AUTHOR=Kemkar Sharvari , Tao Mengdi , Ghosh Alokendra , Stamatakos Georgios , Graf Norbert , Poorey Kunal , Balakrishnan Uma , Trask Nathaniel , Radhakrishnan Ravi 

TITLE=Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

JOURNAL=Frontiers in Physiology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2024.1473125

DOI=10.3389/fphys.2024.1473125

ISSN=1664-042X

ABSTRACT=Cancer's heterogeneity often undermines the efficacy of conventional treatments. Advances in multiomics and sequencing have provided insights, but the complexity of the data requires robust mathematical models for full interpretation. This review highlights recent advancements in computational methodologies for precision oncology, emphasizing the potential of cancer digital twins to enhance patient-specific decision-making. We propose a framework that integrates agent-based modeling with cellular systems biology models, utilizing patient-specific data to predict tissue-level responses. Additionally, we discuss machine learning approaches to build surrogates for these models, facilitating sensitivity analysis, verification, validation, and uncertainty quantification. These advancements are crucial for improving the accuracy and reliability of clinical predictions. Abstract 2 Introduction 2 Cellular systems biology models to encode cell behavior 5 Multi-agent models for simulating tissue-level spatiotemporal dynamics 7 Embedding cellular models for decision-making in a multi-agent modeling framework 8 Differential equation based and boolean approaches for cellular modeling 8 Machine learning approaches to speed up multi-agent simulations 8 Sensitivity analysis and feature importance in the multi-scale hybrid model 9 Surrogate models for sensitivity analysis and feature importance 10 Clinical exploration of feature importance predictions 11 Verification, Validation, and Uncertainty Quantification (VVUQ) of the multi-scale hybridmodeling framework 12 Discussion 14 Building verifiable cancer digital twins for precision medicine 14 Data-driven methods for multi-modal model interpretability and forecasting 17 Current Limitations and Future Perspectives 19 References 20