Commonly in natural processes, a 'concentration' is a 'random walk' with two 'absorbing' states, (1) concentration 0% and (2) concentration 100%. A state is 'a

Commonly in natural processes, a 'concentration' is a 'random walk' with two 'absorbing' states, (1) concentration 0% and (2) concentration 100%. A state is 'absorbing' if when once reach it, then never leave it. So, the work is talking about concentrations of proteins or their mRNA varying due to thermal 'randomness'. Maybe in this case, a decent first-cut mathematical model of what is going on is just a discrete time, discrete state space Markov process. So here each discrete 'step' in time is one generation of a cell. The 'states' record the concentrations. So, could use, say, some of the 'nonlinearity' effects to estimate the probability of moving in one step from one state (of concentrations) to another. But the big, 'global' issue is that the concentrations just keep changing and stop changing only at the absorbing states of 0% or 100%. For something with more detail than this 'global' view, if have those probabilities, then can use some standard work in such Markov processes to say how fast the concentrations reach the 'absorbing' states of 0% or 100%. The literature on Markov processes is enormous. For a start, consider: Erhan Çinlar, 'Introduction to Stochastic Processes', ISBN 0-13-498089-1, Prentice-Hall, Englewood Cliffs, NJ, 1975.