...when I want to go build some new biotechnology, whether it makes a food that I can eat or a bio-fuel that I can use in my vehicle, or I have some disease I want to try and cure, I don't want that project to be a research project. I want it to be an engineering project.
Just like designing a new bridge or a new car is not a scientific research project, designing biotechnology shouldn't always be a research project. But biology is still too hard, argues Drew Endy, in a reflective interview on The Edge. (Thanks to The Seven Stones for the tipoff).
Endy draws a distinction between those of us trying to reverse engineer complex biological systems and those who want to build them - you could say, systems biologists vs. synthetic biologists:
Engineers hate complexity. I hate emergent properties. I like simplicity. I don't want the plane I take tomorrow to have some emergent property while it's flying.
He seems to also be arguing that if we want to build truly predictive models of biological systems, like, say, an individual yeast, we should work on building biological systems, not just reverse engineering them:
If I wanted to be able to model biological systems, if I wanted to be able to predict their behavior when the environment or I make a change to them, I should be building the biological systems myself.
I understand this to mean that you start by engineering really simple things (individual genes), and move up to more complex things (promoters, chromosomes, genomes).
This sounds like a useful approach, but I still don't see how synthetic biology is going to go from engineering really, really simple systems to systems that approach the complexity of real organisms. In the case of mechanical or electrical engineering, the physical theory behind how these systems behave has been worked out, to a high level of sophistication, for decades. And thus we can engineer, fairly easily, things from thermostats to computers to Boeing planes.
But how do we go from building artificial genes and promoters to artificial metabolic pathways (without just copying and pasting an existing metabolic pathway, with minor tweaks)? Let's say you can cheaply synthesize a 50 million-base artificial chromosome, big enough to hold a set of metabolic or signaling pathways of your custom design. How do you choose what to put on your artificial chromosome?
I don't see how you can do it without a genuinely quantitative, formal, theoretical framework for treating biological systems, which we just don't have yet. To echo Endy's earlier quote on engineering, every new effort to model a biological system is a research project in itself, not a routine engineering task. How do we change that?
It's a fascinating interview, worth checking out.