The logic of the floral transition: Reverse-engineering the switch controlling the identity of lateral organs

Much laboratory work has been carried out to determine the gene regulatory network (GRN) that results in plant cells becoming flowers instead of leaves. However, this also involves the spatial distribution of different cell types, and poses the question of whether alternative networks could produce the same set of observed results. This issue has been addressed here through a survey of the published intercellular distribution of expressed regulatory genes and techniques both developed and applied to Boolean network models. This has uncovered a large number of models which are compatible with the currently available data. An exhaustive exploration had some success but proved to be unfeasible due to the massive number of alternative models, so genetic programming algorithms have also been employed. This approach allows exploration on the basis of both data-fitting criteria and parsimony of the regulatory processes, ruling out biologically unrealistic mechanisms. One of the conclusions is that, despite the multiplicity of acceptable models, an overall structure dominates, with differences mostly in alternative fine-grained regulatory interactions. The overall structure confirms the known interactions, including some that were not present in the training set, showing that current data are sufficient to determine the overall structure of the GRN. The model stresses the importance of relative spatial location, through explicit references to this aspect. This approach also provides a quantitative indication of how likely some regulatory interactions might be, and can be applied to the study of other developmental transitions.为探明促使植物细胞分化为花而非叶片的基因调控网络（gene regulatory network, GRN），学界已开展大量实验室研究，但该过程同时涉及不同细胞类型的空间分布，由此引出一个核心问题：是否存在其他调控网络，同样可产生与观测结果一致的细胞表型？本研究通过梳理已发表的表达调控基因的细胞间分布数据，并结合针对布尔网络模型（Boolean network models）开发并应用的相关分析方法对该问题展开探讨，结果发现大量模型均与当前可得的实验数据兼容。此前虽尝试穷尽枚举所有候选模型，但由于候选模型数量过于庞大而无法实现，因此研究人员转而采用了遗传编程算法（genetic programming algorithms）。该方法可同时基于数据拟合准则与调控过程的简约性开展探索，排除生物学上不符合实际的机制。研究得出的核心结论之一是，尽管存在多种可接受的模型，但存在一个主导性的整体结构，模型间的差异主要体现在精细化调控互作的不同选择上；这一整体结构验证了已知的调控互作，包括部分未纳入训练集的互作，表明当前数据已足以确定该GRN的整体架构。该模型通过明确引入空间位置因素，强调了相对空间位置的重要性，本研究方法还可对部分调控互作的发生概率给出量化评估，并且能够推广应用于其他发育转变过程的研究。