de Boer R J, Perelson A S
Theoretical Division, Los Alamos National Laboratory, NM 87545.
J Theor Biol. 1991 Apr 7;149(3):381-424. doi: 10.1016/s0022-5193(05)80313-3.
The development of the immune repertoire during neonatal life involves a strong selection process among different clones. The immune system is genetically capable of producing a much more diverse set of lymphocyte receptors than are expressed in the actual repertoire. By means of a model we investigate the hypothesis that repertoire selection is carried out during early life by the immune network. We develop a model network in which possibly hundreds of B cell clones proliferate and produce antibodies following stimulation. Stimulation is viewed as occurring through receptor crosslinking and is modeled via a log bell-shaped dose-response function. Through secretion of free antibody B cell clones can stimulate one another if their receptors have complementary shapes. Receptor shapes are modeled as binary strings and complementarity is evaluated by a string matching algorithm. The dynamic behavior of our model is typically oscillatory and for some parameters chaotic. In the case of two complementary B cell clones, the chaotic attractor has a number of features in common with the Lorenz attractor. The networks we model do not have a predetermined size or topology. Rather, we model the bone marrow as a source which generates novel clones. These novel clones can either be incorporated into the network or remain isolated, mimicking the non-network portion of the immune system. Clones are removed from the network if they fail to expand. We investigate the properties of the network as a function of P(match), the probability that two randomly selected immunoglobulins have complementary shapes. As the model networks evolve they develop a number of self-regulatory features. Most importantly, networks attain a specific equilibrium size and generate a characteristic amount of "natural" antibody. Because the network reaches an asymptotic size even though the bone marrow keeps supplying novel clones, clones must compete for presence in the network, i.e. repertoire selection takes place. Networks comprised of cells with multireactive receptors remain small, whereas networks consisting of cells with specific receptors become much larger. We find an inverse relationship between n, the number of clones in a network, and P(match), and a linear relationship between n and M, the rate at which novel clones are produced in the bone marrow. We present a simple phenomenological model for the number of clones in the network that accounts for the inverse relationship between n and P(match), and that can account for the relationship between n and M. Additionally, the phenomenological model suggests that there are two qualitatively different network equilibria.(ABSTRACT TRUNCATED AT 400 WORDS)
新生儿期免疫库的发育涉及不同克隆之间的强烈选择过程。免疫系统在基因上能够产生比实际免疫库中表达的淋巴细胞受体更多样化的集合。通过一个模型，我们研究了免疫网络在生命早期进行免疫库选择的假设。我们构建了一个模型网络，其中可能有数百个B细胞克隆在受到刺激后增殖并产生抗体。刺激被视为通过受体交联发生，并通过对数钟形剂量反应函数进行建模。通过分泌游离抗体，如果B细胞克隆的受体具有互补形状，它们就可以相互刺激。受体形状被建模为二进制字符串，互补性通过字符串匹配算法进行评估。我们模型的动态行为通常是振荡的，对于某些参数是混沌的。在两个互补B细胞克隆的情况下，混沌吸引子具有许多与洛伦兹吸引子相同的特征。我们建模的网络没有预先确定的大小或拓扑结构。相反，我们将骨髓建模为产生新克隆的来源。这些新克隆可以并入网络或保持孤立，模拟免疫系统的非网络部分。如果克隆未能扩增，它们就会从网络中被清除。我们研究了网络的性质作为P（匹配）的函数，P（匹配）是两个随机选择的免疫球蛋白具有互补形状的概率。随着模型网络的演化，它们发展出许多自我调节特征。最重要的是，网络达到特定的平衡大小并产生特征量的“天然”抗体。由于即使骨髓不断供应新克隆，网络也会达到渐近大小，因此克隆必须竞争在网络中的存在，即进行免疫库选择。由具有多反应性受体的细胞组成的网络仍然很小，而由具有特异性受体的细胞组成的网络则变得大得多。我们发现网络中克隆的数量n与P（匹配）之间存在反比关系，并且n与M（骨髓中产生新克隆的速率）之间存在线性关系。我们提出了一个关于网络中克隆数量的简单现象学模型，该模型解释了n与P（匹配）之间的反比关系，并且可以解释n与M之间的关系。此外，现象学模型表明存在两种质上不同的网络平衡。（摘要截断于400字）
