Principles of Computation by Competitive Protein Dimerization Networks

Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) can perform diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough (≥8 proteins), can perform nearly all (~90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing.诸多生物信号通路均采用可通过多种组合方式发生竞争性二聚化的蛋白质。此类二聚化网络可执行生化计算：单体（monomer）的浓度作为输入信号，决定了二聚体（dimer）的浓度（即输出信号）。尽管这类网络广泛存在，但学界对二聚化网络所能实现的输入-输出计算范围（即其“表达能力（expressivity）”），以及该范围如何随网络规模与连接模式改变仍知之甚少。本研究采用系统性计算方法，证实即便仅包含3至6个单体的小型二聚化网络，也可实现多样化的多输入计算任务。此外，二聚化网络具备极强通用性：当其蛋白质组分以不同水平表达时（例如在不同细胞类型中），可执行不同的计算任务。值得注意的是，对于带有随机相互作用亲和力的单个网络而言，当其规模足够大（包含≥8种蛋白质）时，仅通过调控单体的表达水平，即可完成近90%的潜在单输入网络计算任务。由此可见，即便是简单的竞争性二聚化过程，也可为多输入、细胞类型特异性的信号处理提供一套高效强大的架构。

搜集汇总

背景与挑战

背景概述

该数据集研究了竞争性蛋白质二聚化网络的计算表达能力，通过系统计算模拟发现，即使由3-6个单体组成的小网络也能执行多种多输入计算，且网络大小和连接性影响其计算范围。当网络包含至少8个蛋白质时，通过调节单体表达水平，可执行约90%的潜在一输入计算，从而为多输入、细胞类型特异性信号处理提供了强大的计算架构。