SynBioBeta 2026: AI-Enabled Multiscale Systems Biology for Precision Medicine and Regenerative Health

This collection is part of a series of SynBioBeta 2026 Research Topics. Please see other Research Topics in the collection here:SynBioBeta 2026: Programmable Molecular BiologySynBioBeta 2026: Synthetic Biology for Space Health, Closed-Loop Life Support, and Off-World BiotechnologyThe field of precision medicine is undergoing a profound transformation as artificial intelligence (AI), systems biology, and molecular biosciences converge to create integrative frameworks that capture and model biological complexity across multiple scales. Traditional biomedical research has often been constrained by the compartmentalization of biological data, where discoveries at the molecular level rarely translate seamlessly into clinical outcomes. Recent advances in machine learning, multiscale modeling, and high-resolution omics technologies are now breaking these barriers. Landmark studies are demonstrating how integrating genomics, transcriptomics, proteomics, and metabolomics within AI-enabled systems can reveal emergent properties of cells, tissues, and organs.However, despite this progress, major knowledge gaps persist. These include challenges in unifying heterogeneous datasets, interpreting foundation models in the context of biology, and translating molecular insights into tangible patient benefits. What remains missing is a systematic approach to integrate mechanistic and predictive models that can guide regenerative and individualized therapeutic strategies.This Research Topic aims to advance the development of AI-driven frameworks that connect molecular biology with systems-level physiology to enable precision and regenerative medicine. The goal is to highlight computational and experimental approaches that bridge molecular mechanisms, disease modeling, and therapeutic design. Key questions include how multimodal data and large-scale foundation models can inform biological understanding, how cell and tissue regeneration can be computationally optimized, and how mechanistic knowledge can be embedded in predictive models to enhance diagnosis, treatment response, and long-term health outcomes. By bringing together systems biology, synthetic biology, and artificial intelligence, this topic seeks to enable a new generation of biology-driven medicine in which predictive models not only describe disease progression but also actively guide precise, durable interventions.To gather further insights in the intersection of artificial intelligence, systems biology, and translational medicine, we welcome articles addressing, but not limited to, the following themes:o Multimodal, multiscale, and foundation models for biological systemso Single-cell and spatial omics integration for personalized and regenerative healtho Discovery of biomarkers and therapeutic targets using AI-augmented workflowso Data-driven design of engineered cell and gene therapieso Computational-experimental frameworks for regenerative and precision medicineo AI-assisted interpretation of molecular mechanisms underlying therapy response and aging Topic editors: Xinye Ni, Ismail Dergaa Source: https://www.frontiersin.org/research-topics/81045/synbiobeta-2026-ai-enabled-multiscale-systems-biology-for-precision-medicine-and-regenerative-health