Individualisierte Lungentumormodelle als Kombination aus Tissue Engineering und einem in silico Modell

Author(s):  
D Fecher ◽  
A Stratmann ◽  
G Wangorsch ◽  
C Göttlich ◽  
H Walles ◽  
...  
Keyword(s):  
2021 ◽  
Author(s):  
Tudor Vasiliu ◽  
Bogdan Florin Florin Craciun ◽  
Andrei Neamtu ◽  
Lilia Clima ◽  
Dragos Lucian Isac ◽  
...  

The biocompatible hydrophilic polyethylene glycol (PEG) is widely used in biomedical applications, such as drug or gene delivery, tissue engineering or as antifouling in biomedical devices. Experimental studies have shown...


2021 ◽  
Author(s):  
Hygor P. M. Melo ◽  
F. Raquel Maia ◽  
André S. Nunes ◽  
Rui L. Reis ◽  
Joaquim M. Oliveira ◽  
...  

ABSTRACTThe collective dynamics of cells on surfaces and interfaces poses technological and theoretical challenges in the study of morphogenesis, tissue engineering, and cancer. Different mechanisms are at play, including, cell-cell adhesion, cell motility, and proliferation. However, the relative importance of each one is elusive. Here, experiments with a culture of glioblastoma multiforme cells on a substrate are combined with in silico modeling to infer the rate of each mechanism. By parametrizing these rates, the time-dependence of the spatial correlation observed experimentally is reproduced. The obtained results suggest a reduction in cell-cell adhesion with the density of cells. The reason for such reduction and possible implications for the collective dynamics of cancer cells are discussed.


2019 ◽  
Vol 9 (18) ◽  
pp. 3674 ◽  
Author(s):  
Jose A. Sanz-Herrera ◽  
Esther Reina-Romo

Bone tissue engineering is currently a mature methodology from a research perspective. Moreover, modeling and simulation of involved processes and phenomena in BTE have been proved in a number of papers to be an excellent assessment tool in the stages of design and proof of concept through in-vivo or in-vitro experimentation. In this paper, a review of the most relevant contributions in modeling and simulation, in silico, in BTE applications is conducted. The most popular in silico simulations in BTE are classified into: (i) Mechanics modeling and scaffold design, (ii) transport and flow modeling, and (iii) modeling of physical phenomena. The paper is restricted to the review of the numerical implementation and simulation of continuum theories applied to different processes in BTE, such that molecular dynamics or discrete approaches are out of the scope of the paper. Two main conclusions are drawn at the end of the paper: First, the great potential and advantages that in silico simulation offers in BTE, and second, the need for interdisciplinary collaboration to further validate numerical models developed in BTE.


2005 ◽  
Vol 11 (3-4) ◽  
pp. 341-356 ◽  
Author(s):  
John L. Semple ◽  
Nicholis Woolridge ◽  
Charles J. Lumsden

2019 ◽  
Vol 99 (3) ◽  
pp. 429-443
Author(s):  
Pillai M. Mamatha ◽  
Janarthanan Gopinathan ◽  
Venugopal Elakkiya ◽  
M. Sathishkumar ◽  
S. R. Sundarrajan ◽  
...  

2021 ◽  
Author(s):  
Ricardo M Rosales ◽  
Konstantinos A Mountris ◽  
Manuel Doblare ◽  
Manuel M Mazo ◽  
Esther Pueyo

2008 ◽  
Vol 10 (4) ◽  
pp. 547-554 ◽  
Author(s):  
Fabio Galbusera ◽  
Margherita Cioffi ◽  
Manuela T. Raimondi

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hygor P. M. Melo ◽  
F. Raquel Maia ◽  
André S. Nunes ◽  
Rui L. Reis ◽  
Joaquim M. Oliveira ◽  
...  

AbstractThe collective dynamics of cells on surfaces and interfaces poses technological and theoretical challenges in the study of morphogenesis, tissue engineering, and cancer. Different mechanisms are at play, including, cell–cell adhesion, cell motility, and proliferation. However, the relative importance of each one is elusive. Here, experiments with a culture of glioblastoma multiforme cells on a substrate are combined with in silico modeling to infer the rate of each mechanism. By parametrizing these rates, the time-dependence of the spatial correlation observed experimentally is reproduced. The obtained results suggest a reduction in cell–cell adhesion with the density of cells. The reason for such reduction and possible implications for the collective dynamics of cancer cells are discussed.


2019 ◽  
Vol 25 (11) ◽  
pp. 641-654
Author(s):  
Maziyar Keshavarzian ◽  
Clark A. Meyer ◽  
Heather N. Hayenga

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