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Since 5–10% of all bone fractures result in non-healing situations, a thorough understanding of the various bone fracture healing phases is necessary to propose adequate therapeutic strategies. In silico models have greatly contributed to the understanding of the influence of mechanics on tissue formation and resorption during the soft and hard callus phases. However, the late-stage remodeling phase has not been investigated from a mechanobiological viewpoint so far. Here, we propose an in silico multi-tissue evolution model based on mechanical strain accumulation to investigate the mechanobiological regulation of bone remodeling during the late phase of healing. Computer model predictions are compared to histological data of two different pre-clinical studies of bone healing. The model predicted the bone marrow cavity re-opening and the resorption of the external callus. Our results suggest that the local strain accumulation can explain the fracture remodeling process and that this mechanobiological response is conserved among different mammal species. Our study paves the way for further understanding of non-healing situations that could help adapting therapeutic strategies to foster bone healing.

While phylogenetic analysis of transcriptomes of the same tissue is usually congruent with the species tree, the controversy emerges when multiple tissues are included, that is, whether species from the same tissue are clustered together, or different tissues from the same species are clustered together. Recent studies have suggested that phylogenetic network approach may shed some lights on our understanding of multi-tissue transcriptome evolution; yet the underlying evolutionary mechanism remains unclear. In this paper we develop a Brownian-based model of transcriptome evolution under the phylogenetic network that can statistically distinguish between the patterns of species-clustering and tissue-clustering. Our model can be used as a null hypothesis (neutral transcriptome evolution) for testing any correlation in tissue evolution, can be applied to cancer transcriptome evolution to study whether two tumors of an individual appeared independently or via metastasis, and can be useful to detect convergent evolution at the transcriptional level. [ABSTRACT FROM AUTHOR]