def tumorSizeCalcExp(sequence, mRate, size, reachMaxRes):
days = len(sequence)
k = 1
growthRates = np.full(size, .5)
resistances = main.expResAssign(size, 1.001, 0, reachMaxRes)
popArr = main.calcPopDistribution(int(size), .01)
mutationRatesUp, mutationRatesDown = main.detMutationRate(mRate, size)
resultArr = odeint(
main.calculateChange, popArr, np.arange(0, days, 1),
(growthRates, mutationRatesUp, mutationRatesDown, k, resistances,
main.periodicDrugDay(sequence, days), np.zeros(popArr.size)))
return np.sum(resultArr)
def costOfResFunction(sequence, mRate, size, maxResSwitchNumb, n):
days = len(sequence)
k = 1
resistances = main.expResAssign(size, n, 0, maxResSwitchNumb)
popArr = main.calcPopDistribution(int(size), 1)
mutationRatesUp, mutationRatesDown = main.detMutationRate(mRate, size)
growthRate = main.costOfRes(.25, resistances)
resultArr = odeint(
main.calculateChange, popArr, np.arange(0, days, 1),
(growthRate, mutationRatesUp, mutationRatesDown, k, resistances,
main.periodicDrugDay(sequence, days), np.zeros(popArr.size)))
return np.sum(resultArr)
def tumorSizeCalcLinear(sequence, mRate, size):
days = len(sequence)
k = 1
growthRates = np.full(size, .5)
resistances = main.assignResistance(size, [0, 1])
popArr = main.calcPopDistribution(int(size), .01)
mutationRatesUp, mutationRatesDown = main.detMutationRate(
mRate, size, True, False)
resultArr = odeint(
main.calculateChange, popArr, np.arange(0, days, 1),
(growthRates, mutationRatesUp, mutationRatesDown, k, resistances,
main.periodicDrugDay(sequence, days), np.zeros(popArr.size)))
return np.sum(resultArr[50, :])
def varyParametersPop(initialMutRate, endMutRate, maxSize, k, drugWeeks,
timesteps, initPopSize):
xs = np.arange(initialMutRate, endMutRate, .01)
ys = range(2, maxSize + 1)
resultsArr = np.zeros((len(ys), len(xs)))
for x, mRate in enumerate(xs):
for y, size in enumerate(ys):
mutationRateUp, mutationRateDown = main.detMutationRate(
mRate * size, size)
popArr = main.calcPopDistribution(size, initPopSize)
resistances = main.assignResistance(size, [0, 1])
change = np.zeros(size)
growthRates = np.full(size, 1)
results = odeint(main.calculateChange, popArr, timesteps,
(growthRates, mutationRateUp, mutationRateDown, k,
resistances, drugWeeks, change))
resultsArr[y, x] = np.sum(results[:][-1])
return resultsArr, xs, ys
def objectiveFun(sequence):
days = 10
size = 5
k = 1
mRate = .05
growthRates = np.full(size, .5)
popArr = main.calcPopDistribution(size, .01)
resistances = main.expResAssign(size, 2, 0, size - 1)
mutationRatesUp, mutationRatesDown = main.detMutationRate(
mRate, size, True, False)
resultArr = odeint(
main.calculateChange, popArr, np.arange(0, days, 1),
(growthRates, mutationRatesUp, mutationRatesDown, k, resistances,
main.periodicDrugDay(sequence, days), np.zeros(popArr.size)))
weightAns = 0
for i in range(0, len(resistances)):
for j in range(0, len(resultArr)):
weightAns = weightAns + (resistances[i] * resultArr[j][i])
return weightAns
def nextSimulation(initialPop, mRate, k, tolerance, size, days, sequence):
popArr = main.calcPopDistribution(size, initialPop)
resistanceArr = main.assignResistance(size, [0, 1])
growthRates = np.full(size, 1)
mutationRatesUp, mutationRatesDown = main.detMutationRate(mRate, size)
results = odeint(
main.calculateChange, popArr, np.arange(0, 100, 1),
(growthRates, mutationRatesUp, mutationRatesDown, k, resistanceArr,
main.periodicDrugDay(sequence, days), np.zeros(size)))
totalPops = np.sum(results, axis=0)
sumPop = np.sum(totalPops)
mean, standardDev = main.getMeanAndStandardDeviation(popArr)
info.append([sumPop, mean, standardDev])
# For next iteration (essentially a recursion)
tolerance = tolerance - 1
if tolerance > 0:
nextSimulation(initialPop, mRate, k, tolerance, size + 1, days,
sequence)
return info