Computational Aging

One of the most important questions within aging science is identifying good biomarkers of aging.

We need biomarkers that are predictive of aging rate and mortality. We have lots of biomarkers that are associated with aging rate and mortality, like cholesterol readings and epigenetic clocks. But we don't know how predictive they are.

We need these biomarkers to be more frequently measured than a full human lifespan. That way we can test interventions and see how they work. The more frequently these biomarkers can be taken (and still be predictive), the more we can make lots of personalized interventions (e.g. lifestyle, drugs, epigenetic reprogramming, gene editing).

Once we have these biomarkers, I could imagine a device like an Apple Watch giving us personalized recommendations based on population and personal data on how to slow our aging. Apple Watch captures large amounts of physiological and lifestyle data. They are increasing their data-capturing capabilities every year.

Working on biomarkers of aging is likely a very good use of time.

“If I can dream, in a few years we’ll have spatiotemporal single-cell ’omics in living tissues.” – Sten Linnarsson, Karolinska Institute[1]

When we know exactly what is going in every tissue and cell of the live human body, we probably will figure out how to cure aging.

But how much data storage is necessary to track every protein and non-coding RNA in the human body?

Here's a quick estimate:

• 30 trillion cells per human
• 80,000 unique non-coding RNA
• 20,000 unique proteins
• = 5x10^22 measurements per human
• Now, measure this hourly every year (8,760 hrs/yr)
• = 5x10^26 measurements per human per year
• Call it 4 bytes per number (float32)
• = 2x10^27 bytes per human per year
• Convert this into zetabytes at 10^21 bytes per zetabyte
• = 2x10^6 zetabytes per year per human
• Measure this across 10 billion humans
• = 2x10^16 zetabytes for humanity per year

So we need 2 million zetabytes (2x10^6) per human per year and 10 billion times more for every human. Unfortunately, we only captured about 100 zetabytes of data as a species in 2021[2]. This math is probably wrong... but probably at least directionally right.

We don't have the ability to store (or of course yet capture) this amount of data. But single-cell spatial transcriptomic methods will continue to rapidly improve, as will data storage and data science methods. For example, we may get tissue-level live 'omics in the next few decades.

[1] Marx, V. Method of the Year: spatially resolved transcriptomics. Nat Methods 18, 9–14 (2021). https://doi.org/10.1038/s41592-020-01033-y

[2] "Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025." Statistia.com. Accessed June 24, 2022. https://www.statista.com/statistics/871513/worldwide-data-created/

"I finally adopted what I called 'Great Thoughts Time.' When I went to lunch Friday noon, I would only discuss great thoughts after that. By great thoughts I mean ones like: 'What will be the role of computers in all of AT&T?', 'How will computers change science?'" – Richard Hamming, You and Your Research

Here are some of the big open questions I see in aging science right now:

1. Cause and effect of why we age still unknown? New paradigm may be needed?
2. What can partial reprogramming do?
3. How to stave off neuron loss in the brain?
4. How to slow or reverse damage in DNA, epigenome, proteome and cell homeostasis?
5. What do DNA methylation clocks really reflect?
6. What is the best way to measure in vivo aging?
7. How to test effectiveness of new aging therapies on humans?
8. How to use massive new datasets?
9. How to get more talented people into the space?
10. How much of a role does inflammation have in aging? How much could reduction of excessive inflammation lead to longevity?
11. What are we learning from the iPSC programming startups and experiments?
12. Does young blood work? Why and how?
13. What is Michael Snyder doing?
14. How do longer lived organisms reduce cell mutation rate? Can you replicate this in mice and see life extension? (See Cagan et al, 2022)
15. How to keep zest for life past 100? Avoid reduction in dopamine signaling and increased depression?

Of critical importance is identifying the right questions and focusing very aggressively and directly on them. I expect this list above will change dramatically as I pursue this field. I hope to make fast enough progress such that I'm embarrassed by this list within a few months.