Wednesday, February 20, 2008

Making Biology Easy Enough For Engineers

No, I'm not knocking the intelligence of engineers. But we're still not at the point where, in the words of synthetic biologist Drew Endy:

...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.


RBH said...

Would that I were 45 years younger. :(

Mike said...

This is exciting stuff, but it may prove to be a tough nut to crack. People are talking about it a lot, but progress is slow.

Anonymous said...

I wouldn't be so pessimistic as to our chances of understanding cells at the network level. There are quantitative theories on how cellular networks operate but it is only recently that people have actually bothered to make the necessary quantitative measurements to test the theories (and I don't mean the omics crowd). There is a great quote I have which I give below, and shows how dangerous it is to underestimate human creativity and persistence.

“The flying machine which
will really fly might be
evolved by the combined
and continuous efforts of
mathematicians and
mechanicians in from one
million to ten million years”
─ The New York Times
9 October 1903

─ Orville Wright’s Diary
9 October 1903
“We started assembly today”

Mike said...

I don't mean to come off sounding so pessimistic - like human flight 100 years ago, I think the potential in this field captures the imagination like few other fields of science right now.

I'm a little depressed about the current status - I keep going to systems biology conferences, hoping to gear something other than omics or untested, purely computational network models, but I rarely do.

I suppose that should be viewed as an opportunity, rather than a cause for lament - it's an unsolved, exciting problem. What could be more fun than that?