Computational Molecular Biology 2025, Vol.15, No.6, 282-290 http://bioscipublisher.com/index.php/cmb 288 When they work together, they not only stabilize the basic functions of cells but also provide energy and material support for salt resistance. The existence of these "alternative plans", in the final analysis, is actually an orderly self-rescue mechanism. It is not a stopgap measure but part of a systematic response. 7 Challenges and Future Perspectives To be honest, it's not that easy to build a reliable rice metabolism model under salt stress. First of all, no matter how beautifully the model framework is set up, as long as the parameters are not up to standard, such as the kinetic values of those enzymes or the post-translational modification information being scattered, the model may get stuck halfway. Either it cannot simulate anything or the results are ridiculously poor. What's more troublesome is that some variables cannot be taken into account at the very beginning, such as fluctuations in salt concentration and in which compartment metabolic reactions occur in the tissue - these may seem like "details", but once added, the complexity of the model rises sharply. Even if you really want to add it, it's hard to have data to support it. Many people have attempted to solve dynamic simulation problems with more complex formulas, but the result is often that as more formulas are added, the uncertainty also soars. To take it a step further, even if a certain team does have a complete set of data, if the format, naming, and annotation standards are inconsistent, they would have to redo everything on a different platform. This is not an isolated case but a long-standing problem that cannot be avoided in the field of modeling at present - models "don't get along", data cannot be used, and results cannot be reproduced. It is precisely for this reason that an increasing number of researchers have begun to call for model development not to rely solely on individual efforts, and it is really time for community-led standard construction to catch up. However, to be fair, although models are difficult to make, they are indeed very useful. Even though it is not yet perfect, genome-scale metabolic network models can already help us identify potential bottleneck links and even lock onto certain metabolic pathways that can regulate salt tolerance. In this way, it is expected that the metabolic impacts brought about by genetic modification can be simulated in the model in advance, and the laboratory can avoid many detours. For breeding, this approach of directly guiding from genotypes to salt-tolerant phenotypes has accelerated the pace of selection and breeding, and also made the goal of precision agriculture no longer so distant. What is even more worth mentioning is that the integration of artificial intelligence and machine learning is changing the game. The past approach that relied on assumptions and explicit modeling is gradually being replaced by AI tools that can "learn" patterns from omics big data. These algorithms not only fill the "loopholes" in model parameters, but sometimes can also discover some metabolic linkage mechanisms that are simply unimaginable in traditional paths. When these methods mature, the predictive power of the metabolic network will be stronger and its adaptability higher, and it can even be directly used to guide the synthetic biological design and metabolic engineering development of rice. This kind of cross-border integration might be the key for the model to truly "come alive" in the next step. Acknowledgments Sincere thanks to the anonymous peer review for their opinions and suggestions. 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 Arense P., Bernal V., Iborra J., and Cánovas M., 2010, Metabolic adaptation of Escherichia coli to long-term exposure to salt stress, Process Biochemistry, 45(9): 1459-1467. https://doi.org/10.1016/j.procbio.2010.05.022 Blazier A., and Papin J., 2012, Integration of expression data in genome-scale metabolic network reconstructions, Frontiers in Physiology, 3: 299. https://doi.org/10.3389/fphys.2012.00299 Che-Othman M., Jacoby R., Millar A., and Taylor N., 2020, Wheat mitochondrial respiration shifts from the tricarboxylic acid cycle to the GABA shunt under salt stress, New Phytologist, 225(3): 1166-1180. https://doi.org/10.1111/nph.15713
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