Blumenthal David B, Lucchetta Marta, Kleist Linda, Fekete Sándor P, List Markus, Schaefer Martin H
Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Elife. 2024 Dec 11;13:e99951. doi: 10.7554/eLife.99951.
Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.
蛋白质-蛋白质相互作用（PPI）网络中的度分布被认为遵循幂律（PL）。然而，技术和研究偏差会影响检测PPI的实验程序。例如，与癌症相关的蛋白质受到了不成比例的关注。此外，大规模实验中的诱饵蛋白往往有许多假阳性相互作用伙伴。通过研究数千个来源可控的PPI网络的度分布，我们探讨了仅由这些偏差能否解释观察到的PPI网络中的PL分布这一问题。我们的发现得到了数学模型和广泛模拟的支持，并表明研究偏差和技术偏差足以产生观察到的PL分布。因此，从观察到的PPI网络中的PL分布推导关于真实生物相互作用组拓扑结构的假设是有问题的。我们的研究对在网络生物学中将生物网络的PL属性用作建模假设或质量标准提出了质疑。
