RT Journal Article
SR Electronic
T1 Inferring phage-bacteria infection networks from time series data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 051581
DO 10.1101/051581
A1 Luis F. Jover
A1 Justin Romberg
A1 Joshua S. Weitz
YR 2016
UL http://biorxiv.org/content/early/2016/05/03/051581.abstract
AB In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects 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 use in silico experiments to analyze 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 and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.