Computational Molecular Biology 2025, Vol.15, No.6, 282-290 http://bioscipublisher.com/index.php/cmb 284 2.2 Accumulation of secondary metabolites and antioxidants When confronted with salt stress, some responses are rapid and instinctive, such as the activation of the antioxidant system. However, apart from those well-known enzymes (SOD, CAT, POD, APX), there is another category of "marginal roles" that should not be overlooked - such as polyamines, phenolic amides, salicylic acid and serotonin, which are signaling molecules and also have the function of clearing ROS. The accumulation differences of these substances among different varieties often indicate their actual role in salt tolerance regulation. In some salt-tolerant strains, the levels of these metabolites are significantly higher, and it is not difficult to understand what role they play in the defense line against stress (Du et al., 2025). 2.3 Overview of key metabolic pathways involved in stress responses The impact of salt stress is not a disturbance through a single path; rather, it is more like a systematic metabolic "major dispatch". A reduction in chlorophyll content, inhibition of starch synthase expression, and restricted photosynthesis are common phenomena, and energy supply will also be restricted. Meanwhile, the main energy pathways such as glycolysis and the TCA cycle need to adjust their rhythms to meet the survival demands of cells. Rice also activates the synthesis of branched-chain amino acids and sugar alcohol metabolism, which are crucial for alleviating osmotic pressure changes. At the cell membrane stage, lipid metabolism cannot fail either - the change in the ratio of PE to PC is precisely helping cells stabilize their membrane structure. These seemingly independent pathways are actually a kind of coordination mechanism formed by rice under "forced" conditions (Liu et al., 2025; Praphasanobol et al., 2025). 3 Computational Approaches for Metabolic Network Modeling 3.1 Fundamentals of static and dynamic modeling (e.g., GSMM, FBA) Not all metabolic models focus on changes over time. Some, such as Flux equilibrium analysis (FBA), do not deal with the "time" dimension at all - it operates based on steady-state assumptions, does not pursue dynamic simulation, but uses an objective function (often maximizing biomass) to identify possible flux distributions. Relatively speaking, dynamic modeling follows a different path. Methods like set dynamics attempt to incorporate time and regulatory changes. Even if some parameters may not be measurable at all, they can still run and barely provide the behavioral trend of the system. However, before modeling, most people still build a static framework first, such as a genome-scale metabolic model (GSMM). After all, such models can systematically organize metabolic reactions and related genes, laying the foundation for subsequent modeling (Vlassis et al., 2013). 3.2 Network construction and definition of nodes/reactions The construction of a metabolic network may sound like building with blocks, but in fact, it is a process of data blending. Each metabolite in the network is like a node, and each reaction is facilitated by enzymes. However, these reactions are not randomly selected. It depends on which reactions are actually operating under specific conditions. So many times, it is more practical to first pick out a set of core reactions and then gradually build a simplified but reasonable sub-network structure. In order to make these pathways fit the biological system as closely as possible, the modeling process often requires repeated optimization with the aid of algorithms, such as whether the fluxes are consistent and whether they meet the basic metabolic requirements, etc. (Pandey, 2025). 3.3 Visualization and flux estimation tools (e.g., COBRA, CellDesigner) After having the model, without good tools to support it, it's basically impossible to move forward even an inch. The COBRA toolbox is almost the "starting point package" for many people's modeling. It can run FBA, conduct flux analysis, and also support modeling with specific conditions, making it highly versatile. In contrast, CellDesigner does not do much numerical calculation. It is more like a "drawing expert", capable of visualizing complex metabolic pathways and helping you understand reaction structures and molecular interactions at a glance. As for flux estimation, many people will use methods such as Monte Carlo sampling or linear programming. They can outline the flux distribution range under different conditions and also identify the bottleneck points that are "choke points" in the network. Although these tools each have their own strengths, when used together, they can basically cover the entire process from metabolic network modeling to verification (Fallahi et al., 2020).
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