The mystery of the origin and development of life is revealed thanks to computer models
Evolution is very slow, so laboratory observations or experiments are almost impossible here. Evolutionists from the University of Michigan decided to get around this problem and find out the reasons for the observed complexity of the appearance and forms of living things, using an evolution simulator. "Lenta.ru" talks about this study.
Evolutionary biologists are still wondering about the complexity of biological organisms and what role different evolutionary mechanisms play in this. One of these mechanisms is natural selection, due to which new variants (alleles) of genes are spread, which contribute to the survival of individual carriers. This may explain the complexity of living organisms, although not always. Sometimes natural selection prevents change by preserving what the animal already has. In this case, one speaks of stabilizing natural selection.
It has been experimentally proven that natural selection is indeed one of the main causes of evolutionary changes, including the spread of new adaptive traits in a population. For example, the American biologist Richard Lenski set up a long-term experiment on the evolution of Escherichia coli. The experiment began in 1988 and continues to this day. Scientists have followed the change of 60 thousand generations of E. coli and found that bacteria, previously unable to feed on sodium citrate, acquired this ability due to mutations in several genes. This gave them an evolutionary advantage among bacteria that grew on citrate-rich media.
Another evolutionary factor is population size. The smaller the population, the stronger the effect of random processes. For example, a natural disaster can lead to the death of all individuals with new alleles, and natural selection will no longer be able to work with them. This is called gene drift, and with every decrease in the number of animals (less than 104 individuals) in the population, the drift increases, weakening the influence of selection.
In molecular evolution, which studies evolutionary mechanisms at the level of genes and their alleles, the role of genetic hitchhiking and drift is well known. Many mutations that lead to the emergence of new gene alleles remain neutral. That is, a new trait either does not arise, and the animal does not change outwardly, or the new trait does not in any way affect the fitness of the individual. The spread of a gene with a neutral mutation, and therefore a trait, is random (gene drift). Another option is also possible. Non-adaptive mechanisms contribute to the accumulation of neutral mutations in the population, which can later lead to the emergence of adaptive traits.
Illustration of gene drift: each time a random number of red and blue balls are transferred from jar to jar, as a result, balls of the same color "win"
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Image: Wikipedia
The size of the animal population in which new alleles spread is very important to the development of complexity. It depends on how much natural selection or gene drift affects. Complexity can develop due to the fact that a number of beneficial mutations arise in a large population, which are favored by natural selection. The larger the population, the more such mutations. Or, in large populations, many accumulating neutral mutations are formed, only some of which are responsible for some external features. These traits add up to the complexity of the organism.
Sometimes evolution comes to a kind of dead end. Paradoxically, negative mutations are sometimes required. Imagine the creature best suited to its environment. Let's say this is a marine animal with a streamlined body and optimal size fins. Any change threatens to upset the balance, and the body will lose its perfection. For example, enlarging fins will become a burden, an animal will lose out to its fellows, and natural selection will not greenlight such a change. However, if a terrible storm occurs and most of the "perfect" individuals die, then genetic drift will come into play. It will allow not only the flawed genes of the large fins to gain a foothold, but also open up space for further evolution. Individuals can either regain optimal fins over time, or compensate for their loss with some other useful properties.
The population that climbs the "hill" of the evolutionary landscape becomes more adaptable, while the top of the hill corresponds to the evolutionary "dead end"
Image: Randy Olson / Wikipedia
To observe all this, very long periods of time are required. Biological experiments supporting evolutionary theories are extremely difficult to implement. Even Lenski's experiment with E. coli, which has a fast generation change and a small genome size, took almost 30 years. To overcome this limitation, evolutionists used the Avida artificial life simulator in their research, published as a press release on Arxiv.org. The aim was to study how population size affects genome size and the totality of all traits (phenotype) of an individual. For simplicity, biologists took a population of asexual organisms and watched "evolution in action."
Avida is an artificial life simulator used for evolutionary biology research. He creates an evolving system of self-replicating (multiplying) computer programs capable of mutating and developing. These digital organisms have an analogue of the genome - a cycle of instructions that allow them to perform any actions, including reproduction. After following certain instructions, the program can copy itself. Organisms compete with each other for a limited resource: computer processor time.
The environment in which digital organisms live and reproduce has a limited number of cells to house programs. When programs take up all the space, new generations are replacing old programs from random cells, regardless of their competitiveness. In this way, a digital analogue of gene drift is achieved. In addition, digital organisms die if they fail to reproduce successfully after a certain number of cycles of instructions.
Image of the Avida world with digital organisms, each of which is a self-replicating program
Image: Elizabeth Ostrowsky / Ostrowsky laboratory
For a program to execute instructions, it requires resources. In Avida, such a resource is a SIP unit (single instruction processing unit), which allows only one instruction to be executed. In total, each organism can have an equal number of SIP units, but in each cycle the resource is unevenly distributed among the programs - depending on the qualities (analog of the phenotype) of digital organisms. If some organism possesses better qualities than another, then it receives more SIP units and manages to execute more instructions in one cycle than its less successful counterpart. Accordingly, it multiplies faster.
The phenotype of a digital organism consists of the features of its "digital metabolism", which give (or do not) enable it to perform certain logical calculations. These traits owe their existence to "genes" that ensure the correct sequence of instructions. Avida checks to see if the body is performing operations correctly and gives it resources according to the amount of code it took to execute the instructions. However, when copying the code, errors may occur - inserting unnecessary fragments or deleting (deleting) existing ones. These mutations alter the ability to compute for better or worse, with insertions enlarging the genome and deletions shrinking.
Digital populations are a convenient object of research. Of course, it will not be possible to test hypotheses related to the influence of genes, epigenetic and other molecular and biochemical factors on evolution. However, they are good at modeling natural selection, drift, and mutation propagation.
The researchers observed the evolution of digital populations of various sizes, from 10 to 10 thousand individuals, passing each through about 250 thousand generations. Not all populations survived during the experiment, most groups of 10 individuals died out. Therefore, scientists simulated the evolution of additional small populations of 12-90 individuals to find out how the likelihood of extinction relates to the development of complexity. Extinction, it turned out, was due to the fact that small populations accumulated deleterious mutations, leading to the appearance of non-viable offspring.
The scientists looked at how genome size changed over the course of the experiment. At the beginning of the "life" of each population, the genome was relatively small, including 50 different instructions. The smallest and largest groups of "organisms" acquired the largest genomes by the end of the experiment, while medium-sized populations shrank their genomes.
Overall, the results showed that very small populations are prone to extinction. The reason for this may be the "Möller ratchet" - the process of irreversible accumulation of harmful mutations in populations of organisms that are incapable of sexual reproduction. Slightly larger populations are unexpectedly able to increase the size of genomes due to mild negative mutations that “roll back” organisms from optimal adaptations. The increase in the size of genomes, in turn, led to the emergence of new phenotypic traits and the complication of the “appearance” of the digital organism.
Large populations also increase genome size and phenotypic complexity, but this is due to rare beneficial mutations. In this case, natural selection acts to promote the spread of such changes. There is also another way of complication: through double mutations, one of which is neutral and does not give any advantages, and the second provides the first with functionality. Medium-sized populations must increase the size of genomes to develop complexity, but beneficial mutations are not so frequent in them, while strong selection removes most of the adaptive changes in genes, and the drift remains too weak. As a result, these populations lag behind small and large populations.
An evolutionary simulator offers an ideal population model and does not fully describe what is happening in reality. For a more complete understanding of the role of adaptive and non-adaptive mechanisms in the development of complexity in living organisms, further research is needed.
Alexander Enikeev