An RBC-MsUQ Framework for Red Blood Cell Morpho-Mechanics
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909730917580800 |
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| author | Wang, Shuo Ma, Lei Guo, Ling Li, Xuejin Zhou, Tao |
| author_facet | Wang, Shuo Ma, Lei Guo, Ling Li, Xuejin Zhou, Tao |
| contents | Characterizing the morpho-mechanical properties of red blood cells (RBCs) is crucial for understanding microvascular transport mechanisms and cellular pathophysiological processes, yet current computational models are constrained by multi-source uncertainties including cross-platform experimental discrepancies and parameter identification stochasticity. We present RBC-MsUQ, a novel multi-stage uncertainty quantification framework tailored for RBCs. It integrates hierarchical Bayesian inference with diverse experimental datasets, establishing prior distributions for RBC parameters via microscopic simulations and literature-derived data. A dynamic annealing technique defines stress-free baselines, while deep neural network surrogates, optimized through sensitivity analysis, achieve sub-10$^{-2}$ prediction errors for efficient simulation approximation. Its two-stage hierarchical inference architecture constrains geometric and shear modulus parameters using stress-free state and stretching data in Stage I and enables full-parameter identification via membrane fluctuation and relaxation tests in Stage II. Applied to healthy and malaria-infected RBCs, the RBC-MsUQ framework produces statistically robust posterior distributions, revealing increased stiffness and viscosity in pathological cells. Quantitative model-experiment validation demonstrates that RBC-MsUQ effectively mitigates uncertainties through cross-platform data fusion, overcoming the critical limitations of existing computational approaches. The RBC-MsUQ framework thus provides a systematic paradigm for studying RBC properties and advancing cellular mechanics and biomedical engineering. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_06852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An RBC-MsUQ Framework for Red Blood Cell Morpho-Mechanics Wang, Shuo Ma, Lei Guo, Ling Li, Xuejin Zhou, Tao Biological Physics Computational Physics Characterizing the morpho-mechanical properties of red blood cells (RBCs) is crucial for understanding microvascular transport mechanisms and cellular pathophysiological processes, yet current computational models are constrained by multi-source uncertainties including cross-platform experimental discrepancies and parameter identification stochasticity. We present RBC-MsUQ, a novel multi-stage uncertainty quantification framework tailored for RBCs. It integrates hierarchical Bayesian inference with diverse experimental datasets, establishing prior distributions for RBC parameters via microscopic simulations and literature-derived data. A dynamic annealing technique defines stress-free baselines, while deep neural network surrogates, optimized through sensitivity analysis, achieve sub-10$^{-2}$ prediction errors for efficient simulation approximation. Its two-stage hierarchical inference architecture constrains geometric and shear modulus parameters using stress-free state and stretching data in Stage I and enables full-parameter identification via membrane fluctuation and relaxation tests in Stage II. Applied to healthy and malaria-infected RBCs, the RBC-MsUQ framework produces statistically robust posterior distributions, revealing increased stiffness and viscosity in pathological cells. Quantitative model-experiment validation demonstrates that RBC-MsUQ effectively mitigates uncertainties through cross-platform data fusion, overcoming the critical limitations of existing computational approaches. The RBC-MsUQ framework thus provides a systematic paradigm for studying RBC properties and advancing cellular mechanics and biomedical engineering. |
| title | An RBC-MsUQ Framework for Red Blood Cell Morpho-Mechanics |
| topic | Biological Physics Computational Physics |
| url | https://arxiv.org/abs/2508.06852 |