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Abstract

Tools from materials science are increasingly used to decrease the variability that often limits the quantitative analysis of cell experiments. Most prominently, micropatterned substrates can be used to normalize cell shape and internal organization. For long-term experiments, however, cells must be able to divide and migrate. This creates the need to design networks of micropatterns that ensure normalization to be maintained even in a dynamic context. The design of such networks requires an efficient model predicting the degree of normalization for dividing and migrating cells. Here we extend earlier formulations of the two-dimensional cellular Potts model with the aim to achieve good agreement with existing cell experiments on cell shape and forces on micropatterns. We show that our model correctly predicts cell spreading, division and migration on micropatterns, both for single cells and cell communities. The inverse problem of network optimization is then addressed with genetic algorithms.