Kant Shashi, Roy Saheli
Department of Biotechnology, School of Biotechnology and Biosciences, Brainware University, Kolkata, India.
Gautam Buddha Mahila College, Magadh University, Gaya, India.
OMICS. 2025 Dec;29(12):576-587. doi: 10.1177/15578100251392371. Epub 2025 Nov 7.
The increasing accessibility of high-throughput omics technologies has represented a paradigm change in systems biology, facilitating the systematic exploration of biological complexity at genomic, transcriptomic, proteomic, and metabolomic levels. Contemporary systems biology more and more depends on integrative multi-omics strategies to unravel the sophisticated, dynamic networks of cellular function and organismal phenotypes. Such methodologies enable scientists to clarify molecular interactions, decipher disease pathology, identify strong biomarkers, and guide precision medicine and synthetic biology initiatives. Recent technological breakthroughs in computational tools, ranging from early or late data integration, network analysis, and machine learning, have overcome obstacles of high-dimensionality, heterogeneity, and perturbations restricted to specific contexts. In this review, we critically assess the principles, methods, and applications of multi-omics integration, with an emphasis on cancer biology, microbial engineering, and synthetic biology. We showcase case studies in which integrative omics provided actionable findings. Finally, we address current limitations (e.g., data heterogeneity, interpretability) and forthcoming solutions (artificial intelligence, single-cell omics, cloud platforms). By closing the gap between molecular layers, multi-omics integration is moving toward predictive models of biological systems and revolutionary biotechnological applications.
高通量组学技术日益普及，标志着系统生物学发生了范式转变，有助于在基因组、转录组、蛋白质组和代谢组水平上对生物复杂性进行系统探索。当代系统生物学越来越依赖整合多组学策略来揭示细胞功能和生物体表型的复杂动态网络。这些方法使科学家能够阐明分子相互作用、解读疾病病理、识别强大的生物标志物，并指导精准医学和合成生物学计划。计算工具方面的最新技术突破，从早期或晚期数据整合、网络分析到机器学习，克服了高维度、异质性以及特定背景下的干扰等障碍。在本综述中，我们批判性地评估了多组学整合的原理、方法和应用，重点关注癌症生物学、微生物工程和合成生物学。我们展示了整合组学提供可操作发现的案例研究。最后，我们讨论了当前的局限性（如数据异质性、可解释性）和即将出现的解决方案（人工智能、单细胞组学、云平台）。通过弥合分子层面之间的差距，多组学整合正朝着生物系统的预测模型和革命性生物技术应用发展。
