Biological networks float between chaotic and robust behavior.


The genomics revolution and modern molecular techniques continue to provide detailed information about many signaling pathways, but a new mathematical model developed by Maximino Aldana and Philippe Cluzel (University of Chicago, Chicago, IL) provides a unique look at the overall architecture of these pathways.

The model is satisfying, says Aldana, because, although it is relatively simple, it accurately reflects a cell's ability to both remain stable in fluctuating environments and to respond and differentiate when the environment changes sufficiently. “It tells you that very simple dynamics can give you what we observe in living organisms, though most people think they are very complex,” says Aldana.

The standard model for signaling networks in cells was published over 30 years ago (Kauffman 1969), but that model fails to predict the overall stability that is characteristic of many living systems. In that model, genes have only two states: on and off. Although Aldana and Cluzel maintained this trait in their model, they modified how the genes are connected to one another. In Kauffman's model, all of the nodes are connected to one another in a homogeneous random manner. In the new version, the researchers used scale-free topology, in which most nodes have relatively few connections and only a few nodes are highly connected.

The best part of this new layout, says Aldana, is not only that the model predicts the dynamic stability required of biological systems, but also that the scale-free topology is consistent with the genetic and molecular data pouring in, which indicates that a few genes in any given pathway have a greater-than-average amount of control over the system. ▪


Aldana, M., and P. Cluzel. 2003. Proc. Natl. Acad. Sci. USA. 10.1073/pnas.1536783100.

Kauffman, S.A.
J. Theor. Biol.