Positional Information Storage in Sequence Patterns

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We build a model of storage of well-defined positional information in probabilistic sequence patterns. Once a pattern is defined, it is possible to judge the effect of any mutation in it. We show that the frequency of beneficial mutations can be high in general and the same mutation can be either advantageous or deleterious depending on the pattern’s context. The model allows to treat positional information as a physical quantity, formulate its conservation law and to model its continuous evolution in a whole genome, with meaningful applications of basic physical principles such as optimal efficiency and channel capacity. A plausible example of optimal solution analytically describes phase transitions-like behavior. The model shows that, in principle, it is possible to store error-free information on sequences with arbitrary low conservation. The described theoretical framework allows one to approach from novel general perspectives such long-standing paradoxes as excessive junk DNA in large genomes or the corresponding G- and C-values paradoxes. We also expect it to have an effect on a number of fundamental concepts in population genetics including the neutral theory, cost-of-selection dilemma, error catastrophe and others.