When it comes to ease of designing, constructing, and testing genomes, not all organisms are created equal. Some have smaller genomes and unicellular lifestyles, while others have larger genomes and undergo complex multicellular development, both of which render genome design and modeling difficult. Some have many more tools available for genome editing, while others are burdened with polyploid genomes that increase the difficulty of constructing and testing new designs. Some organismal phenotypes can be readily measured, while others are subtle and hard to quantify. Most importantly, some replicate in mere minutes and are readily grown in large numbers, while others require years of labor-intensive care to reach adulthood. The advent of new technologies for genome design, construction, and testing have compensated for some of these differences, but accentuated the impact of others.
Dairy cows are classic examples of slow-growing, expensive, multicellular organisms that nonetheless have a large industry invested in their improvement. While cows have been modified through evolutionary engineering since antiquity, their slow growth and large diploid genomes render them recalcitrant to targeted variant construction and testing. Furthermore, in silico
predictive models of mammals do not exist. Nevertheless, milk production has quadrupled over the last 60 years because the industry rigorously measured outputs and applied extremely strong selection in the form of artificial insemination (Funk, 2006
). For decades, top bulls have routinely sired tens of thousands of offspring, efficiently transmitting only the best genes to the next generation—a purely blind evolutionary search, but the most effective strategy available given the constraints of the organism at the time. Thanks to high-throughput sequencing, it is now possible to design strategies to accelerate the rate of improvement. Although we are far from understanding the mechanistic basis of milk production, recent genotyping sequencing efforts have begun to identify the chromosomal regions and individual genes favored by the past few decades of selection (Larkin et al, 2012
). The industry is now implementing rationally designed generations-long strategies to hasten the combination of known beneficial alleles into single genomes using selective breeding and perhaps, eventually, targeted genome editing.
Microbes are the mirror image of domesticated animals in almost every way. Unknown in antiquity due to their microscopic size, they tend toward small haploid genomes that can be grown quickly and in large numbers. Combined with a powerful selection, these traits permit swift evolutionary engineering, as first demonstrated by W.H. Dallinger's nineteenth-century-directed evolution of microbial thermal tolerance from 18°C to an astonishing 70°C over 7 years (Dallinger, 1887
). A dearth of screening and selection technologies impeded further microbial engineering until the latter half of the twentieth century, but the subsequent explosion of such methods has rendered microbes—which combines rapid growth, large population sizes, and powerful selections—the organisms of choice for directed evolution studies. We recently demonstrated that even smaller and faster-replicating genomes can further accelerate and even automate evolutionary engineering (Esvelt et al, 2011
). Our system harnesses filamentous phages, which require only minutes to replicate in host E. coli
cells, to optimize phage-carried exogenous genes in a handful of days without researcher intervention. Compounding their growth advantage is the fact that microbes and phages are also ideal subjects for biological design, modeling, targeted genome editing, and genome synthesis, all of which can focus subsequent evolutionary searches on the regions of sequence space most likely to encode desirable phenotypes. Alternatively, these methods can compensate for the lack of a powerful selection that precludes evolution. Future technologies will ideally extend some of the advantages enjoyed by model organisms, such as E. coli
and S. cerevisiae
to other organisms, enabling more genome engineering endeavors to combine model-driven targeted manipulation with the best growth and selection paradigm available to the target organism.