ImmunoCluster permits nonspecialist users to interrogate liquid and imaging mass, and flow cytometry datasets, resolving novel layers of cellular heterogeneity and insight, as well as producing publishable outputs and figures.

Radial plant growth produces large parts of terrestrial biomass and can be computationally simulated with the help of an instructive framework of intercellular communication loops.

Experimental efforts to validate the output of a computational model that predicts new uses for existing drugs highlights the inherently complex nature of cancer biology.

A computational method has been developed for predicting the cell-type-specific binding of transcription factors to nucleosomes, and involves integration of ChIP-seq, MNase-seq, and DNase-seq data with details of nucleosome structure.

Theoretical ideas have a rich history in many areas of biology, and new theories and mathematical models have much to offer in the future.

Three-dimensional fluorescence imaging of microbial eukaryotes in environmental samples allows accurate automated taxonomic profiling and quantitative data about ultrastructures and interactions of organisms.

With mathematical modeling being an important source of insight for microbial communities, we may need to move beyond commonly-used pairwise models that do not capture microbial interactions.

Data-driven systems biology models of signaling predict cellular response to untested perturbations and can nominate drug combinations to overcome drug resistance in cancer cells.

A newly developed computational framework provides an overview of the extent, diversity, and relevance of the genes of unknown function in genomes and metagenomes.