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Stochastic Model of Seed and Spore Germination

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generate new data at same settings
seed repeatable random numbers
seed
100
no. of points.
21
underlying probability rate function
sigmoid
constant
peaked
point i
12
stochastic model parameters
initial no. of spores
25
P
asym
0.1
t
c
1
50.
m
1
4.
t
c
2
100.
m
2
3.
plot % germinated
fit data
axes maxima
t max.
200.
P
g
max.
0.12
no. germ. max.
130
% germ. max.
100.
Experimentally determined germination curves of microbial spores come in two shapes, sigmoid and nonsigmoid, which can frequently be described by the stretched exponential model. The curve can also be simulated by a stochastic model where the germination probability can vary with time. This Demonstration shows that if the underlying probability of germination is constant, the germination curve is nonsigmoid. If the underlying germination probability rate function curve is itself sigmoid, so is the germination curve. If the germination probability rate function has a maximum, the germination curve can still be sigmoid but with an asymptotic germination level that depends on the peak's location, sharpness, and height.
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