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Kalhor R, Beslon G, Lafond M, Scornavacca C. A Rigorous Framework to Classify the Postduplication Fate of Paralogous Genes. J Comput Biol 2024. [PMID: 39088355 DOI: 10.1089/cmb.2023.0331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2024] Open
Abstract
Gene duplication has a central role in evolution; still, little is known on the fates of the duplicated copies, their relative frequency, and on how environmental conditions affect them. Moreover, the lack of rigorous definitions concerning the fate of duplicated genes hinders the development of a global vision of this process. In this paper, we present a new framework aiming at characterizing and formally differentiating the fate of duplicated genes. Our framework has been tested via simulations, where the evolution of populations has been simulated using aevol, an in silico experimental evolution platform. Our results show several patterns that confirm some of the conclusions from previous studies, while also exhibiting new tendencies; this may open up new avenues to better understand the role of duplications as a driver of evolution.
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Affiliation(s)
- Reza Kalhor
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada
| | | | - Manuel Lafond
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Celine Scornavacca
- Institut des Sciences de l'Evolution de Montpellier (Université de Montpellier, CNRS, IRD, EPHE), Montpellier, France
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2
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Shvartzman B, Ram Y. Self-replicating artificial neural networks give rise to universal evolutionary dynamics. PLoS Comput Biol 2024; 20:e1012004. [PMID: 38547320 PMCID: PMC11003675 DOI: 10.1371/journal.pcbi.1012004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 04/09/2024] [Accepted: 03/17/2024] [Indexed: 04/11/2024] Open
Abstract
In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the self-replicating artificial neural network (SeRANN). We train it to (i) copy its own genotype, like a biological organism, which introduces endogenous spontaneous mutations; and (ii) simultaneously perform a classification task that determines its fertility. Evolving 1,000 SeRANNs for 6,000 generations, we observed various evolutionary phenomena such as adaptation, clonal interference, epistasis, and evolution of both the mutation rate and the distribution of fitness effects of new mutations. Our results demonstrate that universal evolutionary phenomena can naturally emerge in a self-replicator model when both selection and mutation are implicit and endogenous. We therefore suggest that SeRANN can be applied to explore and test various evolutionary dynamics and hypotheses.
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Affiliation(s)
- Boaz Shvartzman
- School of Zoology, Faculty of Life Sciences, Tel Aviv University; Tel Aviv, Israel
- School of Computer Science, Reichman University; Herzliya, Israel
| | - Yoav Ram
- School of Zoology, Faculty of Life Sciences, Tel Aviv University; Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University; Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University; Tel Aviv, Israel
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3
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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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4
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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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5
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McAvoy A, Kates-Harbeck J, Chatterjee K, Hilbe C. Evolutionary instability of selfish learning in repeated games. PNAS NEXUS 2022; 1:pgac141. [PMID: 36714856 PMCID: PMC9802390 DOI: 10.1093/pnasnexus/pgac141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 07/22/2022] [Indexed: 02/01/2023]
Abstract
Across many domains of interaction, both natural and artificial, individuals use past experience to shape future behaviors. The results of such learning processes depend on what individuals wish to maximize. A natural objective is one's own success. However, when two such "selfish" learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest. Here, we explore how a learner can align incentives with a selfish opponent. Moreover, we consider the dynamics that arise when learning rules themselves are subject to evolutionary pressure. By combining extensive simulations and analytical techniques, we demonstrate that selfish learning is unstable in most classical two-player repeated games. If evolution operates on the level of long-run payoffs, selection instead favors learning rules that incorporate social (other-regarding) preferences. To further corroborate these results, we analyze data from a repeated prisoner's dilemma experiment. We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness.
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Affiliation(s)
| | | | | | - Christian Hilbe
- Max Planck Research Group: Dynamics of Social Behavior, Max Planck Institute for Evolutionary Biology, Plön, Germany
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da Silva GM, Yang J, Leang B, Huang J, Weinreich DM, Rubenstein BM. Covalent docking and molecular dynamics simulations reveal the specificity-shifting mutations Ala237Arg and Ala237Lys in TEM beta-lactamase. PLoS Comput Biol 2022; 18:e1009944. [PMID: 35759512 PMCID: PMC9269908 DOI: 10.1371/journal.pcbi.1009944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 07/08/2022] [Accepted: 06/01/2022] [Indexed: 11/18/2022] Open
Abstract
The rate of modern drug discovery using experimental screening methods still lags behind the rate at which pathogens mutate, underscoring the need for fast and accurate predictive simulations of protein evolution. Multidrug-resistant bacteria evade our defenses by expressing a series of proteins, the most famous of which is the 29-kilodalton enzyme, TEM β-lactamase. Considering these challenges, we applied a covalent docking heuristic to measure the effects of all possible alanine 237 substitutions in TEM due to this codon’s importance for catalysis and effects on the binding affinities of commercially-available β-lactam compounds. In addition to the usual mutations that reduce substrate binding due to steric hindrance, we identified two distinctive specificity-shifting TEM mutations, Ala237Arg and Ala237Lys, and their respective modes of action. Notably, we discovered and verified through minimum inhibitory concentration assays that, while these mutations and their bulkier side chains lead to steric clashes that curtail ampicillin binding, these same groups foster salt bridges with the negatively-charged side-chain of the cephalosporin cefixime, widely used in the clinic to treat multi-resistant bacterial infections. To measure the stability of these unexpected interactions, we used molecular dynamics simulations and found the binding modes to be stable despite the application of biasing forces. Finally, we found that both TEM mutants also bind strongly to other drugs containing negatively-charged R-groups, such as carumonam and ceftibuten. As with cefixime, this increased binding affinity stems from a salt bridge between the compounds’ negative moieties and the positively-charged side chain of the arginine or lysine, suggesting a shared mechanism. In addition to reaffirming the power of using simulations as molecular microscopes, our results can guide the rational design of next-generation β-lactam antibiotics and bring the community closer to retaking the lead against the recurrent threat of multidrug-resistant pathogens.
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Affiliation(s)
- Gabriel Monteiro da Silva
- Department of Molecular and Cell Biology, Brown University, Providence, Rhode Island, United States of America
- * E-mail:
| | - Jordan Yang
- Department of Chemistry, Brown University, Providence, Rhode Island, United States of America
| | - Bunlong Leang
- Department of Health and Human Biology, Brown University, Providence, Rhode Island, United States of America
| | - Jessie Huang
- Department of Chemistry, Wellesley College, Wellesley, Massachusetts, United States of America
| | - Daniel M. Weinreich
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
| | - Brenda M. Rubenstein
- Department of Chemistry, Brown University, Providence, Rhode Island, United States of America
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7
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Abstract
In digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition to their complex evolutionary dynamics, many digital evolution systems also produce rich ecological communities. As a result, digital evolution is also a powerful tool for research on eco-evolutionary dynamics. Here, we review the research to date in which digital evolution platforms have been used to address eco-evolutionary (and in some cases purely ecological) questions. This work has spanned a wide range of topics, including competition, facilitation, parasitism, predation, and macroecological scaling laws. We argue for the value of further ecological research in digital evolution systems and present some particularly promising directions for further research.
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Hall RJ, Thorpe S, Thomas GH, Wood AJ. Simulating the evolutionary trajectories of metabolic pathways for insect symbionts in the genus Sodalis. Microb Genom 2020; 6:mgen000378. [PMID: 32543366 PMCID: PMC7478623 DOI: 10.1099/mgen.0.000378] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Accepted: 04/27/2020] [Indexed: 01/13/2023] Open
Abstract
Insect-bacterial symbioses are ubiquitous, but there is still much to uncover about how these relationships establish, persist and evolve. The tsetse endosymbiont Sodalis glossinidius displays intriguing metabolic adaptations to its microenvironment, but the process by which this relationship evolved remains to be elucidated. The recent chance discovery of the free-living species of the genus Sodalis, Sodalis praecaptivus, provides a serendipitous starting point from which to investigate the evolution of this symbiosis. Here, we present a flux balance model for S. praecaptivus and empirically verify its predictions. Metabolic modelling is used in combination with a multi-objective evolutionary algorithm to explore the trajectories that S. glossinidius may have undertaken from this starting point after becoming internalized. The order in which key genes are lost is shown to influence the evolved populations, providing possible targets for future in vitro genetic manipulation. This method provides a detailed perspective on possible evolutionary trajectories for S. glossinidius in this fundamental process of evolutionary and ecological change.
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Affiliation(s)
- Rebecca J. Hall
- Department of Biology, University of York, York, YO10 5NG, UK
- School of Life Sciences, University of Nottingham, Nottingham, NG7 2TQ, UK
| | - Stephen Thorpe
- Department of Biology, University of York, York, YO10 5NG, UK
- Department of Chemistry, University of Oxford, Oxford, OX1 3TA, UK
| | - Gavin H. Thomas
- Department of Biology, University of York, York, YO10 5NG, UK
| | - A. Jamie Wood
- Department of Biology, University of York, York, YO10 5NG, UK
- Department of Mathematics, University of York, York, YO10 5DD, UK
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Borriello E, Walker SI, Laubichler MD. Cell phenotypes as macrostates of the GRN dynamics. JOURNAL OF EXPERIMENTAL ZOOLOGY PART B-MOLECULAR AND DEVELOPMENTAL EVOLUTION 2020; 334:213-224. [DOI: 10.1002/jez.b.22938] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 02/16/2020] [Accepted: 02/17/2020] [Indexed: 01/04/2023]
Affiliation(s)
- Enrico Borriello
- ASU‐SFI Center for Biosocial Complex SystemsArizona State UniversityTempe Arizona
| | - Sara I. Walker
- ASU‐SFI Center for Biosocial Complex SystemsArizona State UniversityTempe Arizona
- Beyond Center for Fundamental Concepts in ScienceArizona State UniversityTempe Arizona
- School of Earth and Space ExplorationArizona State UniversityTempe Arizona
- Blue Marble Space Institute of ScienceSeattle Washington
| | - Manfred D. Laubichler
- ASU‐SFI Center for Biosocial Complex SystemsArizona State UniversityTempe Arizona
- Santa Fe InstituteSanta Fe New Mexico
- Marine Biological LaboratoryWoods Hole Massachusetts
- School of Life SciencesArizona State UniversityTempe Arizona
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10
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Rutten JP, Hogeweg P, Beslon G. Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure. BMC Evol Biol 2019; 19:191. [PMID: 31627727 PMCID: PMC6800497 DOI: 10.1186/s12862-019-1507-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 09/02/2019] [Indexed: 11/24/2022] Open
Abstract
Background Mutators are common in bacterial populations, both in natural isolates and in the lab. The fate of these lineages, which mutation rate is increased up to 100 ×, has long been studied using population genetics models, showing that they can spread in a population following an environmental change. However in stable conditions, they suffer from the increased mutational load, hence being overcome by non-mutators. However, these results don’t take into account the fact that an elevated mutation rate can impact the genetic structure, hence changing the sensitivity of the population to mutations. Here we used Aevol, an in silico experimental evolution platform in which genomic structures are free to evolve, in order to study the fate of mutator populations evolving for a long time in constant conditions. Results Starting from wild-types that were pre-evolved for 300,000 generations, we let 100 mutator populations (point mutation rate ×100) evolve for 100,000 further generations in constant conditions. As expected all populations initially undergo a fitness loss. However, after that the mutator populations started to recover. Most populations ultimately recovered their ancestors fitness, and a significant fraction became even fitter than the non-mutator control clones that evolved in parallel. By analyzing the genomes of the mutators, we show that the fitness recovery is due to two mechanisms: i. an increase in robustness through compaction of the coding part of the mutator genomes, ii. an increase of the selection coefficient that decreases the mean-fitness of the population. Strikingly the latter is due to the accumulation of non-coding sequences in the mutators genomes. Conclusion Our results show that the mutational burden that is classically thought to be associated with mutator phenotype is escapable. On the long run mutators adapted their genomes and reshaped the distribution of mutation effects. Therewith the lineage is able to recover fitness even though the population still suffers the elevated mutation rate. Overall these results change our view of mutator dynamics: by being able to reduce the deleterious effect of the elevated mutation rate, mutator populations may be able to last for a very long time; A situation commonly observed in nature.
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Affiliation(s)
- Jacob Pieter Rutten
- Theoretical Biology and Bioinformatics group,Utrecht University, Padualaan 8, Utrecht, Netherlands.,Université de Lyon, INRIA, CNRS, INSA-Lyon, Beagle Team, LIRIS, UMR5205, Villeurbanne, 69601, France
| | - Paulien Hogeweg
- Theoretical Biology and Bioinformatics group,Utrecht University, Padualaan 8, Utrecht, Netherlands
| | - Guillaume Beslon
- Université de Lyon, INRIA, CNRS, INSA-Lyon, Beagle Team, LIRIS, UMR5205, Villeurbanne, 69601, France.
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11
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LaBar T, Adami C. Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms. PLoS Comput Biol 2016; 12:e1005066. [PMID: 27923053 PMCID: PMC5140054 DOI: 10.1371/journal.pcbi.1005066] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 07/18/2016] [Indexed: 12/02/2022] Open
Abstract
A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large-but not intermediate-sized-populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.
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Affiliation(s)
- Thomas LaBar
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America
- Ecology, Evolutionary Biology, and Behavior Program, East Lansing, Michigan, United States of America
- BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, United States of America
| | - Christoph Adami
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America
- Ecology, Evolutionary Biology, and Behavior Program, East Lansing, Michigan, United States of America
- BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, United States of America
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America
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12
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Gupta A, LaBar T, Miyagi M, Adami C. Evolution of Genome Size in Asexual Digital Organisms. Sci Rep 2016; 6:25786. [PMID: 27181837 PMCID: PMC4867773 DOI: 10.1038/srep25786] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 04/22/2016] [Indexed: 12/21/2022] Open
Abstract
Genome sizes have evolved to vary widely, from 250 bases in viroids to 670 billion bases in some amoebas. This remarkable variation in genome size is the outcome of complex interactions between various evolutionary factors such as mutation rate and population size. While comparative genomics has uncovered how some of these evolutionary factors influence genome size, we still do not understand what drives genome size evolution. Specifically, it is not clear how the primordial mutational processes of base substitutions, insertions, and deletions influence genome size evolution in asexual organisms. Here, we use digital evolution to investigate genome size evolution by tracking genome edits and their fitness effects in real time. In agreement with empirical data, we find that mutation rate is inversely correlated with genome size in asexual populations. We show that at low point mutation rate, insertions are significantly more beneficial than deletions, driving genome expansion and the acquisition of phenotypic complexity. Conversely, the high mutational load experienced at high mutation rates inhibits genome growth, forcing the genomes to compress their genetic information. Our analyses suggest that the inverse relationship between mutation rate and genome size is a result of the tradeoff between evolving phenotypic innovation and limiting the mutational load.
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Affiliation(s)
- Aditi Gupta
- BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA.,Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
| | - Thomas LaBar
- BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA.,Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA.,Program in Ecology, Evolutionary Biology, and Behavior, Michigan State University, East Lansing, MI 48824, USA
| | - Michael Miyagi
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA
| | - Christoph Adami
- BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA.,Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA.,Program in Ecology, Evolutionary Biology, and Behavior, Michigan State University, East Lansing, MI 48824, USA.,Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA
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14
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Cipresso P. Modeling behavior dynamics using computational psychometrics within virtual worlds. Front Psychol 2015; 6:1725. [PMID: 26594193 PMCID: PMC4635205 DOI: 10.3389/fpsyg.2015.01725] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2015] [Accepted: 10/26/2015] [Indexed: 11/20/2022] Open
Abstract
In case of fire in a building, how will people behave in the crowd? The behavior of each individual affects the behavior of others and, conversely, each one behaves considering the crowd as a whole and the individual others. In this article, I propose a three-step method to explore a brand new way to study behavior dynamics. The first step relies on the creation of specific situations with standard techniques (such as mental imagery, text, video, and audio) and an advanced technique [Virtual Reality (VR)] to manipulate experimental settings. The second step concerns the measurement of behavior in one, two, or many individuals focusing on parameters extractions to provide information about the behavior dynamics. Finally, the third step, which uses the parameters collected and measured in the previous two steps in order to simulate possible scenarios to forecast through computational models, understand, and explain behavior dynamics at the social level. An experimental study was also included to demonstrate the three-step method and a possible scenario.
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Affiliation(s)
- Pietro Cipresso
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico ItalianoMilano, Italy
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15
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Doncieux S, Bredeche N, Mouret JB, Eiben AE(G. Evolutionary Robotics: What, Why, and Where to. Front Robot AI 2015. [DOI: 10.3389/frobt.2015.00004] [Citation(s) in RCA: 123] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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16
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Klimenko A, Matushkin Y, Kolchanov N, Lashin S. Modeling evolution of spatially distributed bacterial communities: a simulation with the haploid evolutionary constructor. BMC Evol Biol 2015; 15 Suppl 1:S3. [PMID: 25708911 PMCID: PMC4331802 DOI: 10.1186/1471-2148-15-s1-s3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Multiscale approaches for integrating submodels of various levels of biological organization into a single model became the major tool of systems biology. In this paper, we have constructed and simulated a set of multiscale models of spatially distributed microbial communities and study an influence of unevenly distributed environmental factors on the genetic diversity and evolution of the community members. Results Haploid Evolutionary Constructor software http://evol-constructor.bionet.nsc.ru/ was expanded by adding the tool for the spatial modeling of a microbial community (1D, 2D and 3D versions). A set of the models of spatially distributed communities was built to demonstrate that the spatial distribution of cells affects both intensity of selection and evolution rate. Conclusion In spatially heterogeneous communities, the change in the direction of the environmental flow might be reflected in local irregular population dynamics, while the genetic structure of populations (frequencies of the alleles) remains stable. Furthermore, in spatially heterogeneous communities, the chemotaxis might dramatically affect the evolution of community members.
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