Browse our latest Computational and Systems Biology articles

A combination of electrophysiological and optogenetic approaches revealed a causal role for glutamatergic neurons in the prelimbic cortex in regulating individual differences in decision-making strategies during a threat-avoidance vs. reward-approach conflict paradigm in rats.

Mechanotransduction signaling analysis suggests that mechanical tension can sensitize, desensitize, and reverse cell responses to biochemical agonists, as well as provide mechano-adaptive targets for regionally specific drug responses.

A computational model of the initial visual encoding together with Bayesian image reconstruction quantifies how that encoding, combined with the statistical regularities of natural images, shapes key aspects of visual perception.

Large bursts of brain activity in patients with epilepsy are geometrically related to seizure propagation.

A deep mutational scan of the ADAR2 deaminase domain creates a map for designing tailored ADAR2 variants, and splitting the deaminase between residues 468 and 469 enables highly transcript-specific RNA editing.

Sleep-wake patterns, together with a suprachiasmatic nuclei-independent circadian factor, are necessary and sufficient to maintain high-amplitude nychthemeral rhythms in PERIOD-2.

A new computational analysis of decision making in mice shows sex biases in value-updating while exploring unknown options, with male mice tending to explore longer than females due to updating values more slowly.

An inferred mechanistic model that connects sequence (genotype) to function (constitutive gene expression phenotype) for any random sequence in Escherichia coli reveals the structure of constitutive promoters and how they evolve.

Personalized virtual-heart technology for arrhythmia risk assessment could transform the management of hypertrophic cardiomyopathy patients, eliminating many unnecessary primary-prevention defibrillator deployments while ensuring patients at high risk for arrhythmia are adequately protected.

Optimising the use of transcriptomics enables screening of thousands of compounds and illustrates an approach that yields quantitative pharmacology at the single-gene and pathway level.