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This article is over 4 years old Computerised image of a fragment of the human genome. AP Original story reprinted with permission from Quanta magazine, an editorially independent division of SimonsFoundation. Quanta In what appears to be the first study of its kind, computer scientists report that an algorithm discovered more than 50 years ago in game theory and now widely used in machine learning is mathematically identical to the equations used to describe the distribution of genes within a population of organisms.

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Researchers may be able to use the algorithm, which is surprisingly simple and powerful, to better understand how natural selection works and how populations maintain their genetic diversity.

By viewing evolution as a repeated game, in which individual players, in this case genes, try to find a strategy that creates the fittest population, researchers found that evolution values both diversity and fitness. Some biologists say that the findings are too new and theoretical to be of use; researchers don't yet know how to test the ideas in living organisms.

Others say the surprising connection, published Monday in the advance online version of the Proceedings of the National Academy of Sciences, may help scientists understand a puzzling feature of natural selection: The fittest organisms don't always wipe out their weaker competition. Indeed, as evidenced by the menagerie of life on Earth, genetic diversity reigns.

For example, imagine that you have 10 financial experts giving you advice on how to invest your savings. Each day you have to choose to follow one of them. At the start of the investment period, you know nothing about how well each expert performs.

But every day, the multiplicative weights update algorithm, as it is called, instructs you to boost the probability of choosing the experts who have given the best advice and decrease it for those who have performed poorly.

They were working with equations commonly used in population genetics, first developed nearly a century ago, that describe how the frequencies of certain genetic variations change with each generation. For example, plants that flourish in the current climate might dwindle as global warming alters conditions. When they showed the equations to Umesh Vazirani, pictured, a computer scientist at Berkeley, he noticed parallels to a repeated coordination game — a scenario in game theory in which success depends on the players choosing mutually beneficial options.

Evolution and entropy

As an example, consider a situation in which two prisoners are tempted to turn on each other. If one talks, both lose; if neither talks, both win. Neither prisoner knows what the other will do.

  1. What is important is that problems with reality testing may occur in patients with BPD Fiqueierdo, 2006. For example, Tay—Sachs disease is a genetically heterogeneous Mendelian disorder with an increased prevalence of 0.
  2. Where to go from Here?
  3. Though there is little epidemiological data on sleep disorders among persons diagnosed with BPD, cross-sectional studies show that sleep disorders are prevalent in 15—95. After all, with an estimated prevalence rate of 2.
  4. Instead, evidence is accumulating that founder effects and drift, but not Darwinian selection, might have caused common allele frequency variability, and a causal link between common variants and common disorders has not been substantiated for most disorders 21 , 41 , 42. The pattern is seen in two or more of the following areas.
  5. Schizoid personality disorder is more prevalent in males than females.

This scenario is different than the well-known prisoner's dilemma. Umesh Vazirani, a computer scientist at Berkeley, first noticed that the equations used in population genetics resemble a powerful algorithm in computer science. Peg Skorpinski Viewing the algorithm through the lens of evolution, genes are the players, and each gene has a number of different strategies in the form of genetic variations, or alleles.

One variant of a gene might make a plant tolerate warmer temperatures or drier soil, for instance. The game is played over and over again; at the end of each round, the gene, or player, evaluates how well each of its alleles performed in the current genetic environment and then boosts the weight of the good performers and downsizes the weight of poor performers.

The researchers said the findings will provide a new way to examine the role of sex in evolution. For example, Papadimitriou said he believes that part of its role is to carry out the multiplicative weights update algorithm, though he hasn't yet proven this mathematically.

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Traditional applications of game theory to evolution examine how evolutionary processes shape an individual's behavior. They have also been used to study the evolution of altruism and other properties. The new study focuses on genes rather than individual organisms, and on the genetic makeup of the population instead of behavior.

The approach could illuminate a long-standing mystery in population biology.

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Just as in the financial world, where it's best to keep a diversified portfolio, Vazirani and his collaborators found that the algorithm values both fitness and diversity. You might be tempted to place all your money on a soaring stock. But if circumstances change and that stock starts to tank, you're better off having invested in a more balanced selection. Similarly, an organism's genes may be perfectly tailored to a particular set of environmental conditions, but if those conditions change, a genetically diverse population is more likely to survive.

But they have struggled to explain how such diversity is maintained. In the short term, one would expect diversity to drop as the fittest members of a population spread, knocking out the weaker, genetically dissimilar members.

How do long-term needs surmount the short-term pressures? The findings provide a "speculative suggestion" for how this might happen, though the authors don't propose a specific mechanism, said Nick Barton, a biologist at the Institute of Science and Technology in Austria who was not involved in the study.

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Stearns and others in the field say it's too soon to assess how the findings will affect our understanding of evolution. Even though the connection between different fields is interesting, "it does not actually help us understand biological evolution," said Chris Adami, a physicist and computational biologist at Michigan State University, who was not involved in the study.

Although mathematicians and computer scientists regularly publish in the field, biologists disagree over how much their contributions have done to shape it. Sometimes it can take decades before the right technology or approach arises to test a new theory, Stearns said.

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The equations in the study are based on certain assumptions that may limit their applicability to the real world. For example, the equations don't account for mutations, which would introduce new alleles, or strategies, into the game.

Adding this factor makes the mathematics much more complex. Some say this simplification is a serious drawback, while others maintain that it is not so important in the short term, when existing variations have the strongest impact.

  1. Similarly, an organism's genes may be perfectly tailored to a particular set of environmental conditions, but if those conditions change, a genetically diverse population is more likely to survive.
  2. Single gene Mendelian transmission. The results suggest that individuals exposed to severe traumatic events during childhood are more likely to develop dissociative symptoms.
  3. Based on recent results, it should be recognized that these approaches are at least complementary in studies of schizophrenia and other neuropsychiatric disorders. Disorders of emotional processes in patients with BPD seem to occur not only in the waking state, but also during dreaming, as in the case of nightmares Simor et al.
  4. In the case of dreams, conscious cognition, which is the most important cue that would help differentiate between internally generated memories and those generated externally is not present.
  5. You might be tempted to place all your money on a soaring stock.

But to know whether that means anything, you have to start departing from that point. According to the standard view of evolution, the further a generation lies in the past, the less impact it has on the present — your ancestors from 1,000 years ago probably had less effect on your fitness than your grandparents.

But if the Berkeley team's insights hold up, "it shows us that every past generation contributes equally to what happens in the next generation," Stearns said.

This view that evolution optimizes not just mean fitness but mean fitness and entropy is not well known, "but I think it's a deep observation," Adami said. The Berkeley team isn't the first to highlight the role entropy might play in evolution. But until now, the subject has mainly been of interest to mathematicians rather than biologists.