AI-Enabled Systems Biology for Mapping Metabolic Vulnerabilities in Cancer: From Multi-Omics Integration to Precision Therapeutic Targeting

Cancer metabolism has re-emerged as a central hallmark of tumor initiation, progression, and therapeutic resistance. Advances in high-throughput technologies have enabled the comprehensive profiling of cancer metabolic states across genomic, transcriptomic, proteomic, metabolomic, and epigenomic layers. However, translating these complex, multi-dimensional datasets into actionable biological insight remains a major challenge. AI-enabled systems biology provides a powerful framework to integrate multi-omics data, construct predictive network models, and uncover context-specific metabolic vulnerabilities that are otherwise obscured by pathway-centric analyses.Recent developments in machine learning, deep learning, and network-based modeling have accelerated the identification of metabolic rewiring events, synthetic lethal interactions, and adaptive metabolic programs driving cancer heterogeneity and drug resistance. When coupled with experimental validation and precision medicine strategies, these approaches hold significant promise for guiding rational therapeutic targeting, biomarker discovery, and drug repurposing.This Research Topic aims to unify computational, experimental, and translational efforts employing AI-driven systems biology to unravel the multifaceted metabolic dependencies in cancer. The objective is to accelerate the translation of multi-omics data into actionable strategies that inform precision therapeutic interventions, targeting metabolic vulnerabilities with more predictive, individualized approaches.To gather further insights into the mechanisms and applications of AI-enabled systems biology in cancer metabolism, this Research Topic will focus on studies that integrate multi-omics data, develop computational models, and validate therapeutic hypotheses. We welcome articles addressing, but not limited to, the following themes:• AI-based integration of multi-omics data for cancer metabolism analysis• Systems-level modeling of metabolic networks and flux regulation• Identification of metabolic vulnerabilities and synthetic lethal targets• Single-cell and spatial omics approaches in metabolic reprogramming• AI-guided discovery of metabolic drug targets, biomarkers, and combination therapies• Translational studies linking metabolic modeling to therapeutic responsePlease note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Abdullahi Ibrahim Uba, Yudibeth Sixto-López, Kuldeep Roy Source: https://www.frontiersin.org/research-topics/79841/ai-enabled-systems-biology-for-mapping-metabolic-vulnerabilities-in-cancer-from-multi-omics-integration-to-precision-therapeutic-targeting