Geneticists are by now familiar with Bioinformatics, which uses massive computer power to manage our massive datasets. They are less familiar with information theory, which concerns the analysis of how the computers themselves function – ie how information can be transmitted or manipulated. Information theory’s founder, Claude Shannon, based it on gas theory, but he focused on electronic information. However, Shannon recognised that the theory applies to any kind of information, and he even made an early attempt to apply it to genes - hampered by not knowing that genes were DNA.
Around the same time, geneticists such as Kimura were beginning to use the same gas theory, to produce the equations that fill our genetics textbooks today, which forecast heterozygosity, numbers of allelic types, etc under a wide range of conditions. Surprisingly, Shannon’s method was taken up enthusiastically not by geneticists, but in ecology, where it is used only as a raw measurement of diversity, with limited ability to make testable predictions.
Two important things have now changed. First, it has been recognised that ecology and evolution share four basic processes – dispersal, random change, adaptation, and generation of novelty (speciation, mutation, and recombination). Secondly, the last decade has seen a burgeoning of uses of Shannon information for molecular diversity, including predictive equations, at all levels from sequences and expression in cells through to macroevolution.
As a result, we are set to see analyses integrated at all these levels, with the bonus that information/entropy provides a general predictive approach, not just for genetics and ecology, but for other studies such as the physical environment, and may even extend to assist with evolutionary programming in AI.
For further detail, see: Sherwin et al. 2017. TREE doi.org/10.1016/j.tree.2017.09.012
Geneticists are by now familiar with Bioinformatics, which uses massive computer power to manage our massive datasets. They are less familiar with information theory, which concerns the analysis of how the computers themselves function – ie how information can be transmitted or manipulated. Information theory’s founder, Claude Shannon, based it on gas theory, but he focused on electronic information. However, Shannon recognised that the theory applies to any kind of information, and he even made an early attempt to apply it to genes - hampered by not knowing that genes were DNA.
Around the same time, geneticists such as Kimura were beginning to use the same gas theory, to produce the equations that fill our genetics textbooks today, which forecast heterozygosity, numbers of allelic types, etc under a wide range of conditions. Surprisingly, Shannon’s method was taken up enthusiastically not by geneticists, but in ecology, where it is used only as a raw measurement of diversity, with limited ability to make testable predictions.
Two important things have now changed. First, it has been recognised that ecology and evolution share four basic processes – dispersal, random change, adaptation, and generation of novelty (speciation, mutation, and recombination). Secondly, the last decade has seen a burgeoning of uses of Shannon information for molecular diversity, including predictive equations, at all levels from sequences and expression in cells through to macroevolution.
As a result, we are set to see analyses integrated at all these levels, with the bonus that information/entropy provides a general predictive approach, not just for genetics and ecology, but ...
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