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To target complex, multi-factorial diseases more effectively, there has been an emerging trend of multi-target drug development based on network biology, as well as an increasing interest in traditional Chinese medicine (TCM) that applies a more holistic treatment to diseases. Thousands of years' clinic practices in TCM have accumulated a considerable number of formulae that exhibit reliable in vivo efficacy and safety. However, the molecular mechanisms responsible for their therapeutic effectiveness are still unclear. The development of network-based systems biology has provided considerable support for the understanding of the holistic, complementary and synergic essence of TCM in the context of molecular networks. This review introduces available sources and methods that could be utilized for the network-based study of TCM pharmacology, proposes a workflow for network-based TCM pharmacology study, and presents two case studies on applying these sources and methods to understand the mode of action of TCM recipes.

Background The traditional Chinese medicine (TCM) formula Qing-Fei-Pai-Du decoction (QFPDD) was the most widely used prescription in China's campaign to contain COVID-19, which has exhibited positive effects. However, the underlying mode of action is largely unknown. Purpose A systems pharmacology strategy was proposed to investigate the mechanisms of QFPDD against COVID-19 from molecule, pathway and network levels. Study design and methods The systems pharmacological approach consisted of text mining, target prediction, data integration, network study, bioinformatics analysis, molecular docking, and pharmacological validation. Especially, we proposed a scoring method to measure the confidence of targets identified by prediction and text mining, while a novel scheme was used to identify important targets from 4 aspects. Results 623 high-confidence targets of QFPDD's 12 active compounds were identified, 88 of which were overlapped with genes affected by SARS-CoV-2 infection. These targets were found to be involved in biological processes related with the development of COVID-19, such as pattern recognition receptor signaling, interleukin signaling, cell growth and death, hemostasis, and injuries of the nervous, sensory, circulatory, and digestive systems. Comprehensive network and pathway analysis were used to identify 55 important targets, which regulated 5 functional modules corresponding to QFPDD's effects in immune regulation, anti-infection, anti-inflammation, and multi-organ protection, respectively. Four compounds (baicalin, glycyrrhizic acid, hesperidin, and hyperoside) and 7 targets (AKT1, TNF-α, IL6, PTGS2, HMOX1, IL10, and TP53) were key molecules related to QFPDD's effects. Molecular docking verified that QFPDD's compounds may bind to 6 host proteins that interact with SARS-CoV-2 proteins, further supported the anti-virus effect of QFPDD. At last, in intro experiments validated QFPDD's important effects, including the inhibition of IL6, CCL2, TNF-α, NF-κB, PTGS1/2, CYP1A1, CYP3A4 activity, the up-regulation of IL10 expression, and repressing platelet aggregation. Conclusion This work illustrated that QFPDD could exhibit immune regulation, anti-infection, anti-inflammation, and multi-organ protection. It may strengthen the understanding of QFPDD and facilitate more application of this formula in the campaign to SARS-CoV-2.

Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the network concepts are of great significance. Benefiting from the advances of network science and high-throughput biomedical technologies, studying the biological systems from network biology has attracted much attention in recent years, and networks have long been central to our understanding of biological systems, in the form of linkage maps among genotypes, phenotypes, and the corresponding environmental factors. In this review, we summarize the recent developments of computational network biology, first introducing various types of biological networks and network structural properties. We then review the network-based approaches, ranging from some network metrics to the complicated machine-learning methods, and emphasize how to use these algorithms to gain new biological insights. Furthermore, we highlight the application in neuroscience, human disease, and drug developments from the perspectives of network science, and we discuss some major challenges and future directions. We hope that this review will draw increasing

Traditional Chinese medicines (TCMs) have important therapeutic value in long-term clinical practice. However, because TCMs contain diverse ingredients and have complex effects on the human body, the molecular mechanisms of TCMs are poorly understood. In this work, we determined the gene expression profiles of cells in response to TCM components to investigate TCM activities at the molecular and cellular levels. MCF7 cells were separately treated with 102 different molecules from TCMs, and their gene expression profiles were compared with the Connectivity Map (CMAP). To demonstrate the reliability and utility of our approach, we used nitidine chloride (NC) from the root of Zanthoxylum nitidum, a topoisomerase I/II inhibitor and α-adrenoreceptor antagonist, as an example to study the molecular function of TCMs using CMAP data as references. We successfully applied this approach to the four ingredients in Danshen and analyzed the synergistic mechanism of TCM components. The results demonstrate that our newly generated TCM data and related methods are valuable in the analysis and discovery of the molecular actions of TCM components. This is the first work to establish gene expression profiles for the study of TCM components and serves as a template for general TCM research.

Background: The exploration of the structural topology and the organizing principles of genomebased large-scale metabolic networks is essential for studying possible relations between structure and functionality of metabolic networks. Topological analysis of graph models has often been applied to study the structural characteristics of complex metabolic networks.

Link prediction aims to uncover missing links or predict the emergence of future relationships from the current network structure. Plenty of algorithms have been developed for link prediction in unweighted networks, but only a few have been extended to weighted networks. In this paper, we present what we call a “reliable-route method” to extend unweighted local similarity indices to weighted ones. Using these indices, we can predict both the existence of links and their weights. Experiments on various real-world networks suggest that our reliable-route weighted resource-allocation index performs noticeably better than others with respect to weight prediction. For existence prediction it is either the highest or very close to the highest. Further analysis shows a strong positive correlation between the clustering coefficient and prediction accuracy. Finally, we apply our method to the prediction of missing protein-protein interactions and their confidence scores from known PPI networks. Once again, our reliable-route method shows the highest accuracy.

Nowadays there is a multitude of measures designed to capture different aspects of network structure. To be able to say if a measured value is expected or not, one needs to compare it with a reference model (null model). One frequently used null model is the ensemble of graphs with the same set of degrees as the original network. Here, we argue that this ensemble can give more information about the original network than effective values of network structural quantities. By mapping out this ensemble in the space of some low-level network structure--in our case, those measured by the assortativity and clustering coefficients--one can, for example, study where in the valid region of the parameter space the observed networks are. Such analysis suggests which quantities (or combination of quantities) are actively optimized during the evolution of the network. We use four very different biological networks to exemplify our method. Among other things, we find that high clustering might be a force in the evolution of protein interaction networks. We also find that all four networks are conspicuously robust to both random errors and targeted attacks.