Models for navigating biological complexity in breeding improved crop plants.
Progress in breeding higher-yielding crop plants would be greatly accelerated if the phenotypic consequences of making changes to the genetic makeup of an organism could be reliably predicted. Developing a predictive capacity that scales from genotype to phenotype is impeded by biological complexities associated with genetic controls, environmental effects and interactions among plant growth and development processes. Plant modelling can help navigate a path through this complexity. Here we profile modelling approaches for complex traits at gene network, organ and whole plant levels. Each provides a means to link phenotypic consequence to changes in genomic regions via stable associations with model coefficients. A unifying feature of the models is the relatively coarse level of granularity they use to capture system dynamics. Much of the fine detail is not directly required. Robust coarse-grained models might be the tool needed to integrate phenotypic and molecular approaches to plant breeding.
Hammer
Graeme
G
APSRU, School of Land and Food Sciences, University of Queensland, Brisbane, QLD 4072, Australia. g.hammer@uq.edu.au
Cooper
Mark
M
Tardieu
François
F
Welch
Stephen
S
Walsh
Bruce
B
van Eeuwijk
Fred
F
Chapman
Scott
S
Podlich
Dean
D
D017360:Arabidopsis / Q000235:genetics / Q000254:growth & development; D001947:Breeding; D018556:Crops, Agricultural / Q000235:genetics* / Q000254:growth & development; D035264:Flowers / Q000235:genetics / Q000254:growth & development; D053263:Gene Regulatory Networks; D005838:Genotype; D008957:Models, Genetic; D010641:Phenotype; D018515:Plant Leaves / Q000235:genetics / Q000254:growth & development; D045868:Sorghum / Q000235:genetics / Q000254:growth & development; D003313:Zea mays / Q000235:genetics / Q000254:growth & development