Viruses depend on the host cells for replication and manipulate host cellular processes to assemble their components. Therefore, studying viral infections at the cellular level involves exploring interactions between viral and host proteins. These interactions can be structured as a bipartite network, where one set of nodes represents host proteins, another set represents viral proteins, and the edges symbolize the interactions between these two sets of nodes. Network theory offers a conceptual framework for identifying key proteins in the network, often referred to as centrality measures. Additionally, the projection of the bipartite network to one mode provides insights into the interactions between host proteins based on how they interact with the viral proteins. This chapter deals with the analysis of such a bipartite network of virus and host protein interactions by providing stepwise codes in the R programming language. The workflow is easily adaptable to virus–host interaction data derived from diverse experiments or databases.

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Bipartite Graph Analysis of the Virus–Host Protein Interaction Network to Identify Key Proteins Mediating the Infection

  • Surabhi Singh,
  • Shubhada R. Hegde

摘要

Viruses depend on the host cells for replication and manipulate host cellular processes to assemble their components. Therefore, studying viral infections at the cellular level involves exploring interactions between viral and host proteins. These interactions can be structured as a bipartite network, where one set of nodes represents host proteins, another set represents viral proteins, and the edges symbolize the interactions between these two sets of nodes. Network theory offers a conceptual framework for identifying key proteins in the network, often referred to as centrality measures. Additionally, the projection of the bipartite network to one mode provides insights into the interactions between host proteins based on how they interact with the viral proteins. This chapter deals with the analysis of such a bipartite network of virus and host protein interactions by providing stepwise codes in the R programming language. The workflow is easily adaptable to virus–host interaction data derived from diverse experiments or databases.