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基因组复杂性和情境依赖功能的演变

James O McInerney¹

¹Department of Evolution, Ecology and Behaviour, Institute of Infection, Veterinary, and Ecological Sciences, Biosciences Building, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK.

Molecular biology and evolution

|February 25, 2026

概括

基因组像大型语言模型一样起作用,编码取决于上下文的概率,而不是固定的基因功能. 基因组复杂性量化了基因不相容性,解释了基因流中的健身成本.

科学领域:

基因组学就是基因组学.
进化生物学进化生物学
计算生物学计算生物学

背景情况:

传统遗传学假定固定的基因功能,但泛基因组学和GWAS显示了取决于环境的有机功能.
基因组背景显著影响基因和生物体的表型,即使是核心基因.

研究的目的:

提出一种新的框架,将基因组视为对功能结果的概率分布,类似于大型语言模型 (LLM).
引入和定义"基因组复杂性",作为基因组背景下的遗传元素不相容的信息理论衡量标准.
为预测基因整合潜力和理解进化过程提供可测试的框架.

主要方法:

基因组表现和LLM注意力机制之间的概念类比.
介绍了信息理论概念"基因组复杂性".
证明困惑性作为水平基因转移 (HGT) 和内进化的健身成本指标.

主要成果:

基因组和LLM一样,编码的是函数的概率分布,而不是固定的函数.
基因组表观类似于注意力机制,其中上下文权重是遗传因素的影响.
基因组复杂性量化了遗传元素的不相容性,解释了基因流中的健康成本.

结论:

一个概率学,LLM启发的框架重新定义了我们对基因功能和基因组相互作用的理解.
基因组复杂性为基因与环境相互作用和进化融合提供了可量化的指标.
这种观点推动了合成生物学,进化建模以及我们对基因组适应的理解.

关键词:

基因组复杂性基因组复杂性横向基因转移是指水平基因转移.信息理论信息理论机器学习机器学习突变突变是一种突变.

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