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However, the physiological impacts of these dietary shifts remain largely obscured when evaluated solely by traditional performance metrics such as Feed Conversion Ratio (FCR) and Specific Growth Rate (SGR). This \u2018Black Box\u2019 approach fails to detect sub-clinical metabolic disorders, gut dysbiosis, and molecular stress responses until phenotypic losses occur. This review provides a comprehensive synthesis of how omics technologies \u2013 nutrigenomics, proteomics, metabolomics, and metagenomics \u2013 are elucidating the molecular mechanisms underlying fish nutrition. We examine the capacity of transcriptomics to identify early markers of soybean meal-induced enteritis and the role of proteomics in assessing muscle quality beyond mere gene expression. Furthermore, we highlight the integration of these layers into a \u2018Systems Biology\u2019 approach, utilizing multi-omics and bioinformatics to unravel the complex diet-microbiota-host axis. Finally, the review discusses the transition towards \u2018Precision Aquafeed.\u2019 It identifies the current challenges in cost, data standardization, and bioinformatics that must be overcome to implement these high-throughput tools in commercial feed formulation.<\/jats:p>","DOI":"10.1007\/s10142-026-01937-6","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T06:52:29Z","timestamp":1781592749000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Omics technologies in aquafeed: unlocking the black box towards systems biology"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5264-7103","authenticated-orcid":false,"given":"Mustafa","family":"\u00d6z","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enes","family":"\u00dcst\u00fcner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"1937_CR1","doi-asserted-by":"publisher","first-page":"967205","DOI":"10.3389\/fmolb.2022.967205","volume":"9","author":"FE Agamah","year":"2022","unstructured":"Agamah F, E, Bayjanov J, R, Niehues A, Njoku K, F, Skelton M, Mazandu G, K, t Hoen P, A (2022) Computational approaches for network-based integrative multi-omics analysis. 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