Biological Data

Integrated Computational Frameworks for Multiscale Modeling of Cancer Prevention: A Review

Document Type : Review Article

Authors

1 Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Zanjan, Zanjan, Iran

Abstract

Cancer develops through interconnected molecular, cellular, and tissue-level processes that span multiple spatial and temporal scales. Although multiscale experimental data have expanded opportunities for quantitative analysis, challenges such as limited data availability, biological heterogeneity, and the need for interpretable predictions still restrict progress. A broad range of mathematical and computational approaches has been developed to address these issues. Population-level models based on ordinary differential equations remain central to studies of tumor growth, immune dynamics, and treatment effects, while stochastic versions capture uncertainty in early carcinogenic events and variability in therapeutic outcomes. Spatially resolved methods, including partial differential equations, cellular automata, and multiphase models, incorporate microenvironmental heterogeneity, chemical gradients, and mechanical influences that shape tumor behavior. At the cellular scale, agent-based models represent heterogeneous populations and local interactions, and intracellular network models together with evolutionary game-theoretic approaches clarify regulatory mechanisms and competitive dynamics. Recent efforts increasingly focus on hybrid and multiscale frameworks that integrate these strategies to connect molecular events with emergent tissue-level patterns. This review examines how cancer develops across interconnected molecular, cellular, and tissue-level processes spanning multiple spatial and temporal scales.

Keywords

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Biological Data
Volume 1, Issue 1 - Serial Number 1
June 2026
Pages 20-43
  • Receive Date: 13 March 2026
  • Revise Date: 31 March 2026
  • Accept Date: 17 April 2026
  • First Publish Date: 01 June 2026