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INSILICO-CELL Report Summary

Project ID: 306571
Funded under: FP7-IDEAS-ERC
Country: Spain

Final Report Summary - INSILICO-CELL (Predictive modelling and simulation in mechano-chemo-biology: a computer multi-approach)

The INSILICO-CELL project consists of an extensive integrated experimental and computational strategy, providing a radical new approach to unravel how mechanical and chemical conditions regulate cell migration to improve tissue regeneration. Individual 3D cell migration is a consequence of the competition among different capacities developed by cells: deform itself crossing the pores, exert forces and deform the surrounding matrix, and finally, degradation or erosion of this surrounding matrix. Certainly, these conclusions have to be considered with cautious, because these capacities can be different in function of the type of the cell, the surrounding matrix and the mechano-chemical environment, dominating one mechanism over another.
Collective cell migration is much more complex problem, where cell-cell and cell-matrix interactions can determine different emergent patterns of migration. Thus, we developed a generalized clutch model, in which local stick-slip dynamics define cell-matrix adhesions and cell-cell junctions. With this approach, we can simulate different collective patterns of migration in agreement with in-vitro experiments, such as, wound healing, durotaxis, jamming, angiogenesis and formation of spheroids.
INSILICO-CELL has also provided the development of different experimental and computational technologies. From a computational point of view, we have demonstrated that the use of hybrid techniques, combining finite element with particle-based approaches, is the most adequate way to simulate multicellular systems. From a experimental perspective, we have developed multiple cell in-vitro cultures by means of microfluidic chips that allow to understand the role of mechano-chemical on osteoblast and fibroblast migration. From microscope-based images we have quantified cell migration patterns, cell shape and matrix remodelling. These quantitative data have been used to calibrate parameters’model in order to achieve a real integration of experiments and simulations by means of Bayesian optimization.

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