From genes to flower patterns and evolution:: Dynamic models of gene regulatory networks
From genes to flower patterns and evolution:: Dynamic models of gene regulatory networks
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DOI：
10.1007/s00344-006-0068-8
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发表时间：
2006-12-01
影响因子：
4.8
通讯作者：
Alvarez-Buylla, Elena R.
中科院分区：
文献类型：
--
作者：
Chaos, Alvaro;Aldana, Max;Alvarez-Buylla, Elena R.
基因和蛋白质形成复杂的动力学系统或基因调控网络（GRN），可以达到几个稳定状态（吸引子）。这些可能与不同的细胞类型有关。在植物中，ABC组合模型建立了花器官细胞特化所必需的基因组合。我们已经开发了动态基因调控网络（GRN）模型，以了解如何在花器官原基规格的ABC和非ABC基因的协同作用的结果，建立基因活性的组合选择。我们的分析表明，花器官规格GRN达到6个吸引子的基因配置中观察到的原始细胞类型在花发育的早期阶段和4个，对应于该地区的花序分生组织。这表明，这是整体GRN动态，而不是精确的信号，ABC模型的基础。此外，我们的分析表明，稳定状态的GRN是强大的随机改变的逻辑功能，定义基因的相互作用。在这里，我们更新了GRN模型，并系统地改变了所有逻辑函数的输出，并解决了在哪些情况下恢复原始吸引子的问题。然后，我们减少了原来的三态GRN到两个状态（布尔）GRN，并进行了相同的系统扰动分析。有趣的是，布尔GRN达到了与三态GRN相同的吸引子数量和类型，并且它以与原始GRN相同的方式对扰动做出响应。这些结果表明，一个布尔模型是足以捕捉的动态功能的花卉网络和花卉GRN的鲁棒性提供额外的支持。这些研究结果进一步支持GRN模型为ABC模型提供了一个动力学解释，并且花GRN鲁棒性可能是真双子叶植物中广泛保护花的背后。其他方面的花器官排列和ABC基因表达模式的进化进行了讨论，在这里提出的方法的背景下。
Genes and proteins form complex dynamical systems or gene regulatory networks (GRN) that can reach several steady states (attractors). These may be associated with distinct cell types. In plants, the ABC combinatorial model establishes the necessary gene combinations for floral organ cell specification. We have developed dynamic gene regulatory network (GRN) models to understand how the combinatorial selection of gene activity is established during floral organ primordia specification as a result of the concerted action of ABC and non-ABC genes. Our analyses have shown that the floral organ specification GRN reaches six attractors with gene configurations observed in primordial cell types during early stages of flower development and four that correspond to regions of the inflorescence meristem. This suggests that it is the overall GRN dynamics rather than precise signals that underlie the ABC model. Furthermore, our analyses suggest that the steady states of the GRN are robust to random alterations of the logical functions that define the gene interactions. Here we have updated the GRN model and have systematically altered the outputs of all the logical functions and addressed in which cases the original attractors are recovered. We then reduced the original three-state GRN to a two-state (Boolean) GRN and performed the same systematic perturbation analysis. Interestingly, the Boolean GRN reaches the same number and type of attractors as reached by the three-state GRN, and it responds to perturbations in a qualitatively identical manner as the original GRN. These results suggest that a Boolean model is sufficient to capture the dynamical features of the floral network and provide additional support for the robustness of the floral GRN. These findings further support that the GRN model provides a dynamical explanation for the ABC model and that the floral GRN robustness could be behind the widespread conservation of the floral plan among eudicotyledoneous plants. Other aspects of evolution of flower organ arrangement and ABC gene expression patterns are discussed in the context of the approach proposed here.