Martínez-Sosa Pablo, Mendoza Luis
Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Apartado Postal 70228, Ciudad Universitaria, CP04510 México, D.F., Mexico.
Biosystems. 2013 Aug;113(2):96-103. doi: 10.1016/j.biosystems.2013.05.007. Epub 2013 Jun 4.
There is a vast amount of molecular information regarding the differentiation of T lymphocytes, in particular regarding in vitro experimental treatments that modify their differentiation process. This publicly available information was used to infer the regulatory network that controls the differentiation of T lymphocytes into CD4(+) and CD8(+) cells. Hereby we present a network that consists of 50 nodes and 97 regulatory interactions, representing the main signaling circuits established among molecules and molecular complexes regulating the differentiation of T cells. The network was converted into a continuous dynamical system in the form of a set of coupled ordinary differential equations, and its dynamical behavior was studied. With the aid of numerical methods, nine fixed point attractors were found for the dynamical system. These attractors correspond to the activation patterns observed experimentally for the following cell types: CD4(-)CD8(-), CD4(+)CD8(+), CD4(+) naive, Th1, Th2, Th17, Treg, CD8(+) naive, and CTL. Furthermore, the model is able to describe the differentiation process from the precursor CD4(-)CD8(-) to any of the effector types due to a specific series of extracellular signals.
关于T淋巴细胞的分化，尤其是关于改变其分化过程的体外实验性处理，存在大量的分子信息。这些公开可用的信息被用于推断控制T淋巴细胞分化为CD4(+)和CD8(+)细胞的调控网络。在此，我们展示了一个由50个节点和97个调控相互作用组成的网络，它代表了在调节T细胞分化的分子和分子复合物之间建立的主要信号传导回路。该网络被转化为一组耦合常微分方程形式的连续动力系统，并对其动力学行为进行了研究。借助数值方法，为该动力系统找到了九个定点吸引子。这些吸引子对应于以下细胞类型在实验中观察到的激活模式：CD4(-)CD8(-)、CD4(+)CD8(+)、CD4(+)幼稚型、Th1、Th2、Th17、Treg、CD8(+)幼稚型和CTL。此外，由于特定系列的细胞外信号，该模型能够描述从前体CD4(-)CD8(-)到任何一种效应器类型的分化过程。
