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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.
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