Share Dialog

Share Dialog

>300 subscribers

>300 subscribers

Picture yourself bridging Atoms & Bits

Extending human healthspan through epigenetics

Picture yourself bridging Atoms & Bits

Extending human healthspan through epigenetics

1. The primary approach is epigenetic editing

Our body has millions of cells that contain the same DNA.

Each of these cells use certain parts of the DNA to effectively serve their purposes (i.e. tongue cell vs. liver cell).

For a long time, biologists thought only the DNA mattered. But it turns out, the way the DNA is used by each cell is just as important.

This 'use' of DNA depends on a process called epigenetics, which involves various mechanisms that can turn genes on or off without changing the DNA itself.

Epigenetics controls which parts of the DNA are active in different cells at different times. So, even though a tongue cell and a liver cell have the same DNA, epigenetics ensures that each cell type uses only the relevant parts of the DNA needed for its specific functions.

Understanding and manipulating these epigenetic controls can allow scientists to make older cells behave like younger ones and improve their overall function.

This is the core of what NewLimit aims to achieve.

From their website:

NewLimit plans to initially focus on this mechanism: epigenetic reprogramming. Put simply, we want to figure out a way to restore the regenerative potential we all had when we were younger, but somehow lost.

Now, the epigenetic editing may sound incredible at first glance, but it's not a straightforward task to figure out which cells can be edited, how they should be edited, and the impacts of any changes.

The hurdle here is the physical limitations in how many different permutations of gene editing can be done before any meaningful progress can be done.

So how can NewLimit speed that up?

By being strategic about which experiments to run. Instead of shooting in the dark, how can the team use existing data to figure out the highest chances of success?

With the help of machine learning models, can the team perhaps run 10% of the experiments needed to end up with the same conclusions?

After reading through their docs, it's clear the team is intentional about being for-profit and wants to execute like a lean startup would.

Though this is a decades long mission, that shouldn't mean launches should have to be scaled on that timeframe.

They're working on immune specific cases as a start and will be branching out from there.

It was cool to see how they split up their team into 4 divisions:

- Readers: study cell's current epigenetic structure

- Writers: build tools to rewrite the structure

- Modelers: predicting how to best reprogram cells

- Product: getting the tech out to the real world safely

The founders initially invested $110m of their own money and later raised a $40m series A from Dimension, Founders Fund, & KPCB.

Currently, the team is around 15 employees and they're actively recruiting for research and ops positions.

If you're interested in learning more, I highly recommend checking out the team's substack - they give detailed progress updates on the bits and atoms side every few months.

1. The primary approach is epigenetic editing

Our body has millions of cells that contain the same DNA.

Each of these cells use certain parts of the DNA to effectively serve their purposes (i.e. tongue cell vs. liver cell).

For a long time, biologists thought only the DNA mattered. But it turns out, the way the DNA is used by each cell is just as important.

This 'use' of DNA depends on a process called epigenetics, which involves various mechanisms that can turn genes on or off without changing the DNA itself.

Epigenetics controls which parts of the DNA are active in different cells at different times. So, even though a tongue cell and a liver cell have the same DNA, epigenetics ensures that each cell type uses only the relevant parts of the DNA needed for its specific functions.

Understanding and manipulating these epigenetic controls can allow scientists to make older cells behave like younger ones and improve their overall function.

This is the core of what NewLimit aims to achieve.

From their website:

NewLimit plans to initially focus on this mechanism: epigenetic reprogramming. Put simply, we want to figure out a way to restore the regenerative potential we all had when we were younger, but somehow lost.

Now, the epigenetic editing may sound incredible at first glance, but it's not a straightforward task to figure out which cells can be edited, how they should be edited, and the impacts of any changes.

The hurdle here is the physical limitations in how many different permutations of gene editing can be done before any meaningful progress can be done.

So how can NewLimit speed that up?

By being strategic about which experiments to run. Instead of shooting in the dark, how can the team use existing data to figure out the highest chances of success?

With the help of machine learning models, can the team perhaps run 10% of the experiments needed to end up with the same conclusions?

After reading through their docs, it's clear the team is intentional about being for-profit and wants to execute like a lean startup would.

Though this is a decades long mission, that shouldn't mean launches should have to be scaled on that timeframe.

They're working on immune specific cases as a start and will be branching out from there.

It was cool to see how they split up their team into 4 divisions:

- Readers: study cell's current epigenetic structure

- Writers: build tools to rewrite the structure

- Modelers: predicting how to best reprogram cells

- Product: getting the tech out to the real world safely

The founders initially invested $110m of their own money and later raised a $40m series A from Dimension, Founders Fund, & KPCB.

Currently, the team is around 15 employees and they're actively recruiting for research and ops positions.

If you're interested in learning more, I highly recommend checking out the team's substack - they give detailed progress updates on the bits and atoms side every few months.