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MAtrix Résumé de rapport

Project ID: 308223
Financé au titre de: FP7-IDEAS-ERC
Pays: Belgium

Mid-Term Report Summary - MATRIX (In silico and in vitro Models of Angiogenesis: unravelling the role of the extracellular matrix)

Angiogenesis, the formation of new blood vessels from existing blood vessels, is a process that is fundamental to normal tissue growth, wound repair and disease. The control of angiogenesis is of utmost importance for regenerative medicine as well as the treatment of major diseases like cancer. Cells that make up new blood vessels apply mechanical forces to the tissues (the so called extracellular matrix) in which they are growing. These forces are necessary for the cells to move through the extracellular matrix and to organize themselves into a new blood vessel. It remains a challenge to measure these forces and to understand the governing mechanisms and their detailed role in angiogenesis. The ERC project ‘MAtrix’ follows an interdisciplinary approach to address the importance of cell-matrix forces in the formation of new blood vessels, by integrating computational (‘in silico’) modelling with in vitro experiments.
MAtrix has so far resulted in novel in silico models of active cell mechanical behavior, matrix mechanical behavior and their interaction. The models also address the interaction between chemical and mechanical (physical) signals (see also Heck et al., Mathematical Modelling of Natural Phenomena 2015, for a more detailed explanation), in a sense that the activity of specific molecules is coupled to a cell’s potential to apply forces and move forward, and vice versa. We have furthermore contributed to a more accurate measurement of the forces a cell is exerting during movement (see Jorge Peñas et al., PLOS One 2015). Finally, we have established novel in silico models that allow to address the interplay between oxygen (necessary for cell survival), angiogenesis and tissue regeneration. We have applied these models to suggest alternative designs of cellular therapies for regenerative medicine (see e.g. Carlier et al., PLoS Computational Biology 2014).

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