Stochastic Model of Seed and Spore Germination

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.