Pluripotent stem cells, in the recent years, have been demonstrated to mimic different aspects of metazoan embryonic development in vitro. This has led to the establishment of synthetic embryology: a field that makes use of in vitro stem cell models to investigate developmental processes that would be otherwise inaccessible in vivo. Currently, a plethora of engineering-inspired techniques, including microfluidic devices and bioreactors, exist to generate and culture organoids at high throughput. Similarly, data analysis and deep learning-based techniques, that were established in in vivo models, are now being used to extract quantitative information from synthetic systems. Finally, theory and data-driven in silico modeling are starting to provide a system-level understanding of organoids and make predictions to be tested with further experiments. Here, we discuss our vision of how engineering, data science and theoretical modeling will synergize to offer an unprecedented view of embryonic development. For every one of these three scientific domains, we discuss examples from in vivo and in vitro systems that we think will pave the way to future developments of synthetic embryology.
Keywords: Data science; Deep learning; Engineering; High-throughput; Microfluidics; Modeling; Organoids; Synthetic embryology.
Copyright © 2020. Published by Elsevier Inc.