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The extinction of bacterial populations by antibiotics is stochastic and can be predicted only probabilistically, not deterministically.

A combined experimental and modeling approach provides insight into potential biases when inferring transcription rates from static mRNA distributions, and shows that correcting for cell-cycle phase and post-transcriptional noise provides rates that agree with live-cell transcription measurements.

A systematic comparison of experimentally measured and theoretically predicted magnitudes of organelle abundance fluctuations suggests that budding yeast produces the maximum level of variability in organelle abundance that can be generated by organelle biogenesis pathways.

Neuronal activity in the striatum keeps track of elapsed time during the time production task while that in the cerebellum correlates with stochastic variation of self-timing in the range of several hundreds of milliseconds.

Zebrafish implement a stochastic recursive algorithm during prey capture that reflects an implicit physical model of the world.

Computational modeling of a central decision neuron of Drosophila reveals an electrotonically compact architecture that is ideally suited to support efficient memory storage within a stochastically connected memory circuit.

Stochastic mRNA and non-coding RNA hybridisation has a major impact on protein expression.

Stochasticity introduced computationally into a gene expression oscillator creates heterogeneity in the time of differentiation of identical cells and offers robustness to the progenitor state and the outcome of cell division.

During Escherichia coli systemic infection, the replication of a minuscule fraction of the inoculum drives organ-specific clearance failures, systemic dissemination, and stochastic infection outcomes.

Quantitative statistical modelling reveals local coordination of stochastic gene transcription dynamics in pituitary tissue, which is relevant for integrated tissue responses to physiological stimuli.