Uncovering the Potential Mechanism of Xue Fu Zhu Yu Decoction in the Treatment of Intracerebral Hemorrhage

Background Chinese herbal medicine (CHM) is characterized by “multi- compounds, multi-targets and multi-pathway”, which has advanced benefits for the preventing and treating complex diseases, but still exists unsolved issues, mainly include unclear material basis and underling mechanism of prescription. Integrated pharmacology is a hot cross research area based on system biology, mathematic and poly-pharmacology. It can systematically and comprehensively investigate the therapeutic reaction of compounds or drugs on pathogenic genes network, and is especially suitable for the study of complex CHM systems. Intracerebral Hemorrhage (ICH) is one of the main causes of death among Chinese residents, which is characterized by high mortality and high disability rate. In recent years, the treatment of ICH by CHM has been deeply researched. Xue Fu Zhu Yu Decoction (XFZYD), one of the commonly used prescriptions in treating ICH at clinic level, has not been clear about its mechanism in treating ICH. Methods Here, we established a strategy, which based on compounds-targets, pathogenetic genes, network analysis and node importance calculation. Using this strategy, the core compounds group (CCG) of XFZYD was predicted and validated by in vitro experiments. The molecular mechanism of XFZYD in treating ICH was deduced based on CCG and their targets. Results The results show that the CCG with 43 compounds predicted by this model is highly consistent with the corresponding Compound-Target (C-T) network in terms of gene coverage, enriched pathway coverage and accumulated contribution of key nodes at 89.49%, 88.72% and 90.11%, respectively, which confirmed the reliability and accuracy of the effective compound group optimization and mechanism speculation strategy proposed by us. Conclusions Our strategy of optimizing the effective compound groups and inferring the mechanism provides a strategic reference for explaining the optimization and inferring the molecular mechanism of prescriptions in treating complex diseases of CHM.

Cost-effective Network Disintegration through Targeted Enumeration

We live in a hyperconnected world---connectivity that can sometimes be detrimental. Finding an optimal subset of nodes or links to disintegrate harmful networks is a fundamental problem in network science, with potential applications to anti-terrorism, epidemic control, and many other fields of study. The challenge of the network disintegration problem is to balance the effectiveness and efficiency of strategies. In this paper, we propose a cost-effective targeted enumeration method for network disintegration. The proposed approach includes two stages: searching candidate objects and identifying an optimal solution. In the first stage, we use rank aggregation to generate a comprehensive node importance ranking, upon which we identify a small-scale candidate set of nodes to remove. In the second stage, we use an enumeration method to find an optimal combination among the candidate nodes. Extensive experimental results on synthetic and real-world networks demonstrate that the proposed method achieves a satisfying trade-off between effectiveness and efficiency. The introduced two-stage targeted enumeration framework can also be applied to other computationally intractable combinational optimization problems, from team assembly, via portfolio investment, to drug design.

Overlapping Community Detection Based on Node Importance and Adjacency Information

Detecting the community structure and predicting the change of community structure is an important research topic in social network research. Focusing on the importance of nodes and the importance of their neighbors and the adjacency information, this article proposes a new evaluation method of node importance. The proposed overlapping community detection algorithm (ILE) uses the random walk to select the initial community and adopts the adaptive function to expand the community. It finally optimizes the community to obtain the overlapping community. For the overlapping communities, this article analyzes the evolution of networks at different times according to the stability and differences of social networks. Seven common community evolution events are obtained. The experimental results show that our algorithm is feasible and capable of discovering overlapping communities in complex social network efficiently.

Node Importance Based Protection in Power-Grid Optical Backbone Communication Networks

Power-grid optical backbone communication network is a special communication network serving for power system. With the development of new internet technology, there are more and more services carried by power-grid optical backbone communication networks. It plays an important role in the protection of nodes, especially important nodes which often carry important information of the network, when the network is under heavy traffic load. Hench, to solve this problem, we propose the concept of node importance and design a node importance-based protection algorithm under heavy traffic load scenario in power-grid optical backbone communication networks. Simulation results show that the proposed node importance based protection algorithm can obviously reduce blocking probability of the important nodes and improve the performance of the entire network in terms of blocking probability.

Detecting Critical Functional Ingredients Group and Mechanism of Xuebijing Injection in Treating Sepsis

Sepsis is a systemic inflammatory reaction caused by various infectious or noninfectious factors, which can lead to shock, multiple organ dysfunction syndrome, and death. It is one of the common complications and a main cause of death in critically ill patients. At present, the treatments of sepsis are mainly focused on the controlling of inflammatory response and reduction of various organ function damage, including anti-infection, hormones, mechanical ventilation, nutritional support, and traditional Chinese medicine (TCM). Among them, Xuebijing injection (XBJI) is an important derivative of TCM, which is widely used in clinical research. However, the molecular mechanism of XBJI on sepsis is still not clear. The mechanism of treatment of “bacteria, poison and inflammation” and the effects of multi-ingredient, multi-target, and multi-pathway have still not been clarified. For solving this issue, we designed a new systems pharmacology strategy which combines target genes of XBJI and the pathogenetic genes of sepsis to construct functional response space (FRS). The key response proteins in the FRS were determined by using a novel node importance calculation method and were condensed by a dynamic programming strategy to conduct the critical functional ingredients group (CFIG). The results showed that enriched pathways of key response proteins selected from FRS could cover 95.83% of the enriched pathways of reference targets, which were defined as the intersections of ingredient targets and pathogenetic genes. The targets of the optimized CFIG with 60 ingredients could be enriched into 182 pathways which covered 81.58% of 152 pathways of 1,606 pathogenetic genes. The prediction of CFIG targets showed that the CFIG of XBJI could affect sepsis synergistically through genes such as TAK1, TNF-α, IL-1β, and MEK1 in the pathways of MAPK, NF-κB, PI3K-AKT, Toll-like receptor, and tumor necrosis factor signaling. Finally, the effects of apigenin, baicalein, and luteolin were evaluated by in vitro experiments and were proved to be effective in reducing the production of intracellular reactive oxygen species in lipopolysaccharide-stimulated RAW264.7 cells, significantly. These results indicate that the novel integrative model can promote reliability and accuracy on depicting the CFIGs in XBJI and figure out a methodological coordinate for simplicity, mechanism analysis, and secondary development of formulas in TCM.

Characterizing cycle structure in complex networks

A cycle is the simplest structure that brings redundant paths in network connectivity and feedback effects in network dynamics. An in-depth understanding of which cycles are important and what role they play on network structure and dynamics, however, is still lacking. In this paper, we define the cycle number matrix, a matrix enclosing the information about cycles in a network, and the cycle ratio, an index that quantifies node importance. Experiments on real networks suggest that cycle ratio contains rich information in addition to well-known benchmark indices. For example, node rankings by cycle ratio are largely different from rankings by degree, H-index, and coreness, which are very similar indices. Numerical experiments on identifying vital nodes for network connectivity and synchronization and maximizing the early reach of spreading show that the cycle ratio performs overall better than other benchmarks. Finally, we highlight a significant difference between the distribution of shorter cycles in real and model networks. We believe our in-depth analyses on cycle structure may yield insights, metrics, models, and algorithms for network science.