Browse our latest Computational and Systems Biology articles

Encoding individual behavioral traits into a low-dimensional latent representation enables the accurate prediction of decision-making patterns across distinct task conditions.

Targeted computational redesign of an IL-7 superkine with enhanced folding efficiency and receptor affinity demonstrates superior immune-stimulatory and antitumor activity over wild-type IL-7.

Multi area RNN models fitted to in-vivo cortical activity predict behavioral changes induced by optogenetic perturbations, if biologically informed connectivity constraints on the optogenetically targeted inhibitory neurons are applied.

A simple statistical model captures the essential features of object motion in a large database of natural scenes, helping to shed light on the challenges posed to sensory and motor systems.

A new CRISPR-based approach for tuning gene expression up and down allowed identification of how other genes' expression responds to changes of transcription factors.

Machine learning models reveal that histone marks are predictive of gene expression across human cell types and highlight important nuances between natural control and the effects of CRISPR-Cas9-based epigenome editing.

A low-dimensional voice latent space derived from deep learning captures speaker-identity representations in the temporal voice areas and supports reconstruction of voices preserving identity information.

Collective frequency-selective resonance in depolarised cardiac tissue enables reversible switching between quiescent and ectopic oscillations through hidden bi-stability, which is shaped by light intensity, illuminated area, and pacing frequency.

The morphogenesis of brain folding patterns across species is studied by physical gel models, simulations, and a quantitative morphometric analysis.

In spontaneous intracerebral hemorrhage patients, features of hematoma expansion can be visualized and predicted from non-contrast computed tomography using transport-based morphometry.