Scale-Free Spanning Trees and Their Application in Genomic Epidemiology

Abstract

We study the algorithmic problem of finding the most "scale-free-like"spanning tree of a connected graph. This problem is motivated by the fundamental problem of genomic epidemiology: given viral genomes sampled from infected individuals, reconstruct the transmission network ("who infected whom"). We use two possible objective functions for this problem and introduce the corresponding algorithmic problems termed m-SF (-scale free) and s-SF Spanning Tree problems. We prove that those problems are APX- and NP-hard, respectively, even in the classes of cubic and bipartite graphs. We propose two integer linear programming (ILP) formulations for the s-SF Spanning Tree problem, and experimentally assess its performance using simulated and experimental data. In particular, we demonstrate that the ILP-based approach allows for accurate reconstruction of transmission histories of several hepatitis C outbreaks.