Physics Department, McGill University, Montreal, Quebec, Canada H3A 2T8.
Phys Rev Lett. 2013 May 24;110(21):218102. doi: 10.1103/PhysRevLett.110.218102. Epub 2013 May 21.
Many biological networks have to filter out useful information from a vast excess of spurious interactions. In this Letter, we use computational evolution to predict design features of networks processing ligand categorization. The important problem of early immune response is considered as a case study. Rounds of evolution with different constraints uncover elaborations of the same network motif we name "adaptive sorting." Corresponding network substructures can be identified in current models of immune recognition. Our work draws a deep analogy between immune recognition and biochemical adaptation.
许多生物网络必须从大量虚假交互中筛选出有用信息。在这封信中，我们使用计算进化来预测处理配体分类的网络的设计特征。早期免疫反应的重要问题被视为一个案例研究。在具有不同约束条件的进化回合中，我们发现了一种名为“自适应排序”的相同网络基序的改进。在当前的免疫识别模型中可以识别相应的网络子结构。我们的工作在免疫识别和生化适应之间建立了深刻的类比。
