Can genomics provide a new level of strategic intelligence about

rapidly evolving pathogens? We have developed a new approach to

measure the rates of all possible evolutionary pathways in a genome, as

a conditional Ka/Ks network, and have applied this to several datasets,

including clinical sequencing of 50,000 HIV-1 samples. These data

reveal specific accessory mutations that greatly accelerate the evolution

of multi-drug resistance, and other “kinetic trap” mutations that block it.

Our analysis was highly reproducible in four independent datasets, and

can decipher a pathogen’s evolutionary pathways to multi-drug

resistance even while such mutants are still rare.