Foundations of Quantitative Viral Ecology
Microbes are essential to human health and the maintenance of planetary-scale biogeochemical cycles. Yet, microbes get sick too. There are an estimated 1030 viruses on Earth, the vast majority of which infect microbes. Ecologically relevant viral infections of microbes have long been thought to culminate in the death of the infected microbial cell via lysis and the release of new virus particles. However, recent discoveries show that virus genomes are incorporated into nearly every microbial genome, fundamentally altering host cell physiology and behavior. These long-term, i.e., ‘persistent’, viral infections may help cells grow, defend cells against (deadly) lytic infections by other viruses, or act as weapons against competitors – raising new questions on the rules of viral life. Ongoing work aims to understand virus-host interactions across a continuum from lysis to latency, including:
- Cell-centric measures of viral fitness
- Coevolutionary dynamics of viruses and bacteria
- Linking individual infection outcomes to population and ecosystem dynamics.
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A random bipartite network. Image from Weitz et al., 2013.
BiMat
BiMat is a MATLAB package designed for the analysis and visualization of bipartite ecological networks, though it may be used for any type of bipartite networks. The package aims to consolidate some of the most popular algorithms and metrics for the analysis of bipartite ecological networks under the same MATLAB environment. More specifically, the package focuses on bipartite modularity and nestedness values. Further, BiMat includes the necessary tools for analyzing the statistical significance of these values, together with tools for visualizing bipartite networks in such a way that any of these patterns becomes more apparent to the user.
