MOTIVATION: Hormone pathway interactions are crucial in shaping plant development, such as synergism between the auxin and brassinosteroid pathways in cell elongation. Both hormone pathways have been characterized in detail, revealing several feedback loops. The complexity of this network, combined with a shortage of kinetic data, renders its quantitative analysis virtually impossible at present. RESULTS: As a first step towards overcoming these obstacles, we analyzed the network using a Boolean logic approach to build models of auxin and brassinosteroid signaling, and their interaction. To compare these discrete dynamic models across conditions, we transformed them into qualitative continuous systems, which predict network component states more accurately and can accommodate kinetic data as they become available. To this end, we developed an extension for the SQUAD software, allowing semi-quantitative analysis of network states. Contrasting the developmental output depending on cell type-specific modulators enabled us to identify a most parsimonious model, which explains initially paradoxical mutant phenotypes and revealed a novel physiological feature. AVAILABILITY: The package SQUADD is freely available via the Bioconductor repository at http://www.bioconductor.org/help/bioc-views/release/bioc/html/SQUADD.html.
MOTIVATION: Hormone pathway interactions are crucial in shaping plant development, such as synergism between the auxin and brassinosteroid pathways in cell elongation. Both hormone pathways have been characterized in detail, revealing several feedback loops. The complexity of this network, combined with a shortage of kinetic data, renders its quantitative analysis virtually impossible at present. RESULTS: As a first step towards overcoming these obstacles, we analyzed the network using a Boolean logic approach to build models of auxin and brassinosteroid signaling, and their interaction. To compare these discrete dynamic models across conditions, we transformed them into qualitative continuous systems, which predict network component states more accurately and can accommodate kinetic data as they become available. To this end, we developed an extension for the SQUAD software, allowing semi-quantitative analysis of network states. Contrasting the developmental output depending on cell type-specific modulators enabled us to identify a most parsimonious model, which explains initially paradoxical mutant phenotypes and revealed a novel physiological feature. AVAILABILITY: The package SQUADD is freely available via the Bioconductor repository at http://www.bioconductor.org/help/bioc-views/release/bioc/html/SQUADD.html.
Authors: Leah R Band; John A Fozard; Christophe Godin; Oliver E Jensen; Tony Pridmore; Malcolm J Bennett; John R King Journal: Plant Cell Date: 2012-10-30 Impact factor: 11.277
Authors: G Wilma van Esse; Simon van Mourik; Hans Stigter; Colette A ten Hove; Jaap Molenaar; Sacco C de Vries Journal: Plant Physiol Date: 2012-07-16 Impact factor: 8.340