Counting Subnetworks Under Gene Duplication in Genetic Regulatory Networks

Ashley Scruse, Jonathan Arnold, Robert Robinson
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Abstract

Gene duplication is a fundamental evolutionary mechanism that contributes to biological complexity and diversity (Fortna et al., 2004). Traditionally, research has focused on the duplication of gene sequences (Zhang, 1914). However, evidence suggests that the duplication of regulatory elements may also play a significant role in the evolution of genomic functions (Teichmann and Babu, 2004; Hallin and Landry, 2019). In this work, the evolution of regulatory relationships belonging to gene-specific-substructures in a GRN are modeled. In the model, a network grows from an initial configuration by repeatedly choosing a random gene to duplicate. The likelihood that the regulatory relationships associated with the selected gene are retained through duplication is determined by a vector of probabilities. Occurrences of gene-family-specific substructures are counted under the gene duplication model. In this thesis, gene-family-specific substructures are referred to as subnetwork motifs. These subnetwork motifs are motivated by network motifs which are patterns of interconnections that recur more often in a specialized network than in a random network (Milo et al., 2002). Subnetwork motifs differ from network motifs in the way that subnetwork motifs are instances of gene-family-specific substructures while network motifs are isomorphic substructures. These subnetwork motifs are counted under Full and Partial Duplication, which differ in the way in which regulation relationships are inherited. Full duplication occurs when all regulatory links are inherited at each duplication step, and Partial Duplication occurs when regulation inheritance varies at each duplication step. Moments for the number of occurrences of subnetwork motifs are determined in each model. The results presented offer a method for discovering subnetwork motifs that are significant in a GRN under gene duplication.
计算遗传调控网络中基因复制下的子网络
基因复制是一种基本的进化机制,有助于提高生物学的复杂性和多样性(Fortna 等人,2004 年)。然而,有证据表明,调控元件的复制也可能在基因组功能的进化中发挥重要作用(Teichmann andBabu, 2004; Hallin and Landry, 2019)。在这项工作中,我们模拟了基因组网络中属于基因特异性子结构的调控关系的进化。在该模型中,通过重复选择随机基因进行复制,网络从初始配置开始生长。与所选基因相关的调控关系通过复制得以保留的可能性由概率向量决定。在基因复制模型下,基因家族特异性子结构的出现率被计算在内。在本论文中,基因家族特异的子结构被称为子网络主题(subnetwork motifs)。子网络动机是受网络动机(network motifs)的启发而产生的,网络动机是指在专门网络中比在随机网络中更经常出现的互连模式(Milo et al.子网络图案与网络图案的不同之处在于,子网络图案是基因家族特定子结构的实例,而网络图案是同构的子结构。这些子网络基元被归入完全复制和部分复制范畴,两者在调控关系的遗传方式上有所不同。完全复制发生在每个复制步骤都继承所有调控联系的情况下,而部分复制发生在每个复制步骤的调控继承都不同的情况下。在每个模型中都确定了子网络图案出现次数的矩。研究结果提供了一种方法,用于发现在基因重复情况下对 GRN 有重要意义的子网络主题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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