Blum C F, Heramvand N, Khonsari A S, Kollmann M
Institute for Mathematical Modeling of Biological Systems, Heinrich-Heine University of Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany.
Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829, Köln, Germany.
Nat Commun. 2018 Jan 9;9(1):133. doi: 10.1038/s41467-017-02489-x.
Generating a comprehensive map of molecular interactions in living cells is difficult and great efforts are undertaken to infer molecular interactions from large-scale perturbation experiments. Here, we develop the analytical and numerical tools to quantify the fundamental limits for inferring transcriptional networks from gene knockout screens and introduce a network inference method that is unbiased with respect to measurement noise and scalable to large network sizes. We show that network asymmetry, knockout coverage and measurement noise are central determinants that limit prediction accuracy, whereas the knowledge about gene-specific variability among biological replicates can be used to eliminate noise-sensitive nodes and thereby boost the performance of network inference algorithms.
生成活细胞中分子相互作用的全面图谱是困难的，因此人们付出了巨大努力从大规模扰动实验中推断分子相互作用。在这里，我们开发了分析和数值工具来量化从基因敲除筛选中推断转录网络的基本限制，并引入了一种对测量噪声无偏且可扩展到大型网络规模的网络推断方法。我们表明，网络不对称性、敲除覆盖率和测量噪声是限制预测准确性的核心决定因素，而关于生物重复样本中基因特异性变异性的知识可用于消除对噪声敏感的节点，从而提高网络推断算法的性能。
