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

Dinh, Jean-Louis; Farcot, Etienne; Hodgman, Charlie

Abstract

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.

Citation

Dinh, J., Farcot, E., & Hodgman, C. (in press). The logic of the floral transition: reverse-engineering the switch controlling the identity of lateral organs. PLoS Computational Biology, 13(9), Article e1005744. https://doi.org/10.1371/journal.pcbi.1005744

• Journal Article Type: Article
• Acceptance Date: Aug 23, 2017
• Online Publication Date: Sep 20, 2017
• Deposit Date: Oct 2, 2017
• Publicly Available Date: Oct 2, 2017
• Journal: PLOS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 9
• Article Number: e1005744
• DOI: https://doi.org/10.1371/journal.pcbi.1005744
• Public URL: https://nottingham-repository.worktribe.com/output/883730
• Publisher URL: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005744