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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Synteny and Evolution02:31

Synteny and Evolution

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John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
Around 80 million years ago, the human and mice lineages diverged from the common ancestor. During the course of evolution, the ancestral...
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Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Exon Recombination02:32

Exon Recombination

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The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon...
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Updated: Jun 17, 2025

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
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模拟分子进化的进化算法:一个新的现场提案.

James S L Browning1, Daniel R Tauritz1, John Beckmann2

  • 1Department of Computer Science and Software Engineering, Samuel Ginn College of Engineering, 3101 Shelby Center, Auburn, AL 36849-5347, United States.

Briefings in bioinformatics
|August 12, 2024
PubMed
概括
此摘要是机器生成的。

研究人员正在使用计算进化来扩大大自然有限的蛋白质"词汇". 这个领域融合了进化算法和机器学习,设计了新的,定制的蛋白质,在生物技术和医学中具有潜在的应用.

关键词:
人工智能的人工智能是人工智能.生物技术是生物技术.计算生物学是计算生物学.计算进化的演化.进化算法是指进化的算法.遗传编程是一种基因编程.分子进化分子演变.蛋白质组学 蛋白质组学

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Last Updated: Jun 17, 2025

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科学领域:

  • 计算生物学 计算生物学
  • 生物化学 生物化学
  • 进化生物学 进化生物学

背景情况:

  • DNA编码蛋白质,对新陈代谢过程至关重要.
  • 基因组测序揭示了蛋白质的多样性,但已知的功能性家族只是可能的序列的一小部分.
  • 一种有限的天然蛋白质.
  • 词汇和词汇的使用量.
  • 阻碍了对新功能的发现.

研究的目的:

  • 探索自然蛋白质谱的扩展.
  • 为了设计新的,定制的蛋白质,超越现有的自然限制.
  • 引入一个新的子领域,即模拟分子进化的进化算法 (EASME).

主要方法:

  • 进化算法,机器学习和生物信息学的整合.
  • 应用DNA字符串表示和生物学准确的分子进化.
  • 利用生物信息学告知的健身功能来指导蛋白质设计.

主要成果:

  • 开发一个用于设计新型蛋白质的计算框架.
  • 展示了制造具有所需功能的定制蛋白质的潜力.
  • 建立模拟分子进化的进化算法 (EASME) 作为一个可行的子领域.

结论:

  • 计算进化提供了一种强大的方法来扩大蛋白质谱.
  • 设计者蛋白质可以被创造出来,以满足特定的,潜在的灭绝或新的功能.
  • EASME提供了一个强大的方法来推进蛋白质工程和发现.