The evolution of in-silico physiological models of bone remodelling: From cell population models to micro-MPA approaches
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PUBLIC SUMMARY
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In-silico models of bone are crucial for understanding the behaviour of bone under certain conditions.
There are five types of in-silico models: bone cell population, bone tissue dynamics, FE, AI, and micro MPA models.
Micro MPA models can include bone mediators, cells, as well as a response to 3D stress.
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ABSTRACT
Bone remodelling is a mechanically regulated, multiscale process that maintains skeletal integrity and is disrupted in common metabolic bone diseases. Experiments, however, cannot easily resolve the coupled cellular, biochemical, and mechanical feedback that drive long-term outcomes. In this Review, we examine five in-silico modelling classes used to study bone remodelling: bone cell population dynamics models, bone tissue dynamics models, finite element models, AI models, and three-dimensional micro-multiphysics agent-based models. We compare these approaches through biological fidelity, computational tractability, and translational readiness. FEM excels at macro-scale mechanics but depends on explicit material laws. Cell population models remain dominant for long-horizon and pharmacological simulations because they are interpretable and efficient, although their non-spatial structure limits prediction of microarchitecture. Tissue dynamics models capture curvature-controlled growth efficiently but often rely on simplified mechanobiology. Micro-multiphysics agent-based models provide the strongest link between cell behaviour, mechanics, and microarchitecture, but face major challenges in parameter identifiability, scalable computation, and validation. AI models can bypass explicit material laws and accelerate other methods through surrogate modelling, but risk non-physical predictions without appropriate constraints. We conclude that the field’s next step is to develop integrated, testable workflows that improve calibration and validation while making assumptions explicit. In this context, AI is best viewed as an enabling tool for surrogate modelling, inverse problems, and data-assimilation workflows, rather than as a replacement for mechanistic understanding. -
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Altaleb J., Hansen U., Abel R., et al. (2026). The evolution of in-silico physiological models of bone remodelling: From cell population models to micro-MPA approaches. The Innovation Life 4:100247. https://doi.org/10.59717/j.xinn-life.2026.100247
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Altaleb J., Hansen U., Abel R., et al. (2026). The evolution of in-silico physiological models of bone remodelling: From cell population models to micro-MPA approaches. The Innovation Life 4:100247. https://doi.org/10.59717/j.xinn-life.2026.100247
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Content
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Figure 1.
Evolutionary hierarchy of physiological bone remodelling models
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Figure 2.
Visual representation of model depicting the interaction between bone cells
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Figure 3.
Visual representation of the representative volume element
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Figure 4.
Schematic explaining how the Wnt pathway operates
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Figure 5.
Schematic representation of how PK-PD modelling is implemented in cell population models
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Figure 6.
All relevant mathematical models of bone tissue growth in the literature and how they evolve from each other
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Figure 7.
The influence of surface curvature on tissue growth mediated by surface-bound cells
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Figure 8.
Curvature’s effect on cell distribution and tissue development
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Figure 9.
Figure depicting Kameo et al.’s 2020 model2
