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

A novel computation tool for microbial community modeling predicts the evolution and diversification of E. coli in laboratory evolution experiments and gives insight into the underlying metabolic processes.

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.

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.

Predators may amplify multiple-host parasite spreading in systems with multiple transmission routes.

Genetic barriers to gene flow maintain divergent linked substitution rates in hybridization.

A mathematical modelling approach to understanding zebrafish stripe pattern formation exemplifies a biological rule-set sufficient to generate wild-type and a diverse range of mutant patterns.

Mathematical and experimental analyses suggest that despite their complex architectures, multiple metazoan signaling pathways act in physiological contexts as linear signal transmitters.

Mathematical models of the build-up and depletion of the hypnozoite reservoir in the liver can inform the design of treatment strategies for preventing Plasmodium vivax relapse infections.

Evolutionary graph theory solves the longstanding puzzle of why diverse infectious diseases and cancers show similar (approximately lognormal) distributions of their incubation periods.