Ecological Scaling of Temporal Fluctuations with Bacterial Abundance in Gut Microbiota Depends on Functional Properties of Individual Microbial Species and Bacterial Communities.

Macroecological relationships that describe various statistical associations between species' abundances, their spatial, and temporal variability are among the most general laws in ecology and biology. One of the most commonly observed relationships is a power-law scaling between means and variances of temporal species abundances, known in ecology as Taylor's law. Taylor's law has been observed across many ecosystems, from diverse plant and animal ecosystems to complex microbial communities.

Cancer cells depend on environmental lipids for proliferation when electron acceptors are limited.

Production of oxidized biomass, which requires regeneration of the cofactor NAD, can be a proliferation bottleneck that is influenced by environmental conditions. However, a comprehensive quantitative understanding of metabolic processes that may be affected by NAD deficiency is currently missing. Here, we show that de novo lipid biosynthesis can impose a substantial NAD consumption cost in proliferating cancer cells.

High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.

Motivation: Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above.

Macroecological dynamics of gut microbiota.

The gut microbiota is now widely recognized as a dynamic ecosystem that plays an important role in health and disease. Although current sequencing technologies make it possible to explore how relative abundances of host-associated bacteria change over time, the biological processes governing microbial dynamics remain poorly understood. Therefore, as in other ecological systems, it is important to identify quantitative relationships describing various aspects of gut microbiota dynamics.

Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks.

Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae.

One third of dynamic protein expression profiles can be predicted by a simple rate equation.

Cells respond to environmental stimuli with expression changes at both the mRNA and protein level, and a plethora of known and unknown regulators affect synthesis and degradation rates of the resulting proteins. To investigate the major principles of gene expression regulation in dynamic systems, we estimated protein synthesis and degradation rates from parallel time series data of mRNA and protein expression and tested the degree to which expression changes can be modeled by a simple linear differential equation. Examining three published datasets for yeast responding to diamide, rapamycin, and sodium chloride treatment, we find that almost one-third of genes can be well-modeled, and the estimated rates assume realistic values.