Introduce computational and synthetic biology tools to control and steer tissue development
Engineering 3D tissues requires simultaneous control over a complex and tightly choreographed interplay between cellular communication, cellular self-organization programs and cell-cell and cell-matrix interactions. Recent advances in synthetic biology are opening the possibility to use synthetic gene and signaling circuits and biomaterials to not only probe morphogenesis but also to re-construct it and direct it. This new field of synthetic morphogenesis could provide a scalable pipeline to develop multicellular living machines engineered with novel functions not seen in nature.
Organoids are 3D cell cultures that mimic tissue architecture and recapitulate histological features, cellular diversity and gene expression of the corresponding parental organs. Due to the multiscale structure and interactions present within these systems and a poor understanding of the underlying biology, optimization of organoid production is difficult. However, when large data sets are available, machine-learning techniques are capable of predictive power without a detailed understanding of the underlying biology. CLT focuses on developing new standards for modeling disease using organoids and establishing infrastructure for recording biological signals during organoid growth thereby generating large datasets which will be used to train machine-learning models that capture the underlying process.