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

- Date: 11/27/2006

Garry Odell (University of Washington)

University of British Columbia

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.

10th Anniversary Speaker Series 2006