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

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Dinh:2017:PLOScb,

• author = "Jean-Louis Dinh and Etienne Farcot and Charlie Hodgman",
• title = "The logic of the floral transition: Reverse-engineering the switch controlling the identity of lateral organs",
• journal = "PLOS Computational Biology",
• year = "2017",
• month = sep # " 20",
• keywords = "genetic algorithms, genetic programming",
• language = "en",
• oai = "oai:eprints.nottingham.ac.uk:46904",
• ISSN = "1553-7358",
• bibsource = "OAI-PMH server at eprints.nottingham.ac.uk",
• publisher = "Public Library of Science",
• URL = "http://eprints.nottingham.ac.uk/46904/",
• URL = "http://eprints.nottingham.ac.uk/46904/1/Farcot_logic.pdf",
• URL = "http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005744",
• DOI = "doi:10.1371/journal.pcbi.1005744",
• size = "25 pages",
• 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.",
• notes = "'genetic programming algorithm has been employed to find suitable [graphical GRN] models that explain all observed data.'

  also known as \cite{oai:eprints.nottingham.ac.uk:46904}",

}

Genetic Programming entries for Jean-Louis Dinh Etienne Farcot T Charles Hodgman

Citations