This post is short. Most of the material will be in the Github repo that lists all the formulas that model biological processes.
“All models are wrong, but some are useful” -George Box
The quantification of biology, in my opinion, creates one of the largest opportunities for social good in human history. If you look at many of the historical problems we have solved, a lot of them are human created. Even climate change can be linked to us.
This then poses a logical question- if we keep having to solve the same problems over and over again, are we actually learning? Is the highest leverage bottleneck to human evolution just understanding what makes us, us?
I would argue yes. Like any other engineering system, when you are able to quantify something, you can then predict the outcome of that thing, meaning you can build predictable systems on top of it (which will eventually be replaced with better ones and we expand our vocabulary and descriptive variables).
When you build predictable systems, you can then reliably invent. You can set constants as the North Star for these systems, then reinvest excess cash flows into inventing new tools that produce new insights, leading to new inventions, etc.
This simple thought is exciting. Though models are always one step behind reality, they are still incredibly useful. Look at the hedge fund industry, specifically the companies like Renaissance and Citadel with fully vertically integrated models.