INRIA project-team Virtual Plants, joint with CIRAD and INRA, Montpellier Cedex 5, France.
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Apdo. 20-126, 01000 México, D.F., México.
Sci Rep. 2017 Feb 10;7:42023. doi: 10.1038/srep42023.
Molecular regulation was initially assumed to follow both a unidirectional and a hierarchical organization forming pathways. Regulatory processes, however, form highly interlinked networks with non-hierarchical and non-unidirectional structures that contain statistically overrepresented circuits or motifs. Here, we analyze the behavior of pathways containing non-unidirectional (i.e. bidirectional) and non-hierarchical interactions that create motifs. In comparison with unidirectional and hierarchical pathways, our pathways have a high diversity of behaviors, characterized by the size and number of attractors. Motifs have been studied individually showing that feedback circuit motifs regulate the number and size of attractors. It is less clear what happens in molecular networks that usually contain multiple feedbacks. Here, we find that the way feedback circuits couple to each other (i.e., the combination of the functionalities of feedback circuits) regulate both the number and size of the attractors. We show that the different expected results of epistasis analysis (a method to infer regulatory interactions) are produced by many non-hierarchical and non-unidirectional structures. Thus, these structures cannot be correctly inferred by epistasis analysis. Finally, we show that the combinations of functionalities, combined with other network properties, allow for a better characterization of regulatory structures.
分子调控最初被认为遵循着一个单向和分层的组织形式,形成了途径。然而,调控过程形成了高度互联的网络,具有非层次化和非单向的结构,其中包含具有统计学上显著代表性的电路或基序。在这里,我们分析了包含非单向(即双向)和非分层相互作用的途径,这些相互作用创造了基序。与单向和分层途径相比,我们的途径具有多样化的行为,其特征是吸引子的大小和数量。已经研究了基序,表明反馈电路基序调节吸引子的数量和大小。在通常包含多个反馈的分子网络中,情况就不那么清楚了。在这里,我们发现反馈电路相互耦合的方式(即反馈电路功能的组合)调节了吸引子的数量和大小。我们表明,上位性分析(一种推断调控相互作用的方法)的不同预期结果是由许多非层次化和非单向结构产生的。因此,这些结构不能通过上位性分析正确推断。最后,我们表明,功能组合与其他网络特性相结合,可以更好地表征调控结构。