Modeling our biology in detail

“Restoring order to the whole system is surely the eventual future of medicine. Unraveling the systems biology of aging is going to take incomprehensible amounts of data, enormous computing power, and smart computational biologists, working in tandem with those in the lab. Replacing numerical with narrative representation has revolutionized whole fields of science in the past, and the data and computation revolution in biology has only just begun. Once we can model our biology in detail, we’ll be able to reprogram it. Human beings will finally be negligibly senescent, biologically immortal, and ageless… It should be our collective mission.” - Andrew Steele, Ageless, 2020

One of the surprising things about aging science is how little we still know. We know far more than we did 40 years ago, thanks to a generation of pioneering investigators. But it seems that we have some puzzle pieces without the picture of how they fit together.

Another surprising thing is how little we know about our own bodies and health. We have high-level markers, such as blood panels, w physiological measures, etc. And we mostly rely on how we feel. If we feel good, we typically don't worry about our health. If we don't feel good, then something might be wrong. I generally feel great, young, energetic and healthy. Still, I know that damage from aging is accumulating throughout my 37 trillion cells and the tens of millions of biomolecules in each cell. Rust, damage and waste are taking root.

We don't yet know where this damage is happening. I can see the sun damage on my skin. I can feel my creaky ankles. But how is my heart aging? How is my brain aging? Where are little cancers forming? Which tissues are increasingly damaged that don't yet have noticeable dysfunction? It's hard to manage what you can't measure.

To Steele's quote, I believe it is critical to measure and model our biology. We need to know what's going on and intervene before it's too late. This will be a foundation for slowing and reversing aging. It's a massive challenge and will require new methods, data and computational techniques. It's where I expect to spend the next 20+ years of my career.