title: Inferring phage-bacteria infection networks from time-series data.
abstract: In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infect which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time-series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We usein silicoexperiments to analyse the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network. This approach also mitigates against the possibility of evolutionary changes to relevant phenotypes during the time course of measurement.
authors.affiliation: School of Physics , Atlanta, GA, USA.
authors.forename: Luis F
authors.initials: LF
authors.lastname: Jover
authors.affiliation: School of Electrical and Computer Engineering , Atlanta, GA, USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Romberg
authors.affiliation: School of Biological Sciences, Georgia Institute of Technology , Atlanta, GA, USA.
authors.forename: Joshua S
authors.identifier: 0000-0002-3433-8312
authors.initials: JS
authors.lastname: Weitz
keywords: complex communities; inference; microbial ecology; nonlinear dynamics; viral ecology