肿瘤微环境数字建模的计算生物学方法进展

Tumor progression arises from tightly coupled cellular, vascular, metabolic, immune, and biomechanical processes within the tumor microenvironment (TME), forming a heterogeneous multiscale system that cannot be fully characterized through experimental observation alone. Computational modeling provides a quantitative framework for integrating these mechanisms, but the diversity of available methods complicates model selection, parameterization, validation, and clinical translation. This review presents a problem-driven synthesis of computational approaches for TME modeling. Continuous, discrete, and discrete–continuous hybrid paradigms are compared in terms of biological resolution, data requirements, interpretability, and computational cost, and are linked to five mechanistic domains: tumor-cell dynamics, vascular transport, extracellular matrix and mechanics, metabolism and hypoxia, and immune interactions. Model credibility is examined through parameter identifiability, uncertainty quantification, numerical validity, biological fidelity, and clinical relevance, with emphasis on interpreting performance according to intended use and validation context. Machine learning is analyzed by function within mechanistic–ML integration: surrogate models accelerate forward simulation, inverse methods estimate latent parameters and states, patient-specific workflows combine multimodal clinical data with mechanistic models, and physics-informed neural networks embed governing equations into learning. We further discuss longitudinal personalization, physics-aware multiscale integration, clinically deployable digital twins, external generalization, and synthetic-data-to-clinical transfer. A translation-readiness pathway is proposed from mechanistic hypothesis generation to adaptive digital-twin decision support. Credible TME modeling should prioritize the minimum sufficient model, explicit intended use, transparent uncertainty, and evidence matched to the biological or clinical question.