Human Biological Datacenter to Launch to Train World Model of Human Biology
Vivodyne confirms that it grows over 20 types of different human organ tissues, both healthy and with patient-linked diseases, including liver, lungs, gut, bone marrow, pancreas, kidney, eyes, and lymph nodes. The post Human Biological Datacenter to Launch to Train World Model of Human Biology appeared first on GEN - Genetic Engineering and Biotechnology News .
Vivodyne has launched what it calls the world's largest human biological datacenter, featuring 12 robotic HIVE laboratories and the ability to perform controlled trials on 3.1 million large human tissues annually - roughly twice the scale of all clinical trials in the U.S. combined. The company has also unveiled its Series 2 TissueDisk, a wafer-scale biological chip that simultaneously grows hundreds of large, functional, living human tissues, manufactured end-to-end on Vivodyne's own robotic production line.
Eight major pharmaceutical companies have taken advantage of early access to this platform, according to Vivodyne, to discover and test potential medicines "in humans" before they reach the patient. This combination of the TissueDisk and robotic HIVE laboratories represents a novel environment where reinforcement learning - a technique that has propelled the growth of AI language models - can be applied to understand human physiology.
This approach enables millions of therapeutic interventions to be introduced into living human tissues, with their causal biological consequences measured using advanced modalities like 3D scanning, transcriptomic sequencing, and deep proteomic analysis. Georgescu envisions this as the foundation of the first comprehensive world model of human biology, a necessary step given the limitations of single-target drugs in addressing complex diseases like cancer, fibrosis, autoimmune disorders, and neurological conditions.
By generating and training on vast quantities of human data - grown as functional human tissues in the lab - Vivodyne aims to create an AI capable of learning from richer, more diverse datasets than ever before. This could potentially revolutionize pharmaceutical research by providing a safer, more efficient way to understand how drugs interact with the complex human body.
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