Breckenridge, CO, United States · ISCB
Biological discovery is undergoing a transformation driven by the convergence of advanced imaging technologies, data science, and computational modeling. Modern microcopy and multi-scale imaging techniques now reveal structural and functional insights from molecular to tissue levels. At the same time, artificial intelligence (AI) and physics-based models are providing powerful frameworks for interpreting and predicting biological behavior. Despite these advances, integrating these diverse methods remains a significant challenge. This Keystone Symposia conference on Integrative Structural Biology: Bridging Fields, Techniques, and Scales addresses the critical need to integrate heterogeneous imaging modalities, aided by computational and deep learning-driven approaches, to derive mechanistic and predictive insights from complex biological data. Attendees will have the opportunity to establish new cross-disciplinary collaborations, learn about integration and validation strategies, and collectively discuss new roadmaps and directions for the field. These will enable the establishment of standard data formats and acquisition modalities for large datasets, ready for deep learning approaches. By bringing together imaging scientists, computational biologists, data scientists, and systems biologists, the meeting aims to foster dialogue that defines emerging frontiers in data integration, standardization, accessibility, and visualization. This cross-disciplinary exchange is essential for overcoming current challenges and accelerating advancements. Previous meetings have often centered on individual structural biology techniques but have rarely emphasized interdisciplinary approaches. While these meetings typically acknowledge the importance of diverse methodologies, they neglect to address the practical integration of these approaches. In contrast, this meeting’s carefully curated program prioritizes integrative approaches, ensuring that the meeting moves beyond acknowledgment to actionable convergence.