Computational Molecular Biology 2024, Vol.14, No.4, 134-144 http://bioscipublisher.com/index.php/cmb 134 Research Insight Open Access Dynamic Modeling in Systems Biology: From Pathway Analysis to Whole-Cell Simulations Jiayao Zhou , Shudan Yan Institute of Life Sciences, Jiyang Colloge of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding author: jiayao zhou@jicat.org Computational Molecular Biology, 2024, Vol.14, No.4 doi: 10.5376/cmb.2024.14.0016 Received: 16 May, 2024 Accepted: 22 Jun., 2024 Published: 08 Jul., 2024 Copyright © 2024 Zhou and Yan, 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: Zhou J.Y., and Yan S.D., 2024, Dynamic modeling in systems biology: from pathway analysis to whole-cell simulations, Computational Molecular Biology, 14(4): 134-144 (doi: 10.5376/cmb.2024.14.0016) Abstract Systems biology is an important research field for understanding complex biological systems. By integrating various omics data and computational models, it reveals the interactions and dynamic behaviors of different biomolecules within the organism. Dynamic modeling, as a core tool in systems biology, helps researchers construct multi-scale biological system models through analysis of metabolic pathways, signal transduction pathways, etc., extending from the cellular level to whole cell simulations. This study is based on the latest research progress and explores the application of dynamic modeling in gene regulatory networks, drug discovery, personalized medicine, and synthetic biology, with a particular focus on the challenges and prospects of whole cell simulation. Dynamic modeling helps to enhance the understanding of biological systems and provides new solutions for fields such as personalized therapy and drug development. Future research will focus on how to address the challenges of data integration, model complexity, and computational power to drive further development in systems biology. Keywords Systems biology; Dynamic modeling; Metabolic pathways; Whole-cell simulations; Personalized medicine 1 Introduction Systems biology is an integrative discipline that connects molecular components across different biological scales, such as cells, tissues, and organs, to physiological functions, aiming to uncover the dynamic behavior of complex biological systems. This field combines quantitative reasoning, computational models, and high-throughput experimental techniques to understand the flow of information from genes to biological functions at various levels (Tavassoly et al., 2018). By synthesizing multidimensional data from cells and molecules, systems biology provides a crucial framework for generating hypotheses, guiding experimental design, and predicting mechanisms (Eddy et al., 2015). This approach not only deepens our understanding of biological complexity but also advances quantitative pharmacology and precision medicine (Tavassoly et al., 2018). Dynamic modeling, a core tool in systems biology, enables researchers to simulate and analyze the temporal changes of biological systems. These models often use mathematical techniques such as ordinary differential equations to describe changes in biochemical networks, offering insights into the dynamic behavior of signaling and gene regulatory networks (Linden et al., 2022). Despite challenges with limited and noisy experimental data, dynamic modeling provides valuable perspectives on the multi-scale dynamics of biological systems (Tavassoly et al., 2018; Linden et al., 2022). The use of integrated modeling and time-series simulation allows researchers to go beyond static snapshots and capture the dynamic responses of biological systems under different conditions (Musilová and Sedlář, 2021). These models not only contribute to basic research but also have broad applications in drug discovery, disease treatment, and synthetic biology. This study reviews existing methods of dynamic modeling and explores their applications and challenges in systems biology. We will analyze the advantages and limitations of these methods, and demonstrate the potential of dynamic modeling in interpreting complex biological systems through practical case studies. At the same time, future research directions will be proposed to further improve the predictive ability and application value of dynamic models.
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