Faculty of Biology and Bernstein Center for Computational Neuroscience, Ludwig-Maximilians-Universität München, Munich, Germany.
Commun Biol. 2024 Jun 3;7(1):678. doi: 10.1038/s42003-024-06342-y.
Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and incompleteness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be determined from the statistical moments of observable network components alone, as long as the functional relevance of the network components lies in their mean values and the mean of each latent variable maps onto a scaled expectation of a binary variable. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the test to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons.
破译大型生物网络的功能组织是当前数学方法面临的一个主要挑战。一种常见的方法是将网络分解为在很大程度上独立的功能模块，但考虑到测量的不确定性和不完整性，从网络活动推断这些模块及其组织是困难的。通常，整体功能组织的某些部分，如中间处理步骤，是潜在的。我们表明，只要网络组件的功能相关性在于它们的平均值，并且每个潜在变量的平均值映射到二进制变量的缩放期望，那么仅从可观察网络组件的统计矩就可以确定隐藏结构。我们推导了一个基于相关性的统计检验来验证生物网络的功能是否允许分层模块化。我们将该检验应用于基因调控网络、锥体神经元的树突和尖峰神经元网络。
