Cohen Alan A, Leblanc Sebastien, Roucou Xavier
Groupe de Recherche PRIMUS, Département de Médecine de Famille et de Médecine d'Urgence, Université de Sherbrooke, Sherbrooke, QC, Canada.
Centre de Recherche, Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, QC, Canada.
Front Physiol. 2021 Feb 10;12:624097. doi: 10.3389/fphys.2021.624097. eCollection 2021.
Physiological and biochemical networks are highly complex, involving thousands of nodes as well as a hierarchical structure. True network structure is also rarely known. This presents major challenges for applying classical network theory to these networks. However, complex systems generally share the property of having a diffuse or distributed signal. Accordingly, we should predict that system state can be robustly estimated with sparse sampling, and with limited knowledge of true network structure. In this review, we summarize recent findings from several methodologies to estimate system state via a limited sample of biomarkers, notably Mahalanobis distance, principal components analysis, and cluster analysis. While statistically simple, these methods allow novel characterizations of system state when applied judiciously. Broadly, system state can often be estimated even from random samples of biomarkers. Furthermore, appropriate methods can detect emergent underlying physiological structure from this sparse data. We propose that approaches such as these are a powerful tool to understand physiology, and could lead to a new understanding and mapping of the functional implications of biological variation.
生理和生化网络高度复杂，包含数千个节点以及层次结构。真正的网络结构也鲜为人知。这给将经典网络理论应用于这些网络带来了重大挑战。然而，复杂系统通常具有扩散或分布式信号的特性。因此，我们可以预测，通过稀疏采样以及对真实网络结构的有限了解，系统状态能够得到可靠估计。在本综述中，我们总结了几种通过生物标志物的有限样本估计系统状态的方法的最新研究结果，特别是马氏距离、主成分分析和聚类分析。虽然这些方法在统计学上较为简单，但明智地应用时能对系统状态进行新颖的表征。总体而言，即使从生物标志物的随机样本中通常也能估计系统状态。此外，适当的方法可以从这些稀疏数据中检测出潜在的生理结构。我们认为，诸如此类的方法是理解生理学的有力工具，可能会带来对生物变异功能影响的新认识和新映射。
