David Sachs, PhD

David Sachs, PhD, is an Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. He is developing a new micro-organ model of the human cardiovascular system, grown from stem cells inside a microfluidic chip, under the control of a custom robotic platform. The chip will be used to study heart disease and development, including cardiovascular stiffening diseases due to microgravity, and is currently scheduled to launch to the International Space Station in March of 2025. Recent funding will expand the chip with the addition of a liver micro-organ, moving toward the longer term goal of AI guided manufacturing of a comprehensive human micro-organ-on-chip system designed to be readily accessible to a diversity of collaborators. Prior to his work at Mount Sinai, he led the Advanced Application Development group at the MEMS semiconductor startup InvenSense, including the algorithm and digital architecture design of motion sensing devices that have been distributed in billions of units. He has undergraduate degrees in physics and piano performance, a master’s degree from the MIT Media Lab, and a PhD in Biomedical Sciences from Mount Sinai.

Modeling human physiology, development, and disease in vitro is currently accomplished with iPSC derived spheroids and organ-on-chip devices. However, these are limited in that organ-on-chip systems lack important cellular diversity and microphysiology, and spheroids do not capture the tube-like geometry that is essential for most organ functions and multi-organ connectivity. To address the need for more accurate human organ models, we are developing a micro-organ-on-chip system in which iPSCs are seeded into a microfluidic chip and differentiated into tube-shaped organoids in situ, in a system that is guided, but not restricted, by the microfluidic geometry. Starting with the cardiovascular system as a proof of concept, this platform has the potential to simultaneously capture cellular diversity, microphysiology, organ function, and multi-organ connectivity. Custom robotic systems were also developed to control both the seeding and monitoring processes, achieving a controllable variety of micro-organ geometries. As natural organ development leverages continuous feedback from neighboring organ systems, our current engineering efforts are to shift our automation method to deep reinforcement learning, in order to increase the repeatability and throughput of the system via real-time feedback control of organ differentiation.