What do you get when you mix theorists in computer science with evolutionary biologists? You get an algorithm to explain sex. It turns out that 155 years after Charles Darwin first published “On the Origin of Species,” vexing questions remain about key aspects of evolution, such as how sexual recombination and natural selection produced the teeming diversity of life that exists today.
The answer could lie in the game that genes play during sexual recombination, and computer theorists at UC Berkeley have identified an algorithm to describe the strategy used by these genes in this game. Their proposal, published today (Monday, June 16) in the online Early Edition of the Proceedings of the National Academy of Sciences, addresses the dueling evolutionary forces of survival of the fittest and of diversity. “There is a paradox in evolution,” said study co-author Umesh Vazirani, UC Berkeley professor of electrical engineering and computer sciences and director of the Berkeley Quantum Computation Center. “Suppose the mixing of genes through sexual recombination helps create a perfect individual. That perfection gets lost in the next generation because with sex, the offspring only inherits half the perfect parent’s genes. If sexual recombination speeds up the rate at which good solutions are found, it also speeds up the rate at which those solutions are broken apart.”
This question was among many challenges in evolutionary biology tackled this past spring at the Simons Institute for the Theory of Computing at UC Berkeley. The institute brought together theoretical computer scientists with researchers from evolutionary biology, physics, probability, and statistics to look at evolution through the lens of computation.
“The key to this work is the making of a connection between three theoretical fields: algorithms, game theory and evolutionary theory,” said Livnat. “This new bridge is an uncommon advance that opens up possibilities for cross-fertilization between the fields in the future.”