Quarterly (winter, spring, summer, fall)
128 pp. per issue
7 x 10, illustrated
ISSN
1064-5462
E-ISSN
1530-9185
2014 Impact factor:
1.39

Artificial Life

Fall 2012, Vol. 18, No. 4, Pages 445-460
(doi: 10.1162/ARTL_a_00076)
© 2012 Massachusetts Institute of Technology
Computational Modeling of the Regulatory Network Organizing the Wound Response in Arabidopsis thaliana
Article PDF (473.61 KB)
Abstract

Plants are frequently wounded by mechanical impact or by insects, and their ability to adequately respond to wounding is essential for their survival and reproductive success. The wound response is mediated by a signal transduction and regulatory network. Molecular studies in Arabidopsis have identified the COI1 gene as a central component of this network. Current models of these networks qualitatively describe the wound response, but they are not directly assessed using quantitative gene expression data. We built a model comprising the key components of the Arabidopsis wound response using the transsys framework. For comparison, we constructed a null model that is devoid of any regulatory interactions, and various alternative models by rewiring the wound response model. All models were parametrized by computational optimization to generate synthetic gene expression profiles that approximate the empirical data set. We scored the fit of the synthetic to the empirical data with various distance measures, and used the median distance after optimization to directly and quantitatively assess the wound response model and its alternatives. Discrimination of candidate models depends substantially on the measure of gene expression profile distance. Using the null model to assess quality of the distance measures for discrimination, we identify correlation of log-ratio profiles as the most suitable distance. Our wound response model fits the empirical data significantly better than the alternative models. Gradual perturbation of the wound response model results in a corresponding gradual decline in fit. The optimization approach provides insights into biologically relevant features, such as robustness. It is a step toward enabling integrative studies of multiple cross-talking pathways, and thus may help to develop our understanding how the genome informs the mapping of environmental signals to phenotypic traits.