The authors assembled a comprehensive dermal fibroblast signaling network to bridge cell-scale mechanotransduction with tissue-level growth relevant to skin expansion (TE). Literature curation produced a network composed of 96 nodes and 151 reactions. Network inputs include mechanical stretch and eight biochemical ligands: TGFbeta, PDGF, FGF, IL1, IL6, TNF-alpha, AngII, and ET1. Model outputs of interest focus on extracellular matrix (ECM) enzymes (proMMP1/2/9, MMP1/2/9), ECM proteins (CImRNA, collagen I, fibronectin), and fibroblast functional readouts (alphaSMA, proliferation).
The curated signaling network was implemented as a logic-based system of ordinary differential equations (ODEs). According to the authors, this formulation reproduced 82% of the calibration dataset and agreed with independent validation data. The implementation captures how biochemical inputs and mechanical stretch propagate through the network to regulate ECM production and fibroblast activity, enabling linkage from molecular signaling to tissue-relevant outputs.
Sensitivity analysis of the model revealed a tension-dependent organization of regulatory control. At baseline mechanical tension, model outputs are influenced by many positive regulators (referred to as boosters) and a single dominant inhibitory node, LATS1/2. When tension is increased, regulatory control becomes more consolidated: fewer nodes exert dominant influence and new, tension-specific regulators emerge, including integrin (ITGB1). These results indicate that the network’s control architecture reorganizes as a function of mechanical context, altering which nodes most strongly affect ECM and fibroblast phenotypes.
Multiple signaling axes converge on a limited set of central regulators in the network, producing notable crosstalk. The authors highlight pronounced interaction between TGFbeta signaling and mechanical tension as a primary example. This convergence suggests that distinct upstream stimuli—mechanical stretch and soluble ligands—can produce integrated fibroblast outputs through shared regulatory hubs, which in turn may determine ECM deposition and remodeling outcomes during TE.
To connect cell-scale signaling to macroscopic tissue behavior, the authors linked collagen production outputs from the fibroblast network to a tissue-level growth formulation. This coupling establishes a bidirectional mechanical–biochemical feedback: mechanical stretch alters fibroblast signaling and ECM synthesis, and the resulting ECM accumulation modifies tissue mechanics and growth. The integrated framework thus enables simulation of how cellular responses scale up to affect tissue expansion over time.
When applied to experimental measurements, the coupled multiscale model reproduced tension-induced skin growth measured in a porcine TE model reported by the authors. This agreement indicates that the model’s combination of mechanotransduction signaling and tissue growth rules can capture key features of TE-driven skin growth in an in vivo-like system.
The preprint indicates data and code resources are available; a GitHub repository is listed in the footnotes. The authors declare no competing interests. It is important to note that this report is a preprint and has not undergone peer review. Specific methodological and quantitative details beyond those summarized here are contained in the full manuscript and supplementary materials referenced by the authors; any details not explicitly reported in the abstract or visible sections of the source were not added beyond what the source states.
By providing a calibrated, literature-grounded signaling network coupled to tissue growth, the framework offers a platform to explore targeted interventions in TE. The identification of tension-dependent regulators—such as LATS1/2 at baseline and ITGB1 under high tension—and the central role of TGFbeta cross-talk suggest candidate nodes for experimental perturbation in future studies aimed at modulating ECM deposition or fibroblast activity during expansion.
This work is presented as a preprint on bioRxiv (doi provided by the authors). The study was funded in part by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR074525). The GitHub link for the project appears in the article footnotes for users seeking data and code.