Network Motif Analysis Research Articles

Inter-firm patent litigation networks: a study of network motif analysis

Despite the recent complex intertwining of firms in fierce intellectual property disputes, the formation mechanisms of patent infringement litigation relationships between firms have been little studied from a network science perspective. We construct an inter-firm patent litigation network using longitudinal data on patent infringement litigation and the firms involved in the US, and examine its structural characteristics and the mechanisms of tie formation through network motif analysis, linking it to existing knowledge on negative ties. The results reveal the significant influence of mechanisms such as homophily, popularity, and activity on network formation, with reciprocity emerging as a pivotal factor. The absence of triadic closure is also observed. This distinct structural pattern is consistent across different technological fields and throughout the 20-year period. Furthermore, our analysis delves into the rapid countersuit strategies common within this network and provides valuable insights into patent litigation strategies between firms.

Rail Transit Networks and Network Motifs: A Review and Research Agenda

The railway plays an essential role in urban and intercity transport of goods and people. Intercity and urban rail transit infrastructures contribute to the economic and environmental sustainability of global economies. Those infrastructures can be modeled as complex networks, so that we can evaluate system properties of the network structure. This stream of research has focused on the topological analysis of global network structure, but little research exists that examines how local network structures affect system properties. The local structure of complex networks can be examined with network motif analysis, as those network motifs are the building blocks of networked systems. Nevertheless, there has been scarce attention given to local network properties in rail transit networks. We contribute to covering this gap in the literature with a literature review of motif analysis research and its application to weighted and unweighted rail transit networks, also covering the current state-of-the-art of network motif decomposition and analysis. We demonstrate that network motif analysis is not only applicable, but also beneficial for the design and planning of rail transit networks, enhancing their sustainability by improving efficiency, reducing environmental impact, and optimizing resource allocation. Based on our findings, we propose future research directions that involve applying motif analysis to enhance the sustainability features of both unweighted and weighted rail transit networks.

Are the dynamics of wall turbulence in minimal channels and larger domain channels equivalent? A graph-theoretic approach

This work proposes two algorithmic approaches to extract critical dynamical mechanisms in wall-bounded turbulence with minimum human bias. In both approaches, multiple types of coherent structures are spatiotemporally tracked, resulting in a complex multilayer network. Network motif analysis, i.e., extracting dominant non-random elemental patterns within these networks, is used to identify the most dominant dynamical mechanisms. Both approaches, combined with network motif analysis, are used to answer whether the main dynamical mechanisms of a minimal flow unit (MFU) and a larger unconstrained channel flow, labeled a full channel (FC), at Reτ ≈ 180, are equivalent. The first approach tracks traditional coherent structures defined as low- and high-speed streaks, ejections, and sweeps. It is found that the roll-streak pairing, consistent with the current understanding of self-sustaining processes, is the most significant and simplest dynamical mechanism in both flows. However, the MFU has a timescale for this mechanism that is approximately 2.83 times slower than that of the FC. In the second approach, we use semi-Lagrangian wavepackets and define coherent structures from their energetic streak, roll, and small-scale phase space. This method also shows similar motifs for both the MFU and FC. It indicates that, on average, the most dominant phase-space motifs are similar between the two flows, with the significant events taking place approximately 2.21 times slower in the MFU than in the FC. This value is more consistent with the implied timescale ratio of only the slow speed streaks taking part in the roll-streak pairing extracted using the first multi-type spatiotemporal approach, which is approximately 2.17 slower in the MFU than the FC.

Detecting the horizontal/vertical price relationship patterns in the global oil industry chain through network analysis

The investigation of price relationship patterns within the horizontal regional and vertical industry chain domains of oil market represents a crucial yet under-researched area. This study tackles the inherent complexities in identifying these patterns, arising from three key challenges: the presence of numerous downstream petrochemical products, the interconnectedness of oil product prices across two domains, and the existence of price influence loop. To address these challenges, this study selected 51 oil products from various regions and different segments of oil industry chain, and calculated and tested their price lead-lag relationships. Subsequently, we established and analyzed the price lead-lag relationship networks within two domains. Furthermore, we expanded network topological structure analysis from a single domain to encompass both domains. Finally, employing the network motif analysis method, we successfully detected the price relationship patterns, taking into consideration the intricate nature of price influence loops. Our findings illuminate the general characteristics of the oil market within two domains but also shed light on previous ignored price lead-lag relationships. The thorough analysis and conclusions drawn from this study offer detailed policy implications to downstream refineries and policy makers.

Network Motif Detection in the Network of Inflammatory Markers and Depression Symptoms among Patients with Stable Coronary Heart Disease: Insights from the Heart and Soul Study

Background. Network motif analysis is a technique used to explore recurrent and statistically significant subgraphs within a network. Applying a motif analysis to the complex network of inflammation and depression may yield nuanced insight into the specific interaction mechanisms between inflammatory markers and individual depression symptoms, which is our aim. Methods. This cross-sectional study is based on patients with stable coronary heart disease (CHD). A partial correlation network was initially constructed to link inflammatory markers, including C-reactive protein (CRP), Interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), monocyte chemoattractant protein-1 (MCP-1), individual depression symptoms, and covariates. A network-centric approach searched all possible non-isomorphic subgraph patterns of size k = 4 in the network. Results. Although CRP, IL-6, and TNF-α displayed an insignificant association with specific depression symptoms, the motif analysis revealed various subgraph patterns of interactions between depression symptoms associated with MCP-1. Generally, MCP-1 formed a closed loop with psychomotor problems and sleep disturbances, and this configuration was connected in various forms with other symptoms, particularly cognitive (e.g., feelings of worthlessness, concentration difficulty, and suicidal ideation) and neurovegetative/somatic (e.g., appetite changes and fatigue) symptoms. Moreover, MCP-1 was frequently associated with a closed-loop triangle comprising cognitive and neurovegetative/somatic symptoms but not with mood symptoms (e.g., loss of interest and feelings of sadness). Conclusions. The findings provide insight into how MCP-1 may be involved in the pathology of depression among patients with stable CHD in a more precise manner. This study also proposes future directions for research on depression.

The seizure classification of focal epilepsy based on the network motif analysis

Due to the complexity of focal epilepsy and its risk for transiting to the generalized epilepsy, the development of reliable classification methods to accurately predict and classify focal and generalized seizures is critical for the clinical management of patients with epilepsy. In order to holistically understand the seizure propagation behavior of focal epilepsy, we propose a three-node motif reduced network by respectively simplifying the focal region, surrounding healthy region and their critical regions as the single node. Because three-node motif can richly characterize information evolutions, the motif analysis method could comprehensively investigate the seizure behavior of focal epilepsy. Firstly, we define a new seizure propagation marker value to capture the seizure onsets and intensity. Based on the three-node motif analysis, it is shown that the focal seizure and spreading can be categorized as inhibitory seizure, focal seizure, focal-critical seizure and generalized seizures, respectively. The four types of seizures correspond to specific modal types respectively, reflecting the strong correlation between seizure behavior and information flow evolution. In addition, it is found that the intensity difference of outflow and inflow information from the critical node (connection heterogeneity) and the excitability of the critical node significantly affected the distribution and transition of the four seizure types. In particular, the method of local linear stability analysis also verifies the effectiveness of four types of seizures classification. In sum, this paper computationally confirms the complex dynamic behavior of focal seizures, and the study of criticality is helpful to propose novel seizure control strategies.

Spatiotemporal heterogeneity reveals urban-rural differences in post-disaster recovery

A post-disaster recovery process necessitates significant financial and time investment. Previous studies have found the importance of post-disaster spatial recovery heterogeneity, but the recovery heterogeneity has not been extended to the directed recovery relationships despite the significance of sequential recovery plans. Identifying a causal structure between county-level time series data can reveal spatial relationships in the post-disaster recovery process. This study uses a causal discovery method to reveal the spatiotemporal relationships between counties before, during, and after Hurricane Irma in 2017. This study proposes node aggregation methods at different time scales to obtain internally validated causal links. This paper utilizes points of interest data with daily location information from mobile phones and county-level daily nighttime light data. We find intra-regional homogeneity, inter-regional heterogeneity, and a hierarchical structure among urban, suburban, and rural counties based on a network motif analysis. Subsequently, this article suggests county-level post-disaster sequential recovery plans using the causal graph methods. These results help policymakers develop recovery scenarios and estimate the corresponding spatial recovery impacts.

Abnormal and Changing Information Interaction in Adults with Attention-Deficit/Hyperactivity Disorder Based on Network Motifs.

Network motif analysis approaches provide insights into the complexity of the brain's functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients.

Exploring the Project-Based Collaborative Networks between Owners and Contractors in the Construction Industry: Empirical Study in China

In the project-based construction industry, organizations build collaborative relationships through specific projects. The owners and contractors who are the key project stakeholders have gradually formed a complex project-based industry-level collaborative network in many different projects, closely related to knowledge exchange and industry development. Based on the data set of the National Quality Engineering Award (NQEA) projects in China from 2013 to 2021, we empirically analyze the characteristics and evolution of project-based collaborative networks between owners and contractors in the construction industry by using social network analysis (SNA) and network motif analysis (NMA) method. The results show that (1) the owner–contractor collaborative network exhibits small-world network characteristics. The island effect caused by small groups in the network makes the overall connectivity of the network low. During the study period, the collaborative network became more compact. (2) State-owned construction companies, such as China Construction Third Engineering Bureau Corporation Limited, China Construction Eighth Engineering Bureau Corporation Limited, and China Construction Second Engineering Bureau Corporation Limited, with high degree centrality and betweenness centrality, are the core companies in the collaborative network. In China, state-owned construction enterprises are favored by owners and have established collaborative relationships with many owners and contractors. (3) There are two local collaborative patterns in the collaborative network: motif and anti-motif. Motifs include some triangle-based tight collaborative patterns, while anti-motifs involve some loose binary collaborative patterns. The results help understand the structure and evolution of the industry-level collaborative relationship network between owners and contractors and can provide references for owners and contractors to develop relationship cultivation strategies more effectively.

Creating adaptive social-ecological fit: The role of regional actors in the governance of Sea-level rise adaptation in San Francisco bay

Sea level rise is one of the most pressing climate adaptation issues around the world. Often, coastal communities are interdependent in their exposure to sea level rise – if one builds a seawall, it will push water to another – and would benefit from a coordinated adaptive response. The literature on social-ecological systems (SES) calls for actors placed at higher levels of governance (e.g. regional government in a metropolitan area) to improve coordination between local managers by serving as brokers. However, we lack empirical insight on how higher-level actors might improve coordination in practice, and theoretical development on the implications of their intermediation. To address these gaps, we study the case of adaptation to sea level rise in the San Francisco Bay Area. We build a social-ecological network of social actors and shoreline segments using original survey data and simulated scenarios of tidal and traffic interdependencies between shoreline segments. We perform a frequency analysis of network motifs that operationalize social-ecological ‘fit’ in the context of the Bay Area. We find that regional actors and non-governmental organizations increase social-ecological fit by providing intermediation between actors who work on different shoreline segments, whether interdependent or not. This shows that these actors provide adaptive social-ecological fit, future-proofing the Bay Area to current and future climate adaptation challenges.

ChromNetMotif: a Python tool to extract chromatin-sate marked motifs in a chromatin interaction network.

Analysis of network motifs is crucial to studying the robustness, stability, and functions of complex networks. Genome organization can be viewed as a biological network that consists of interactions between different chromatin regions. These interacting regions are also marked by epigenetic or chromatin states which can contribute to the overall organization of the chromatin and proper genome function. Therefore, it is crucial to integrate the chromatin states of the nodes when performing motif analysis in chromatin interaction networks. Even though there has been increasing production of chromatin interaction and genome-wide epigenetic modification data, there is a lack of publicly available tools to extract chromatin state-marked motifs from genome organization data. We develop a Python tool, ChromNetMotif, offering an easy-to-use command line interface to extract chromatin-state-marked motifs from a chromatin interaction network. The tool can extract occurrences, frequencies, and statistical enrichment of the chromatin state-marked motifs. Visualization files are also generated which allow the user to interpret the motifs easily. ChromNetMotif also allows the user to leverage the features of a multicore processor environment to reduce computation time for larger networks. The output files generated can be used to perform further downstream analysis. ChromNetMotif aims to serve as an important tool to comprehend the interplay between epigenetics and genome organization. ChromNetMotif is available at https://github.com/lncRNAAddict/ChromNetworkMotif.

Role of a ZF-HD Transcription Factor in miR157-Mediated Feed-Forward Regulatory Module That Determines Plant Architecture in Arabidopsis.

In plants, vegetative and reproductive development are associated with agronomically important traits that contribute to grain yield and biomass. Zinc finger homeodomain (ZF-HD) transcription factors (TFs) constitute a relatively small gene family that has been studied in several model plants, including Arabidopsis thaliana L. and Oryza sativa L. The ZF-HD family members play important roles in plant growth and development, but their contribution to the regulation of plant architecture remains largely unknown due to their functional redundancy. To understand the gene regulatory network controlled by ZF-HD TFs, we analyzed multiple loss-of-function mutants of ZF-HD TFs in Arabidopsis that exhibited morphological abnormalities in branching and flowering architecture. We found that ZF-HD TFs, especially HB34, negatively regulate the expression of miR157 and positively regulate SQUAMOSA PROMOTER BINDING–LIKE 10 (SPL10), a target of miR157. Genome-wide chromatin immunoprecipitation sequencing (ChIP-Seq) analysis revealed that miR157D and SPL10 are direct targets of HB34, creating a feed-forward loop that constitutes a robust miRNA regulatory module. Network motif analysis contains overrepresented coherent type IV feedforward motifs in the amiR zf-HD and hbq mutant background. This finding indicates that miRNA-mediated ZF-HD feedforward modules modify branching and inflorescence architecture in Arabidopsis. Taken together, these findings reveal a guiding role of ZF-HD TFs in the regulatory network module and demonstrate its role in plant architecture in Arabidopsis.

Evolutionary analysis of the global rare earth trade networks

This study used social network analysis, spatial measurement, and network statistical analysis to study the relationships among countries in the global rare earth trade. Using data on rare earth products from the UN Comtrade database, the global rare earth trade network and its evolving topological characteristics are analyzed for the period 1999–2020. Spatial correlation analysis using the global Moran index showed that countries tended to carry out rare earth trade with neighboring countries, and there were certain spatial aggregations in the trade patterns. A temporal exponential random graph model (TERGM) was used to analyze the influencing factors and evolution of the global rare earth trade, and network motif analysis was used to analyze the influence of network topology on the trading network structure. The results showed that the global rare earth trade model presented the following characteristics: trade from one core country to many other countries, reciprocity between trading countries, and trade around near neighboring rare earth-rich countries. Other factors, such as countries with the same GDP levels and World Trade Organization (WTO) member countries, influenced the global rare earth trade but not the formation of the rare earth trade network. In examining the temporal dependence of rare earth trade networks over a 22-year period, it was found that the linkages of rare earth trade networks among countries remained relatively stable, pointing to the long-term dependence of countries with scarce rare earth resources on resource-rich countries.

Discussion of “Co-citation and Co-authorship Networks of Statisticians”

We thank the authors for their new contribution to a high quality data set and interesting findings from the modeling and analysis of the co-citation and co-authorship networks of statisticians. Leveraging this data set, there are lots of additional questions that might be answered, and analyses done. Network motif analysis is one such. In this discussion, we construct temporal citation networks among statisticians using the publication data provided in the paper, and focus on analyzing the frequency and distribution of temporal motifs in such dynamic networks. This analysis provides initial insights into the temporal patterns of citing behaviors among authors of various statistics journals from 1975 to 2015.

Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward Loops.

The regulatory relationships between genes and proteins in a cell form a gene regulatory network (GRN) that controls the cellular response to changes in the environment. A number of inference methods to reverse engineer the original GRN from large-scale expression data have recently been developed. However, the absence of ground-truth GRNs when evaluating the performance makes realistic simulations of GRNs necessary. One aspect of this is that local network motif analysis of real GRNs indicates that the feed-forward loop (FFL) is significantly enriched. To simulate this properly, we developed a novel motif-based preferential attachment algorithm, FFLatt, which outperformed the popular GeneNetWeaver network generation tool in reproducing the FFL motif occurrence observed in literature-based biological GRNs. It also preserves important topological properties such as scale-free topology, sparsity, and average in/out-degree per node. We conclude that FFLatt is well-suited as a network generation module for a benchmarking framework with the aim to provide fair and robust performance evaluation of GRN inference methods.

Analysis of the Spatio-Temporal Evolution and Factors Influencing the Freight Network in the Middle Reaches of the Yangtze River

The study uses python software to crawl O-D big data on the freight information platform and construct a frequency matrix based on freight connections between cities, then forming a freight network. There are 31 cities in the middle reaches of the Yangtze river that form the subject of the research. The study adopts the methods of the node degree, community analysis, network motif analysis, and multielement regression analysis to assess the differences of the spatio-temporal evolution and factors influencing the freight network in 2014 and 2018. The following conclusions can be drawn: (1) the freight network has experienced a change in pattern from “island” to “radial,” and the tightness of the freight network is strengthened. (2) The circulation accumulation of elements causes the change of node degree to have a high tendency of agglomeration of capital and central cities. (3) The phenomenon of “enclave freight” and “freight union” exists in the inner-city group, but the “freight alliance” formed by the “enclave” is relatively loose. (4) With the increase in the scale of the freight network, the module characteristics are gradually simplified. (5) Science and technology run through the entire process of the formation and development of the urban freight network.

Colored Network Motif Analysis by Dynamic Programming Approach: An Application in Host Pathogen Interaction Network

Network motifs are subgraphs of a network which are found with significantly higher frequency than that expected in similar random networks. Motifs are small building blocks of a network and they have emerged as a way to uncover topological properties of complex networks. A special yet not much explored type of motif is the 'colored motif' where color (type) of each node, and hence the edges, in the motif is distinguishable from each other. A traditional motif is defined as a recurring structure in a network, whereas colored motif introduces detailed information about the color of the nodes. G-trie is a data structure to efficiently store a given set of subgraphs by exploiting the topological overlaps within them. In this article we have implemented a modified g-trie to store colored subgraphs and developed a method to discover colored motifs. Our method uses an approximate enumeration for counting the subgraphs to reduce the runtime. We have applied our method to find colored motifs of size three in a host pathogen protein-protein interaction network having two types of proteins namely HIV-1 and human proteins, and four types of edges. Here, we have discovered eight motifs, six of which contain both HIV-1 and human proteins, while the remaining two contain only human proteins.

Inter-organisational patent opposition network: how companies form adversarial relationships

Much of the research on networks using patent data focuses on citations and the collaboration networks of inventors, hence regarding patents as a positive sign of invention. However, patenting is, most importantly, a strategic action used by companies to compete with each other. This study sheds light on inter-organisational adversarial relationships in patenting for the first time. We constructed and analysed the network of companies connected via patent opposition relationships that occurred between 1980 and 2018. A majority of the companies are directly or indirectly connected to each other and hence form the largest connected component. We found that, in the network, many companies disapprove patents in various industrial sectors as well as those owned by foreign companies. The network exhibits heavy-tailed, power-law-like degree distribution, and assortative mixing. We further investigated the dynamics of the formation of this network by conducting a temporal network motif analysis, with patent co-ownership among the companies being considered. By regarding opposition as a negative relationship and patent co-ownership as a positive relationship, we analysed where collaboration may occur in the opposition network and how such positive relationships would interact with negative relationships. The results identified the structurally imbalanced triadic motifs and the temporal patterns of the occurrence of triads formed by a mixture of positive and negative relationships. Our findings suggest that the mechanisms of the emergence of the inter-organisational adversarial relationships may differ from those of other types of negative relationships, hence necessitating further research.

The parable of arable land: Characterizing large scale land acquisitions through network analysis.

Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasing oil prices and rise of green capitalism that in turn impacts the demand in biofuel. A visible indicator of these phenomena is the increase, in recent years, of Large Scale Land Acquisitions (LSLAs) by private companies or states. Such land investments often lead to conflicts with local population and have raised issues regarding people’s rights, the role of different production models and land governance. The aim of this work is to show how publicly available data about LSLAs can be modeled into complex network structures, thus showing how the application of advanced network analysis techniques can be used to better understand land trade dynamics. We use data collected by the Land Matrix Initiative on LSLAs to model three land trade networks: a multi-sector network, a network centered on the mining sector and a network centered on the agriculture one. Then we provide an extended analysis of such networks which includes: (i) a structural analysis, (ii) the definition of a score, namely LSLA-score, which allows to rank the countries based on their investing/target role in the land trade network, (iii) an analysis of the land trade context which takes into account the LSLA-score ranking and the correlation between network features and several country development indicators, (iv) an analysis centered on the discover and analysis of network motifs (i.e., recurring patterns in the land trade network), which provides insights into complex and diverse relations between countries. Our analyses showed how the land trade market is massively characterized by a Global North-Global South dynamic, even if the investing power of emerging economies also has a major impact in creating relations between different sub-regions of the world. Moreover, the analyses on the mining and agriculture sectors highlighted how the role of several countries in the trade network may drastically change depending of the investment sector, showing diverse hierarchies between investor, intermediate and target countries.

P0538HYPERIN PREVENTS ISCHEMIC/REPERFUSION AKI VIA STK40

Abstract Background and Aims Ischemia-reperfusion injury (IRI) is the outcome of an inflammatory process and tubular cell death that is triggered by undergoing a transient reduction or cessation of blood flow and following by reperfusion. Unresolved IRI can contribute to chronic kidney disease even death. Our aims is to investigate the protective effect of hyperin on ischemia-reperfusion renal injury (IRI) and its possible mechanism. Method ① The transcriptome chip data of multiple IRI models were selected from the NCBI GEO DateSets database and a number of key proteins that could participate in IRI were screened out (the fold increase was greater than 2 fold and was statistically significant). Network and transcript binding motif analysis was performed to determine the best binding protein. ② C57BL / 6J mice were selected and randomly divided into normal group, sham operation group, IRI group (bilateral renal pedicle clamping for 45min), hyperin + IRI group (50mg / kg.d per day, 7 days before surgery ), DMSO + IRI group (7 days before the operation, the same amount of DMSO was administered to the stomach every day, and the operation was the same as AKI), with 6 rats in each group. Renal tissue and blood were collected 24 hours after operation for testing. ③ In vitro experiments, human proximal tubule epithelial cells (HK-2) were selected and divided into hypoxia 3, 6, 9, 12, 24, 36, and 48h for reoxygenation of 1, 3, and 6h respectively. Relevant indicators for RT-PCR detection were determined Optimal hypoxia time. The drug safe concentration was selected according to 0, 5, 10, 25, 50, 100, 200, 400 μg / ml hyperin pre-treatment for 12 hours, and the CCK8 reagent was added for 2 hours to measure the absorbance at 450 nm. The cells were randomly divided into normal group, hypoxia group, hypoxia + DMSO group, hypoxia + hyperin group, and related indexes were detected by RT-PCR and Western Blot. ④ Obtain the tertiary structure of the protein and the three-dimensional structure of the hyperin molecule from the RCSB Protein Data Bank website and the PubChem compound database, and use molecular docking technology to determine the proteins that can bind to hyperin using autodock software and analyze their binding ability. Results Bioinformatics analysis suggested that STK40 protein is one of the key factors of IRI and may be a target for preventing and treating diseases. In vivo experiments showed that compared with the normal group and the sham operation group, the levels of serum creatinine, blood urea nitrogen, and kim-1 in rats were significantly increased after AKI, and HE staining of pathological sections showed an increase in renal tubular injury scores. Significantly decreased (P<0.05); RT-PCR results showed that kim-1, caspase-3, NF-κB, IL-6, TNF-α increased significantly after AKI, STK40, Bcl2 / BAX decreased, and the above after hyperin The indicators changed in opposite directions (P <0.05). In vitro experiments: The best time for hypoxia is 24h hypoxia + 1h reoxygenation; compared with the control group, the drug concentration is <100 μg / mL and the cell proliferation activity rate is> 90%, so the hyperin concentration was selected as 50 μg / mL (P < 0.05); RT-PCR results showed that Hif1-α, caspase-3, NF-κB, IL-6, TNF-α significantly increased, and STK40, Bcl2 / BAX decreased compared with the normal group. After administration of hyperin, the above indexes changed in opposite directions (P <0.05). Conclusion In this study, using molecular docking technology and constructing IRI mice model, it was confirmed that hyperin can reduce IRI and exert a protective effect on IRI by inhibiting STK40 expression.