Evolution resembles not just a tree with diverging branches, but a woven tapestry. Threads of ancestry cross when species exchange genes — for example, bacteria pass drug resistance to each other, and plants crossbreed to produce hybrids. Scientists have learned to untangle this knot: they analyze mutations — random typos in the assembly instructions for an organism (DNA).
Three simple rules for how these typos accumulate create a unique pattern. Just as astronomers determine a star's composition from its light, a method called spectroscopy, biologists see hidden crossings by the degree of disorder in mutations, or entropy. It turns out you can almost always tell a simple branching line from a tangled network.
Mathematically proven: even a single gene exchange millions of years ago leaves an indelible trace in DNA. Similar methods are used by linguists for language family trees and programmers for analyzing complex circuits. Just as physics has the Standard Model of elementary particles, in biology simple models of mutations reveal the history of life written in carbon compounds.
as written by the authors arXiv:2607.12919
Phylogenetic networks generalize phylogenetic trees to evolutionary histories that include reticulate events such as recombination, horizontal gene transfer, and hybridization. Under a Markov model of nucleotide substitution, a phylogenetic network determines a distribution of leaf-patterns. Here, we study the identifiability of the network topology from this distribution under the Jukes-Cantor (JC), Kimura 2-parameter (K2P), and Kimura 3-parameter (K3P) models. Our first result is that the semi-directed network parameter of a level-1 phylogenetic network (modulo redirecting triangles) is fully identifiable under all three models, on a biologically reasonable parameter space in which substitution rates are probabilistic and mixing parameters are non-trivial (i.e., not 0 or 1). In contrast to the generic identifiability established in prior work, this holds at every point of the parameter space, not merely off of a measure-zero subset. Our second result distinguishes phylogenetic networks from phylogenetic trees, on the same parameter space, under JC and K2P. We prove that no phylogenetic network and phylogenetic tree can induce the same leaf-pattern distribution unless the network is a tree, possibly augmented with certain substructures called $$2$$-blobs. This means the presence of reticulate evolution creates, in most cases, a detectable signature in the leaf-pattern distribution. More broadly, these results have consequences for identifiability beyond the models and network classes studied here, including for several coalescent-based models.
The Jukes–Cantor model (1969) treats any substitution of a letter in DNA as equally likely, like a random typo in a word.
Fantastic chimeras are not fiction: science can already recognize genetic traces of hybrids.