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J Exp Zool B Mol Dev Evol. 2023 Mar;340(2):143-161. doi: 10.1002/jez.b.23068. Epub 2021 Jul 12.
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A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data.单细胞 RNA 测序数据调控网络推断方法的综合调查。
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa190.
4
Genomic Basis of Striking Fin Shapes and Colors in the Fighting Fish.打斗鱼惊人的鳍形状和颜色的基因组基础。
Mol Biol Evol. 2021 Jul 29;38(8):3383-3396. doi: 10.1093/molbev/msab110.
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Modularity and hierarchy in biological systems: Using gene regulatory networks to understand evolutionary change.生物系统的模块化和层次结构:利用基因调控网络理解进化变化。
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GRN 概念作为进化发育生物学的指导。

The GRN concept as a guide for evolutionary developmental biology.

机构信息

Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.

School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia.

出版信息

J Exp Zool B Mol Dev Evol. 2023 Mar;340(2):92-104. doi: 10.1002/jez.b.23132. Epub 2022 Mar 28.

DOI:10.1002/jez.b.23132
PMID:35344632
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9515236/
Abstract

Organismal phenotypes result largely from inherited developmental programs, usually executed during embryonic and juvenile life stages. These programs are not blank slates onto which natural selection can draw arbitrary forms. Rather, the mechanisms of development play an integral role in shaping phenotypic diversity and help determine the evolutionary trajectories of species. Modern evolutionary biology must, therefore, account for these mechanisms in both theory and in practice. The gene regulatory network (GRN) concept represents a potent tool for achieving this goal whose utility has grown in tandem with advances in "omic" technologies and experimental techniques. However, while the GRN concept is widely utilized, it is often less clear what practical implications it has for conducting research in evolutionary developmental biology. In this Perspective, we attempt to provide clarity by discussing how experiments and projects can be designed in light of the GRN concept. We first map familiar biological notions onto the more abstract components of GRN models. We then review how diverse functional genomic approaches can be directed toward the goal of constructing such models and discuss current methods for functionally testing evolutionary hypotheses that arise from them. Finally, we show how the major steps of GRN model construction and experimental validation suggest generalizable workflows that can serve as a scaffold for project design. Taken together, the practical implications that we draw from the GRN concept provide a set of guideposts for studies aiming at unraveling the molecular basis of phenotypic diversity.

摘要

生物体的表型主要源于遗传的发育程序,这些程序通常在胚胎和幼年阶段执行。这些程序不是自然选择可以随意塑造的空白石板。相反,发育机制在塑造表型多样性方面起着至关重要的作用,并有助于确定物种的进化轨迹。因此,现代进化生物学必须在理论和实践中考虑这些机制。基因调控网络(GRN)概念代表了实现这一目标的有力工具,其效用随着“组学”技术和实验技术的进步而同步增长。然而,尽管 GRN 概念得到了广泛应用,但对于在进化发育生物学中开展研究的实际影响,往往不太清楚。在本文中,我们试图通过讨论如何根据 GRN 概念设计实验和项目来澄清这一点。我们首先将熟悉的生物学概念映射到 GRN 模型的更抽象组件上。然后,我们回顾了如何将各种功能基因组方法用于构建此类模型,并讨论了当前用于对由此产生的进化假设进行功能测试的方法。最后,我们展示了 GRN 模型构建和实验验证的主要步骤如何提出可推广的工作流程,为项目设计提供了一个框架。总之,我们从 GRN 概念中得出的实际影响为旨在揭示表型多样性的分子基础的研究提供了一组指导方针。