Browse our latest Computational and Systems Biology articles

Asymmetries between the shape of the left and right human cortex are highly unique to individuals, akin to a neuroanatomical fingerprint, related to cognitive function, and primarily driven by person-specific environmental influences.

Deep mutational scanning of homologous proteins shows conservation in allosteric mechanisms but differences in molecular details within the protein family.

An evolutionary algorithm is used to build gene networks implementing cell size control, and suggests multiple ways for evolution to first build sizers and turn them into adders depending on evolutionary constraints.

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.

Context factors such as experimental task and model parameterization can significantly impact modeling results, such that across tasks, the same participants show different fitted parameter values, and parameters capture different cognitive processes across tasks.

CD4+ T cells responding to chronic lymphocytic choriomeningitis virus infection display transcriptional heterogeneity that is marked by both lineage-specific gene expression profiles and core gene expression programs that are upregulated and conserved across multiple distinct populations of T helper cells.

The organization of olfactory projection neurons and their synaptic connectivity with third-order neurons studied along with olfactory responses suggest that the labeled-line design is partially at work in the Drosophila olfactory system.

Molecular dynamics simulations reveal that D-serine competes with glutamate for binding to the NMDA receptor, a finding supported by electrophysiology experiments with consequences for D-serine-focused therapeutic strategies for myriad neurological disorders.

Rendering compound vision at high speed with high precision, enabling rapid, data-driven exploration of the compound eye design space.

AIDPS derived by 76 machine-learning algorithm combinations in 13 independent multicenter cohorts exhibited superior capability than clinical traits and 86 published prognostic signatures and could serve as an ideal biomarker for stratified management and individualized treatment of pancreatic cancer.