Mechano-biological and bio-mechanical pathways in cutaneous wound healing

Abstract

Skin injuries heal through coordinated action of fibroblast-mediated extracellular matrix (ECM) deposition, ECM remodeling, and wound contraction. Defects involving the dermis result in fibrotic scars featuring increased stiffness and altered collagen content and organization. Although computational models are crucial to unravel the underlying biochemical and biophysical mechanisms, simulations of the evolving wound biomechanics are seldom benchmarked against measurements. Here, we leverage recent quantifications of local tissue stiffness in murine wounds to refine a previously-proposed systems bio-chemo mechanobiological finite-element model. Fibroblasts are considered as the main cell type involved in ECM remodeling and wound contraction. Tissue rebuilding is coordinated by the release and diffusion of a cytokine wave, e.g. TGF-beta, itself developed in response to an earlier inflammatory signal triggered by platelet aggregation. We calibrate a model of the evolving wound biomechanics through a custom-developed hierarchical Bayesian inverse analysis. Further calibration is based on published biochemical and morphological murine wound healing data over a 21-day healing period. The calibrated model recapitulates the temporal evolution of: inflammatory signal, fibroblast infiltration, collagen buildup, and wound contraction. Moreover, it enables in silico hypothesis testing, which we explore by: (i) quantifying the alteration of wound contraction profiles corresponding to the measured variability in local wound stiffness; (ii) proposing alternative constitutive links connecting the dynamics of the biochemical fields to the evolving mechanical properties; (iii) discussing the plausibility of a stretch- vs. stiffness-mediated mechanobiological coupling. Ultimately, our model challenges the current understanding of wound biomechanics and mechanobiology, beside offering a versatile tool to explore and eventually control scar fibrosis after injury.

Impact of Wound Dressing on Mechanotransduction within Tissues of Chronic Wounds

“…Computational models have enhanced the understanding about the interplay of biological, chemical, and physical events during healing of a wound by providing quantities that are otherwise hard to determine [ 31 , 32 , 33 ]. These studies have applied either ordinary [ 32 ] or partial [ 33 ] differential equations (ODEs or PDEs, respectively) or with agent based models or developed a purely mechanical model including tissue growth or coupled basic biochemical fields with tissue nonlinear mechanics [ 31 ]. However, focus of all previous studies have been on modeling the complex tissue repair processes not on how a dressing may play role on tissue remodeling through mechanotransduction.…”

Mechano-biological and bio-mechanical pathways in cutaneous wound healing

“…Aiming to establish a versatile in silico tool to test alternative hypotheses on the biomechanical and mechano-biological pathways involved in wound healing, we then calibrated our systems-mechanobiological FE model [39,42] to recapitulate the temporal evolution of several key biochemical and morphological aspects of murine wound healing over a 21-day period (Fig 5). Altering the tissue biomechanical parameters according to the 95% CI obtained from the Bayesian calibration procedure allowed us to assess their influence on the healing outcome in terms of wound permanent contracture and visible area changes.…”

“…The 2D Lagrangian FE model that we use is publicly available [42] and follows closely our original formulation [39]. Here, we briefly state the main equations and modeling assumptions, both for completeness and to reflect changes from our previous work.…”

“…Specifically, we adopt a gradient-based approach known as Automated Differential Variational Inference (ADVI) [59] and assume that the posterior follows a spherical Gaussian distribution without correlation of parameters, which we estimate by maximizing the ELBO [58]. The corresponding code is publicly available [42].…”

Role of mechanotransduction on decision making for treatment of chronic wounds

“…Computational models will assist in building this understanding. They have enhanced knowledge on the interaction of biological, physical, and chemical processes and occurrences throughout the wound healing process through providing of otherwise hard to determine quantities 56–58 . One study focused on modeling complicated tissue repair using ordinary differential equations with agent‐based models, 57 whereas the other used secondary differential equations 58 .…”