Multiscale Mechanistic Modeling is a rapidly growing field within systems biology that leverages computational and mathematical frameworks to dissect complex biological phenomena across spatial and temporal scales. This approach has become indispensable for unraveling the intricate, multi-layered organization of biological systems, from molecular interactions to organ-level dynamics and population-wide effects. Despite remarkable advancements in quantitative modeling and the emergence of hybrid methods, there remains a significant need to bridge the diverse scales characteristic of living systems, ensure the harmonization of heterogeneous data types, and accurately capture the interplay of physical, chemical, and biological mechanisms.

The challenge of integrating data and mechanisms across different organizational levels has prompted ongoing debate on the most effective strategies for multiscale modeling. Several notable studies have shown how agent-based models, hybrid discrete-continuum frameworks, and the integration of mechanistic insights with machine learning or AI approaches can provide predictive power and mechanistic depth. However, the translation of these advances into cohesive, generalizable frameworks remains an active area of research. Open questions persist regarding the scalability, transparency, and reproducibility of these models, especially as the field continues to grapple with issues like data integration, model validation, and the prediction of emergent system-level behavior. Thus, comprehensive reviews are critical to synthesize current findings, highlight transformative technologies, and chart new directions for research.

This Research Topic aims to provide in-depth reviews of computational and mathematical modeling methodologies that span multiple biological scales, emphasizing both foundational concepts and innovative approaches within the context of mechanistic systems biology. By consolidating the state-of-the-art, this collection endeavors to identify gaps in current understanding and foster a unified vision for the future of multiscale modeling.

To gather further insights into the frontiers and challenges of multiscale mechanistic modeling, we welcome review articles that address, but are not limited to, the following themes:

· Agent-based modeling for multiscale biological systems
· Integrative approaches combining mechanistic modeling with machine learning or AI
· Data-driven modeling methodologies interacting with mechanistic frameworks
· Hybrid models that blend discrete and continuum methods
· Approaches integrating biochemical, biomechanical, and bioelectrical mechanisms
· Techniques for merging and harmonizing multi-omics and cross-scale experimental data
· Validation, scalability, and reproducibility in multiscale modeling
· Predictive modeling and simulation of interventions or perturbations across scales

This Research Topic welcomes Review, Systematic Review, and Mini Review articles that explore the current landscape, technological innovations, theoretical advances, and future challenges in multiscale mechanistic modeling for systems biology.

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Reviews in Multiscale Mechanistic Modeling