2010
DOI: 10.1146/annurev-arplant-042809-112213
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Computational Morphodynamics: A Modeling Framework to Understand Plant Growth

Abstract: Computational morphodynamics utilizes computer modeling to understand the development of living organisms over space and time. Results from biological experiments are used to construct accurate and predictive models of growth. These models are then used to make novel predictions providing further insight into the processes in question, which can be tested experimentally to either confirm or rule out the validity of the computational models. This review highlights two fundamental issues: (1.) models should span… Show more

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Cited by 78 publications
(62 citation statements)
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“…Since any of these microscopic reorganizations would lead to indistinguishable macroscopic deformations, all of these possibilities must be considered in phenotypic analysis of tissue morphogenesis [16,17]. In the context of plant morphodynamics [18], our study has emphasized that in addition to differential cell division and isotropic cell expansion, differential cell anisotropy can also play a dominant role in evolutionarily significant shape change. Petal spur sculpting and spur-length diversity across the genus Aquilegia, even in its most extreme expressions, can be explained solely through variation in cell anisotropy.…”
Section: Discussionmentioning
confidence: 96%
“…Since any of these microscopic reorganizations would lead to indistinguishable macroscopic deformations, all of these possibilities must be considered in phenotypic analysis of tissue morphogenesis [16,17]. In the context of plant morphodynamics [18], our study has emphasized that in addition to differential cell division and isotropic cell expansion, differential cell anisotropy can also play a dominant role in evolutionarily significant shape change. Petal spur sculpting and spur-length diversity across the genus Aquilegia, even in its most extreme expressions, can be explained solely through variation in cell anisotropy.…”
Section: Discussionmentioning
confidence: 96%
“…The mathematical model described below seeks to explore how different processes interact to produce the gibberellin distribution and how this distribution is able, through the gibberellin signaling pathway, to determine the DELLA distribution. A multiscale modeling approach is essential to understanding the complexity of such a system (12)(13)(14); for example, a genetic mutation may alter both the gibberellin pathway and the growth dynamics, and modeling can integrate these perturbations to predict the DELLA distribution. In this work, we focus on the root elongation zone.…”
mentioning
confidence: 99%
“…We previously found that the CgAUX1 gene that encodes an auxin influx carrier functionally equivalent to Arabidopsis AtAUX1 is expressed in the vascular tissues and in Frankia-infected cells in C. glauca nodules (Péret et al, 2007). To test whether this CgAUX1 expression might be sufficient to explain the pattern of auxin accumulation, we used a computational modeling approach similar to the ones that have been applied successfully to study developmental processes such as phyllotaxis (de Reuille et al, 2006;Chickarmane et al, 2010). Auxin fluxes and accumulation patterns in a tissue can be inferred from the cellular localization of transporters.…”
Section: Resultsmentioning
confidence: 99%