Stochastic tuning of gene expression could be a common mechanism through which eukaryotic cells adapt to challenging external environments, potentially including survival of infectious organisms within the host and adaptation of cancer cells to chemotherapy.

A new high-throughput method for single-cell RNA-seq in yeast cells shows how stochastic expression of glucose-repressed genes contributes to cell-to-cell differences during adaptation to an environmental change.

Visualized odor encounters show that Drosophila navigate spatiotemporally complex odor plumes using random walks biased by the timing of brief and unpredictable odor encounters.

How gene expression noise is regulated is critical for cell fate and tissue patterning.

An optimal wire and function trade-off emerges from noisy growth and stochastic retraction during Drosophila class I ventral posterior dendritic arborisation (c1vpda) dendrite development.

A reanalysis of the stochastic model of organelle production (Mukherji and O'Shea, 2014) suggests that this model requires significant further discussion.

Real-time single-molecule visualization of transcription and splicing in living cells reveals that RNA synthesis and processing can occur through multiple pathways on the same gene.

Microbial macroecological patterns under coarse-graining are captured by a null model of ecology, a result which was leveraged to successfully predict the relationship between fine and coarse-grained diversity.

New modelling, statistics, and experiments show that cellular populations of mitochondrial DNA (mtDNA) evolve during development according to solvable stochastic dynamics involving binomial partitioning and random turnover, facilitating a predictive and quantitative theory of the mtDNA bottleneck.

Multivariate stochastic volatility models provide insights into understanding the dynamics and connectivity of neural signals during cognitive tasks.