For making genetic networks operate robustly, unintelligent non-design suffices

Five years ago we (George von Dassow, Ed Munro, Eli Meir, and Garrett Odell) made mathematical/computer models of two ancient and famous genetic networks that act early in diverse embryos to establish spatial gene expression patterns prefiguring the body plan. Our models revealed these networks to be astonishingly robust. That is, they continue to make the correct spatial pattern in the face of thousand-fold variations in the strengths of most functional forms of interactions among participating genes. After getting over my surprise that it was even possible to design networks with such properties, I now believe only networks having this kind of robustness can be functionally heritable in polymorphic populations. What general design features might endow genetic networks with the kind of extreme robustness we found in two real networks?

To probe for answers, I wrote a computer program that haphazardly generates randomly connected networks made from about the same number of biochemically sensible parts that constitute the segment polarity and neurogenic networks. We (Bjorn Millard, Ed Munro, and I) devised computer algorithms that discover and catalog the stable expression patterns any network can make, and, from all these, distills those patterns the network can make robustly with respect to variations of its parameters. The bottom line is that 19 out of 20 random networks that our program created (i.e. networks devoid of any purposeful design whatever) could make at least one, and usually many, complex stable spatial expression patterns with the same high robustness that the real, evolved, segment-polarity and neurogenic networks exhibit. Several of the random, non-designed networks turn out to be much more robust than either real network. Only 1 out of 20 random networks is a complete loser; it did not make any interesting pattern at all.

Our algorithms for finding patterns any network can stabilize show that it's possible to replace the network's differential equation model, which keeps track of continuous concentrations of gene products changing continously through time, by discrete logic models with quantized far-apart concentrations. Unfortunately, there are many different ways to do this -- different ways for different parameter values -- no way appropriate for all parameter values.

The in silico result that thoughtless, haphazard, non-design produces networks whose robustness seems inspired begs questioning what else unintelligent non-design might be capable of.