On June 9th, 2022, Daniel Muratore successfully defended his dissertation entitled “Emergence of Marine Biogeochemical Dynamics Across Scales Drive by Complex Microbial and Viral Communities,’’ earning his Ph.D. in quantitative biosciences.

Congratulations, Daniel!

To learn more about Daniel’s dissertation, read the abstract below.

Marine microbial populations are subject to the dual pressures of bottom-up nutrient limitation and top-down infection by abundant viruses. However, top-down and bottom-up controls do not act independently. Environmental conditions also have a large impact on the ecology and evolution of viral populations. This thesis explores the mutual feedbacks between the bottom-up and top-down drivers of marine microbial ecosystems.

The first part of this thesis studies two Lagrangian field campaigns conducted in oligotrophic gyres – one in the North Pacific and one in the Sargasso Sea. Emergent ecosystem-level diel cycles in nutrient uptake and assimilation show partitioning of key limiting resources. We also identify diel coordination of viral gene transcription across vast viral diversity.

The second part studies eco-evolutionary responses of marine virus infection strategies and variable environmental conditions. A comparative metagenomic study of genomic and proteomic nitrogen content across the Eastern Tropical North Pacific oxygen minimum zone identifies genome streamlining in bacteria, archaea, and viruses across nitrogen gradients. Then, we construct and analyze a game theoretic model of the evolution of strategies for viruses and their hosts in iron-limited environments, where viruses can use specialized host iron uptake mechanisms to facilitate infection. We identify conditions for the coexistence of hosts with and without this iron uptake capacity, and viruses that do or do not leverage it for infection.

On November 18th, 2021, Ashley Coenen successfully defended her dissertation entitled “Inferring ecological interactions from dynamics in phage-bacteria communities,’’ earning her Ph.D. in physics.

To learn more about Ashley’s dissertation, read the abstract below.

Characterizing how viruses interact with microbial hosts is critical to understanding microbial community structure and function. However, existing methods for quantifying bacteria-phage interactions are not widely applicable to natural communities. First, many bacteria are not culturable, preventing direct experimental testing. Second, “-omics” based methods, while high in accuracy and specificity, have been shown to be extremely low in power. Third, inference methods based on time-series or co-occurrence data, while promising, have for the most part not been rigorously tested. This thesis work focuses on this final category of quantification strategies: inference methods.

In this thesis, we further our understanding of both the potential and limitations of several inference methods, focusing primarily on time-series data with high time resolution. We emphasize the quantification of efficacy by using time-series data from multi-strain bacteria-phage communities with known infection networks. We employ both in silico simulated bacteria-phage communities as well as an in vitro community experiment. We review existing correlation-based inference methods, extend theory and characterize tradeoffs for model-based inference which uses convex optimization, characterize pairwise interactions in a 5×5 virus-microbe community experiment using Markov chain Monte Carlo, and present analytic tools for microbiome time-series analysis when a dynamical model is unknown. Together, these chapters bridge gaps in existing literature in inference of ecological interactions from time-series data.