Video: The right kinds of analysis could advance disease prevention – fundamentally.
Have you ever wondered whether modern medicine is going to give Ponce de Leon’s fabled Fountain of Youth a run for its money?
Check out this presentation – it’s going to blow your mind.
It might sound like fantasy, but in some ways, we’re talking about real technologies with the power to save, and change, lives.
Discussing the proposition of “making aging optional,” MIT Professor Manolis Kellis starts with something fundamental: causality in epidemiology.
Where he goes from there is instructive to someone thinking about real longevity: he addresses genomics, a scientific discipline with the power to help us figure out how disease works, and then, how to beat it.
“How do you develop therapeutics, without mechanism?” Kellis asks.
(image caption: The whole talk really asks intriguing questions about healthcare applications)
The answer, he adds, involves looking at things on a cellular level, and a molecular level, at what Kellis calls “single-cell resolution.”
Also, as Kellis contends, “AI begins with data.”
When you listen to all of this, you begin to think deeply about the drivers of disease – and, in Kellis’s words, the “regulatory control regimens” – and the cell types that make a difference.
Kellis explains how you can intervene for everyone by looking at specific examples, by making predictions and then making changes.
As Kellis talks about “perturbing” the genomic sequencing, he gives a concrete example, of thermogenic and lipogenic models that can change the science of combating obesity.
With genomic editing, he suggests, we may soon be able to control an individual’s weight by flipping thermogenic and lipogenic models “like a switch.” Think about this: if you really can do that, tomorrow’s people will be able to stay thin without specific diet and fitness interventions, just because the cells will be burning calories instead of storing them. It really starts to make you think about those deep-science interventions that replace that you might call “simple outcomes” (like TV and fast food, and obesity). This is part of what’s strikingly interesting here.
Another very interesting example that Kellis provides is around Alzheimer’s, a top-of-mind cognitive disease afflicting the lives of many elderly people.
Be sure to study this part of the data-rich slides that Kellis is presenting, and think about this essential concept – it’s the intervening in lipid transport, in some ways, that affects the myelin sheath of the infrastructure that supports our cognitive process.
Then when you support the proper brain structures, you can actually rebuild cognition!
Does it sound too good to be true?
There’s a lot more scientific detail in the video. Check it out and develop your own conclusions.
But the idea that causality can help with what Kellis calls “mediation analysis” is a pretty powerful proposition.
He also goes into methods, like multi-tissue profiling and multimodal analytics that could drive change for clinics and doctors, for patients in hospitals – for anyone involved in clinical care models.
Then, too, in terms of behavioral medicine, we see explicitly how these applications could help control or even practically eliminate conditions like schizophrenia, bipolar, even autism.
When Kellis mentions disease hallmarks, and the classification of subtypes of patients, something we suppose AI is abundantly able to do, we get to a broader understanding of how the medicines of tomorrow may work: for example, as the speaker posits, at a phenotypic level…
There’s a lot more in here about AI-driven drug discovery with large language models, and with, for example, the analysis of protein folding and function, and applications of biotech, finance, and pharma, and doctor-patient hospital scenarios – watch it from beginning to end, to develop a better idea of where genomics and related work is probably headed.