Designing cultured tissue moulds using evolutionary strategies

Abstract

There is an unmet need for artificial intelligence techniques that can speed up the design of growth strategies for cultured tissues. Cultured tissue is increasingly important for a range of applications such as cultivated meat, pharmaceutical assays and regenerative medicine. In this paper, we introduce a method based around evolutionary strategies, machine learning and biophysical simulations that can be used to speed up the process of identifying new tissue growth strategies for these diverse applications. We demonstrate the method by designing tethering strategies to grow tissues containing various cell types with desirable properties such as high cellular alignment and uniform density.