Multi-scale design in layered synthetic biological systems

Author(s):  
Thomas Prescott ◽  
Antonis Papachristodoulou
2021 ◽  
Author(s):  
Davide Cazzaro ◽  
Alessio Trivella ◽  
Francesco Corman ◽  
David Pisinger

2018 ◽  
Vol 498 (2) ◽  
pp. 296-304 ◽  
Author(s):  
Fabio Sterpone ◽  
Sébastien Doutreligne ◽  
Thanh Thuy Tran ◽  
Simone Melchionna ◽  
Marc Baaden ◽  
...  

Author(s):  
H. Issa ◽  
M.Z. Tzen ◽  
M. Lenczner ◽  
R. Habib ◽  
E. Ostrosi ◽  
...  
Keyword(s):  

2011 ◽  
Vol 173 (2) ◽  
pp. 541-551 ◽  
Author(s):  
J.-M. Commenge ◽  
M. Saber ◽  
L. Falk

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Pinar Acar

Abstract We present a new sampling method for the multi-scale design of polycrystalline materials, which improves the computational time efficiency compared to the existing computational approaches. The solution strategy aims to find microstructure designs that optimize component-scale mechanical properties. The microstructure is represented with a probabilistic texture descriptor that quantifies the volume fractions of different crystallographic orientations. However, the original microstructure design space is high-dimensional and thus optimization in this domain is not favorable. Instead, we generate property closures, which are the reduced spaces of volume-averaged material properties that are computed in terms of the microstructural texture descriptors. We observe that the traditional design approaches which are based on sampling in the original microstructure space and sampling on the property closure are inefficient as they lead to highly concentrated design samples in the solution space. Therefore, we introduce a new sampling method in the property closure, which creates simplexes using the triangulation of the property hull and then generating samples for each simplex. Example problems include the optimization of Galfenol and α-titanium microstructures to improve non-linear material properties. The new sampling approach is shown to obtain better solutions while decreasing the required computational time compared to the previous microstructure design methods.


2020 ◽  
Vol 375 (1807) ◽  
pp. 20190377
Author(s):  
Andreas Deutsch ◽  
Peter Friedl ◽  
Luigi Preziosi ◽  
Guy Theraulaz

Collective migration has become a paradigm for emergent behaviour in systems of moving and interacting individual units resulting in coherent motion. In biology, these units are cells or organisms. Collective cell migration is important in embryonic development, where it underlies tissue and organ formation, as well as pathological processes, such as cancer invasion and metastasis. In animal groups, collective movements may enhance individuals' decisions and facilitate navigation through complex environments and access to food resources. Mathematical models can extract unifying principles behind the diverse manifestations of collective migration. In biology, with a few exceptions, collective migration typically occurs at a ‘mesoscopic scale’ where the number of units ranges from only a few dozen to a few thousands, in contrast to the large systems treated by statistical mechanics. Recent developments in multi-scale analysis have allowed linkage of mesoscopic to micro- and macroscopic scales, and for different biological systems. The articles in this theme issue on ‘Multi-scale analysis and modelling of collective migration’ compile a range of mathematical modelling ideas and multi-scale methods for the analysis of collective migration. These approaches (i) uncover new unifying organization principles of collective behaviour, (ii) shed light on the transition from single to collective migration, and (iii) allow us to define similarities and differences of collective behaviour in groups of cells and organisms. As a common theme, self-organized collective migration is the result of ecological and evolutionary constraints both at the cell and organismic levels. Thereby, the rules governing physiological collective behaviours also underlie pathological processes, albeit with different upstream inputs and consequences for the group. This article is part of the theme issue ‘Multi-scale analysis and modelling of collective migration in biological systems’.


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