Computational Molecular Biology 2024, Vol.14, No.2, 45-53 http://bioscipublisher.com/index.php/cmb 45 Research Report Open Access Modeling Biological Networks: Computational Approaches to Network Dynamics Guoliang Chen, Minghua Li Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: minghua li@cuixi.org Computational Molecular Biology, 2024, Vol.14, No.2 doi: 10.5376/cmb.2024.14.0006 Received: 28 Jan., 2024 Accepted: 11 Mar., 2024 Published: 29 Mar., 2024 Copyright © 2024 Chen and Li, 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: Chen G.L., and Li M.H., 2024, Modeling biological networks: computational approaches to network dynamics, Computational Molecular Biology, 14(2): 45-53 (doi: 10.5376/cmb.2024.14.0006) Abstract Biological networks are important tools for understanding the complexity and functionality of biological systems, and their dynamic analysis can reveal the dynamic behavior of biological processes. However, the high complexity and diversity of biological networks pose urgent challenges for research, requiring the development and application of advanced computational methods. This study reviews the different types of biological networks and their functional roles in biology, and explores in detail network dynamics calculation methods including graph theory, agent-based modeling, differential equations, etc. In addition, we also focus on dynamic modeling of gene regulatory networks, protein-protein interaction networks, and metabolic networks, analyzing the applications and limitations of these methods in practical biological systems. In order to provide a comprehensive reference for researchers in the field of biological network dynamics. Keywords Biological networks; Network dynamics; Computational methods; Gene regulatory networks; Protein-protein interaction networks 1 Introduction Biological networks, encompassing gene regulatory networks (GRNs), protein-protein interaction networks, and metabolic pathways, are fundamental to understanding the complex interactions that govern cellular processes. These networks are integral to various biological functions, including cell differentiation, metabolism, and signal transduction (Karlebach and Shamir, 2008; Wang and Gao, 2010). The advent of high-throughput technologies and computational methods has enabled the detailed mapping and analysis of these networks, providing insights into their structure and function (Covert et al., 2004; Glass et al., 2013). The integration of experimental data with computational models has become essential for elucidating the intricate dynamics of biological systems (Mangan et al., 2016; Manipur et al., 2020). Understanding the dynamics of biological networks is crucial for several reasons. Firstly, it allows researchers to predict the behavior of these networks under different conditions, which is vital for identifying the mechanisms underlying diseases caused by dysregulated cellular processes (Liu et al., 2020; Jolly and Roy, 2022). Secondly, dynamic models can facilitate the development of biotechnological applications by providing faster and more cost-effective alternatives to experimental approaches (Liu et al., 2020). Moreover, the study of network dynamics can reveal emergent properties and interactions that are not apparent from static network analyses, thereby offering a more comprehensive understanding of biological systems (Boccaletti et al., 2006; Paulevé et al., 2020). The application of control theory and other mathematical frameworks to these networks has further enhanced our ability to analyze and manipulate their behavior (Jolly and Roy, 2022). This study attempts to emphasize the methods used, the challenges encountered, and the progress made in this field. Specifically studying various modeling techniques, including Boolean modeling, differential equations, and data-driven methods, as well as their applications in understanding gene regulatory networks, metabolic pathways, and other biological systems. We will discuss the integration of high-throughput data and computational models, as well as the impact of these methods on future research and biotechnology innovation. We hope to provide valuable resources for researchers and practitioners interested in dynamic modeling of biological networks.
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