Hotton Scott, Colvin Mike E
School of Natural Sciences and UC Merced Center for Computational Biology, University of California, Merced, CA 95344, USA.
J Theor Biol. 2007 Apr 21;245(4):610-26. doi: 10.1016/j.jtbi.2006.11.006. Epub 2006 Nov 15.
Cell differentiation often appears to be a stochastic process particularly in the hemopoietic system. One of the earliest stochastic models for the growth of stem cell populations was proposed by Till et al. in 1964. In this model there are just two cell types: stem cells and specialized cells. At each time step there is a fixed probability that a stem cell differentiates into a specialized cell and a fixed probability that it undergoes mitosis to produce two stem cells. Even though this model is conceptually simple the myriad of possible outcomes has made it difficult to analyse. We present original closed-form expressions for the probability functions and a fast algorithm for computing them. Renewed interest in stem cells has raised questions about the effect de-differentiation has on stem cell populations. We have extended the stochastic model to include de-differentiation and show that even a small amount of de-differentiation can have a large effect on stem cell population growth.
细胞分化通常似乎是一个随机过程，尤其是在造血系统中。1964年，蒂尔等人提出了最早的关于干细胞群体生长的随机模型之一。在这个模型中，只有两种细胞类型：干细胞和特化细胞。在每个时间步，干细胞分化为特化细胞有一个固定概率，进行有丝分裂产生两个干细胞也有一个固定概率。尽管这个模型在概念上很简单，但众多可能的结果使其难以分析。我们给出了概率函数的原始封闭形式表达式以及计算它们的快速算法。对干细胞的新兴趣引发了关于去分化对干细胞群体影响的问题。我们扩展了随机模型以包括去分化，并表明即使少量的去分化也会对干细胞群体生长产生很大影响。
