Multiscale Modeling of Bone Healing: Toward a Systems Biology Approach

Cerebral palsy is a neuromusculoskeletal disorder associated with muscle weakness, altered muscle architecture, and progressive musculoskeletal symptoms that worsen with age. Pathological changes at the level of the whole muscle have been shown; however, it is unclear why this progression of muscle impairment occurs at the cellular level. The process of muscle regeneration is complex, and the interactions between cells in the muscle milieu should be considered in the context of cerebral palsy. In this work, we built a coupled mechanobiological model of muscle damage and regeneration to explore the process of muscle regeneration in typical and cerebral palsy conditions, and whether a reduced number of satellite cells in the cerebral palsy muscle environment could cause the muscle regeneration cycle to lead to progressive degeneration of muscle. The coupled model consisted of a finite element model of a muscle fiber bundle undergoing eccentric contraction, and an agent-based model of muscle regeneration incorporating satellite cells, inflammatory cells, muscle fibers, extracellular matrix, fibroblasts, and secreted cytokines. Our coupled model simulated damage from eccentric contraction followed by 28 days of regeneration within the muscle. We simulated cyclic damage and regeneration for both cerebral palsy and typically developing muscle milieus. Here we show the nonlinear effects of altered satellite cell numbers on muscle regeneration, where muscle repair is relatively insensitive to satellite cell concentration above a threshold, but relatively sensitive below that threshold. With the coupled model, we show that the fiber bundle geometry undergoes atrophy and fibrosis with too few satellite cells and excess extracellular matrix, representative of the progression of cerebral palsy in muscle. This work uses in silico modeling to demonstrate how muscle degeneration in cerebral palsy may arise from the process of cellular regeneration and a reduced number of satellite cells.

Section: Introductionmentioning

confidence: 99%

A Coupled Mechanobiological Model of Muscle Regeneration In Cerebral Palsy

Khuu

Fernandez

Handsfield

2021

“…Simulation results led to non-union with bone marrow cavity capping, as observed in experimental studies on non-unions ( Mehta et al, 2011 ; Schlundt et al, 2018 ). This approach has been already used to model impaired healing in silico ( Borgiani, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Mechano-Biological Computer Model of Scaffold-Supported Bone Regeneration: Effect of Bone Graft and Scaffold Structure on Large Bone Defect Tissue Patterning

Perier-Metz

Duda

Checa

2020

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Large segmental bone defects represent a clinical challenge for which current treatment procedures have many drawbacks. 3D-printed scaffolds may help to support healing, but their design process relies mainly on trial and error due to a lack of understanding of which scaffold features support bone regeneration. The aim of this study was to investigate whether existing mechano-biological rules of bone regeneration can also explain scaffold-supported bone defect healing. In addition, we examined the distinct roles of bone grafting and scaffold structure on the regeneration process. To that end, scaffold-surface guided migration and tissue deposition as well as bone graft stimulatory effects were included in an in silico model and predictions were compared to in vivo data. We found graft osteoconductive properties and scaffold-surface guided extracellular matrix deposition to be essential features driving bone defect filling in a 3D-printed honeycomb titanium structure. This knowledge paves the way for the design of more effective 3D scaffold structures and their pre-clinical optimization, prior to their application in scaffold-based bone defect regeneration.

“…There is a wide range of in silico models available in the literature to investigate different aspects of bone regeneration in silico (see Doblaré et al, 2004 ; Giorgi et al, 2016 ; Borgiani et al, 2017 ; Ghiasi et al, 2017 for recent reviews), but most of them focus only on the repair and remodeling phases, thus ignoring the inflammation phase. At the same time, many approaches modeling immune and inflammatory responses in other tissues have been presented in the last two decades (see Section 3.2 ).…”

Section: In Silico Modelingmentioning

confidence: 99%

Towards in silico Models of the Inflammatory Response in Bone Fracture Healing

Lafuente-Gracia

Borgiani

Nasello

et al. 2021

Self Cite

In silico modeling is a powerful strategy to investigate the biological events occurring at tissue, cellular and subcellular level during bone fracture healing. However, most current models do not consider the impact of the inflammatory response on the later stages of bone repair. Indeed, as initiator of the healing process, this early phase can alter the regenerative outcome: if the inflammatory response is too strongly down- or upregulated, the fracture can result in a non-union. This review covers the fundamental information on fracture healing, in silico modeling and experimental validation. It starts with a description of the biology of fracture healing, paying particular attention to the inflammatory phase and its cellular and subcellular components. We then discuss the current state-of-the-art regarding in silico models of the immune response in different tissues as well as the bone regeneration process at the later stages of fracture healing. Combining the aforementioned biological and computational state-of-the-art, continuous, discrete and hybrid modeling technologies are discussed in light of their suitability to capture adequately the multiscale course of the inflammatory phase and its overall role in the healing outcome. Both in the establishment of models as in their validation step, experimental data is required. Hence, this review provides an overview of the different in vitro and in vivo set-ups that can be used to quantify cell- and tissue-scale properties and provide necessary input for model credibility assessment. In conclusion, this review aims to provide hands-on guidance for scientists interested in building in silico models as an additional tool to investigate the critical role of the inflammatory phase in bone regeneration.