Coupled physiology and gene expression, measured over a two-day time course, reveals specific time-of-day responses to the early stages of drought in Brassica rapa.

Regulatory networks of genes controlling different aspects of insect reproduction have been identified by a systems-level analysis of quantitative phenotypic information obtained from the loss of individual cell signaling genes.

Integrating decades of small-scale experiments with human gene expression data provides a systems-level view of the coordinated molecular processes triggered by spinal cord injury, and their relationship to recovery.

Realistic reaction-diffusion signaling networks that include cell-autonomous factors can robustly form self-organizing spatial patterns for any combination of diffusion coefficients without requiring differential diffusivity.

Graph analysis revealed that spontaneous fluctuations of episodic-memory encoding performance associate with time-varying brain connectivity networks.

Single cell expression data can be used to determine how regulatory transcription factors and target genes are connected, and is especially useful when studying transcription factors controlling heterogeneous cell states.

Basal ganglia gene coexpression patterns shift across the sensorimotor critical period for vocal learning.

A neural network model with asymmetric synaptic interactions can store and retrieve multiple continuous memory sequences with their temporal structure.

Conventional studies have focused on enzymatic residues directly involved in catalysis; dissecting a potential interaction network within which these ‘catalytic residues’ are embedded provides insights fundamental to enzyme function, evolution, and engineering.

A gene network analysis approach reveals a conserved small regulatory RNA that is crucial for bacterial cell survival across distinct stress conditions.