Molecular Systems Biology | The Seven Stones

Non-adaptive forces in the evolution of genetic networks

(via Gene Expression)


A few days ago, an exciting review by Michael Lynch was published in Nature Reviews Genetics (The evolution of genetic networks by non-adaptive processes, Lynch 2007a ), a close follow-up of another review, published in PNAS a few months ago (The frailty of adaptive hypotheses for the origins of organismal complexity, Lynch 2007b). Michael Lynch has also written a book on the topic: The Origins of Genome Architecture (read a review)

The architecture of biological networks are often hypothesized as being “shaped” by adaptive evolution to confer global properties such as redundancy, robustness, modularity, complexity and evolvability. Lynch has some robust comments (others have some too, see Jonathan Eisen’s “adaptationomics awards”) on the “vast majority of biologists engaged in evolutionary studies [who] interpret virtually every aspect of biodiversity in adaptive terms” (Lynch 2007b). In contrast to what he perceives as a widespread belief, Lynch states clearly:

It is an open question as to whether pathway complexity is a necessary prerequisite for the evolution of complex phenotypes, or whether the genome architectures of multicellular species are simply more conducive to the passive emergence of network connections.(Lynch 2007a)

Beyond its somewhat controversial tone, Lynch’s central lesson is the need to adopt a population genetics viewpoint (“nothing in evolution makes sense except in light of population genetics”) and he reminds us that, beside natural selection, three additional non-adaptive processes drive the evolution of living organisms: genetic drift, mutation and recombination. By analyzing the interplay between relative rates of loss and gain of regulatory sites (which depend both on mutation rate and mutational target size such as non-coding DNA), population size and recombination frequency, he demonstrates that purely non-adaptive forces can, in principle, determine the level of connectivity of regulatory networks—for example, determine the predominance of highly connected network motifs over linear pathways—without invoking any inherent advantages of the respective architectures on biological functions related for example to development or metabolism. It appears thus that, depending on the population genetics parameters, network structure can be profoundly “shaped” by the mere physical processes of mutation and recombination. At the very least, Lynch proposes that such models should be considered as “null hypothesis” when claiming that selection is engaged in a given aspect of organisms complexity.

In his review of Lynch’s book, Massimo Pigliucci draws our attention to the fact that “the genome is only part of the story, arguably the simplest part to figure out”, and that one of the greatest current challenges is to explain how phenotypes evolve. Lynch also recognizes that his models are simplified and do not, for example, consider kinetic or dynamical properties of biological networks. But here is a naive question: would it be possible to design an experimental strategy to test directly, in the lab, the evolution of simple (synthetic?) genetic circuits and observe the trends in connectivity under non-selective conditions or are the timescales involved too unrealistic?


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