Computational Molecular Biology 2025, Vol.15, No.6, 282-290 http://bioscipublisher.com/index.php/cmb 285 4 Data Integration and Model Reconstruction 4.1 Strategies for integrating multi-omics data (transcriptomics, metabolomics, proteomics) Take the case of salt stress for example. Looking at just one piece of data is very likely to fall into the trap of one-sided interpretation. For instance, in the transcriptome, it seems that the gene expression has increased, but sometimes the corresponding enzyme shows no significant movement. However, the changes in metabolites provided by the metabolome do not always indicate which pathway has truly been activated. This kind of misalignment is not uncommon. After all, gene expression, protein abundance and metabolic levels do not always align in rhythm. It is precisely for this reason that data from multiple omics must be uploaded simultaneously in order to piece together a reliable metabolic panorama. Methods like Flux Balance analysis (FBA) can be regarded as a common integration approach. Although it relies on a static model, different reactions can be weighted in the model, and the transcriptome can be used to "indirectly" regulate the reaction intensity. As for the metabolome, some people simply use reverse modeling to study the covariance between metabolites and, in turn, infer the strength of reactions and the direction of networks. In this way, it's not just about seeing who appears on the Internet, but also estimating who has actually moved and how much they have moved. Of course, these methods are not perfect. Sometimes, it also depends on the quality of the data. But the benefits are obvious - especially under salt stress, the dynamic adjustment of metabolism is the key point, and the fusion of multi-omics just helps us grasp the rhythm of these changes (Blazier and Papin, 2012; Töpfer et al., 2015). 4.2 Mapping metabolic pathways using public databases (KEGG, MetaCyc) Sometimes, building a model doesn't start from an "open space". Databases like KEGG and MetaCyc are like architectural drawings. They not only provide structural information such as pathways and enzyme functions, but also help us match the annotated genes with the corresponding metabolic reactions one by one. With the help of tools like RAVEN 2.0, these data can be quickly transformed into a preliminary metabolic network sketch. Of course, these paths may not be fully applicable to every species, but they remain the fundamental framework for integrating omics data. For those who want to simulate the metabolic response of rice under salt stress, without this step, it is basically impossible to move forward (Karlsen et al., 2018; Wang et al., 2018). 4.3 Extraction and optimization of salt stress-specific metabolic networks Although genome-wide metabolic models are good, under specific stress conditions, full acceptance seems too "heavy". In fact, what is more crucial is to screen out those reactions that are truly active under salt stress. Algorithms like fastcore are designed for this purpose - they can extract a core sub-network from a large model that only retains high-throughput responses, eliminate edge paths, and reduce the complexity of the model. Then, if the model prediction does not match the experimental observation, the reaction flow can still be adjusted through optimization methods to make the overall performance more in line with the real physiological data. This approach is not omnipotent, but it is a very practical entry point in studying salt stress responses and identifying regulatory nodes (Li et al., 2023). 5 Simulation and Analysis of Salt Stress Scenarios 5.1 Comparative analysis of metabolic flux under normal and stress conditions When rice is exposed to salt stress, the metabolic system's response is not ambiguous (Figure 2). When the simulation results were compared, the differences became apparent - especially in photosynthesis, respiration, and the synthesis of antioxidant substances, the fluxes of several key pathways all changed. In fact, once salt gets involved, the utilization of hexose is easily disturbed, while the process of breathing alone "works overtime" instead, with a significant increase in flow. This kind of phenomenon has long been reflected in the equilibrium analysis combining transcriptome and metabolome data. These changes do not seem accidental and are quite consistent with the physiological phenomena observed in the experiment. Ultimately, behind these adjustments, it might be that rice is redistributing energy and resources to cope with the pressure brought by stress.
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