Computational Molecular Biology 2024, Vol.14, No.1, 28-35 http://bioscipublisher.com/index.php/cmb 29 1 Overview of Network Biology 1.1 Basic concepts and research scope of network biology Network Biology is an interdisciplinary field dedicated to studying biomolecular networks and their interactions. Rooted in the principles of Systems Biology, Network Biology integrates multi-omics data and utilizes graph theory, statistics, and computational algorithms to deeply analyze the complex relationships between biomolecules. Its research scope is extensive, covering gene regulatory networks, protein interaction networks, metabolic networks, and more. Within gene regulatory networks, Network Biology focuses on the transcriptional regulatory relationships between genes, exploring how they work together to regulate gene expression in organisms. In protein interaction networks, it aims to reveal the interactions between proteins and how these interactions affect cellular functions and life processes (Becerra-Flores and Cardozo, 2020). Network Biology not only focuses on the topological structure and dynamic characteristics of networks but also delves into the functional significance of these characteristics. By constructing and analyzing biomolecular networks, Network Biology can reveal the mechanisms of synergistic action between biomolecules and how these mechanisms affect growth, development, disease occurrence, and other processes in organisms. 1.2 Applications of network biology in protein interaction networks, gene regulatory networks, etc. The application of Network Biology in areas such as protein interaction networks and gene regulatory networks not only deepens our understanding of life phenomena but also provides new strategies and tools for disease treatment and new drug development. In protein interaction networks, Network Biology constructs maps of protein interactions, systematically revealing the physical and functional connections between proteins. Li et al. (2019) used high-throughput technologies and bioinformatics methods to identify and quantify the interactions between proteins, thus understanding their key roles in cellular signal transduction, metabolic regulation, and disease occurrence. These studies have deepened our understanding of protein functional diversity and complexity and have provided potential targets and new approaches for disease treatment. In gene regulatory networks, Network Biology plays an equally important role. Gene regulatory networks are complex systems for gene expression regulation within cells, involving interactions among transcription factors, RNA molecules, and epigenetic modifications. Network Biology can analyze the topological structure of gene regulatory networks, identify key regulatory nodes and pathways, and thereby understand the mechanisms and dynamic changes of gene expression regulation. These studies can reveal the complex relationships between genes and phenotypes, providing strong support for the elucidation of disease mechanisms and the development of precision medicine. 1.3 Unique advantages of network biology in revealing protein functions and disease relationships Network Biology offers a novel perspective and method for understanding the relationships between protein functions and diseases. It can comprehensively analyze protein interaction networks and systematically study the interactions among proteins, revealing their functional localization and mechanisms of action within cells. This global analysis approach overcomes the limitations of traditional biological methods, which can only study single proteins or simple pathways, allowing for a deeper understanding of the complex roles of proteins in the onset and progression of diseases (Liu et al., 2020). Network Biology can integrate multi-omics data, merging information from genomics, transcriptomics, proteomics, and other levels, thereby more comprehensively revealing the relationships between protein functions and diseases. This cross-omics analysis method can uncover potential associations that traditional methods may miss, providing new ideas for disease diagnosis and treatment. Qin et al. (2020) found that Network Biology also has predictive capabilities, able to predict new disease-related proteins and drug targets based on known protein interaction networks and gene regulatory networks. This predictive ability not only helps to identify potential disease risks in advance but also provides new candidate molecules for drug development, accelerating the process of pharmaceutical research.
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