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A Diffusion-Reaction Model for Predicting Enzyme-Mediated Dynamic Hydrogel Stiffening.

Hung-Yi Liu1, Chien-Chi Lin2,3

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This study models enzyme-mediated hydrogel stiffening, crucial for mimicking tumor microenvironments. Diffusion path length and incubation time, not network crosslinking, critically control tyrosinase distribution and gel stiffening.

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Area of Science:

  • Biomaterials Science
  • Tissue Engineering
  • Biomedical Engineering

Background:

  • Dynamic hydrogels with tunable mechanical properties are essential for studying cell fate and recapitulating the tumor microenvironment.
  • Enzyme-mediated stiffening hydrogels offer a method to mimic the temporal stiffening observed in vivo.

Purpose of the Study:

  • To develop and validate a mathematical model for enzyme diffusion and reaction in hydrogels.
  • To identify critical factors influencing enzyme-mediated hydrogel stiffening.
  • To understand the mechanistic basis of dynamic hydrogel stiffening for biomedical applications.

Main Methods:

  • Utilized Fick's second law of diffusion to model enzyme diffusion in poly(ethylene glycol) (PEG)-peptide hydrogels.
  • Employed the Michaelis-Menten model to estimate enzyme-mediated secondary crosslinking.
  • Experimentally validated model predictions using a 1D diffusion setup with 8-arm PEG-norbornene and peptide crosslinkers.

Main Results:

  • Model predictions indicated that increased network crosslinking did not significantly impede enzyme diffusion.
  • Diffusion path length and enzyme incubation time were identified as critical factors for tyrosinase distribution and crosslink formation.
  • Enzyme-stiffened hydrogels demonstrated elastic properties comparable to traditional chemically crosslinked hydrogels.

Conclusions:

  • The study provides a mechanistic understanding of enzyme-mediated hydrogel stiffening.
  • The developed model accurately predicts enzyme diffusion and reaction kinetics within hydrogel networks.
  • This work advances the design of dynamic hydrogels for applications in regenerative medicine and disease modeling.