International Journal of Aquaculture, 2025, Vol.15, No.4, 184-196 http://www.aquapublisher.com/index.php/ija 194 biological network (Miao et al., 2021). Specifically, transcriptomics can provide a list of differential genes and expression correlations, proteomics can verify the actual abundance and modification status of key regulatory proteins, and epigenomics (methylation group, histone modification group) reveals which gene regulation may be apparently affected. Hormone level data and physiological indicators are also important variables in modeling. Modern systems biology provides a variety of methods to combine different types of data (Tanvir et al., 2023). For example, network inference algorithms in machine learning can be used to integrate transcription factor-DNA binding data, miRNA target gene data with expression data to reconstruct a directed regulatory network, and then superimpose the effects of hormones and environmental factors on the nodes in it. In the study of grouper, there have been attempts to integrate genome and transcriptome data analysis: Hawe's team assembled high-quality reference genomes, combined with different gender and sexual reversal transcriptome data, drew heat maps and pathway maps of grouper gender-related genes, and used known gender pathway genes to annotate and verify the results (Hawe et al., 2019). In the future, kinetic modeling methods can be used: fit differential equation models through time series data, simulate the changes in key genes and hormone concentrations over time, so as to infer the regulatory relationship and intensity between each component. This strategy based on multiomics and computational models will help us to quantitatively understand grouper gender regulation networks (Valous et al., 2024). 7.3 Identification and functional verification of key regulatory factors of network nodes In the gender regulatory network built, there are often some "key nodes" that play a decisive role in network behavior. Identifying these key regulators and verifying their functions is of great significance for a deep understanding of the gender-determining mechanisms. Dmrt1, Foxl2, Cyp19a1a, Amh, etc. are obviously the central nodes of grouper gender network, and their state transitions basically determine the fate of gonadal tissue (Zhang et al., 2017). Therefore, functional verification work for these genes is also underway. For example, knocking out or mutating these genes through gene editing technology and observing the impact on the gender phenotype is the most direct verification method. In addition, transgene overexpression or gene therapy can also be used, such as overexpressing dmrt1 or amh in female fish, to simulate its overactivation to see if it is sufficient to induce sexual reversal (Li et al., 2021). Through the results of these functional experiments, we can determine the causal relationships and necessary conditions in the network model, and then optimize the network structure. In addition to the genetic level, hormone nodes in the regulatory network can also perform functional verification. For example, the administration of hormone receptor antagonists is observed to block the expected gender switch response; or to verify whether the axis is necessary for gender switch through hypothalamic-pituitary damage/transplantation experiments. Further, cross-species verification can also be tried: expressing the important gender genes of grouper in pattern fish to detect whether they can interfere with the gender development of pattern fish, thereby providing evidence for the function of grouper genes from the side. It should be emphasized that there are multiple redundancy and compensation mechanisms in the gender regulation network. Acknowledgements During this study, we sincerely thank my colleagues for their literature and the two review experts for their suggestions on the revisions. Conflict of Interest Disclosure The authors confirm that the study was conducted without any commercial or financial relationships and could be interpreted as a potential conflict of interest. References Augstenová B., and Ma W.J., 2025, Decoding Dmrt1: insights into vertebrate sex determination and gonadal sex differentiation, Journal of Evolutionary Biology, 2025: voaf031. https://doi.org/10.1093/jeb/voaf031 Dong J.J., Li J., Hu J., Sun C.F., Tian Y.Y., Li W.H., Yan N.N., Sun C.X., Sheng X.H., Yang S., Shi Q., and Ye X., 2020, Comparative genomics studies on the dmrt gene family in fish, Frontiers in Genetics, 11 563947. https://doi.org/10.3389/fgene.2020.563947
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