Trabecular bone remodeling in the aging mouse: A micro-multiphysics agent-based in silico model using single-cell mechanomics

Boaretti, Daniele; Marques, F. C.; Ledoux, Charles; Singh, Amit; Kendall, Jack J.; Wehrle, Esther; Kuhn, Gisela; Bansod, Yogesh Deepak; Schulte, Friederike A.; Müller, Ralph

doi:10.3389/fbioe.2023.1091294

Cited by 7 publications

(9 citation statements)

References 79 publications

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“…Briefly, the micro-MPA model coupled micro-finite element (micro-FE), micro-finite difference, and agent-based modelling to simulate revascularisation, bone formation, resorption, and tissue differentiation in a mouse femoral osteotomy defect. The micro-FE and micro-finite difference sub-models of the micro-MPA model run on open-source libraries ( Boaretti et al, 2023 ) which are widely adopted and maintained by the high-performance computing community. The model was highly repeatable as all random seeds were fixed, i.e., two identical models running on different cores produced the same outcomes.…”

Section: Methodsmentioning

confidence: 99%

“…The cytokine and oxygen concentrations were simulated using a linear backward time-centred space (BTCS) finite-difference solver. The details of the solver are described in more detail by Tourolle (2019) and Boaretti et al (2023) . The reaction-diffusion-decay (RDD) step of the micro-finite difference model consisted of many sub-steps, which were scaled with the global update interval.…”

Section: Methodsmentioning

confidence: 99%

“…The reaction-diffusion-decay (RDD) step of the micro-finite difference model consisted of many sub-steps, which were scaled with the global update interval. The stability of the RDD step was ensured for each chemical species and verified prior to the start of the study ( Boaretti et al, 2023 ). The simulation domain was discretised at the same resolution as the micro-FE model where each element (voxel) was assigned isotropic diffusivity for each cytokine.…”

Section: Methodsmentioning

confidence: 99%

“…Further, cytokine binding was modelled in a reaction step that captured receptor-ligand kinetics and ligand-ligand, e.g., osteoprotegerin (OPG) and receptor activator of nuclear factor kB ligand (RANKL), interactions. The linear reaction kinetics and implementation thereof were described previously ( Tourolle, 2019 ; Boaretti et al, 2023 ). The finite-difference model also included cytokine decay where each cytokine was assigned an exponential decay constant ( see Supplementary Table S1 ).…”

Section: Methodsmentioning

confidence: 99%

“…This initial implementation was mainly concerned with development of the callus as a result of osteoblast polarisation. Adaptions of this framework successfully simulated homeostatic bone remodelling in ageing mouse populations ( Boaretti et al, 2023 ) and human biopsies ( Tourolle et al, 2021 ). By focusing on bone regeneration, we adapted the framework to include revascularisation and the remodelling of the fracture callus via resorption and de novo tissue formation.…”

Section: Introductionmentioning

confidence: 99%

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An in silico micro-multiphysics agent-based approach for simulating bone regeneration in a mouse femur defect model

Kendall,

Ledoux,

Marques

et al. 2023

Front. Bioeng. Biotechnol.

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Bone defects represent a challenging clinical problem as they can lead to non-union. In silico models are well suited to study bone regeneration under varying conditions by linking both cellular and systems scales. This paper presents an in silico micro-multiphysics agent-based (micro-MPA) model for bone regeneration following an osteotomy. The model includes vasculature, bone, and immune cells, as well as their interaction with the local environment. The model was calibrated by time-lapsed micro-computed tomography data of femoral osteotomies in C57Bl/6J mice, and the differences between predicted bone volume fractions and the longitudinal in vivo measurements were quantitatively evaluated using root mean square error (RMSE). The model performed well in simulating bone regeneration across the osteotomy gap, with no difference (5.5% RMSE, p = 0.68) between the in silico and in vivo groups for the 5-week healing period – from the inflammatory phase to the remodelling phase – in the volume spanning the osteotomy gap. Overall, the proposed micro-MPA model was able to simulate the influence of the local mechanical environment on bone regeneration, and both this environment and cytokine concentrations were found to be key factors in promoting bone regeneration. Further, the validated model matched clinical observations that larger gap sizes correlate with worse healing outcomes and ultimately simulated non-union. This model could help design and guide future experimental studies in bone repair, by identifying which are the most critical in vivo experiments to perform.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An in silico micro-multiphysics agent-based approach for simulating bone regeneration in a mouse femur defect model

Kendall,

Ledoux,

Marques

et al. 2023

Front. Bioeng. Biotechnol.

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AOP Report: Development of an adverse outcome pathway for deposition of energy leading to bone loss

Sandhu,

Keyworth,

Karimi‐Jashni

et al. 2024

Environ and Mol Mutagen

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Bone loss, commonly seen in osteoporosis, is a condition that entails a progressive decline of bone mineral density and microarchitecture, often seen in post‐menopausal women. Bone loss has also been widely reported in astronauts exposed to a plethora of stressors and in patients with osteoporosis following radiotherapy for cancer. Studies on mechanisms are well documented but the causal connectivity of events to bone loss development remains incompletely understood. Herein, the adverse outcome pathway (AOP) framework was used to organize data and develop a qualitative AOP beginning from deposition of energy (the molecular initiating event) to bone loss (the adverse outcome). This qualitative AOP was developed in collaboration with bone loss research experts to aggregate relevant findings, supporting ongoing efforts to understand and mitigate human system risks associated with radiation exposures. A literature review was conducted to compile and evaluate the state of knowledge based on the modified Bradford Hill criteria. Following review of 2029 studies, an empirically supported AOP was developed, showing the progression to bone loss through many factors affecting the activities of bone‐forming osteoblasts and bone‐resorbing osteoclasts. The structural, functional, and quantitative basis of each proposed relationship was defined, for inference of causal changes between key events. Current knowledge and its gaps relating to dose‐, time‐ and incidence‐concordance across the key events were identified, as well as modulating factors that influence linkages. The new priorities for research informed by the AOP highlight areas for improvement to enable development of a quantitative AOP used to support risk assessment strategies for space travel or cancer radiotherapy.

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Mechanostat parameters estimated from time-lapsedin vivomicro-computed tomography data of mechanically driven bone adaptation are logarithmically dependent on loading frequency

Marques

Boaretti

Walle

et al. 2023

Preprint

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Mechanical loading is a key factor governing bone remodeling and adaptation. Both preclinical and clinical studies have demonstrated its effects on bone tissue, which were also notably predicted in the mechanostat theory. Indeed, existing methods to quantify bone mechanoregulation have successfully associated the frequency of remodeling events with local mechanical signals, combining time-lapsed in vivo micro-computed tomography (micro-CT) imaging and micro-finite element (micro-FE) analysis. However, a correlation between the local surface velocity of remodeling events and mechanical signals has not been shown. As many degenerative bone diseases have also been linked to impaired bone remodeling, this relationship could provide an advantage in detecting the effects of such conditions and advance our understanding of the underlying mechanisms. Therefore, in this study, we introduce a novel method to estimate remodeling velocity (RmV) curves from time-lapsed in vivo mouse caudal vertebrae data under static and cyclic mechanical loading. These curves can be fitted with piecewise linear functions as proposed in the mechanostat theory. Accordingly, new remodeling parameters can be derived from such data, including formation saturation levels (FSL), resorption velocity modulus (RVM), and remodeling thresholds (RmT). Our results revealed that the norm of the gradient of strain energy density (gradSED) yielded the highest accuracy to quantify mechanoregulation data using micro-FE analysis with homogeneous material properties, while effective strain was the best predictor for micro-FE analysis with heterogeneous material properties. Furthermore, RmV curves could be accurately described with piecewise linear and hyperbola functions (root mean square error below 0.2 um/day for weekly analysis) and several remodeling parameters determined from these curves followed a logarithmic relationship with loading frequency, especially FSL and RmT values for both weekly and four-weekly analysis. Crucially, RmV curves and derived parameters could detect differences in mechanically driven bone adaptation, which complemented previous results showing a logarithmic relationship between loading frequency and net change in bone volume fraction over four weeks. Together, we expect this data to support the calibration of in silico models of bone adaptation and the characterization of the effects of mechanical loading and pharmaceutical treatment interventions in vivo.

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Trabecular bone remodeling in the aging mouse: A micro-multiphysics agent-based in silico model using single-cell mechanomics

Cited by 7 publications

References 79 publications

An in silico micro-multiphysics agent-based approach for simulating bone regeneration in a mouse femur defect model

An in silico micro-multiphysics agent-based approach for simulating bone regeneration in a mouse femur defect model

AOP Report: Development of an adverse outcome pathway for deposition of energy leading to bone loss

Mechanostat parameters estimated from time-lapsedin vivomicro-computed tomography data of mechanically driven bone adaptation are logarithmically dependent on loading frequency

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