Rethinking embryology in vitro: A synergy between engineering, data science and theory

Area of Science:

• Developmental Biology
• Stem Cell Biology
• Bioengineering

Background:

• Pluripotent stem cells can model embryonic development in vitro.
• Synthetic embryology utilizes these models for studying inaccessible developmental processes.
• Organoid generation and analysis benefit from engineering and data science advancements.

Purpose of the Study:

• To present a vision for the synergistic integration of engineering, data science, and theoretical modeling in synthetic embryology.
• To highlight how these integrated approaches can provide unprecedented insights into embryonic development.
• To discuss examples from both in vivo and in vitro systems that will drive future synthetic embryology research.

Main Methods:

• Leveraging engineering-inspired techniques like microfluidic devices and bioreactors for high-throughput organoid generation and culture.
• Applying data analysis and deep learning techniques, adapted from in vivo models, for quantitative analysis of synthetic systems.
• Utilizing theory-driven and data-driven in silico modeling for system-level understanding and prediction in organoid development.

Main Results:

• Demonstration of pluripotent stem cells mimicking embryonic development in vitro.
• Establishment of synthetic embryology as a field for studying developmental processes.
• Integration of engineering, data science, and modeling for a comprehensive view of development.

Conclusions:

• The synergy between engineering, data science, and theoretical modeling promises to revolutionize synthetic embryology.
• This interdisciplinary approach will unlock new avenues for understanding embryonic development.
• Future developments in synthetic embryology will be significantly shaped by these integrated scientific domains.