嵌入式计算进化:研究随机性和形态复杂性进化的模型。

IF 2.2 4区 生物学 Q2 BIOLOGY
Integrative Organismal Biology Pub Date : 2024-08-21 eCollection Date: 2024-01-01 DOI:10.1093/iob/obae032
E Aaron, J H Long
{"title":"嵌入式计算进化:研究随机性和形态复杂性进化的模型。","authors":"E Aaron, J H Long","doi":"10.1093/iob/obae032","DOIUrl":null,"url":null,"abstract":"<p><p>For an integrated understanding of how evolutionary dynamics operate in parallel on multiple levels, computational models can enable investigations that would be otherwise infeasible or impossible. We present one modeling framework, <i>Embodied Computational Evolution</i> (<i>ECE</i>), and employ it to investigate how two types of randomness-genetic and developmental-drive the evolution of morphological complexity. With these two types of randomness implemented as germline mutation and transcription error, with rates varied in an [Formula: see text] factorial experimental design, we tested two related hypotheses: ( <i><b>H<sub>1</sub></b> </i> ) Randomness in the gene transcription process alters the direct impact of selection on populations; and ( <i><b>H<sub>2</sub></b> </i> ) Selection on locomotor performance targets morphological complexity. The experiment consisted of 121 conditions; in each condition, nine starting phenotypic populations developed from different randomly generated genomic populations of 60 individuals. Each of the resulting 1089 phenotypic populations evolved over 100 generations, with the autonomous, self-propelled individuals under directional selection for enhanced locomotor performance. As encoded by their genome, individuals had heritable morphological traits, including the numbers of segments, sensors, neurons, and connections between sensors and motorized joints that they activated. An individual's morphological complexity was measured by three different metrics derived from counts of the body parts. In support of <i><b>H<sub>1</sub></b> </i> , variations in the rate of randomness in the gene transcription process varied the dynamics of selection. In support of <i><b>H<sub>2</sub></b> </i> , the morphological complexity of populations evolved adaptively.</p>","PeriodicalId":13666,"journal":{"name":"Integrative Organismal Biology","volume":"6 1","pages":"obae032"},"PeriodicalIF":2.2000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413536/pdf/","citationCount":"0","resultStr":"{\"title\":\"Embodied Computational Evolution: A Model for Investigating Randomness and the Evolution of Morphological Complexity.\",\"authors\":\"E Aaron, J H Long\",\"doi\":\"10.1093/iob/obae032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>For an integrated understanding of how evolutionary dynamics operate in parallel on multiple levels, computational models can enable investigations that would be otherwise infeasible or impossible. We present one modeling framework, <i>Embodied Computational Evolution</i> (<i>ECE</i>), and employ it to investigate how two types of randomness-genetic and developmental-drive the evolution of morphological complexity. With these two types of randomness implemented as germline mutation and transcription error, with rates varied in an [Formula: see text] factorial experimental design, we tested two related hypotheses: ( <i><b>H<sub>1</sub></b> </i> ) Randomness in the gene transcription process alters the direct impact of selection on populations; and ( <i><b>H<sub>2</sub></b> </i> ) Selection on locomotor performance targets morphological complexity. The experiment consisted of 121 conditions; in each condition, nine starting phenotypic populations developed from different randomly generated genomic populations of 60 individuals. Each of the resulting 1089 phenotypic populations evolved over 100 generations, with the autonomous, self-propelled individuals under directional selection for enhanced locomotor performance. As encoded by their genome, individuals had heritable morphological traits, including the numbers of segments, sensors, neurons, and connections between sensors and motorized joints that they activated. An individual's morphological complexity was measured by three different metrics derived from counts of the body parts. In support of <i><b>H<sub>1</sub></b> </i> , variations in the rate of randomness in the gene transcription process varied the dynamics of selection. In support of <i><b>H<sub>2</sub></b> </i> , the morphological complexity of populations evolved adaptively.</p>\",\"PeriodicalId\":13666,\"journal\":{\"name\":\"Integrative Organismal Biology\",\"volume\":\"6 1\",\"pages\":\"obae032\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413536/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Integrative Organismal Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/iob/obae032\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integrative Organismal Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/iob/obae032","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

为了综合理解进化动力学如何在多个层面上并行运作,计算模型可以帮助我们进行原本不可行或不可能进行的研究。我们提出了一个建模框架,即 "嵌入式计算进化(ECE)",并利用它来研究两种随机性--遗传随机性和发育随机性--如何驱动形态复杂性的进化。通过将这两种随机性作为种系突变和转录错误来实现,并在[公式:见正文]因子实验设计中改变其比率,我们检验了两个相关假设:(H1)基因转录过程中的随机性改变了选择对种群的直接影响;(H2)对运动性能的选择以形态复杂性为目标。实验包括 121 个条件;在每个条件中,9 个起始表型种群由不同的随机产生的 60 个个体的基因组种群发展而来。由此产生的 1089 个表型群体中的每一个都经过了 100 代的进化,其中自主、自走的个体受到定向选择,以提高运动性能。根据基因组编码,个体具有可遗传的形态特征,包括其激活的节段、传感器、神经元以及传感器和运动关节之间的连接数量。个体的形态复杂性是通过对身体各部分的计数得出的三个不同指标来衡量的。为支持 H1,基因转录过程中随机率的变化改变了选择的动态。支持 H2 的证据是,种群的形态复杂性发生了适应性进化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Embodied Computational Evolution: A Model for Investigating Randomness and the Evolution of Morphological Complexity.

For an integrated understanding of how evolutionary dynamics operate in parallel on multiple levels, computational models can enable investigations that would be otherwise infeasible or impossible. We present one modeling framework, Embodied Computational Evolution (ECE), and employ it to investigate how two types of randomness-genetic and developmental-drive the evolution of morphological complexity. With these two types of randomness implemented as germline mutation and transcription error, with rates varied in an [Formula: see text] factorial experimental design, we tested two related hypotheses: ( H1 ) Randomness in the gene transcription process alters the direct impact of selection on populations; and ( H2 ) Selection on locomotor performance targets morphological complexity. The experiment consisted of 121 conditions; in each condition, nine starting phenotypic populations developed from different randomly generated genomic populations of 60 individuals. Each of the resulting 1089 phenotypic populations evolved over 100 generations, with the autonomous, self-propelled individuals under directional selection for enhanced locomotor performance. As encoded by their genome, individuals had heritable morphological traits, including the numbers of segments, sensors, neurons, and connections between sensors and motorized joints that they activated. An individual's morphological complexity was measured by three different metrics derived from counts of the body parts. In support of H1 , variations in the rate of randomness in the gene transcription process varied the dynamics of selection. In support of H2 , the morphological complexity of populations evolved adaptively.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.70
自引率
6.70%
发文量
48
审稿时长
20 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信