4-S model
Sequencing, Synthesis, Scale, Software
Technology is dramatically changing our relationship with biology. After a productive century with many foundational discoveries, our capabilities are beginning to compound in ways that they haven’t before. This is especially true for genetics. Just a matter of decades ago, a PhD in genetics often consisted of sequencing a gene. Now, graduate students sequence genomes, and carry out massive functional studies perturbing every gene that is expressed.
What’s different?
One simple model that has gained traction is: read, write, edit. This model leans heavily into the analogy of DNA as the source code of an organism. We have made enormous strides in DNA sequencing—letting us read the source code. Modern DNA synthesis technology lets us write new code. You can likely guess where DNA editing technologies like CRISPR fit in the model.
While this model is useful, I prefer to focus on the broadly enabling technologies themselves, a level of abstraction one layer below their purposes. When I think about what is different in the world of genetic technologies, I think about Sequencing, Synthesis, Scale, and Software. I find that this mental model helps frame a wide range of current research. It also helps explain the emergence of a new phenotype of biotech startup.
I’ve written a series a posts about this model. The links to each post are aggregated below.
