# Yeast

## 1070 days ago by jogr5598

# Yeast program, to study growth of yeast using Euler's method # First, specify the starting and ending points, stepsize, and total number of observation points tstart=0 tfin=60 stepsize=0.1 length=(tfin-tstart)/stepsize+1 # Next, specify the intial amount of yeast Y; alcohol A; the carrying capacity b; the toxicity coefficient c; and the growth rate k Y=0.5 A=0 b=10 c=0.1 k=0.2 t=tstart # Next we create lists to store our computed values of t, A and Y Yvalues=[] Avalues=[] tvalues=[] # The following loop does three things: # (1) stores the current values of Y and t into the lists created above; # (2) computes the next value of Y using Euler's method; # (3) increases t by the stepsize for i in range(length): # Store current values of Y, A, and t Yvalues.append(Y) Avalues.append(A) tvalues.append(t) # Compute rate of change using logistic equation and appropriate rate equation Yprime=k*Y*(1-Y/b)-c*A*Y Aprime=.05*Y # Net change equals rate of change times stepsize, for Y and A DeltaY=Yprime*stepsize DeltaA=Aprime*stepsize # New values equal current values plus net change, for Y, A, and t Y=Y+DeltaY A=A+DeltaA t=t+stepsize # Next time through the loop, the above new values play the role of current values # Zip the t values with the S/I/R values into lists of ordered pairs, and create plots of these Yplot=list_plot(list(zip(tvalues,Yvalues)),plotjoined=True,marker='o',color='blue') Aplot=list_plot(list(zip(tvalues,Avalues)),plotjoined=True,marker='o',color='red') # Now plot the computed Y and A values against the corresponding points in the domain show(Yplot+Aplot,axes_labels=['$t$ (hours)','$Y,A$ (pounds)'])
# Yeast program, to study growth of yeast using Euler's method # First, specify the starting and ending points, stepsize, and total number of observation points tstart=0 tfin=60 stepsize=0.1 length=(tfin-tstart)/stepsize+1 # Next, specify the intial amount of yeast Y; alcohol A; sugar S; the carrying capacity b; the toxicity coefficient c; and the growth rate k Y=0.5 A=0 S=25 b=.4 c=0.1 k=0.2 t=tstart # Next we create lists to store our computed values of t, A S, and Y Yvalues=[] Avalues=[] Svalues=[] tvalues=[] # The following loop does three things: # (1) stores the current values of Y and t into the lists created above; # (2) computes the next value of Y using Euler's method; # (3) increases t by the stepsize for i in range(length): # Store current values of Y, A, S, and t Yvalues.append(Y) Avalues.append(A) Svalues.append(S) tvalues.append(t) # Compute rate of change using logistic equation and appropriate rate equation Yprime=k*Y*(1-Y/(b*S))-c*A*Y Aprime=.05*Y Sprime=-0.15*Y # Net change equals rate of change times stepsize, for Y, S and A DeltaY=Yprime*stepsize DeltaA=Aprime*stepsize DeltaS=Sprime*stepsize # New values equal current values plus net change, for Y, A, S, and t Y=Y+DeltaY A=A+DeltaA S=S+DeltaS t=t+stepsize # Next time through the loop, the above new values play the role of current values # Zip the t values with the S/I/R values into lists of ordered pairs, and create plots of these Yplot=list_plot(list(zip(tvalues,Yvalues)),plotjoined=True,marker='o',color='blue') Aplot=list_plot(list(zip(tvalues,Avalues)),plotjoined=True,marker='o',color='red') Splot=list_plot(list(zip(tvalues,Svalues)),plotjoined=True,marker='o',color='green') # Now plot the computed Y, S and A values against the corresponding points in the domain show(Yplot+Aplot+Splot,axes_labels=['$t$ (hours)','$Y,A,S$ (pounds)'])