In method 2 we assume that initially synchronization is successful in gathering all cells at some common ''initial state'' before event S, and cells have a certain probability per unit of time (g(t)) at time t to be transferred from this ''initial state'' to S. We assume deterministic kinetics once S is entered, and therefore we can represent a cell with the time spent in this stage. So let l(a) be the value of some trait of an individual cell which, a time units ago, entered stage S. Let L(t) be the sample output defined as in (3), but now p(t,a) is the probability distribution describing the collection of cells which are a units of time in stage S at time t. Because the age clock doesn't start ticking until the cell enters S, negative ages are not allowed. In an initially synchronous population p and g relate as follows
Again our goal is to find l(a) from the experimental time series L(t)
and experimental information about the distribution g(t). So far we have
not been able to find an easy computation scheme for a general probability
function, like series (4) found for synchronization method 1.
For the uniform distribution on the interval [0,b] and exponential
distribution with parameter 
, the inverse function of (6)
read, respectively,
and
in which 
is the first derivative of L with respect to time. The
transformation rule (8) for the exponential distribution is
exactly equivalent to the one obtained for an exponential distribution in
method 1.
