OMICS: A Journal of Integrative Biology

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.

Artificial intelligence (AI) marks an era in systems science when digital technologies are transforming big data-driven knowledge production and their applications toward public policy and governance including health care innovation, be they in internal medicine, surgery, biotechnology, or public health. The anticipations for an increase in throughput and efficiency of science and medicine are also accompanied by political and moral corollaries of AI. There is a need to explore and better understand the role of AI within the conceptual frames of the information society, knowledge society, and innovation ecosystems, as well as governance guided by critical policy studies. This article reviews and explores the political and normative implications of AI for a systems science audience and in relation to AI’s generative nature, which can redirect human behavior and, to a certain extent, shape societies, not to mention cultures and practices in science and innovation ecosystems in the 21st century.

Microtubule-associated serine/threonine-protein kinase 3 (MAST3) is a member of the MAST kinase family implicated in neuronal and immune pathways and is predicted to associate with cytoskeletal regulation. However, insights into its functional role in cytoskeletal organization remain unexplored. In this study, we performed a large-scale phosphoproteomic analysis of MAST3 using 562 datasets to delineate its functional network. We identified four predominant phosphosites, S134, S146, S792, and S793, based on the frequency of detection and differential regulation, with S134 and S146 localized within the Domain of Unknown Function domain, a noncatalytic region. These phosphosites exhibited distinct coregulatory profiles, suggesting regulation through noncatalytic domains. Coregulated phosphosites were enriched for cytoskeleton-associated functions, including actin filament organization, microtubule organization, and spindle assembly. Additionally, predicted downstream substrates such as KIF15, EPB41L1, CP110, and HNRNPU, and binary interactors including LMNA, CKAP4, and CAMSAP2, further support the involvement of MAST3 in cytoskeletal regulation. The convergence of these cytoskeletal partners across phosphosites, substrates, and interactors suggests that MAST3 may act as a key modulator of cytoskeletal organization through phosphorylation-dependent protein–protein interactions. Notably, frequent phosphorylation of S146 across cancer types points to a potential tumor-specific regulatory role. Together, these findings provide the first systems-level insight into the role of MAST3 in cytoskeletal regulation and disease relevance.

Lung adenocarcinoma (LUAD) remains the most common subtype of lung cancer, characterized by high heterogeneity and poor survival outcomes. Although transcriptomic and metabolomic alterations have been individually studied, integrated multi-omics analyses are needed to uncover the convergent pathways that drive tumor progression. Differentially expressed genes (DEGs) were identified from the GSE229253 transcriptomic dataset comprising LUAD tumor and adjacent normal tissues, while significantly altered metabolites were obtained from the Lung Cancer Metabolome Database. The top 10 DEGs and metabolites were analyzed using the search tool for interacting chemicals (STITCH) to construct gene-metabolite networks, and Integrated Molecular Pathway Level Analysis (IMPaLA) was employed for integrated pathway enrichment to identify overlapping molecular processes. Transcriptomic profiling revealed 973 DEGs (410 upregulated and 563 downregulated), and metabolomic analysis identified significant alterations in metabolites linked to redox balance, amino acid derivatives, and nucleotide metabolism. Integration through STITCH generated a network of 16 nodes and 9 edges, highlighting gene-metabolite associations of probable biological relevance. Joint pathway enrichment analysis using IMPaLA consistently identified glycosylation-related pathways, particularly O-linked glycosylation of mucins, as major axes of convergence between transcriptomic and metabolomic alterations in LUAD (joint p = 0.00129–0.00434). Several genes (B3GNT6, FEZF1-AS1, and LCAL1) and metabolites (isoleucylleucine, leucylleucine, and isoleucylvaline) are probable novel candidates, warranting further investigation. These findings provide systems-level evidence that aberrant glycosylation is likely a central hallmark of LUAD, underscore the potential of glycosylation pathways as biomarkers and therapeutic targets, and demonstrate the utility of cross-omics approaches to unpack the molecular complexity of lung cancer.