Dinh Jean-Louis, Farcot Etienne, Hodgman Charlie
Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington, United Kingdom.
School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.
PLoS Comput Biol. 2017 Sep 20;13(9):e1005744. doi: 10.1371/journal.pcbi.1005744. eCollection 2017 Sep.
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.
为了确定导致植物细胞形成花而非叶的基因调控网络（GRN），人们开展了大量实验室工作。然而，这也涉及到不同细胞类型的空间分布，并引发了一个问题，即其他网络是否能产生相同的一组观测结果。本文通过对已发表的表达调控基因的细胞间分布以及开发并应用于布尔网络模型的技术进行调查，解决了这个问题。这揭示了大量与当前可用数据兼容的模型。详尽的探索取得了一些成功，但由于替代模型数量众多，证明是不可行的，因此也采用了遗传编程算法。这种方法允许在数据拟合标准和调控过程简约性的基础上进行探索，排除生物学上不现实的机制。其中一个结论是，尽管存在多种可接受的模型，但一个总体结构占主导地位，差异主要体现在替代的细粒度调控相互作用上。总体结构证实了已知的相互作用，包括一些在训练集中不存在的相互作用，表明当前数据足以确定GRN的总体结构。该模型通过明确提及这一方面，强调了相对空间位置的重要性。这种方法还提供了一些调控相互作用可能性的定量指标，并且可以应用于其他发育转变的研究。
