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Ortega R, Fortuna MA. avidaR: an R library to perform complex queries on an ontology-based database of digital organisms. PeerJ Comput Sci 2023; 9:e1568. [PMID: 37810343 PMCID: PMC10557521 DOI: 10.7717/peerj-cs.1568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 08/14/2023] [Indexed: 10/10/2023]
Abstract
Digital evolution is a branch of artificial life in which self-replicating computer programs-digital organisms-mutate and evolve within a user-defined computational environment. In spite of its value in biology, we still lack an up-to-date and comprehensive database on digital organisms resulting from evolution experiments. Therefore, we have developed an ontology-based semantic database-avidaDB-and an R package-avidaR-that provides users of the R programming language with an easy-to-use tool for performing complex queries without specific knowledge of SPARQL or RDF. avidaR can be used to do research on robustness, evolvability, complexity, phenotypic plasticity, gene regulatory networks, and genomic architecture by retrieving the genomes, phenotypes, and transcriptomes of more than a million digital organisms available on avidaDB. avidaR is already accepted on CRAN (i.e., a comprehensive collection of R packages contributed by the R community) and will make biologists better equipped to embrace the field of digital evolution.
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Affiliation(s)
- Raúl Ortega
- Estación Biológica de Doñana, Consejo Superior de Investigaciones Científicas, Seville, Spain
| | - Miguel Angel Fortuna
- Estación Biológica de Doñana, Consejo Superior de Investigaciones Científicas, Seville, Spain
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Ortega R, Wulff E, Fortuna MA. Ontology for the Avida digital evolution platform. Sci Data 2023; 10:608. [PMID: 37689762 PMCID: PMC10492814 DOI: 10.1038/s41597-023-02514-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 08/30/2023] [Indexed: 09/11/2023] Open
Abstract
The Ontology for Avida (OntoAvida) aims to develop an integrated vocabulary for the description of Avida, the most widely used computational approach for performing experimental evolution using digital organisms-self-replicating computer programs that evolve within a user-defined computational environment. The lack of a clearly defined vocabulary makes some biologists feel reluctant to embrace the field of digital evolution. This integrated framework empowers biologists by equipping them with the necessary tools to explore and analyze the field of digital evolution more effectively. By leveraging the vocabulary of Avida, researchers can gain deeper insights into the evolutionary processes and dynamics of digital organisms. In addition, OntoAvida allows researchers to make inference based on certain rules and constraints, facilitate the reproducibility of in silico evolution experiments and trace the provenance of the data stored in avidaDB-an RDF database containing the genomes, transcriptomes, and phenotypes of more than a million digital organisms. OntoAvida is part of the Open Biological and Biomedical Ontologies (OBO Foundry) and is available at http://www.obofoundry.org/ontology/ontoavida.html .
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Affiliation(s)
- Raúl Ortega
- Computational Biology Lab, Estación Biológica de Doñana (EBD), Spanish National Research Council (CSIC), Seville, Spain
| | - Enrique Wulff
- Instituto de Ciencias Marinas de Andalucía (ICMAN), Spanish National Research Council (CSIC), Puerto Real, Cádiz, Spain
| | - Miguel A Fortuna
- Computational Biology Lab, Estación Biológica de Doñana (EBD), Spanish National Research Council (CSIC), Seville, Spain.
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Kamiura R, Mizuuchi R, Ichihashi N. Plausible pathway for a host-parasite molecular replication network to increase its complexity through Darwinian evolution. PLoS Comput Biol 2022; 18:e1010709. [PMID: 36454734 PMCID: PMC9714742 DOI: 10.1371/journal.pcbi.1010709] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 11/04/2022] [Indexed: 12/05/2022] Open
Abstract
How the complexity of primitive self-replication molecules develops through Darwinian evolution remains a mystery with regards to the origin of life. Theoretical studies have proposed that coevolution with parasitic replicators increases network complexity by inducing inter-dependent replication. Particularly, Takeuchi and Hogeweg proposed a complexification process of replicator networks by successive appearance of a parasitic replicator followed by the addition of a new host replicator that is resistant to the parasitic replicator. However, the feasibility of such complexification with biologically relevant molecules is still unknown owing to the lack of an experimental model. Here, we investigated the plausible complexification pathway of host-parasite replicators using both an experimental host-parasite RNA replication system and a theoretical model based on the experimental system. We first analyzed the parameter space that allows for sustainable replication in various replication networks ranging from a single molecule to three-member networks using computer simulation. The analysis shows that the most plausible complexification pathway from a single host replicator is the addition of a parasitic replicator, followed by the addition of a new host replicator that is resistant to the parasite, consistent with the previous study by Takeuchi and Hogeweg. We also provide evidence that the pathway actually occurred in our previous evolutionary experiment. These results provide experimental evidence that a population of a single replicator spontaneously evolves into multi-replicator networks through coevolution with parasitic replicators.
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Affiliation(s)
- Rikuto Kamiura
- Department of Life Science, Graduate School of Arts and Science, The University of Tokyo, Tokyo, Japan
| | - Ryo Mizuuchi
- JST, PRESTO, Kawaguchi, Saitama, Japan
- Komaba Institute for Science, The University of Tokyo, Tokyo, Japan
| | - Norikazu Ichihashi
- Department of Life Science, Graduate School of Arts and Science, The University of Tokyo, Tokyo, Japan
- Komaba Institute for Science, The University of Tokyo, Tokyo, Japan
- Research Center for Complex Systems Biology, Universal Biology Institute, The University of Tokyo, Tokyo, Japan
- * E-mail:
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Fortuna MA, Beslon G, Ofria C. Editorial: Digital evolution: Insights for biologists. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.1037040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Evolutionary transition from a single RNA replicator to a multiple replicator network. Nat Commun 2022; 13:1460. [PMID: 35304447 PMCID: PMC8933500 DOI: 10.1038/s41467-022-29113-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 02/22/2022] [Indexed: 11/26/2022] Open
Abstract
In prebiotic evolution, self-replicating molecules are believed to have evolved into complex living systems by expanding their information and functions open-endedly. Theoretically, such evolutionary complexification could occur through successive appearance of novel replicators that interact with one another to form replication networks. Here we perform long-term evolution experiments of RNA that replicates using a self-encoded RNA replicase. The RNA diversifies into multiple coexisting host and parasite lineages, whose frequencies in the population initially fluctuate and gradually stabilize. The final population, comprising five RNA lineages, forms a replicator network with diverse interactions, including cooperation to help the replication of all other members. These results support the capability of molecular replicators to spontaneously develop complexity through Darwinian evolution, a critical step for the emergence of life. Long-term experimental evolution shows that a single polymerase-encoding RNA replicator can evolve into a complex replicator network, shedding light on how a molecular replicator could have developed complexity before the emergence of life.
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Vostinar AE, Skocelas KG, Lalejini A, Zaman L. Symbiosis in Digital Evolution: Past, Present, and Future. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.739047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Symbiosis, the living together of unlike organisms as symbionts, is ubiquitous in the natural world. Symbioses occur within and across all scales of life, from microbial to macro-faunal systems. Further, the interactions between symbionts are multimodal in both strength and type, can span from parasitic to mutualistic within one partnership, and persist over generations. Studying the ecological and evolutionary dynamics of symbiosis in natural or laboratory systems poses a wide range of challenges, including the long time scales at which symbioses evolve de novo, the limited capacity to experimentally control symbiotic interactions, the weak resolution at which we can quantify interactions, and the idiosyncrasies of current model systems. These issues are especially challenging when seeking to understand the ecological effects and evolutionary pressures on and of a symbiosis, such as how a symbiosis may shift between parasitic and mutualistic modes and how that shift impacts the dynamics of the partner population. In digital evolution, populations of computational organisms compete, mutate, and evolve in a virtual environment. Digital evolution features perfect data tracking and allows for experimental manipulations that are impractical or impossible in natural systems. Furthermore, modern computational power allows experimenters to observe thousands of generations of evolution in minutes (as opposed to several months or years), which greatly expands the range of possible studies. As such, digital evolution is poised to become a keystone technique in our methodological repertoire for studying the ecological and evolutionary dynamics of symbioses. Here, we review how digital evolution has been used to study symbiosis, and we propose a series of open questions that digital evolution is well-positioned to answer.
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Ratcliff WC, Herron M, Conlin PL, Libby E. Nascent life cycles and the emergence of higher-level individuality. Philos Trans R Soc Lond B Biol Sci 2018; 372:rstb.2016.0420. [PMID: 29061893 DOI: 10.1098/rstb.2016.0420] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2017] [Indexed: 12/12/2022] Open
Abstract
Evolutionary transitions in individuality (ETIs) occur when formerly autonomous organisms evolve to become parts of a new, 'higher-level' organism. One of the first major hurdles that must be overcome during an ETI is the emergence of Darwinian evolvability in the higher-level entity (e.g. a multicellular group), and the loss of Darwinian autonomy in the lower-level units (e.g. individual cells). Here, we examine how simple higher-level life cycles are a key innovation during an ETI, allowing this transfer of fitness to occur 'for free'. Specifically, we show how novel life cycles can arise and lead to the origin of higher-level individuals by (i) mitigating conflicts between levels of selection, (ii) engendering the expression of heritable higher-level traits and (iii) allowing selection to efficiently act on these emergent higher-level traits. Further, we compute how canonical early life cycles vary in their ability to fix beneficial mutations via mathematical modelling. Life cycles that lack a persistent lower-level stage and develop clonally are far more likely to fix 'ratcheting' mutations that limit evolutionary reversion to the pre-ETI state. By stabilizing the fragile first steps of an evolutionary transition in individuality, nascent higher-level life cycles may play a crucial role in the origin of complex life.This article is part of the themed issue 'Process and pattern in innovations from cells to societies'.
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Affiliation(s)
- William C Ratcliff
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Matthew Herron
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Peter L Conlin
- Department of Biology and BEACON Center for the Study of Evolution in Action, University of Washington, Seattle, WA 98195, USA
| | - Eric Libby
- Santa Fe Institute, Santa Fe, NM 87501, USA
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Hochberg ME, Marquet PA, Boyd R, Wagner A. Innovation: an emerging focus from cells to societies. Philos Trans R Soc Lond B Biol Sci 2018; 372:rstb.2016.0414. [PMID: 29061887 DOI: 10.1098/rstb.2016.0414] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2017] [Indexed: 12/20/2022] Open
Abstract
Innovations are generally unexpected, often spectacular changes in phenotypes and ecological functions. The contributions to this theme issue are the latest conceptual, theoretical and experimental developments, addressing how ecology, environment, ontogeny and evolution are central to understanding the complexity of the processes underlying innovations. Here, we set the stage by introducing and defining key terms relating to innovation and discuss their relevance to biological, cultural and technological change. Discovering how the generation and transmission of novel biological information, environmental interactions and selective evolutionary processes contribute to innovation as an ecosystem will shed light on how the dominant features across life come to be, generalize to social, cultural and technological evolution, and have applications in the health sciences and sustainability.This article is part of the theme issue 'Process and pattern in innovations from cells to societies'.
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Affiliation(s)
- Michael E Hochberg
- Institut des Sciences de l'Evolution, Université de Montpellier, 34095 Montpellier, France .,Santa Fe Institute, Santa Fe, NM 87501, USA.,Institute for Advanced Study in Toulouse, 31015 Toulouse, France
| | - Pablo A Marquet
- Santa Fe Institute, Santa Fe, NM 87501, USA.,Departamento de Ecologı́a, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile.,Instituto de Ecología y Biodiversidad (IEB), Casilla 653, Santiago, Chile.,Instituto de Sistemas Complejos de Valparaíso (ISCV), Artillería 4780, Valparaíso, Chile
| | - Robert Boyd
- Santa Fe Institute, Santa Fe, NM 87501, USA.,School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287, USA
| | - Andreas Wagner
- Santa Fe Institute, Santa Fe, NM 87501, USA.,Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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