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September 6, 2014 15:53
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Stochastic simulation of Patient and Virus population dynamics
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import numpy | |
import random | |
import pylab | |
from VirusClasses import * | |
def simulationDelayedTreatment(numTrials): | |
""" | |
Runs simulations and make histograms for problem 1. | |
Runs numTrials simulations to show the relationship between delayed | |
treatment and patient outcome using a histogram. | |
Histograms of final total virus populations are displayed for delays of 300, | |
150, 75, 0 timesteps (followed by an additional 150 timesteps of | |
simulation). | |
numTrials: number of simulation runs to execute (an integer) | |
""" | |
cured = 0 | |
x = 75 | |
virusList = [] | |
for trial in range(1, numTrials+1): | |
viruses = [] | |
virusTotals = [] | |
for i in range(1, numTrials + 1): | |
virus = ResistantVirus(0.1, 0.05, {'guttagonol': False}, 0.005) | |
viruses.append(virus) | |
treatedPatient = TreatedPatient(viruses, 1000) | |
for i in range(1, x+1): | |
treatedPatient.update() | |
y = treatedPatient.getTotalPop() | |
virusTotals.append(y) | |
treatedPatient.addPrescription('guttagonol') | |
for i in range(1, 151): | |
treatedPatient.update() | |
y = treatedPatient.getTotalPop() | |
virusTotals.append(y) | |
result = virusTotals[-1] | |
virusList.append(result) | |
if result <=50: | |
cured +=1 | |
print cured | |
pylab.hist(virusList, bins =20) | |
pylab.show() | |
def simulationTwoDrugsDelayedTreatment(numTrials): | |
""" | |
Runs simulations and make histograms for problem 2. | |
Runs numTrials simulations to show the relationship between administration | |
of multiple drugs and patient outcome. | |
Histograms of final total virus populations are displayed for lag times of | |
300, 150, 75, 0 timesteps between adding drugs (followed by an additional | |
150 timesteps of simulation). | |
numTrials: number of simulation runs to execute (an integer) | |
""" | |
cured = 0 | |
x = 150 | |
virusList = [] | |
for trial in range(numTrials): | |
viruses = [] | |
virusTotals = [] | |
for i in range(100): | |
virus = ResistantVirus(0.1, 0.05, | |
{'guttagonol': False, 'grimpex': False}, | |
0.01) | |
viruses.append(virus) | |
treatedPatient = TreatedPatient(viruses, 1000) | |
for i in range(150): | |
treatedPatient.update() | |
y = treatedPatient.getTotalPop() | |
virusTotals.append(y) | |
treatedPatient.addPrescription('guttagonol') | |
for i in range(x): | |
treatedPatient.update() | |
y = treatedPatient.getTotalPop() | |
virusTotals.append(y) | |
treatedPatient.addPrescription('grimpex') | |
for i in range(150): | |
treatedPatient.update() | |
y = treatedPatient.getTotalPop() | |
virusTotals.append(y) | |
result = virusTotals[-1] | |
virusList.append(result) | |
if result <=50: | |
cured +=1 | |
print cured | |
pylab.hist(virusList, bins =20) | |
pylab.show() | |
simulationTwoDrugsDelayedTreatment(100) | |
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import numpy | |
import random | |
import pylab | |
class NoChildException(Exception): | |
""" | |
NoChildException is raised by the reproduce() method in the SimpleVirus | |
and ResistantVirus classes to indicate that a virus particle does not | |
reproduce. | |
""" | |
class SimpleVirus(object): | |
""" | |
Representation of a simple virus (does not model drug effects/resistance). | |
""" | |
def __init__(self, maxBirthProb, clearProb): | |
""" | |
Initialize a SimpleVirus instance, saves all parameters as attributes | |
of the instance. | |
maxBirthProb: Maximum reproduction probability (a float between 0-1) | |
clearProb: Maximum clearance probability (a float between 0-1). | |
""" | |
self.maxBirthProb = maxBirthProb | |
self.clearProb = clearProb | |
def getMaxBirthProb(self): | |
""" | |
Returns the max birth probability. | |
""" | |
return self.maxBirthProb | |
def getClearProb(self): | |
""" | |
Returns the clear probability. | |
""" | |
return self.clearProb | |
def doesClear(self): | |
""" Stochastically determines whether this virus particle is cleared from the | |
patient's body at a time step. | |
returns: True with probability self.getClearProb and otherwise returns | |
False. | |
""" | |
if random.random()<self.getClearProb(): | |
return True | |
return False | |
def reproduce(self, popDensity): | |
""" | |
Stochastically determines whether this virus particle reproduces at a | |
time step. Called by the update() method in the Patient and | |
TreatedPatient classes. The virus particle reproduces with probability | |
self.getMaxBirthProb * (1 - popDensity). | |
If this virus particle reproduces, then reproduce() creates and returns | |
the instance of the offspring SimpleVirus (which has the same | |
maxBirthProb and clearProb values as its parent). | |
popDensity: the population density (a float), defined as the current | |
virus population divided by the maximum population. | |
returns: a new instance of the SimpleVirus class representing the | |
offspring of this virus particle. The child should have the same | |
maxBirthProb and clearProb values as this virus. Raises a | |
NoChildException if this virus particle does not reproduce. | |
""" | |
if random.random()<=(self.getMaxBirthProb()*(1-popDensity)): | |
simplevirus = SimpleVirus(self.getMaxBirthProb(), self.getClearProb()) | |
return simplevirus | |
else: | |
raise NoChildException | |
class Patient(object): | |
""" | |
Representation of a simplified patient. The patient does not take any drugs | |
and his/her virus populations have no drug resistance. | |
""" | |
def __init__(self, viruses, maxPop): | |
""" | |
Initialization function, saves the viruses and maxPop parameters as | |
attributes. | |
viruses: the list representing the virus population (a list of | |
SimpleVirus instances) | |
maxPop: the maximum virus population for this patient (an integer) | |
""" | |
self.viruses = viruses | |
self.maxPop = maxPop | |
def getViruses(self): | |
""" | |
Returns the viruses in this Patient. | |
""" | |
return self.viruses | |
def getMaxPop(self): | |
""" | |
Returns the max population. | |
""" | |
return self.maxPop | |
def getTotalPop(self): | |
""" | |
Gets the size of the current total virus population. | |
returns: The total virus population (an integer) | |
""" | |
return len(self.getViruses()) | |
def update(self): | |
""" | |
Update the state of the virus population in this patient for a single | |
time step. update() should execute the following steps in this order: | |
- Determine whether each virus particle survives and updates the list | |
of virus particles accordingly. | |
- The current population density is calculated. This population density | |
value is used until the next call to update() | |
- Based on this value of population density, determine whether each | |
virus particle should reproduce and add offspring virus particles to | |
the list of viruses in this patient. | |
returns: The total virus population at the end of the update (an | |
integer) | |
""" | |
virusesCopy = self.viruses[:] | |
for virus in virusesCopy: | |
if virus.doesClear() == True: | |
self.viruses.remove(virus) | |
popDensity = (float(self.getTotalPop())/float(self.getMaxPop())) | |
virusesCopy2 = self.viruses[:] | |
for virus in virusesCopy2: | |
if self.getTotalPop() >= self.getMaxPop(): | |
break | |
try: self.viruses.append(virus.reproduce(popDensity)) | |
except NoChildException: | |
pass | |
return self.viruses | |
def simulationWithoutDrug(numViruses, maxPop, maxBirthProb, clearProb, | |
numTrials): | |
""" | |
Run the simulation and plot the graph for problem 3 (no drugs are used, | |
viruses do not have any drug resistance). | |
For each of numTrials trial, instantiates a patient, runs a simulation | |
for 300 timesteps, and plots the average virus population size as a | |
function of time. | |
numViruses: number of SimpleVirus to create for patient (an integer) | |
maxPop: maximum virus population for patient (an integer) | |
maxBirthProb: Maximum reproduction probability (a float between 0-1) | |
clearProb: Maximum clearance probability (a float between 0-1) | |
numTrials: number of simulation runs to execute (an integer) | |
""" | |
virusTotals = [0 for i in range(300)] | |
viruses = [] | |
virusAvgs = [] | |
for x in range(numViruses): | |
virus = SimpleVirus(maxBirthProb, clearProb) | |
viruses.append(virus) | |
patient = Patient(viruses, maxPop) | |
for e in range(numTrials): | |
virusPops = [] | |
for i in range(300): | |
patient.update() | |
x = patient.getTotalPop() | |
virusTotals[i] += x | |
for i in range(300): | |
virusAvgs.append((float(virusTotals[i])/float(numTrials))) | |
pylab.plot(range(300), virusAvgs) | |
pylab.title('SimpleVirus simulation') | |
pylab.xlabel('Time step') | |
pylab.ylabel('Average Virus Population') | |
pylab.legend() | |
pylab.figure(1) | |
pylab.show() | |
class ResistantVirus(SimpleVirus): | |
""" | |
Representation of a virus which can have drug resistance. | |
""" | |
def __init__(self, maxBirthProb, clearProb, resistances, mutProb): | |
""" | |
Initialize a ResistantVirus instance, saves all parameters as attributes | |
of the instance. | |
maxBirthProb: Maximum reproduction probability (a float between 0-1) | |
clearProb: Maximum clearance probability (a float between 0-1). | |
resistances: A dictionary of drug names (strings) mapping to the state | |
of this virus particle's resistance (either True or False) to each drug. | |
e.g. {'guttagonol':False, 'srinol':False}, means that this virus | |
particle is resistant to neither guttagonol nor srinol. | |
mutProb: Mutation probability for this virus particle (a float). This is | |
the probability of the offspring acquiring or losing resistance to a drug. | |
""" | |
SimpleVirus.__init__(self, maxBirthProb, clearProb) | |
self.resistances = resistances | |
self.mutProb = mutProb | |
def getResistances(self): | |
""" | |
Returns the resistances for this virus. | |
""" | |
return self.resistances | |
def getMutProb(self): | |
""" | |
Returns the mutation probability for this virus. | |
""" | |
return self.mutProb | |
def isResistantTo(self, drug): | |
""" | |
Get the state of this virus particle's resistance to a drug. This method | |
is called by getResistPop() in TreatedPatient to determine how many virus | |
particles have resistance to a drug. | |
drug: The drug (a string) | |
returns: True if this virus instance is resistant to the drug, False | |
otherwise. | |
""" | |
if drug not in self.resistances.keys(): | |
return False | |
return self.resistances[drug] | |
def reproduce(self, popDensity, activeDrugs): | |
""" | |
""" | |
for drug in activeDrugs: | |
if self.isResistantTo(drug)==False: | |
raise NoChildException | |
if random.random() < self.getMaxBirthProb()*(1-popDensity): | |
newResistances = self.resistances.copy() | |
for drug in self.resistances.keys(): | |
if self.resistances[drug]==True: | |
if random.random() > (1-self.mutProb): | |
newResistances[drug] = False | |
else: | |
if random.random() < self.mutProb: | |
newResistances[drug] = True | |
newVirus = ResistantVirus(self.maxBirthProb, self.clearProb, newResistances, self.mutProb) | |
return newVirus | |
else: | |
raise NoChildException | |
class TreatedPatient(Patient): | |
""" | |
Representation of a patient. The patient is able to take drugs and his/her | |
virus population can acquire resistance to the drugs he/she takes. | |
""" | |
def __init__(self, viruses, maxPop): | |
""" | |
Initialization function, saves the viruses and maxPop parameters as | |
attributes. Also initializes the list of drugs being administered | |
(which should initially include no drugs). | |
viruses: The list representing the virus population (a list of | |
virus instances) | |
maxPop: The maximum virus population for this patient (an integer) | |
""" | |
Patient.__init__(self, viruses, maxPop) | |
self.listOfDrugs = [] | |
def addPrescription(self, newDrug): | |
""" | |
Administer a drug to this patient. After a prescription is added, the | |
drug acts on the virus population for all subsequent time steps. If the | |
newDrug is already prescribed to this patient, the method has no effect. | |
newDrug: The name of the drug to administer to the patient (a string). | |
postcondition: The list of drugs being administered to a patient is updated | |
""" | |
if newDrug in self.listOfDrugs: | |
pass | |
else: | |
self.listOfDrugs.append(newDrug) | |
def getPrescriptions(self): | |
""" | |
Returns the drugs that are being administered to this patient. | |
returns: The list of drug names (strings) being administered to this | |
patient. | |
""" | |
return self.listOfDrugs | |
def getResistPop(self, drugResist): | |
""" | |
Get the population of virus particles resistant to the drugs listed in | |
drugResist. | |
drugResist: Which drug resistances to include in the population (a list | |
of strings - e.g. ['guttagonol'] or ['guttagonol', 'srinol']) | |
returns: The population of viruses (an integer) with resistances to all | |
drugs in the drugResist list. | |
""" | |
count = 0 | |
for virus in self.viruses: | |
for drug in drugResist: | |
hasResistance = 1 | |
if virus.isResistantTo(drug): | |
hasResistance *= 1 | |
else: | |
hasResistance = 0 | |
break | |
count += hasResistance | |
return count | |
def removePresciption(newDrug): | |
if newDrug not in self.listOfDrugs: | |
pass | |
else: | |
self.listOfDrugs.remove(newDrug) | |
def update(self): | |
""" | |
Update the state of the virus population in this patient for a single | |
time step. update() should execute these actions in order: | |
- Determine whether each virus particle survives and update the list of | |
virus particles accordingly | |
- The current population density is calculated. This population density | |
value is used until the next call to update(). | |
- Based on this value of population density, determine whether each | |
virus particle should reproduce and add offspring virus particles to | |
the list of viruses in this patient. | |
The list of drugs being administered should be accounted for in the | |
determination of whether each virus particle reproduces. | |
returns: The total virus population at the end of the update (an | |
integer) | |
""" | |
virusesCopy = self.viruses[:] | |
for virus in virusesCopy: | |
if virus.doesClear() == True: | |
self.viruses.remove(virus) | |
popDensity = (float(self.getTotalPop())/float(self.getMaxPop())) | |
virusesCopy2 = self.viruses[:] | |
for virus in virusesCopy2: | |
try: self.viruses.append(virus.reproduce(popDensity, self.listOfDrugs)) | |
except NoChildException: | |
pass | |
return self.getTotalPop() | |
def simulationWithDrug(numViruses, maxPop, maxBirthProb, clearProb, resistances, | |
mutProb, numTrials): | |
""" | |
Runs simulations and plots graphs. | |
For each of numTrials trials, instantiates a patient, runs a simulation for | |
150 timesteps, adds guttagonol, and runs the simulation for an additional | |
150 timesteps. At the end plots the average virus population size | |
(for both the total virus population and the guttagonol-resistant virus | |
population) as a function of time. | |
numViruses: number of ResistantVirus to create for patient (an integer) | |
maxPop: maximum virus population for patient (an integer) | |
maxBirthProb: Maximum reproduction probability (a float between 0-1) | |
clearProb: maximum clearance probability (a float between 0-1) | |
resistances: a dictionary of drugs that each ResistantVirus is resistant to | |
(e.g., {'guttagonol': False}) | |
mutProb: mutation probability for each ResistantVirus particle | |
(a float between 0-1). | |
numTrials: number of simulation runs to execute (an integer) | |
""" | |
viruses = [] | |
virusTotals = [0 for i in range(300)] | |
virusResPop = [0 for i in range(300)] | |
virusAvgs = [] | |
virusResAvgs = [] | |
for virus in range(numViruses): | |
virus = ResistantVirus(maxBirthProb, clearProb, resistances, mutProb) | |
viruses.append(virus) | |
for e in range(numTrials): | |
treatedPatient = TreatedPatient(viruses, maxPop) | |
for i in range(150): | |
treatedPatient.update() | |
x = treatedPatient.getTotalPop() | |
virusTotals[i] += x | |
y = treatedPatient.getResistPop(['guttagonol']) | |
virusResPop[i] += y | |
treatedPatient.addPrescription('guttagonol') | |
for i in range(150, 300): | |
treatedPatient.update() | |
x = treatedPatient.getTotalPop() | |
virusTotals[i] += x | |
y = treatedPatient.getResistPop(['guttagonol']) | |
virusResPop[i] += y | |
for i in range(300): | |
virusAvgs.append((float(virusTotals[i])/float(numTrials))) | |
virusResAvgs.append((float(virusResPop[i])/float(numTrials))) | |
pylab.plot(range(300), virusAvgs, label = 'total pop') | |
pylab.plot(range(300), virusResAvgs, label = 'resistant pop') | |
pylab.title('ResistantVirus simulation') | |
pylab.xlabel('time step') | |
pylab.ylabel('# viruses') | |
pylab.legend() | |
pylab.figure(1) | |
pylab.show() | |
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