Computational Molecular Biology 2024, Vol.14, No.5, 211-219 http://bioscipublisher.com/index.php/cmb 211 Systematic Review Open Access Emerging Trends in Systems Biology: Multi-Omics Integration and Beyond Ning Wang, Guocheng Zhang, Manman Li Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China Corresponding author: manman.li@hibio.org Computational Molecular Biology, 2024, Vol.14, No.5 doi: 10.5376/cmb.2024.14.0024 Received: 15 Aug., 2024 Accepted: 20 Sep., 2024 Published: 25 Oct., 2024 Copyright © 2024 Wang et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang N., Zhang G.C., and Li M.M., 2024, Emerging trends in systems biology: multi-omics integration and beyond, Computational Molecular Biology, 14(5): 211-219 (doi: 10.5376/cmb.2024.14.0024) Abstract This study analyzes the framework and key technologies of multi-omics integration, including the combination of genomics, transcriptomics, proteomics, metabolomics, and epigenomics. It also discusses the computational tools and data analysis methods used in multi-omics integration, such as network construction, machine learning, and big data visualization, which are essential for processing and interpreting multi-omics data. With the rapid advancement of multi-omics technologies, data integration offers a holistic view of biological systems, enabling a deeper understanding of complex biological processes. Through case studies in fields such as personalized medicine and agriculture, this study demonstrates the practical applications of these integrative approaches, highlighting the importance of multi-omics in advancing personalized medicine, agriculture, and environmental research. Additionally, it aims to address the technical challenges in multi-omics data integration and provide insights into future directions, including real-time integration and the application of artificial intelligence. Keywords Systems biology; Multi-omics integration; Data visualization; Personalized medicine; Artificial intelligence (AI) 1 Introduction Systems biology is a holistic approach to understanding the complex interactions within biological systems. Unlike traditional biological research, which often focuses on individual components, systems biology aims to integrate various biological data to model and understand the dynamic interactions within a system. This approach is crucial for deciphering the intricate networks of genes, proteins, and metabolites that drive cellular functions and organismal phenotypes (MacLeod and Nersessian, 2016; Veenstra, 2020). The field has evolved significantly over the past few decades, driven by advancements in high-throughput technologies and computational methods, enabling researchers to analyze thousands of molecules simultaneously and understand their interdependencies. The advent of multi-omics technologies has revolutionized systems biology by providing comprehensive datasets that encompass various biological layers, including genomics, transcriptomics, proteomics, and metabolomics. These technologies allow for the large-scale quantification of biomolecules, facilitating a deeper understanding of biological processes and interactions (Dhillon et al., 2020). The integration of these diverse datasets, although challenging due to their heterogeneity, has become essential for revealing novel insights into complex biological systems (Huan et al., 2017). Techniques such as network analysis, machine learning, and pathway enrichment are increasingly employed to integrate and interpret multi-omics data, thereby enhancing our understanding of biological functions and disease mechanisms (Pazhamala et al., 2021). This study will explore emerging trends in systems biology, with a particular focus on the integration of multi-omics data. By reviewing recent advancements and methodologies in this field, it aims to highlight the potential of multi-omics integration in providing a holistic view of biological systems. This approach not only enhances our understanding of fundamental biological processes but also holds significant implications for various applications, including disease prediction, biomarker discovery, and crop improvement. The study emphasizes the importance of multi-omics integration in advancing systems biology and its potential to transform both biological research and its applications. 2 Framework and Key Technologies of Multi-Omics Integration 2.1 Integration of genomics and transcriptomics
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