Computational Molecular Biology 2024, Vol.14, No.4, 134-144 http://bioscipublisher.com/index.php/cmb 141 7.2 Scalability and computational limitations Scalability and computational efficiency are critical concerns in the simulation of large-scale biological models. Whole-cell simulations, for instance, involve extensive biochemical reaction networks that are computationally expensive to simulate, particularly when using detailed stochastic methods (Yeom et al., 2021). High-performance computing (HPC) platforms and parallel computing techniques have been employed to mitigate these limitations. For example, the use of a parallel implementation of the stochastic simulation algorithm (SSA) has been shown to accelerate the simulation of whole-cell models (Yeom et al., 2021). Moreover, modular approaches that divide the cell into subunits and simulate them in parallel can significantly reduce computational time, as demonstrated in the simulation of Escherichia coli (Das and Mitra, 2021). Despite these advancements, the need for more efficient algorithms and computational frameworks remains, especially as models become more complex and detailed (Stumpf, 2021). 7.3 Future trends in systems biology modeling The future of systems biology modeling is likely to be shaped by several emerging trends. One significant trend is the increasing use of hybrid systems modeling, which combines continuous and discrete dynamics to better capture the complexity of biological systems (Dobbe and Tomlin, 2015). This approach allows for the simulation of phenomena such as gene switching and mutations, which are not adequately represented by traditional continuous models. Another promising direction is the application of data-driven modeling techniques, such as neural networks, to learn and predict the dynamics of biological systems from experimental data (Legaard et al., 2021). These techniques can complement traditional mechanistic models, providing a more flexible and scalable approach to modeling complex biological processes. Additionally, the continued development of high-throughput technologies and the accumulation of large-scale biological data will drive the need for more sophisticated and integrative modeling approaches (Ji et al., 2017). The integration of these trends will likely lead to more accurate and comprehensive models, facilitating new discoveries and applications in biotechnology, energy and personalized medicine (Hernandez et al., 2020; Meyer and Saez-Rodriguez, 2021; Lin, 2024). 8 Concluding Remarks Dynamic modeling in systems biology has made significant progress, particularly in the development of whole-cell models (WCMs). These models integrate vast knowledge of cellular processes, revealing the complex interactions between mechanisms within cells. Research on WCMs has emphasized the importance of data quality for model parameterization and validation, highlighting the necessity of high-quality data to improve the accuracy of model predictions. Moreover, modular modeling approaches, such as bond graphs, have facilitated the construction of large-scale dynamic models by ensuring consistency with physical conservation laws, enhancing model reusability and scalability. Simplified assumptions continue to be key in ensuring that models remain representative and operational in complex biological systems. The future of systems biology and whole-cell simulations holds great promise. High-performance computing platforms will provide the necessary power to simulate complex biosystems, speeding up simulations and improving analytical precision. The development of new standards and simulation tools will enhance the reproducibility of models, fostering scientific discoveries. Integrating molecular dynamics simulations with whole-cell models will offer deeper insights into cellular processes at the atomic level, narrowing the gap between computational predictions and experimental observations. Crowdsourced initiatives like the DREAM challenges will continue to drive the field forward by providing unbiased standards for evaluating modeling methods. To further advance dynamic modeling, efforts should first focus on improving data quality and availability. High-quality experimental data are essential for accurate model parameterization and validation, and standardizing data collection and sharing processes is crucial. Additionally, scalable and modular modeling frameworks should be developed, as modular approaches simplify the construction of large-scale models while maintaining physical consistency. Improving computational tools and resources is also essential; investments in high-performance
RkJQdWJsaXNoZXIy MjQ4ODYzNA==