Researchers at the University of California, Berkeley, have identified an algorithm to illustrate gene strategy during reproduction.
The research team, made up of evolutionary and computer scientists, aimed to understand how genetic diversity is always maintained, despite natural selection.
The research, published in the Proceedings of the National Academy of Sciences, is the first to combine algorithm, evolution theory and game theory.
Evolutionary game theory
The mixing of genes to create a new genetic set or ‘sexual or genetic recombination’ can be, according to the research, illustrated by the algorithm called the multiplicative weight update algorithm (MWUA).
This algorithm works by ‘maximising the trade-off between going all-in on a successful genetic trait and hedging its bets by minimizing its commitment to any one trait.’ (Sarah Young, UC Berkley Media Centre).
Rather than building only upon successful traits, genes follow the strategy of the algorithm. Mimicking the behaviour of a patient gambler or a pension pot nearing maturity, genes invest in all traits, just in case one trait is needed in future.
Researcher Umesh Vazirani, talks of the ‘paradox in evolution’. In its aim to create the perfect human, genes knit to make the ideal genetic set , only for it to be diluted in the next generation when the genetic combination is then halved again.
It’s a risky business. Does the genetic mix further improve on the ideal, or are half of the ideal traits lost?
It makes sense to invest in all traits, just in case. You don’t want to lose a trait through the unpredictable game of sexual reproduction.
Umesh Vazirani said; “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.”
Genetic winners and losers
Researcher Christos Papadimitriou, in questioning why there is so much genetic combination if the point of evolution is to select the best genes; “It’s the same (in evolutionary biology),” he told UC Berkley Media Centre. “The players who participate in the game are the genes, and they look at how the various alleles performed in the previous generation and boost the good performers and decrease a little the bad performers.”
The research study concentrated on the ‘weak’ selection in evolution to view the outcome in genetic selection. The analysis showed that selection appear to offer ‘gentle advantages’ that create slow and gradual improvements over time, rather than a sudden leap.
Christos Papadimitrio: “We noticed that with variation, genes have a preference for a 50-50 distribution rather than a 90-10 distribution. If we use a gambling analogy, genes don’t want to go all-in. They want to hedge their bets. Even if there is an extremely successful genetic trait, evolution doesn’t want to let the genes for the other traits go extinct in case they’re needed later.”
“Now we’re noticing that nature uses this algorithm in evolution. It makes it easier to understand why evolution has been so successful,” Christos Papadimitriou.
Research in PNAS:
Algorithms, games, and evolution by Erick Chastaina, Adi Livnat, Christos Papadimitriouc, and Umesh Vaziranic
Berkeley media release: http://newscenter.berkeley.edu/2014/06/16/algorithm-explains-sex-in-evolution/
Image with thanks to creative commons licensed (BY) flickr photo by jared: http://flickr.com/photos/generated/942345473