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a Dep. of Agron., Kansas State Univ., Manhattan, KS 66506
b Div. of Biol., Kansas State Univ., Manhattan, KS 66506
* Corresponding author (welchsm{at}ksu.edu)
Received for publication May 1, 2001. Crop simulation models incorporate many physiological processes within sophisticated mathematical frameworks. However, the control mechanisms for these processes tend to be ad hoc, empirical, and indirectly inferred from data and may lack realistic plasticity. Using model organisms like Arabidopsis thaliana, genomic scientists are rapidly disentangling the networks of genes that exert physiological control. As yet, however, these networks are qualitative in nature, depicting promotion and inhibition pathways but not supporting quantitative predictions of overall integrated effects. We believe (i) that neural networks can provide the quantification that current genetic networks lack and (ii) that taxonomic conservation of central genetic mechanisms will make networks developed for model plants also useful in crops. This paper presents evidence supporting the first point based on a neural network with eight nodes corresponding to A. thaliana genes controlling inflorescence timing. The nodes were linked into photoperiod and autonomous pathways abstracted from an existing qualitative genetic network model. Growth chamber data on transition timing were collected at 16 and 24°C for seven A. thaliana strains possessing loss-of-function mutations at the network loci. An eighth strain served as a common wild-type control. The neural network model reproduced the time course of the transition at both temperatures for all eight genotypes. Results included tracking a novel, temperature-dependent exchange in transition order exhibited by two mutants whose duplication is not possible by usual crop simulation methods. Furthermore, the ability to imitate the data appeared to have a desirable sensitivity to assumed network structure.
Abbreviations: ES, Excel Solver (software) • G x E, genotype x environment (interaction) • LS, least squares • MQL, Marquardt–Levenberg (algorithm) • RMSE, root mean square error • SCE, simulated complex evolution • SSE, sum of squared errors
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