Computational Molecular Biology 2024, Vol.14, No.4, 134-144 http://bioscipublisher.com/index.php/cmb 138 regulatory networks. By specifying rules for interactions, these models can simulate the system's behavior and predict the outcomes of perturbations. This approach has been successfully applied to various biological systems, providing valuable insights into their regulatory mechanisms and potential therapeutic targets. In summary, dynamic modeling approaches, including differential equation models, stochastic models, and rule-based modeling, offer powerful tools for understanding the complex behavior of biological systems. Each approach has its strengths and limitations, and their combined use can provide a more comprehensive understanding of the underlying biological processes. 4 Whole-Cell Simulations: Moving Toward Comprehensive Models 4.1 Challenges in whole-cell modeling Whole-cell modeling represents a significant challenge in computational systems biology due to the complexity and scale of the task. One of the primary obstacles is the inference of parameters and the selection among competing models, which requires reliable construction methods and efficient computational techniques (Stumpf et al., 2021). The development and curation of these models are labor-intensive, and only a few comprehensive models have been developed to date (Georgouli et al., 2023). Additionally, the integration of diverse intracellular pathways through various computational methods, such as stochastic simulation, is time-consuming and computationally expensive (Yeom et al., 2021). The need for high-performance computing platforms to run these models efficiently is another critical challenge (Georgouli et al., 2023). 4.2 Integrating omics data The integration of omics data is crucial for the development of accurate and comprehensive whole-cell models. Omic technologies, such as genomics, transcriptomics, proteomics, and metabolomics, provide a complete readout of the molecular state of a cell at different biological scales. Genome-scale models (GEMs) have been used to interpret and integrate multi-omic data, converting biological reactions into mathematical formulations that can be modeled using optimization principles (Dahal et al., 2020). The integration of omics data with whole-cell models requires appropriate computational methods and data-sharing practices to ensure the accuracy and completeness of the models. Recent advancements in measurement technology and bioinformatics have facilitated the integration of omics data, but challenges remain in developing scalable and comprehensive models (Goldberg et al., 2017). 4.3 Case studies of whole-cell simulations Several case studies highlight the progress and potential of whole-cell simulations. For instance, a high-performance whole-cell simulation of Escherichia coli (E. coli) was developed using modular cell biology principles and a Brownian dynamics-based parallel simulation framework (Das and Mitra, 2021). This approach involved dividing the bacterium into subcells and utilizing Hamiltonian mechanics-based equations of motion to simulate the system. The simulation demonstrated scalability and efficiency, particularly when tested on high-end CPU-GPU clusters. Another study presented a parallel implementation of the stochastic simulation algorithm (SSA) applied to a whole-cell reaction network, which aimed to speed up the simulation process and accelerate the development of comprehensive models (Yeom et al., 2021). These case studies illustrate the potential of whole-cell simulations to provide valuable insights into cellular processes and highlight the importance of computational efficiency and scalability in developing comprehensive models. 5 Applications of dynamic modeling in systems biology 5.1 Drug discovery and therapeutic target identification Dynamic modeling in systems biology has significantly advanced drug discovery and therapeutic target identification. By integrating high-throughput data and computational models, researchers can identify potential drug targets and understand the mechanisms of drug action. For instance, systems biology approaches have been utilized to predict novel interactions between ligands and targets, facilitating the development of multi-target drugs (Yadav and Tripathi, 2018). Molecular dynamics (MD) simulations provide detailed insights into protein-ligand interactions, which are crucial for understanding the structure-function relationship of targets and guiding the drug discovery process (Liu et al., 2018). Additionally, chemoinformatics combined with systems dynamics simulations has revolutionized drug lead optimization and personalized therapy by integrating molecular and systems-level data (Wang et al., 2016).
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