Flowering time control: gene network modelling and the link to quantitative genetics
Stephen M. Welch A E , Zhanshan Dong A B , Judith L. Roe C and Sanjoy Das DA Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA.
B Current address: Pioneer Hi-Bred International, Inc., 7300 NW 62nd Ave, Johnston, IA 50131, USA.
C Division of Biology, Kansas State University, Manhattan, KS 66506, USA.
D Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA.
E Corresponding author. Email: welchsm@ksu.edu
Australian Journal of Agricultural Research 56(9) 919-936 https://doi.org/10.1071/AR05155
Submitted: 9 May 2005 Accepted: 20 June 2005 Published: 28 September 2005
Abstract
Flowering is a key stage in plant development that initiates grain production and is vulnerable to stress. The genes controlling flowering time in the model plant Arabidopsis thaliana are reviewed. Interactions between these genes have been described previously by qualitative network diagrams. We mathematically relate environmentally dependent transcription, RNA processing, translation, and protein–protein interaction rates to resultant phenotypes. We have developed models (reported elsewhere) based on these concepts that simulate flowering times for novel A. thaliana genotype–environment combinations. Here we draw 12 contrasts between genetic network (GN) models of this type and quantitative genetics (QG), showing that both have equal contributions to make to an ideal theory. Physiological dominance and additivity are examined as emergent properties in the context of feed-forwards networks, an instance of which is the signal-integration portion of the A. thaliana flowering time network. Additivity is seen to be a complex, multi-gene property with contributions from mass balance in transcript production, the feed-forwards structure itself, and downstream promoter reaction thermodynamics. Higher level emergent properties are exemplified by critical short daylength (CSDL), which we relate to gene expression dynamics in rice (Oryza sativa). Next to be discussed are synergies between QG and GN relating to the quantitative trait locus (QTL) mapping of model coefficients. This suggests a new verification test useful in GN model development and in identifying needed updates to existing crop models. Finally, the utility of simple models is evinced by 80 years of QG theory and mathematical ecology.
Additional keywords: regulation, differential equations, photothermal, pathways.
Acknowledgments
An earlier version of this work is in the published Proceedings of the 4th International Crop Science Congress (26 September–4 October 2004, Brisbane, Australia). The Congress organisers kindly gave their permission to publish this updated version, for which we are grateful. We also benefited from the inputs of Mark Cooper and Graeme Hammer; open discussions with other authors in this Special Issue, especially Francois Tardieu; and comments from anonymous reviewers. This work was supported in part by NSF Projects 32115 and 0425759, USDA Project 2003–35304–13217, and Hatch Project KAES 0507, all at Kansas State University. This is Contribution No. 05-191-J from the Kansas Agricultural Experiment Station.
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