Deep learning using neural networks relies on a class of machine-learnable models
constructed using 'differentiable programs'. These programs can combine mathematical …

Much has been made about JLW Thudichum's colorful, and one could say clairvoyant,
naming of sphingosine “in commemoration of the many enigmas which it presented to the …

Practical experiments drive important scientific discoveries in biology, but theory‐based
research studies also contribute novel—sometimes paradigm‐changing—findings. Here, we …

Mathematical modeling of biological processes provides deep insights into complex cellular
systems. While quantitative and continuous models such as differential equations have been …

With the release of the landmark report Toxicity Testing in the 21st Century: A Vision and a
Strategy, the US National Academy of Sciences, in 2007, precipitated a major change in the …

G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to
respond to specific cues in their environment. However, the relationship between stimulus …

Advances in molecular biology and toxicology are paving the way for major improvements in
the evaluation of the hazards posed by the large number of chemicals found at low levels in …

Quantitative computational models play an increasingly important role in modern biology.
Such models typically involve many free parameters, and assigning their values is often a …

In metastatic cancer, the degree of heterogeneity of the tumor microenvironment (TME) and
its molecular underpinnings remain largely unstudied. To characterize the tumor–immune …

What can we learn about controlling a system solely from its underlying network structure?
Here we adapt a recently developed framework for control of networks governed by a broad …