Michael J. Follows

Proteomes reflect size-based growth constraints across diverse microbes

The paper uses proteomic data to show that microbial growth rates are constrained by size-related factors, with intermediate-sized cells having the highest growth potential.

Invariant scaling of the Species Abundance Distribution in observed and simulated marine plankton communities

The paper analyzes Species Abundance Distributions (SADs) in marine plankton communities from surveys and simulations, finding invariant scaling despite environmental and taxonomic variation.

Estimating absolute microbial abundances from metabarcoding anchored to cytometry data

This paper calibrates metabarcoding data using flow cytometry to estimate absolute microbial abundances and gene concentrations in oceanic samples.

Nutrient response strategies drive coastal range shifts of phytoplankton taxa

The paper examines how nutrient response strategies of two rare phytoplankton taxa contribute to their increasing prevalence in Cape Cod Bay over 20 years.

Four Potential Mechanisms Underlying Phytoplankton-Bacteria Interactions Assessed Using Experiments and Models

The paper assesses four mechanisms of phytoplankton-bacteria interactions using models and experiments, finding two distinct modes of interaction and highlighting the need for additional mechanisms like allelopathy.

Cellular-level control on global ocean deoxygenation driven by phytoplankton ecophysiology

The paper estimates phytoplankton elemental composition and shows how cellular-level changes affect global ocean oxygen consumption and hypoxic zone expansion.

Microbial reaction rate estimation using proteins and proteomes

The paper estimates microbial reaction rates using proteomic data, finding that global proteomes can predict reaction rates with high accuracy without needing detailed mechanistic knowledge.

Reverse engineering environmental metatranscriptomes clarifies best practices for eukaryotic assembly

The paper introduces a metatranscriptome assembly pipeline and validates it using simulated data, then applies it to real datasets to improve taxonomic and functional discovery.