Making Simple Model Systems to Study the Design Principles of Biological Networks

The speaker at our departmental seminar last week, Wenying Shou, discussed a system of cooperating yeast strains she has constructed, forthcoming in PNAS (link to abstract, full article requires subscription). She created two yeast strains which absolutely depend on each other for survival. Yeast are normally free-living single celled organisms. But Shou knocked out a gene required to make lysine (an amino acid, critical in many proteins) in one yeast strain, and a gene required to make adenine (needed to make DNA and RNA) in the other yeast strain. These yeast strains are perfectly fine on their own, as long as you supplement their broth or culture medium with adenine or lysine - if they can't make it, the yeast cells can pull it in from their environment. However, if you put these yeast strains alone in a culture medium without adenine or lysine, they die out.

Shou showed that you can put these two engineered strains together so that they each supply the missing nutrient required by the other strain (with a little bit of tweaking, described in the paper). The strain that cannot produce its own adenine does produce the lysine that's required by the other strain and visa versa. So what happens is this: you start out with a culture containing both strains, in medium missing lysine and adenine. The lysine-defective strain starts to die off because there is no lysine available. As the cells die, they release adenine (which they can synthesize) into the medium, which is quickly taken up and used by the other adenine-defective strain, which doesn't produce adenine. Soon enough, the adenine-defective strain sucks up all the adenine released by the first strain, and now the adenine-defective strain is dying. As it dies, it releases lysine, which the first strain, now near complete extinction, can take up, enabling it to start growing robustly again. And so this whole system goes back and forth, each strain supplying a missing nutrient to the other.

The beauty of this system is that all of the relevant parameters can be measured, such as how much adenine a cell releases when it dies, how fast a strain grows at a certain concentration of nutrient, etc. Shou was able to write a set of equations describing this relatively simple system, and create a phase diagram based on two variables - the number of cells from each strain used to start the cooperative culture:



So we now have a prediction - the two strains will cooperate and survive if you start them on one side of the phase diagram, but they will die out if you start them on the other side. If you start your culture with a number of cells that lies right on the dividing line, sometimes your culture will survive and sometimes it won't. That's exactly what Shou found when she started her cultures with varying numbers of cells from each strain.

I think this is a nice model system to play with, but the problem is that I'm not sure yet what key questions it can answer for us. The ostensible rationale for this system is to study cooperation in nature, but frankly I think this is too disconnected from real cooperating species found in nature to gain much insight. Furthermore, since cooperation is already established in this system, you're not really studying how cooperating systems evolve in the first place. In the paper itself, Shou and her co-authors barely make reference to the decades of field and theoretical studies that have analyzed natural cooperation, and I don't think they've identified any questions that field biologists would love to see answered with this model system.

But this is beside the point. Our inability to engineer any but the simplest cellular networks from scratch suggests that we are missing an important part of the picture of how cells work. We can't build a cell from scratch. One way forward is to build simple systems like Shou's, with measurable parameters and study the dynamics to learn more about the principles that underlie simple biological networks. The dynamics that Shou has produced so far are maybe a little too simple (if you read the paper, you'll notice that the system merely converges to a point attractor), but I don't doubt this system has potential. As more simple systems like this one become available, we will need to define the specific important questions we want to answer. We probably have most of the mathematical and physical tools we need, yet we are lacking the useful concepts that will enable us to seriously study how a cellular system works on a quantitative level.