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.

Other Information: 

10th Anniversary Speaker Series 2006