Computational Molecular Biology 2024, Vol.14, No.4, 134-144 http://bioscipublisher.com/index.php/cmb 142 computing infrastructure and the development of efficient algorithms will accelerate the execution of whole-cell models and enhance their capacity to handle complex systems. To foster reproducibility, new standards and protocols must be established to ensure models can be reliably replicated and validated across studies. Lastly, collaborative and crowdsourced efforts should be encouraged; supporting initiatives like the DREAM challenges will drive innovation and improve the quality of model assessments. Acknowledgments We are deeply grateful for Dr. Li's expertise and patience in this study. we also thank the two anonymous peer reviewers for their careful review and valuable comments on this manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Costello Z., and Martín H.G., 2018, A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data, NPJ Systems Biology and Applications, 4(1): 1-14. https://doi.org/10.1038/s41540-018-0054-3 Dahal S., Yurkovich J.T., Xu H., Palsson B.O., and Yang L., 2020, Synthesizing systems biology knowledge from omics using genome‐scale models, Proteomics, 20(17-18): 1900282. https://doi.org/10.1002/pmic.201900282 Das B., and Mitra P., 2021, High-performance whole-cell simulation exploiting modular cell biology principles, Journal of Chemical Information and Modeling, 61(3): 1481-1492. https://doi.org/10.1021/acs.jcim.0c01282 Dix A., Vlaic S., Vlaic S., Guthke R., and Linde J., 2016, Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers, Clinical Microbiology and Infection, 22(7): 600-606. https://doi.org/10.1016/j.cmi.2016.04.014 Dobbe R., and Tomlin C.J., 2015, Hybrid systems modeling for (cancer) systems biology, BioRxiv, 2015: 035022. https://doi.org/10.1101/035022 Eddy J., Funk C., and Price N., 2015, Fostering synergy between cell biology and systems biology, Trends in Cell Biology, 25(8)p: 440-445. https://doi.org/10.1016/j.tcb.2015.04.005 Georgouli K., Yeom J.S., Blake R.C., and Navid A., 2023, Multi-scale models of whole cells: progress and challenges, Frontiers in Cell and Developmental Biology, 11: 1260507. https://doi.org/10.3389/fcell.2023.1260507 Getz M.C., Nirody J.A., and Rangamani P., 2018, Stability analysis in spatial modeling of cell signaling, Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 10(1): e1395. https://doi.org/10.1002/wsbm.1395 Goldberg A.P., Szigeti B., Chew Y.H., Sekar J.A.P., Roth Y.D., and Karr J.R., 2017, Emerging whole-cell modeling principles and methods, Current Opinion in Biotechnology, 51: 97-102. https://doi.org/10.1016/j.copbio.2017.12.013 Groß A., Kracher B., Kraus J.M., Kühlwein S.D., Pfister A.S., Wiese S., Luckert K., Pötz O., Joos T., Daele D., Raedt L., Kühl M., and Kestler H., 2019, Representing dynamic biological networks with multi-scale probabilistic models, Communications Biology, 2(1): 21. https://doi.org/10.1038/s42003-018-0268-3 Helikar T., Cutucache C.E., Dahlquist L.M., Herek T.A., Larson J.J., and Rogers J.A., 2015, Integrating interactive computational modeling in biology curricula, PLoS Computational Biology, 11(3): e1004131. https://doi.org/10.1371/journal.pcbi.1004131 Hernandez C., Thomas-Chollier M., Naldi A., and Thieffry D., 2020, Computational verification of large logical models—application to the prediction of t cell response to checkpoint inhibitors, Frontiers in Physiology, 11: 558606. https://doi.org/10.3389/fphys.2020.558606 Hou J., Acharya L., Zhu D., and Cheng J., 2016, An overview of bioinformatics methods for modeling biological pathways in yeast, Briefings in Functional Genomics, 15(2): 95-108. https://doi.org/10.1145/3459930.3471161 Ji Z., Yan K., Li W., Hu H., and Zhu X., 2017, Mathematical and computational modeling in complex biological systems, BioMed Research International, 2017(1): 5958321. https://doi.org/10.1155/2017/5958321
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