Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Join them; it only takes a minute:

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

Can I better explain cell lineages using PDEs or stochastic?

share|cite|improve this question
This is a major change to the question and renders the previous answers non-responsive. Please return to the prior question, then ask a new question if you want. I suspect this question needs much more detail to get a reasonable answer. – Ross Millikan Feb 12 '13 at 18:51

In the limit of large numbers of cells, if $a(t)$, $b(t)$ and $c(t)$ are the numbers of cells of type $A$, $B$ and $C$ then the average dynamics are described by the ODEs

$$\begin{align} \dot{a} & = 0 \\ \dot{b} & = pa + 2pc \\ \dot{c} & = (1-p)a + (1-2p)c \end{align}$$

The first of these means that $a(t)=a_0$ always. The third can then be solved to give

$$c(t) = \frac{(1-2p)c_0+ (1-p)a_0}{1-2p} e^{(1-2p)t} - \frac{(1-p)a_0}{1-2p}$$

which leads to a solution for the second equation by integrating:

$$b(t) = \frac{(1-2p)c_0+ (1-p)a_0}{(1-2p)^2} e^{(1-2p)t} - \frac{(1-2p^2)a_0}{1-2p}t + \frac{(1-2p)^2b_0 - (1-2p)c_0 - (1-p)a_0}{(1-2p)^2}$$

This is assuming that $p\neq 1/2$. In that case the equations are somewhat simplified ($c(t)$ is linear in $t$ and $b(t)$ is quadratic).

share|cite|improve this answer

First of all, I think you should find interesting this wiki.article on branching processes.

Next, the answer on your question depends on the precise description of the model you're dealing with - namely, would you like to work in a continuous time setting or in a discrete time. In both cases I would suggest to use Markov Chains: continuous or discrete since the distribution of number of cells for your model depends only on the current state, not on the whole history.

Dependence of probabilities on time may make analysis more difficult because time-dependent Markov Chains are not as popular in the literature as time-independent ones. Sometimes dependence on time may be changed to the more deep dependence on the state (say, when you have more cells than they consume more sources and hence they divide more rare).

With regards to ODE's or PDE's - I don't think that you need them unless you want to consider non-individual cells but rather the density of them spreading in some volume. Another useful wiki.article on Gillespie algorithm, you may also would like to take a look on papers cited there.

share|cite|improve this answer

Your Answer


By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.