Advances in computational biology approaches for digital modeling of tumor microenvironment.
- 1Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, 219 Ning Liu Road, Nanjing, Jiangsu Province 210044, China.
- 2Department of Neurosurgery, The First Affiliated Hospital of Xinjiang Medical University, No. 137 Liyushan South Road, Xinshi District, Urumqi, Xinjiang Uygur Autonomous Region 830054, China.
- 3State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, No. 2 Sipailou, Xuanwu District, Nanjing, Jiangsu Province 210096, China.
Abstract
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.
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BibTeX
@article{ming2026advances,
title = {{Advances in computational biology approaches for digital modeling of tumor microenvironment.}},
author = {Wenlong Ming and Huimin Lu and Tianhao Zeng and Kun Luo and Jun Xu and Hongde Liu},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag595},
url = {https://europepmc.org/article/MED/42949536},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Advances in computational biology approaches for digital modeling of tumor microenvironment.
AU - Wenlong Ming
AU - Huimin Lu
AU - Tianhao Zeng
AU - Kun Luo
AU - Jun Xu
AU - Hongde Liu
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag595
UR - https://europepmc.org/article/MED/42949536
AB - 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.
SN - 1467-5463
SN - 1477-4054
ER -