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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.

A computational model, based on single-cell features like contractility and polarizability, quantitatively describes cellular dynamics from the single cell level up to small cohorts and confluent tissues.

Selection bias, also known as collider bias, likely explains the apparent protective effect of G6PD deficiency against cerebral falciparum malaria.

The provided framework based on biological models and clinical literature can aid in the optimization of visual cortical prostheses through computational or behavioral simulation experiments, serving as a flexible tool for computational, clinical, and behavioral neuroscientists working on visual neuroprosthetics.

Methylated CpG sites are saturated for T mutations in a sample of 390K human exomes, providing a test case for inferences about fitness effects in human genes, and insight into the interpretation of mutations as pathogenic using reference datasets.

Computer simulations considering actions of the intranuclear enzyme, Topoisomerase-II, in a mechanistic scheme elucidated a capability of enzymes to contribute to controlling chromatin spatial organizations and discovered new characteristic features in eu- and heterochromatin organization caused by the enzymatic activity.

Estimating cell type composition from a gene expression mixture remains a challenging task, as evidenced by how a simple regression model outperforms many state-of-the-art spatial deconvolution methods.

Bacterial population can coordinate individuals of different phenotypes by spatial modulation of their run-and-tumble behaviors, resulting in collective group migration with an ordered structure of phenotypes.

Central vestibular regions in the brainstem and cerebellum perform dynamic Bayesian inference to combine motor commands and sensory signals into an optimal estimate of self-motion.

Emergent property inference, a novel machine learning methodology, learns distributions of neural circuit model parameters that produce computational properties and provides novel scientific insight through the quantification of the rich parametric structure it captures.