Combined experimental and computational characterization of crosslinked collagen-based hydrogels

Valero, C. ; Amaveda, H. (Universidad de Zaragoza) ; Mora, M. (Universidad de Zaragoza) ; García-Aznar, J.M. (Universidad de Zaragoza)

Resumen: Collagen hydrogels are widely used for in-vitro experiments and tissue engineering applications. Their use has been extended due to their biocompatibility with cells and their capacity to mimic biological tissues; nevertheless their mechanical properties are not always optimal for these purposes. Hydrogels are formed by a network of polymer filaments embedded on an aqueous substrate and their mechanical properties are mainly defined by the filament network architecture and the individual filament properties. To increase properties of native collagen, such as stiffness or strain-stiffening, these networks can be modified by adding crosslinking agents that alter the network architecture, increasing the unions between filaments. In this work, we have investigated the effect of one crosslinking agent, transglutaminase, in collagen hydrogels with varying collagen concentration. We have observed a linear dependency of the gel rigidity on the collagen concentration. Moreover, the addition of transglutaminase has induced an earlier strain-stiffening of the collagen gels. In addition, to better understand the mechanical implications of collagen concentration and crosslinkers inclusion, we have adapted an existing computational model, based on the worm-like chain model (WLC), to reproduce the mechanical behavior of the collagen gels. With this model we can estimate the parameters of the biopolymer networks without more sophisticated techniques, such as image processing or network reconstruction, or, inversely, predict the mechanical properties of a defined collagen network.

Idioma: Inglés

DOI: 10.1371/journal.pone.0195820

Año: 2018

Publicado en: PLoS ONE 13, 4 (2018), e0195820[16 pp]
ISSN: 1932-6203

Factor impacto JCR: 2.776 (2018)

Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 23 / 69 = 0.333 (2018) - Q2 - T2

Factor impacto SCIMAGO: 1.1 - Agricultural and Biological Sciences (miscellaneous) (Q1) - Medicine (miscellaneous) (Q1) - Biochemistry, Genetics and Molecular Biology (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T12
Financiación: info:eu-repo/grantAgreement/ES/DGA/T86
Financiación: info:eu-repo/grantAgreement/EC/FP7/306571/EU/Predictive modelling and simulation in mechano-chemo-biology: a computer multi-approach/INSILICO-CELL
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2015-64221-C2-1-R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/ENE2014-52105-R

Tipo y forma: Article (Published version)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)
Área (Departamento): Área Cienc.Mater. Ingen.Metal. (Dpto. Ciencia Tecnol.Mater.Fl.)