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Biology
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Evolutionary Biology from an Information Theory Approach
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Evolutionary Biology from an Information Theory Approach

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Evolutionary Biology".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 3587

Special Issue Editor

Prof. Dr. Antonio Carvajal-Rodríguez

E-Mail Website
Guest Editor
Department of Biochemistry, Genetics and Immunology, University of Vigo, 36310 Vigo, Spain
Interests: population genomics; evolutionary theory; simulation; information theory; evolutionary patterns

Special Issue Information

Dear Colleagues,

The mathematical theory of information began with the publication by the mathematician Claude Shannon of an article in 1948 where he formalizes the process of information exchange in message communication. It soon became clear that the theory had much broader applications. In biology, it was adopted by ecology and population genetics to measure species diversity, and more recently, it has had a significant impact in fields such as neurobiology and bioinformatics. In evolutionary biology, the basic equations of population genetics describing changes in gene frequency can be reformulated in terms of information. Additionally, the connection of information theory with the evolution of biological complexity and molecular biology has also been described. Recent studies seem to show that information theory may be the appropriate framework to describe evolutionary processes.

This Special Issue aims to contribute to the theoretical development of certain aspects of population genetics from the perspective of information theory. The objective is related to the connection between information and biological evolution. This relationship can be studied at various levels, such as information and changes in gene frequencies within the population, the organization of nucleotide sequences in DNA, information accumulated through natural selection, information in animal behavior, in the evolution of plants, in ecosystem interactions, and so on.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: any aspect of evolutionary biology connected with information theory.

I/We look forward to receiving your contributions.

Prof. Dr. Antonio Carvajal-Rodríguez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evolution
  • population genetics
  • information
  • Kullback–Leibler
  • Jeffreys divergence
  • population stability index

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Published Papers (2 papers)

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Research

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23 pages, 378 KiB  
Article
On Non-Random Mating, Adaptive Evolution, and Information Theory
by Antonio Carvajal-Rodríguez
Biology 2024, 13(12), 970; https://doi.org/10.3390/biology13120970 - 25 Nov 2024
Viewed by 1050
Abstract
Population genetics describes evolutionary processes, focusing on the variation within and between species and the forces shaping this diversity. Evolution reflects information accumulated in genomes, enhancing organisms’ adaptation to their environment. In this paper, I propose a model that begins with the distribution [...] Read more.
Population genetics describes evolutionary processes, focusing on the variation within and between species and the forces shaping this diversity. Evolution reflects information accumulated in genomes, enhancing organisms’ adaptation to their environment. In this paper, I propose a model that begins with the distribution of mating based on mutual fitness and progresses to viable adult genotype distribution. At each stage, the changes result in different measures of information. The evolutionary dynamics at each stage of the model correspond to certain aspects of interest, such as the type of mating, the distribution of genotypes in regard to mating, and the distribution of genotypes and haplotypes in the next generation. Changes to these distributions are caused by variations in fitness and result in Jeffrey’s divergence values other than zero. As an example, a model of hybrid sterility is developed of a biallelic locus, comparing the information indices associated with each stage of the evolutionary process. In conclusion, the informational perspective seems to facilitate the connection between cause and effect and allows the development of statistical tests to perform hypothesis testing against zero-information null models (random mating, no selection, etc.). The informational perspective could contribute to clarify, deepen, and expand the mathematical foundations of evolutionary theory. Full article
(This article belongs to the Special Issue Evolutionary Biology from an Information Theory Approach)

Review

Jump to: Research

16 pages, 1378 KiB  
Review
Pan-Evo: The Evolution of Information and Biology’s Part in This
by William B. Sherwin
Biology 2024, 13(7), 507; https://doi.org/10.3390/biology13070507 - 8 Jul 2024
Viewed by 1765
Abstract
Many people wonder whether biology, including humans, will benefit or experience harm from new developments in information such as artificial intelligence (AI). Here, it is proposed that biological and non-biological information might be components of a unified process, ‘Panevolution’ or ‘Pan-Evo’, based on [...] Read more.
Many people wonder whether biology, including humans, will benefit or experience harm from new developments in information such as artificial intelligence (AI). Here, it is proposed that biological and non-biological information might be components of a unified process, ‘Panevolution’ or ‘Pan-Evo’, based on four basic operations—innovation, transmission, adaptation, and movement. Pan-Evo contains many types of variable objects, from molecules to ecosystems. Biological innovation includes mutations and behavioural changes; non-biological innovation includes naturally occurring physical innovations and innovation in software. Replication is commonplace in and outside biology, including autocatalytic chemicals and autonomous software replication. Adaptation includes biological selection, autocatalytic chemicals, and ‘evolutionary programming’, which is used in AI. The extension of biological speciation to non-biological information creates a concept called ‘Panspeciation’. Panevolution might benefit or harm biology, but the harm might be minimal if AI and humans behave intelligently because humans and the machines in which an AI resides might split into vastly different environments that suit them. That is a possible example of Panspeciation and would be the first speciation event involving humans for thousands of years. This event will not be particularly hostile to humans if humans learn to evaluate information and cooperate better to minimise both human stupidity and artificial simulated stupidity (ASS—a failure of AI). Full article
(This article belongs to the Special Issue Evolutionary Biology from an Information Theory Approach)
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