Feasibility study of fluid-structure-growth modelling in the context of endogenous tissue restoration

Abstract

In situ tissue engineering exploits natural inflammatory responses to turn a synthetic vascular graft into a regenerated, functioning, and living vessel through endogenous tissue restoration. Despite promising preclinical results, challenges remain, especially in high-pressure regimes. To better understand the underlying biological processes and predict long-term outcomes of graft implantation, we propose an in silico approach. We account for the solid mechanics of conduit grafts in high and low pressure regimes, the wall shear stresses, and long-term growth and remodeling through two reduced-order fluid-structure-growth models. The first employs an artificial neural network surrogate model to compute the time-averaged wall shear stress at every growth step, whereas the second evaluates a fully-coupled fluid-structure interaction model every 60 time steps. We thoroughly verify both models and confirm acceptable model-form errors, then calibrate them against data from animal experiments. A validation study comparing in silico predictions with unseen animal data of carotid and pulmonary conduit grafts confirms that the model remains reliable up to at least one month of prediction time, hence in the future enabling in silico optimization of short-term endogenous tissue restoration in tissue engineering vascular grafts.

YOLO-WSR: A wavelet feature and structure guided diffusion model for detection and automated restoration of cultural heritage artifact images
Yi Chen, Xing Wang, Wang Shaohua
PLOS One11 Sep 2026Peer-reviewedCC BY 4.0Full textNeurosciencePLOS
Restoration pathways reshape forest structure and multifunctionality in subtropical plantations and secondary forests
Juyang Wu, Qin Xiang, Xuemei Wang +2
Frontiers in Plant Science21 Oct 2026Peer-reviewedCC BY 4.0Full textEcologyCrossrefPMC
Genetic marker analysis of RAPGEF6 gene and covariance structure modelling for egg
Mohammad Taghi Zarrinnia, Karim Hasanpur, Sadegh Alijani +2
BMC Genomics2 Oct 2026Peer-reviewedCC BY-NC-ND 4.0Full textGenetics & genomicsCrossref
Root Age Structure Governs Long-Term Architecture and Hydraulic Function in Perennial Plants: a Modelling Framework
M. Giraud, L. Fichtl, D. Leitner +3
bioRxiv3 Oct 2026PreprintCC BY-NCFull textPlant biologybioRxiv
Point-Process Modelling of Cell-Type Interaction Structure in CosMx Glioma Data for Hypothesis-Driven Tumor Inference from scRNA-seq
F. P. Carcanholo, M. H. A. Cassiano, E. M. Panepucci +2
bioRxiv2 Oct 2026PreprintCC BY-NCFull textBioinformatics & computational biologybioRxiv

Cite

BibTeX RIS

BibTeX

@misc{maes2026feasibility,
  title = {{Feasibility study of fluid-structure-growth modelling in the context of endogenous tissue restoration}},
  author = {L. Maes and A. Hendrickx and H. Shirazi and D. Ozturk and M. Bastron and T. Turgut and V. Bouwman and N. Götzen and T. Vervenne and M. Rolf and F. Sempertegui and M. Cox and S. Avril and G. A. Holzapfel and B. Meuris and N. Famaey},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.20.760334},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.20.760334v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - Feasibility study of fluid-structure-growth modelling in the context of endogenous tissue restoration
AU  - L. Maes
AU  - A. Hendrickx
AU  - H. Shirazi
AU  - D. Ozturk
AU  - M. Bastron
AU  - T. Turgut
AU  - V. Bouwman
AU  - N. Götzen
AU  - T. Vervenne
AU  - M. Rolf
AU  - F. Sempertegui
AU  - M. Cox
AU  - S. Avril
AU  - G. A. Holzapfel
AU  - B. Meuris
AU  - N. Famaey
PY  - 2026
DA  - 2026/10/20
PB  - bioRxiv
DO  - 10.64898/2026.10.20.760334
UR  - https://www.biorxiv.org/content/10.64898/2026.10.20.760334v1
AB  - In situ tissue engineering exploits natural inflammatory responses to turn a synthetic vascular graft into a regenerated, functioning, and living vessel through endogenous tissue restoration. Despite promising preclinical results, challenges remain, especially in high-pressure regimes. To better understand the underlying biological processes and predict long-term outcomes of graft implantation, we propose an in silico approach. We account for the solid mechanics of conduit grafts in high and low pressure regimes, the wall shear stresses, and long-term growth and remodeling through two reduced-order fluid-structure-growth models. The first employs an artificial neural network surrogate model to compute the time-averaged wall shear stress at every growth step, whereas the second evaluates a fully-coupled fluid-structure interaction model every 60 time steps. We thoroughly verify both models and confirm acceptable model-form errors, then calibrate them against data from animal experiments. A validation study comparing in silico predictions with unseen animal data of carotid and pulmonary conduit grafts confirms that the model remains reliable up to at least one month of prediction time, hence in the future enabling in silico optimization of short-term endogenous tissue restoration in tissue engineering vascular grafts.
ER  -

Abstract

Related

Cite