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A primordial developmental toolkit shared by all vertebrates, and described by a theory of the mathematician Alan Turing, sets the growth pattern for all types of skin structures.
Some researchers are using a complexity framework thought to be purely theoretical to understand evolutionary dynamics in biological and computational systems.
New results emerging from graph theory prove that the way a population is organized can guarantee the eventual triumph of natural selection — or permanently thwart it.
Elastic springs help tiny animals stay fast and strong. New work is finding what size critters must be to benefit from the springs.
The long, variable times that some diseases incubate after infection defies simple explanation. An idealized model of tumor growth offers a statistical solution.
The computer scientist Barbara Engelhardt develops machine-learning models and methods to scour human genomes for the elusive causes and mechanisms of disease.
The evolutionary biologist Jessica Flack seeks the computational rules that groups of organisms use to solve problems.
A disarmingly simple model of ecology does everything well — except predict how rapidly nature can change. Can it become more realistic while still avoiding all of biology’s messy complexities?
The biological world is computational at its core, argues computer scientist Leslie Valiant.