🚀Scaling Synthetic Biology

One of the most exciting technological developments in recent years has been the advent of synthetic biology (synbio): altering microorganisms to better suit our needs. Engineered organisms are bringing about breakthroughs in medicine, materials and agriculture – and the industry’s still in its early stages.

The total addressable market is certainly huge. According to McKinsey research, up to 60% of the global economy’s physical inputs could one day be produced biologically. Over the next couple of decades, the market could be worth several trillion dollars. Another consultancy, BCG, maps out just how wide the impact could be.

On this week’s podcast, I spoke to one of the leaders in the space: Reshma Shetty, COO and co-founder of Ginkgo Bioworks. Ginkgo integrates cutting-edge biology with machine learning and automation to speed up the process of designing, building and testing engineered organisms. Reshma explained to me how Ginkgo’s process works, the company’s platform business model, and just how big she thinks the synbio market could become.

I’ve picked out a couple of particularly juicy ideas below – but the conversation as a whole is well worth listening to if you want to understand the burgeoning synbio industry.

You can 🎧 listen to our discussion here, or 📝 read a full transcript.

Computational DNA

Ginkgo got its start in a computer science lab – and computational thinking is very much a part of the way the company works. Reshma (a trained computer scientist herself) framed Ginkgo’s work in a really fascinating way. There are some very real-world challenges involved in producing engineered cells, but the process has plenty in common with engineering design in other fields, including software design. As Reshma explains:

[Y]ou have an idea for an organism or an application that you want to go after and you think about how you might achieve it… How has nature tackled this problem? Are there ways that we can take what nature has given us and improve it? [...] Then as you sketch the high-level design of the cell, for each step in the design, you're trying to design potential DNA sequences that would have that functionality. It's sort of like if you're a software programmer and you need to code up an algorithm for sorting a list, you go onto your computer, you open it up and you start typing and spec out, here's what I think a software program will look like to sort this list.

Of course, where software works on ones and zeroes, DNA is written in nucleobases, represented by the letters A, C, G and T. Coming up with a cell that has a certain functionality is a question of finding and writing the correct the biological code. In that sense, cell design is a search problem. That’s all very well and good, but it doesn’t mean it’s a simple problem.

It is an unfathomable number of choices, basically […] as many stars as there are in the universe type of thing. Right now, it's just not possible to exhaustively explore that space. And so what we have to do is try to bias our search to where we think designs are that will work.

Ginkgo attempts to speed up that search in a number of ways. The company is constantly gathering experimental data and feeding it back into a codebase, laying the groundwork for faster cell design in the future. On the hardware side, it’s using automation and miniaturisation to get through experiments more quickly. Theoretically, as the Ginkgo codebase grows – and as its lab work becomes speedier – it should be able to race through the search process faster.

At present, synbio is in relatively few areas – but it could well start to address more and more problems. I really enjoyed talking to Reshma about what that change could look like.

If you want to listen to our conversation in full, click here.


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