🧠 AI just made a stunning leap in understanding how we age.
In this episode of AI Revolution, we explore a breakthrough discovery where advanced AI models have decoded the behavior of aging cells — potentially unlocking the door to longer life, disease reversal, and even age reversal. 🔬💥
🧬 Can AI slow or stop aging?
Could this lead to revolutionary anti-aging therapies?
⏳ Is eternal youth closer than we think?
From labs to longevity startups, AI is reshaping human biology faster than anyone expected.
🔔 Like, comment & subscribe for more frontier-shifting insights in AI, biotech, and the future of humanity.
#AIRevolution #AgingBreakthrough #AIAndBiotech #EternalYouth #LongevityScience #AntiAgingAI #ArtificialIntelligence #CellularReprogramming #FutureOfHealth #AIInMedicine #BiotechNews #LifeExtension #HealthTech #HumanLifespan #RegenerativeMedicine #AgeReversal #DeepLearningBiology #AIHealthRevolution #Transhumanism #NextGenScience
In this episode of AI Revolution, we explore a breakthrough discovery where advanced AI models have decoded the behavior of aging cells — potentially unlocking the door to longer life, disease reversal, and even age reversal. 🔬💥
🧬 Can AI slow or stop aging?
Could this lead to revolutionary anti-aging therapies?
⏳ Is eternal youth closer than we think?
From labs to longevity startups, AI is reshaping human biology faster than anyone expected.
🔔 Like, comment & subscribe for more frontier-shifting insights in AI, biotech, and the future of humanity.
#AIRevolution #AgingBreakthrough #AIAndBiotech #EternalYouth #LongevityScience #AntiAgingAI #ArtificialIntelligence #CellularReprogramming #FutureOfHealth #AIInMedicine #BiotechNews #LifeExtension #HealthTech #HumanLifespan #RegenerativeMedicine #AgeReversal #DeepLearningBiology #AIHealthRevolution #Transhumanism #NextGenScience
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TechTranscript
00:00Exploring huge piles of data on genes and cells,
00:05AI has found some amazing secrets.
00:08What exciting mysteries might they solve for us next?
00:11Now, to understand the sheer scale of what AI has achieved,
00:14we need to rewind a bit and look at the story behind it.
00:17So, back in 1889, François-Gilbert Viol, a French doctor,
00:22ventured down from the Andes, took some of his blood,
00:25and looked at it under a microscope.
00:27He found that the red blood cells, crucial for carrying oxygen,
00:31had increased by 42%.
00:33This was Violt's introduction to a fascinating aspect of human biology.
00:38Our bodies can produce these essential cells on demand when necessary.
00:41Early in the 20th century, scientists proposed that a hormone
00:44was behind this capability.
00:46They named it erythropoietin, or red maker, in Greek.
00:49It took 70 years for scientists to isolate erythropoietin
00:53by filtering through 670 gallons of urine.
00:56Fast forward about 50 years, and researchers in Israel identified
01:00a unique kidney cell responsible for producing this hormone
01:03when there's a shortage of oxygen.
01:05They named it the Norn cell, after the Norse gods thought
01:08to determine human destiny.
01:10It took humanity 134 years to uncover the Norn cells.
01:14Last summer, however, computers in California found them by themselves
01:17in just 6 weeks.
01:18This discovery happened when Stanford researchers set up computers
01:21to self-learn biology.
01:23They used an AI program similar to ChatGPT, which learned language
01:26from billions of internet techs.
01:28However, Stanford's team fed their AI raw data on millions of actual cells,
01:32including their chemical and genetic profiles,
01:35without explaining the significance of these details,
01:37or the differences among cells.
01:39The computers analyzed the data independently,
01:42organizing a model of cells based on their similarities in a complex,
01:45multi-dimensional framework.
01:47The outcome was impressive.
01:49The AI could identify a previously unseen cell as one of over a thousand types,
01:53including the Norn cell.
01:54It's incredible because the AI model discovered the Norn cell in the kidney
01:58without being informed of its existence.
02:00Mentioned Jura Leskovic, a computer scientist at Stanford behind the project.
02:05This software is among various new AI-enabled tools, dubbed Foundation Models,
02:10aiming to understand biology's basics.
02:12These models aren't just organizing biological data,
02:15they're uncovering new insights into gene functions and cell development.
02:18As these models grow, incorporating more lab data and computing power,
02:22experts believe they'll lead to even more significant findings,
02:25potentially unveiling mysteries about cancer and other diseases,
02:28or discovering methods to transform cell types.
02:31Discovering something about biology that biologists haven't been able to
02:35would be a landmark moment, and I believe it's coming,
02:38said Dr. Eric Topol, head of the Scripps Research Translational Institute.
02:42The extent of these models' capabilities is up for debate.
02:45While some are skeptical, others are hopeful that Foundation Models
02:49will eventually address one of biology's greatest puzzles,
02:53the distinction between life and non-life.
02:56Now, for years, biologists have been intrigued by how our body's different cells
03:00utilize genes to perform the myriad tasks necessary for survival.
03:05Roughly 10 years ago, large-scale experiments began aimed at identifying
03:09genetic information from individual cells.
03:11The findings were catalogued in extensive databases, or cell atlases,
03:16that grew to contain billions of data points.
03:18Dr. Christina Theodoris, while a medical resident at Boston Children's Hospital,
03:22learned about a new AI model developed by Google in 2017 for translating languages.
03:28This model was trained on millions of English sentences and their translations in German and French,
03:33gaining the ability to translate previously unseen sentences.
03:37Dr. Theodoris speculated on the possibility of a similar model being able to decipher the information contained within cell atlases.
03:44In 2021, she faced challenges in finding a lab willing to explore this idea due to widespread skepticism about its feasibility.
03:51Shirley Liu, a computational biologist at the Dana-Farber Cancer Institute in Boston, decided to give her a chance.
03:58Dr. Theodoris gathered data from 106 human studies, encompassing 30 million cells,
04:04and fed this into her newly developed program GeneFormer.
04:08The model acquired a profound comprehension of gene behavior across various cells.
04:12For instance, it predicted that deactivating a gene named TED4 in a specific heart cell type would cause significant disruption.
04:21When this prediction was tested on real heart cells known as cardiomyocytes, their ability to beat diminished.
04:27In another experiment, Dr. Theodoris and her team introduced GeneFormer to heart cells from both individuals with abnormal heart rhythms and healthy individuals.
04:36They tasked the model with identifying changes needed to restore health to the diseased cells.
04:41GeneFormer suggested diminishing the activity of four genes previously unassociated with heart disease.
04:47Following the model's guidance, Dr. Theodoris' team attempted to suppress these genes.
04:52In two of the four attempts, the treatments enhanced the cell's functionality.
04:55The Stanford team later joined the field of foundation models after contributing to the creation of CellX Gene, one of the largest cell databases globally.
05:04They trained their AI on 33 million cells from this database, focusing on messenger RNA and protein structures, both products of genes.
05:13The resulting model, dubbed Universal Cell Embedding , learned to categorize over a thousand cell types by observing gene activation patterns.
05:21It arranged 36 million cells into clusters based on gene usage, echoing discoveries made by generations of biologists.
05:28UCE also deduced significant insights into cellular development from a single fertilized egg, essentially redefining developmental biology.
05:36It understood that body cells could be classified by their origin from one of the early embryo's three layers.
05:42Furthermore, UCE proved capable of applying its knowledge to unfamiliar species.
05:47When given genetic data from an unknown animal, like a naked mole rat, it could accurately identify many of its cell types.
05:54This model can handle any new organism, chicken, frog, fish, you name it, and still produce meaningful output, Dr. Leskovich explained.
06:01Upon identifying the NORN cells, the team speculated, based on their database, that these cells might exist beyond the kidneys, possibly throughout the body.
06:10Dr. Katalin Sustak, a researcher studying NORN cells, expressed both interest and skepticism about this finding.
06:17While doubting the presence of erythropoietin-producing NORN cells outside the kidneys, she acknowledged the potential for these newly identified cells to sense oxygen similarly.
06:27In essence, UCE might have uncovered a new cell type before traditional biological methods could.
06:33Alright, now, just as ChatGPT can make errors, so can biological AI models.
06:38Kazia Kedzierska, a computational biologist at Oxford University along with her team, put GeneFormer and another foundational AI model,
06:46SZGPT, through rigorous testing.
06:49They challenged these models with unseen cell atlases, asking them to categorize cells into types.
06:55While the AI models excelled at certain tasks, they sometimes underperformed compared to simpler software.
07:01Dr. Kedzierska remains optimistic about the potential of these models, but cautions against using them blindly due to their current limitations.
07:09Dr. Leskovich pointed out that as more data becomes available for training, these models are getting better.
07:15However, he mentioned that the volume of data in cell atlases is relatively small compared to the vastness of information ChatGPT was trained on, expressing a wish for an internet of cells.
07:26The future looks promising, with larger cell atlases being developed and a wider variety of cell data being collected.
07:32This includes detailing the molecules attached to genes and capturing detailed images of cells to identify the precise locations of proteins.
07:40This wealth of information will enhance the Foundation model's ability to understand cell functionality deeply.
07:46Efforts are also being made to merge the self-learned insights of these models with the vast knowledge already documented by biologists.
07:53This includes integrating discoveries from thousands of scientific papers with cell measurement databases, aiming for a comprehensive mathematical model of a cell.
08:02Bo Wang, the creator of SDGPT and a computational biologist at the University of Toronto, believes such a virtual cell could revolutionize biology.
08:11It would enable scientists to simulate experiments digitally, predicting cell behavior in any given scenario, without needing a physical laboratory.
08:18Dr. Quake is intrigued by the possibility that Foundation models might uncover not only the cell types known to exist, but also those that could potentially exist, adhering to the biochemical rules of life.
08:29He imagines creating a map of life's boundaries, exploring beyond which life cannot sustain.
08:34Such a map might even lead to the creation of novel cells not currently found in nature, with Foundation models devising chemical blueprints to convert regular cells into ones with unique capabilities.
08:45These futuristic cells could perform tasks like clearing plaque from blood vessels or examining diseased organs from the inside.
08:52Dr. Quake acknowledges the speculative nature of these ideas, likening them to the science fiction narrative of Fantastic Voyage.
09:01Yet he remains open to the boundless possibilities the future may hold.
09:05Now, let's talk about some of the potential new risks.
09:08If the Foundation models achieve what Dr. Quake envisions, they could introduce various new challenges.
09:13Recently, over 80 biologists and AI specialists have called for regulations on this technology to prevent its potential misuse, such as the development of novel biological weapons derived from artificially created cells.
09:28Concerns about privacy could emerge even more swiftly.
09:31There's hope to develop Foundation models tailored to individual genomes, providing insights into how specific genetic variations influence cellular functions.
09:41This level of personalization could lead to groundbreaking medical discoveries, but might also expose sensitive genetic information of those who contribute their DNA and cell data for research.
09:52Amid these advancements and concerns, some experts question the ultimate capabilities of Foundation models.
09:58Their effectiveness hinges on the quality and scope of the data they're trained on.
10:03Uncovering significant new truths about life may require data we don't yet know how to gather, or may not even realize is necessary.
10:10Sarah Walker, a physicist at Arizona State University who explores the origins of life, acknowledges that while these models could lead to interesting findings,
10:19their potential for fundamental breakthroughs is limited by the current scope of data.
10:24Nevertheless, the success of Foundation models has prompted a re-evaluation of the role of human biologists.
10:30Traditional biology has valued the creativity and labor involved in experimental research that uncovers life's mysteries.
10:37However, computers, by analyzing vast quantities of cell data, might reveal complex patterns and insights in a fraction of the time, challenging our notions of scientific creativity and the future role of biologists in research.
10:50Dr. Quake suggests this shift could necessitate a significant rethinking of creativity's nature in the scientific process, hinting at a future where the academic status quo, particularly for professors accustomed to traditional research methodologies, may be upended.
11:06Alright, don't forget to hit that subscribe button for more updates. Thanks for tuning in and we'll catch you in the next one.
11:12We'll catch you in the next one.