.009455e+06
676.721903e+061.009503e+060.3271060.2141020.1460280.2165070.1414090.0118790.4841760.2...0.0068566.433295e+061.667573e+065.777270e+063.979073e+066.293420e+065.363330e+064.806909e+066.721903e+061.009503e+06
686.722257e+061.009549e+060.3289530.2143630.1453220.2166130.1407500.0117800.4855350.2...0.0069596.434018e+061.667907e+065.778415e+063.979494e+066.294538e+065.363577e+064.807067e+066.722257e+061.009549e+06
696.722602e+061.009595e+060.3307990.2146250.1446200.2167150.1400970.0116830.4868840.2...0.0070616.434719e+061.668233e+065.779528e+063.979903e+066.295624e+065.363816e+064.807220e+066.722602e+061.009595e+06
\n", 1612 | "

700000 rows × 21 columns

\n", 1613 | "
" 1614 | ], 1615 | "text/plain": [ 1616 | " states \\\n", 1617 | " GPPS LS Acetyl-CoA Acetoacetyl-CoA HMG-CoA \n", 1618 | "Strain Time \n", 1619 | "1 0 8.546974e+00 7.102368e+00 0.200000 0.200000 0.200000 \n", 1620 | " 1 3.488546e+06 6.205517e+06 0.202057 0.200050 0.198739 \n", 1621 | " 2 3.857668e+06 7.334069e+06 0.204079 0.200136 0.197191 \n", 1622 | " 3 3.998702e+06 7.807357e+06 0.206089 0.200233 0.195611 \n", 1623 | " 4 4.073159e+06 8.067673e+06 0.208094 0.200337 0.194026 \n", 1624 | " 5 4.119178e+06 8.232364e+06 0.210094 0.200444 0.192443 \n", 1625 | " 6 4.150440e+06 8.345946e+06 0.212090 0.200555 0.190867 \n", 1626 | " 7 4.173062e+06 8.429013e+06 0.214085 0.200668 0.189299 \n", 1627 | " 8 4.190191e+06 8.492407e+06 0.216077 0.200784 0.187740 \n", 1628 | " 9 4.203611e+06 8.542377e+06 0.218068 0.200901 0.186193 \n", 1629 | " 10 4.214409e+06 8.582778e+06 0.220056 0.201020 0.184656 \n", 1630 | " 11 4.223286e+06 8.616118e+06 0.222043 0.201140 0.183130 \n", 1631 | " 12 4.230711e+06 8.644101e+06 0.224029 0.201262 0.181615 \n", 1632 | " 13 4.237014e+06 8.667921e+06 0.226013 0.201386 0.180113 \n", 1633 | " 14 4.242432e+06 8.688442e+06 0.227996 0.201510 0.178621 \n", 1634 | " 15 4.247139e+06 8.706307e+06 0.229977 0.201636 0.177142 \n", 1635 | " 16 4.251266e+06 8.721998e+06 0.231958 0.201764 0.175674 \n", 1636 | " 17 4.254914e+06 8.735891e+06 0.233937 0.201892 0.174218 \n", 1637 | " 18 4.258162e+06 8.748277e+06 0.235915 0.202022 0.172773 \n", 1638 | " 19 4.261073e+06 8.759389e+06 0.237891 0.202153 0.171341 \n", 1639 | " 20 4.263695e+06 8.769414e+06 0.239867 0.202285 0.169920 \n", 1640 | " 21 4.266071e+06 8.778504e+06 0.241841 0.202418 0.168510 \n", 1641 | " 22 4.268233e+06 8.786785e+06 0.243814 0.202553 0.167112 \n", 1642 | " 23 4.270209e+06 8.794358e+06 0.245787 0.202688 0.165726 \n", 1643 | " 24 4.272022e+06 8.801312e+06 0.247758 0.202825 0.164351 \n", 1644 | " 25 4.273691e+06 8.807720e+06 0.249728 0.202962 0.162987 \n", 1645 | " 26 4.275233e+06 8.813643e+06 0.251696 0.203101 0.161635 \n", 1646 | " 27 4.276662e+06 8.819134e+06 0.253664 0.203241 0.160293 \n", 1647 | " 28 4.277990e+06 8.824239e+06 0.255631 0.203381 0.158964 \n", 1648 | " 29 4.279226e+06 8.828998e+06 0.257597 0.203523 0.157645 \n", 1649 | "... ... ... ... ... ... \n", 1650 | "10000 40 6.705666e+06 1.007370e+06 0.276744 0.207560 0.166500 \n", 1651 | " 41 6.706647e+06 1.007499e+06 0.278626 0.207785 0.165691 \n", 1652 | " 42 6.707581e+06 1.007622e+06 0.280508 0.208011 0.164887 \n", 1653 | " 43 6.708472e+06 1.007739e+06 0.282388 0.208239 0.164087 \n", 1654 | " 44 6.709322e+06 1.007851e+06 0.284266 0.208468 0.163290 \n", 1655 | " 45 6.710135e+06 1.007957e+06 0.286144 0.208698 0.162498 \n", 1656 | " 46 6.710913e+06 1.008060e+06 0.288019 0.208930 0.161709 \n", 1657 | " 47 6.711658e+06 1.008157e+06 0.289894 0.209163 0.160924 \n", 1658 | " 48 6.712372e+06 1.008251e+06 0.291767 0.209397 0.160143 \n", 1659 | " 49 6.713057e+06 1.008341e+06 0.293639 0.209633 0.159366 \n", 1660 | " 50 6.713714e+06 1.008428e+06 0.295509 0.209870 0.158593 \n", 1661 | " 51 6.714346e+06 1.008511e+06 0.297379 0.210109 0.157823 \n", 1662 | " 52 6.714954e+06 1.008590e+06 0.299246 0.210348 0.157058 \n", 1663 | " 53 6.715539e+06 1.008667e+06 0.301113 0.210589 0.156296 \n", 1664 | " 54 6.716103e+06 1.008741e+06 0.302978 0.210832 0.155538 \n", 1665 | " 55 6.716646e+06 1.008813e+06 0.304842 0.211076 0.154784 \n", 1666 | " 56 6.717170e+06 1.008881e+06 0.306704 0.211321 0.154034 \n", 1667 | " 57 6.717675e+06 1.008948e+06 0.308566 0.211567 0.153287 \n", 1668 | " 58 6.718163e+06 1.009012e+06 0.310426 0.211815 0.152544 \n", 1669 | " 59 6.718635e+06 1.009074e+06 0.312284 0.212064 0.151805 \n", 1670 | " 60 6.719091e+06 1.009134e+06 0.314141 0.212314 0.151070 \n", 1671 | " 61 6.719532e+06 1.009192e+06 0.315997 0.212566 0.150339 \n", 1672 | " 62 6.719959e+06 1.009248e+06 0.317852 0.212819 0.149611 \n", 1673 | " 63 6.720372e+06 1.009302e+06 0.319705 0.213073 0.148887 \n", 1674 | " 64 6.720772e+06 1.009355e+06 0.321558 0.213328 0.148167 \n", 1675 | " 65 6.721161e+06 1.009405e+06 0.323408 0.213585 0.147450 \n", 1676 | " 66 6.721537e+06 1.009455e+06 0.325258 0.213843 0.146737 \n", 1677 | " 67 6.721903e+06 1.009503e+06 0.327106 0.214102 0.146028 \n", 1678 | " 68 6.722257e+06 1.009549e+06 0.328953 0.214363 0.145322 \n", 1679 | " 69 6.722602e+06 1.009595e+06 0.330799 0.214625 0.144620 \n", 1680 | "\n", 1681 | " ... \\\n", 1682 | " Mev MevP MevPP IPP DMAPP ... \n", 1683 | "Strain Time ... \n", 1684 | "1 0 0.200000 0.200000 0.200000 0.200000 0.2 ... \n", 1685 | " 1 0.201052 0.199443 0.189291 0.211476 0.2 ... \n", 1686 | " 2 0.202244 0.198476 0.174489 0.227601 0.2 ... \n", 1687 | " 3 0.203420 0.197381 0.159719 0.243869 0.2 ... \n", 1688 | " 4 0.204576 0.196228 0.145631 0.259538 0.2 ... \n", 1689 | " 5 0.205714 0.195047 0.132433 0.274364 0.2 ... \n", 1690 | " 6 0.206833 0.193850 0.120194 0.288257 0.2 ... \n", 1691 | " 7 0.207934 0.192647 0.108925 0.301195 0.2 ... \n", 1692 | " 8 0.209018 0.191443 0.098610 0.313189 0.2 ... \n", 1693 | " 9 0.210085 0.190241 0.089211 0.324270 0.2 ... \n", 1694 | " 10 0.211135 0.189044 0.080684 0.334482 0.2 ... \n", 1695 | " 11 0.212168 0.187852 0.072976 0.343874 0.2 ... \n", 1696 | " 12 0.213185 0.186668 0.066031 0.352500 0.2 ... \n", 1697 | " 13 0.214186 0.185492 0.059793 0.360416 0.2 ... \n", 1698 | " 14 0.215171 0.184325 0.054206 0.367677 0.2 ... \n", 1699 | " 15 0.216141 0.183168 0.049212 0.374338 0.2 ... \n", 1700 | " 16 0.217094 0.182021 0.044759 0.380452 0.2 ... \n", 1701 | " 17 0.218033 0.180884 0.040796 0.386070 0.2 ... \n", 1702 | " 18 0.218956 0.179757 0.037275 0.391239 0.2 ... \n", 1703 | " 19 0.219864 0.178641 0.034151 0.396003 0.2 ... \n", 1704 | " 20 0.220757 0.177536 0.031383 0.400404 0.2 ... \n", 1705 | " 21 0.221635 0.176442 0.028933 0.404480 0.2 ... \n", 1706 | " 22 0.222498 0.175359 0.026766 0.408264 0.2 ... \n", 1707 | " 23 0.223347 0.174286 0.024851 0.411789 0.2 ... \n", 1708 | " 24 0.224182 0.173225 0.023160 0.415083 0.2 ... \n", 1709 | " 25 0.225002 0.172175 0.021666 0.418170 0.2 ... \n", 1710 | " 26 0.225808 0.171136 0.020346 0.421075 0.2 ... \n", 1711 | " 27 0.226600 0.170107 0.019181 0.423818 0.2 ... \n", 1712 | " 28 0.227378 0.169090 0.018152 0.426417 0.2 ... \n", 1713 | " 29 0.228142 0.168084 0.017242 0.428888 0.2 ... \n", 1714 | "... ... ... ... ... ... ... \n", 1715 | "10000 40 0.211977 0.161755 0.018448 0.441320 0.2 ... \n", 1716 | " 41 0.212205 0.160907 0.017900 0.443296 0.2 ... \n", 1717 | " 42 0.212429 0.160067 0.017398 0.445219 0.2 ... \n", 1718 | " 43 0.212647 0.159234 0.016936 0.447095 0.2 ... \n", 1719 | " 44 0.212861 0.158409 0.016512 0.448928 0.2 ... \n", 1720 | " 45 0.213069 0.157592 0.016121 0.450720 0.2 ... \n", 1721 | " 46 0.213273 0.156782 0.015761 0.452475 0.2 ... \n", 1722 | " 47 0.213473 0.155980 0.015428 0.454195 0.2 ... \n", 1723 | " 48 0.213668 0.155185 0.015120 0.455885 0.2 ... \n", 1724 | " 49 0.213858 0.154397 0.014835 0.457545 0.2 ... \n", 1725 | " 50 0.214043 0.153617 0.014570 0.459178 0.2 ... \n", 1726 | " 51 0.214224 0.152844 0.014324 0.460786 0.2 ... \n", 1727 | " 52 0.214400 0.152078 0.014094 0.462371 0.2 ... \n", 1728 | " 53 0.214572 0.151319 0.013880 0.463934 0.2 ... \n", 1729 | " 54 0.214739 0.150567 0.013679 0.465477 0.2 ... \n", 1730 | " 55 0.214901 0.149822 0.013491 0.467001 0.2 ... \n", 1731 | " 56 0.215059 0.149084 0.013315 0.468508 0.2 ... \n", 1732 | " 57 0.215213 0.148354 0.013148 0.469998 0.2 ... \n", 1733 | " 58 0.215362 0.147629 0.012991 0.471473 0.2 ... \n", 1734 | " 59 0.215506 0.146912 0.012843 0.472933 0.2 ... \n", 1735 | " 60 0.215647 0.146201 0.012702 0.474380 0.2 ... \n", 1736 | " 61 0.215782 0.145497 0.012568 0.475813 0.2 ... \n", 1737 | " 62 0.215914 0.144800 0.012440 0.477235 0.2 ... \n", 1738 | " 63 0.216041 0.144109 0.012318 0.478644 0.2 ... \n", 1739 | " 64 0.216164 0.143425 0.012202 0.480043 0.2 ... \n", 1740 | " 65 0.216283 0.142747 0.012090 0.481430 0.2 ... \n", 1741 | " 66 0.216397 0.142075 0.011983 0.482808 0.2 ... \n", 1742 | " 67 0.216507 0.141409 0.011879 0.484176 0.2 ... \n", 1743 | " 68 0.216613 0.140750 0.011780 0.485535 0.2 ... \n", 1744 | " 69 0.216715 0.140097 0.011683 0.486884 0.2 ... \n", 1745 | "\n", 1746 | " controls \\\n", 1747 | " Limonene AtoB HMGS HMGR MK \n", 1748 | "Strain Time \n", 1749 | "1 0 0.000000 9.600104e+00 6.536708e-01 5.869749e+00 3.399382e+00 \n", 1750 | " 1 0.000446 2.858194e+06 4.728040e+06 8.770207e+06 2.289815e+06 \n", 1751 | " 2 0.001163 3.467969e+06 6.188499e+06 9.203323e+06 2.787437e+06 \n", 1752 | " 3 0.001952 3.733472e+06 6.898832e+06 9.357360e+06 3.005128e+06 \n", 1753 | " 4 0.002775 3.882075e+06 7.318873e+06 9.436329e+06 3.127242e+06 \n", 1754 | " 5 0.003615 3.977054e+06 7.596379e+06 9.484353e+06 3.205393e+06 \n", 1755 | " 6 0.004466 4.042998e+06 7.793378e+06 9.516641e+06 3.259701e+06 \n", 1756 | " 7 0.005323 4.091456e+06 7.940466e+06 9.539840e+06 3.299633e+06 \n", 1757 | " 8 0.006184 4.128569e+06 8.054477e+06 9.557313e+06 3.330230e+06 \n", 1758 | " 9 0.007047 4.157903e+06 8.145441e+06 9.570947e+06 3.354422e+06 \n", 1759 | " 10 0.007910 4.181672e+06 8.219706e+06 9.581883e+06 3.374031e+06 \n", 1760 | " 11 0.008773 4.201323e+06 8.281483e+06 9.590849e+06 3.390246e+06 \n", 1761 | " 12 0.009635 4.217840e+06 8.333677e+06 9.598333e+06 3.403878e+06 \n", 1762 | " 13 0.010496 4.231918e+06 8.378358e+06 9.604675e+06 3.415498e+06 \n", 1763 | " 14 0.011355 4.244060e+06 8.417039e+06 9.610118e+06 3.425522e+06 \n", 1764 | " 15 0.012213 4.254639e+06 8.450853e+06 9.614840e+06 3.434257e+06 \n", 1765 | " 16 0.013067 4.263939e+06 8.480664e+06 9.618976e+06 3.441937e+06 \n", 1766 | " 17 0.013920 4.272179e+06 8.507142e+06 9.622628e+06 3.448742e+06 \n", 1767 | " 18 0.014770 4.279530e+06 8.530818e+06 9.625876e+06 3.454813e+06 \n", 1768 | " 19 0.015617 4.286129e+06 8.552114e+06 9.628785e+06 3.460263e+06 \n", 1769 | " 20 0.016461 4.292086e+06 8.571371e+06 9.631404e+06 3.465184e+06 \n", 1770 | " 21 0.017302 4.297489e+06 8.588869e+06 9.633775e+06 3.469647e+06 \n", 1771 | " 22 0.018141 4.302413e+06 8.604839e+06 9.635931e+06 3.473715e+06 \n", 1772 | " 23 0.018976 4.306919e+06 8.619471e+06 9.637901e+06 3.477437e+06 \n", 1773 | " 24 0.019808 4.311057e+06 8.632928e+06 9.639707e+06 3.480857e+06 \n", 1774 | " 25 0.020637 4.314872e+06 8.645346e+06 9.641369e+06 3.484008e+06 \n", 1775 | " 26 0.021462 4.318399e+06 8.656840e+06 9.642904e+06 3.486923e+06 \n", 1776 | " 27 0.022285 4.321670e+06 8.667511e+06 9.644326e+06 3.489625e+06 \n", 1777 | " 28 0.023104 4.324711e+06 8.677442e+06 9.645647e+06 3.492139e+06 \n", 1778 | " 29 0.023920 4.327547e+06 8.686709e+06 9.646877e+06 3.494482e+06 \n", 1779 | "... ... ... ... ... ... \n", 1780 | "10000 40 0.004083 6.400325e+06 1.652353e+06 5.725171e+06 3.959854e+06 \n", 1781 | " 41 0.004186 6.402311e+06 1.653266e+06 5.728297e+06 3.961012e+06 \n", 1782 | " 42 0.004289 6.404203e+06 1.654137e+06 5.731278e+06 3.962115e+06 \n", 1783 | " 43 0.004392 6.406009e+06 1.654968e+06 5.734123e+06 3.963168e+06 \n", 1784 | " 44 0.004496 6.407733e+06 1.655762e+06 5.736842e+06 3.964173e+06 \n", 1785 | " 45 0.004599 6.409382e+06 1.656522e+06 5.739442e+06 3.965135e+06 \n", 1786 | " 46 0.004702 6.410960e+06 1.657249e+06 5.741931e+06 3.966054e+06 \n", 1787 | " 47 0.004805 6.412471e+06 1.657946e+06 5.744316e+06 3.966936e+06 \n", 1788 | " 48 0.004908 6.413920e+06 1.658614e+06 5.746604e+06 3.967780e+06 \n", 1789 | " 49 0.005011 6.415310e+06 1.659255e+06 5.748800e+06 3.968591e+06 \n", 1790 | " 50 0.005114 6.416646e+06 1.659872e+06 5.750909e+06 3.969369e+06 \n", 1791 | " 51 0.005217 6.417929e+06 1.660464e+06 5.752938e+06 3.970117e+06 \n", 1792 | " 52 0.005320 6.419164e+06 1.661034e+06 5.754890e+06 3.970837e+06 \n", 1793 | " 53 0.005422 6.420353e+06 1.661584e+06 5.756769e+06 3.971530e+06 \n", 1794 | " 54 0.005525 6.421498e+06 1.662113e+06 5.758580e+06 3.972197e+06 \n", 1795 | " 55 0.005628 6.422601e+06 1.662623e+06 5.760326e+06 3.972841e+06 \n", 1796 | " 56 0.005730 6.423666e+06 1.663115e+06 5.762011e+06 3.973461e+06 \n", 1797 | " 57 0.005833 6.424694e+06 1.663590e+06 5.763638e+06 3.974060e+06 \n", 1798 | " 58 0.005936 6.425686e+06 1.664049e+06 5.765209e+06 3.974639e+06 \n", 1799 | " 59 0.006038 6.426645e+06 1.664493e+06 5.766728e+06 3.975198e+06 \n", 1800 | " 60 0.006141 6.427573e+06 1.664922e+06 5.768197e+06 3.975738e+06 \n", 1801 | " 61 0.006243 6.428470e+06 1.665338e+06 5.769619e+06 3.976261e+06 \n", 1802 | " 62 0.006345 6.429338e+06 1.665740e+06 5.770995e+06 3.976767e+06 \n", 1803 | " 63 0.006448 6.430180e+06 1.666129e+06 5.772329e+06 3.977258e+06 \n", 1804 | " 64 0.006550 6.430995e+06 1.666507e+06 5.773621e+06 3.977733e+06 \n", 1805 | " 65 0.006652 6.431785e+06 1.666873e+06 5.774874e+06 3.978193e+06 \n", 1806 | " 66 0.006754 6.432552e+06 1.667228e+06 5.776090e+06 3.978640e+06 \n", 1807 | " 67 0.006856 6.433295e+06 1.667573e+06 5.777270e+06 3.979073e+06 \n", 1808 | " 68 0.006959 6.434018e+06 1.667907e+06 5.778415e+06 3.979494e+06 \n", 1809 | " 69 0.007061 6.434719e+06 1.668233e+06 5.779528e+06 3.979903e+06 \n", 1810 | "\n", 1811 | " \\\n", 1812 | " PMK PMD Idi GPPS \n", 1813 | "Strain Time \n", 1814 | "1 0 3.886694e+00 5.129363e+00 5.984526e-01 8.546974e+00 \n", 1815 | " 1 4.126997e+06 5.624252e+06 6.243208e+06 3.488546e+06 \n", 1816 | " 2 5.100021e+06 6.354137e+06 6.755196e+06 3.857668e+06 \n", 1817 | " 3 5.535019e+06 6.641433e+06 6.945044e+06 3.998702e+06 \n", 1818 | " 4 5.781585e+06 6.795049e+06 7.044026e+06 4.073159e+06 \n", 1819 | " 5 5.940358e+06 6.890678e+06 7.104782e+06 4.119178e+06 \n", 1820 | " 6 6.051142e+06 6.955940e+06 7.145871e+06 4.150440e+06 \n", 1821 | " 7 6.132837e+06 7.003317e+06 7.175513e+06 4.173062e+06 \n", 1822 | " 8 6.195571e+06 7.039276e+06 7.197906e+06 4.190191e+06 \n", 1823 | " 9 6.245259e+06 7.067501e+06 7.215420e+06 4.203611e+06 \n", 1824 | " 10 6.285586e+06 7.090244e+06 7.229492e+06 4.214409e+06 \n", 1825 | " 11 6.318971e+06 7.108961e+06 7.241047e+06 4.223286e+06 \n", 1826 | " 12 6.347064e+06 7.124634e+06 7.250704e+06 4.230711e+06 \n", 1827 | " 13 6.371030e+06 7.137950e+06 7.258896e+06 4.237014e+06 \n", 1828 | " 14 6.391718e+06 7.149403e+06 7.265932e+06 4.242432e+06 \n", 1829 | " 15 6.409756e+06 7.159359e+06 7.272041e+06 4.247139e+06 \n", 1830 | " 16 6.425623e+06 7.168094e+06 7.277395e+06 4.251266e+06 \n", 1831 | " 17 6.439688e+06 7.175818e+06 7.282125e+06 4.254914e+06 \n", 1832 | " 18 6.452243e+06 7.182698e+06 7.286336e+06 4.258162e+06 \n", 1833 | " 19 6.463517e+06 7.188866e+06 7.290107e+06 4.261073e+06 \n", 1834 | " 20 6.473698e+06 7.194425e+06 7.293504e+06 4.263695e+06 \n", 1835 | " 21 6.482937e+06 7.199463e+06 7.296580e+06 4.266071e+06 \n", 1836 | " 22 6.491359e+06 7.204048e+06 7.299379e+06 4.268233e+06 \n", 1837 | " 23 6.499068e+06 7.208240e+06 7.301937e+06 4.270209e+06 \n", 1838 | " 24 6.506150e+06 7.212087e+06 7.304283e+06 4.272022e+06 \n", 1839 | " 25 6.512680e+06 7.215630e+06 7.306442e+06 4.273691e+06 \n", 1840 | " 26 6.518719e+06 7.218903e+06 7.308437e+06 4.275233e+06 \n", 1841 | " 27 6.524320e+06 7.221937e+06 7.310285e+06 4.276662e+06 \n", 1842 | " 28 6.529530e+06 7.224756e+06 7.312002e+06 4.277990e+06 \n", 1843 | " 29 6.534389e+06 7.227383e+06 7.313601e+06 4.279226e+06 \n", 1844 | "... ... ... ... ... \n", 1845 | "10000 40 6.242538e+06 5.352033e+06 4.799660e+06 6.705666e+06 \n", 1846 | " 41 6.245594e+06 5.352716e+06 4.800098e+06 6.706647e+06 \n", 1847 | " 42 6.248507e+06 5.353366e+06 4.800515e+06 6.707581e+06 \n", 1848 | " 43 6.251288e+06 5.353986e+06 4.800913e+06 6.708472e+06 \n", 1849 | " 44 6.253944e+06 5.354578e+06 4.801293e+06 6.709322e+06 \n", 1850 | " 45 6.256485e+06 5.355143e+06 4.801656e+06 6.710135e+06 \n", 1851 | " 46 6.258917e+06 5.355685e+06 4.802004e+06 6.710913e+06 \n", 1852 | " 47 6.261247e+06 5.356203e+06 4.802336e+06 6.711658e+06 \n", 1853 | " 48 6.263482e+06 5.356700e+06 4.802655e+06 6.712372e+06 \n", 1854 | " 49 6.265627e+06 5.357176e+06 4.802961e+06 6.713057e+06 \n", 1855 | " 50 6.267687e+06 5.357634e+06 4.803255e+06 6.713714e+06 \n", 1856 | " 51 6.269668e+06 5.358074e+06 4.803537e+06 6.714346e+06 \n", 1857 | " 52 6.271574e+06 5.358497e+06 4.803808e+06 6.714954e+06 \n", 1858 | " 53 6.273410e+06 5.358904e+06 4.804069e+06 6.715539e+06 \n", 1859 | " 54 6.275178e+06 5.359296e+06 4.804321e+06 6.716103e+06 \n", 1860 | " 55 6.276883e+06 5.359674e+06 4.804563e+06 6.716646e+06 \n", 1861 | " 56 6.278528e+06 5.360038e+06 4.804797e+06 6.717170e+06 \n", 1862 | " 57 6.280116e+06 5.360390e+06 4.805023e+06 6.717675e+06 \n", 1863 | " 58 6.281650e+06 5.360729e+06 4.805240e+06 6.718163e+06 \n", 1864 | " 59 6.283132e+06 5.361057e+06 4.805451e+06 6.718635e+06 \n", 1865 | " 60 6.284566e+06 5.361374e+06 4.805654e+06 6.719091e+06 \n", 1866 | " 61 6.285954e+06 5.361681e+06 4.805851e+06 6.719532e+06 \n", 1867 | " 62 6.287297e+06 5.361978e+06 4.806042e+06 6.719959e+06 \n", 1868 | " 63 6.288599e+06 5.362266e+06 4.806226e+06 6.720372e+06 \n", 1869 | " 64 6.289860e+06 5.362544e+06 4.806405e+06 6.720772e+06 \n", 1870 | " 65 6.291083e+06 5.362814e+06 4.806578e+06 6.721161e+06 \n", 1871 | " 66 6.292269e+06 5.363076e+06 4.806746e+06 6.721537e+06 \n", 1872 | " 67 6.293420e+06 5.363330e+06 4.806909e+06 6.721903e+06 \n", 1873 | " 68 6.294538e+06 5.363577e+06 4.807067e+06 6.722257e+06 \n", 1874 | " 69 6.295624e+06 5.363816e+06 4.807220e+06 6.722602e+06 \n", 1875 | "\n", 1876 | " \n", 1877 | " LS \n", 1878 | "Strain Time \n", 1879 | "1 0 7.102368e+00 \n", 1880 | " 1 6.205517e+06 \n", 1881 | " 2 7.334069e+06 \n", 1882 | " 3 7.807357e+06 \n", 1883 | " 4 8.067673e+06 \n", 1884 | " 5 8.232364e+06 \n", 1885 | " 6 8.345946e+06 \n", 1886 | " 7 8.429013e+06 \n", 1887 | " 8 8.492407e+06 \n", 1888 | " 9 8.542377e+06 \n", 1889 | " 10 8.582778e+06 \n", 1890 | " 11 8.616118e+06 \n", 1891 | " 12 8.644101e+06 \n", 1892 | " 13 8.667921e+06 \n", 1893 | " 14 8.688442e+06 \n", 1894 | " 15 8.706307e+06 \n", 1895 | " 16 8.721998e+06 \n", 1896 | " 17 8.735891e+06 \n", 1897 | " 18 8.748277e+06 \n", 1898 | " 19 8.759389e+06 \n", 1899 | " 20 8.769414e+06 \n", 1900 | " 21 8.778504e+06 \n", 1901 | " 22 8.786785e+06 \n", 1902 | " 23 8.794358e+06 \n", 1903 | " 24 8.801312e+06 \n", 1904 | " 25 8.807720e+06 \n", 1905 | " 26 8.813643e+06 \n", 1906 | " 27 8.819134e+06 \n", 1907 | " 28 8.824239e+06 \n", 1908 | " 29 8.828998e+06 \n", 1909 | "... ... \n", 1910 | "10000 40 1.007370e+06 \n", 1911 | " 41 1.007499e+06 \n", 1912 | " 42 1.007622e+06 \n", 1913 | " 43 1.007739e+06 \n", 1914 | " 44 1.007851e+06 \n", 1915 | " 45 1.007957e+06 \n", 1916 | " 46 1.008060e+06 \n", 1917 | " 47 1.008157e+06 \n", 1918 | " 48 1.008251e+06 \n", 1919 | " 49 1.008341e+06 \n", 1920 | " 50 1.008428e+06 \n", 1921 | " 51 1.008511e+06 \n", 1922 | " 52 1.008590e+06 \n", 1923 | " 53 1.008667e+06 \n", 1924 | " 54 1.008741e+06 \n", 1925 | " 55 1.008813e+06 \n", 1926 | " 56 1.008881e+06 \n", 1927 | " 57 1.008948e+06 \n", 1928 | " 58 1.009012e+06 \n", 1929 | " 59 1.009074e+06 \n", 1930 | " 60 1.009134e+06 \n", 1931 | " 61 1.009192e+06 \n", 1932 | " 62 1.009248e+06 \n", 1933 | " 63 1.009302e+06 \n", 1934 | " 64 1.009355e+06 \n", 1935 | " 65 1.009405e+06 \n", 1936 | " 66 1.009455e+06 \n", 1937 | " 67 1.009503e+06 \n", 1938 | " 68 1.009549e+06 \n", 1939 | " 69 1.009595e+06 \n", 1940 | "\n", 1941 | "[700000 rows x 21 columns]" 1942 | ] 1943 | }, 1944 | "metadata": {}, 1945 | "output_type": "display_data" 1946 | }, 1947 | { 1948 | "name": "stderr", 1949 | "output_type": "stream", 1950 | "text": [ 1951 | "/Users/zakcostello/anaconda/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", 1952 | " warnings.warn(mesg, RuntimeWarning)\n" 1953 | ] 1954 | } 1955 | ], 1956 | "source": [ 1957 | "#Pull Data from Simulated Data Set\n", 1958 | "df = read_timeseries_data('data/Fulld10000n0.csv',metabolites,proteins,augment=None,n=100)" 1959 | ] 1960 | }, 1961 | { 1962 | "cell_type": "markdown", 1963 | "metadata": {}, 1964 | "source": [ 1965 | "Once the Data is read in, it is split into test and training groups for building the model and using it for prediction. 100 strains go into each group. Then the training dataframe is used to train the model. Parameters are set to reduce the training time to a manageable duration." 1966 | ] 1967 | }, 1968 | { 1969 | "cell_type": "code", 1970 | "execution_count": null, 1971 | "metadata": {}, 1972 | "outputs": [ 1973 | { 1974 | "name": "stdout", 1975 | "output_type": "stream", 1976 | "text": [ 1977 | "Warning: xgboost.XGBRegressor is not available and will not be used by TPOT.\n", 1978 | "Warning: xgboost.XGBRegressor is not available and will not be used by TPOT.\n" 1979 | ] 1980 | } 1981 | ], 1982 | "source": [ 1983 | "#Sample 100 Strains for Training & 100 Strains For Prediction\n", 1984 | "strains = df.index.get_level_values(0).unique()\n", 1985 | "test_df = df.loc[df.index.get_level_values(0).isin(strains[0:30])]\n", 1986 | "training_df = df.loc[df.index.get_level_values(0).isin(strains[50:])]\n", 1987 | "\n", 1988 | "model = learn_dynamics(training_df,generations=10,population_size=10)" 1989 | ] 1990 | }, 1991 | { 1992 | "cell_type": "markdown", 1993 | "metadata": {}, 1994 | "source": [ 1995 | "Once the Model is constructed, we use the model to predict the metabolite trajectory on the 100 selected test strains. " 1996 | ] 1997 | }, 1998 | { 1999 | "cell_type": "code", 2000 | "execution_count": null, 2001 | "metadata": { 2002 | "scrolled": true 2003 | }, 2004 | "outputs": [], 2005 | "source": [ 2006 | "# Use Model to Metabolite Trajectory for Each Strain\n", 2007 | "trajectory_df = test_df.groupby('Strain').apply(lambda x: simulate_dynamics(model,x,tolerance=5e-4))" 2008 | ] 2009 | }, 2010 | { 2011 | "cell_type": "markdown", 2012 | "metadata": {}, 2013 | "source": [ 2014 | "The Predicted vs Actual Final Productions are pulled from the data and the error distribution is plotted." 2015 | ] 2016 | }, 2017 | { 2018 | "cell_type": "code", 2019 | "execution_count": null, 2020 | "metadata": {}, 2021 | "outputs": [], 2022 | "source": [ 2023 | "#Find the Final Production in each Trajectory for both the real\n", 2024 | "display(trajectory_df)\n", 2025 | "\n", 2026 | "y_p = trajectory_df['Limonene'].loc[trajectory_df.index.get_level_values(1)==69]\n", 2027 | "y = test_df['states']['Limonene'].loc[trajectory_df.index.get_level_values(1)==69]\n", 2028 | "y_err = y-y_p\n", 2029 | "\n", 2030 | "sns.distplot(y_err)\n", 2031 | "plt.show()" 2032 | ] 2033 | }, 2034 | { 2035 | "cell_type": "markdown", 2036 | "metadata": {}, 2037 | "source": [ 2038 | "Finally We Plot the PCA with production predictions overlayed onto the data to demonstrate that there is a relationship that can be determined between the PCA and production. " 2039 | ] 2040 | }, 2041 | { 2042 | "cell_type": "code", 2043 | "execution_count": null, 2044 | "metadata": {}, 2045 | "outputs": [], 2046 | "source": [ 2047 | "#Plot PCA overlayed onto Predictions\n", 2048 | "X = test_df['controls'].loc[trajectory_df.index.get_level_values(1)==69]\n", 2049 | "pca = PCA(2)\n", 2050 | "X_pca = np.transpose(pca.fit_transform(X)).tolist()\n", 2051 | "\n", 2052 | "#Scatter Plot PCA Fit\n", 2053 | "plt.figure()\n", 2054 | "plt.scatter(*X_pca,c=y*1000,cmap=plt.cm.get_cmap('coolwarm'))\n", 2055 | "plt.colorbar()\n", 2056 | "plt.title('Proteomics data overlayed with Actual Final Production')\n", 2057 | "plt.xlabel('Principal Component 1')\n", 2058 | "plt.ylabel('Principal Component 2')\n", 2059 | "plt.show()\n", 2060 | "\n", 2061 | "plt.figure()\n", 2062 | "plt.scatter(*X_pca,c=y_p*1000,cmap=plt.cm.get_cmap('coolwarm'))\n", 2063 | "plt.colorbar()\n", 2064 | "plt.title('Proteomics data overlayed with Predicted Final Production')\n", 2065 | "plt.xlabel('Principal Component 1')\n", 2066 | "plt.ylabel('Principal Component 2')\n", 2067 | "plt.show()" 2068 | ] 2069 | }, 2070 | { 2071 | "cell_type": "code", 2072 | "execution_count": null, 2073 | "metadata": {}, 2074 | "outputs": [], 2075 | "source": [ 2076 | "display(trajectory_df)\n", 2077 | "trajectory_df.to_csv('FullTimeSeriesPredicted.csv')" 2078 | ] 2079 | }, 2080 | { 2081 | "cell_type": "code", 2082 | "execution_count": null, 2083 | "metadata": {}, 2084 | "outputs": [], 2085 | "source": [ 2086 | "import pandas as pd\n", 2087 | "pd.concat([X,y,y_p],axis=1).to_csv('Predicted_Data2.csv')\n", 2088 | "\n", 2089 | "#display(X)\n", 2090 | "#display(y)\n", 2091 | "#display(y_p)" 2092 | ] 2093 | } 2094 | ], 2095 | "metadata": { 2096 | "kernelspec": { 2097 | "display_name": "Python 3", 2098 | "language": "python", 2099 | "name": "python3" 2100 | }, 2101 | "language_info": { 2102 | "codemirror_mode": { 2103 | "name": "ipython", 2104 | "version": 3 2105 | }, 2106 | "file_extension": ".py", 2107 | "mimetype": "text/x-python", 2108 | "name": "python", 2109 | "nbconvert_exporter": "python", 2110 | "pygments_lexer": "ipython3", 2111 | "version": "3.6.1" 2112 | } 2113 | }, 2114 | "nbformat": 4, 2115 | "nbformat_minor": 2 2116 | } 2117 | -------------------------------------------------------------------------------- /notebooks/LearnLimoneneDynamics.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Kinetic Learning Example\n", 8 | "## Simulate Limonene Dynamics" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "from KineticLearning import learn_dynamics,read_timeseries_data,simulate_dynamics\n", 18 | "from IPython.display import display\n", 19 | "import pandas as pd" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": null, 25 | "metadata": { 26 | "collapsed": true 27 | }, 28 | "outputs": [ 29 | { 30 | "name": "stderr", 31 | "output_type": "stream", 32 | "text": [ 33 | "/Users/zakcostello/anaconda/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", 34 | " warnings.warn(mesg, RuntimeWarning)\n" 35 | ] 36 | } 37 | ], 38 | "source": [ 39 | "#Import DataFrame from CSV & Define Important Variables\n", 40 | "controls = ['AtoB', 'GPPS', 'HMGR', 'HMGS', 'Idi','Limonene Synthase', 'MK', 'PMD', 'PMK']\n", 41 | "states = ['Acetyl-CoA','HMG-CoA', 'Mevalonate', 'Mev-P', 'IPP/DMAPP', 'Limonene']\n", 42 | "limonene_df = read_timeseries_data('data/limonene_data.csv',states,controls,time='Hour',strain='Strain',augment=200)" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "## Learn the Dynamics of the Limonene Pathway" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": null, 55 | "metadata": {}, 56 | "outputs": [ 57 | { 58 | "name": "stdout", 59 | "output_type": "stream", 60 | "text": [ 61 | "Warning: xgboost.XGBRegressor is not available and will not be used by TPOT.\n" 62 | ] 63 | } 64 | ], 65 | "source": [ 66 | "model = learn_dynamics(limonene_df,generations=50,population_size=30,verbose=True)" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": null, 72 | "metadata": { 73 | "collapsed": true 74 | }, 75 | "outputs": [], 76 | "source": [ 77 | "strain_df = limonene_df.loc[limonene_df.index.get_level_values(0)=='L2']\n", 78 | "trajectory_df = simulate_dynamics(model,strain_df,verbose=True)" 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": null, 84 | "metadata": { 85 | "collapsed": true 86 | }, 87 | "outputs": [], 88 | "source": [ 89 | "import matplotlib.pyplot as plt\n", 90 | "for metabolite in limonene_df['states'].columns:\n", 91 | " plt.figure()\n", 92 | " ax = plt.gca()\n", 93 | " strain_df['states'].loc[strain_df.index.get_level_values(0)=='L2'].reset_index().plot(x='Time',y=metabolite,ax=ax)\n", 94 | " trajectory_df.plot(x='Time',y=metabolite,ax=ax)\n", 95 | " plt.show()" 96 | ] 97 | } 98 | ], 99 | "metadata": { 100 | "kernelspec": { 101 | "display_name": "Python 3", 102 | "language": "python", 103 | "name": "python3" 104 | }, 105 | "language_info": { 106 | "codemirror_mode": { 107 | "name": "ipython", 108 | "version": 3 109 | }, 110 | "file_extension": ".py", 111 | "mimetype": "text/x-python", 112 | "name": "python", 113 | "nbconvert_exporter": "python", 114 | "pygments_lexer": "ipython3", 115 | "version": "3.6.1" 116 | } 117 | }, 118 | "nbformat": 4, 119 | "nbformat_minor": 2 120 | } 121 | -------------------------------------------------------------------------------- /notebooks/README.md: -------------------------------------------------------------------------------- 1 | # Notebooks 2 | 3 | This directory contains jupyter notebooks which either, reproduce the work outlined in the paper or demonstrate use of the library. 4 | 5 | ## Library Demos 6 | Various demonstrations of how to use the refactored library in practice. 7 | 8 | * [LearnLimoneneDynamics.ipynb](LearnLimoneneDynamics.ipynb) - An example where the dynamics of the limonene pathway are learned and production is maximized by modulating protein levels. 9 | 10 | 11 | ## Paper Reproduction 12 | These notebooks are generally less well maintained than the demo notebooks. These were used and modified heavily during the development of the software. To make the tool usable we heavily refactored the tool. Much of this code is legacy and will be depricated for future use, but it will reproduce the results found in the paper. 13 | 14 | * [ManuscriptNotebook.ipynb](ManuscriptNotebook.ipynb) - Reproduces a bulk of the analysis from the paper. 15 | 16 | * [Create Data Set.ipynb](Create%20Data%20Set.ipynb) - Creates simulated strain data based on a hand built model of the metabolic pathway that results in the production of Limonene from Acetyl-CoA. 17 | 18 | * [Biological_Intuition.ipynb](Biological_Intution.ipynb) - Generates Data to Reproduce Figure 9 From the Paper. 19 | 20 | * [Biological Inutition Plots.ipynb](Biological%20Inutition%20Plots.ipynb) - Creates the Plots for Figure 9 of the Paper. 21 | -------------------------------------------------------------------------------- /notebooks/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JBEI/KineticLearning/c34249105c43dc4b7889bee7ebb82531f3e89fb5/notebooks/__init__.py -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.14.2 2 | TPOT==0.9.2 3 | scipy==1.0.0 4 | networkx==1.11 5 | matplotlib==2.0.2 6 | pandas==0.22.0 7 | ipython==6.4.0 8 | seaborn==0.8.1 9 | scikit_learn==0.19.1 10 | --------------------------------------------------------------------------------