Virtual Plant Tissue: Building Blocks for Next-Generation Plant Growth Simulation

Vos, Dirk De; Dzhurakhalov, A.A.; Stijven, Sean; Klosiewicz, Przemyslaw; Beemster, Gerrit T.S.; Broeckhove, J.

doi:10.3389/fpls.2017.00686

Cited by 11 publications

(12 citation statements)

References 39 publications

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“…Because maize leaves predominantly grow longitudinally, with limited expansion in the lateral and anticlinal direction, we used the modelling software VPTISSUE (De Vos et al, 2017) to represent a section of the leaf by a dynamic lattice or mesh (see Supporting Information Methods S1; Fig. S1) that is restrained to grow only longitudinally.…”

Section: Model Developmentmentioning

confidence: 99%

How grass keeps growing: an integrated analysis of hormonal crosstalk in the maize leaf growth zone

et al. 2019

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Summary We studied the maize leaf to understand how long‐distance signals, auxin and cytokinin, control leaf growth dynamics. We constructed a mathematical model describing the transport of these hormones along the leaf growth zone and their interaction with the local gibberellin (GA) metabolism in the control of cell division. Assuming gradually declining auxin and cytokinin supply at the leaf base, the model generated spatiotemporal hormone distribution and growth patterns that matched experimental data. At the cellular level, the model predicted a basal leaf growth as a result of cell division driven by auxin and cytokinin. Superimposed on this, GA synthesis regulated growth through the control of the size of the region of active cell division. The predicted hormone and cell length distributions closely matched experimental data. To correctly predict the leaf growth profiles and final organ size of lines with reduced or elevated GA production, the model required a signal proportional to the size of the emerged part of the leaf that inhibited the basal leaf growth driven by auxin and cytokinin. Excision and shading of the emerged part of the growing leaf allowed us to demonstrate that this signal exists and depends on the perception of light intensity.

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Section: Model Developmentmentioning

confidence: 99%

How grass keeps growing: an integrated analysis of hormonal crosstalk in the maize leaf growth zone

et al. 2019

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“…Early multicellular models used a structured rectangular grid to depict root topologies (Grieneisen et al ., 2007; Mähönen et al ., 2014; Mironova et al ., 2012), however, recent models use realistic root anatomies, derived either from digitized microscopic images (Band et al ., 2012a; Mellor et al ., 2016, 2022; Moore et al ., 2015, 2017) or idealized using computational tools (Di Mambro et al ., 2017). Different frameworks to develop multicellular models exist, namely CellModeller (Dupuy et al ., 2008, 2010), OpenAlea (Collis et al ., 2022; Pradal et al ., 2008), VirtualPlantTissue (De Vos et al ., 2017; Merks et al ., 2011) and MECHA (Couvreur et al ., 2018). These frameworks use a standard programming language (e.g., java, python, or C++) to couple model components, representing different processes.…”

Section: Introductionmentioning

confidence: 99%

RootSlice– a novel functional-structural model for root anatomical phenotypes

Sidhu

Ajmera

Arya

et al. 2022

Preprint

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Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes.

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“…Temporal analysis of plant growth has traditionally been performed through modeling [12]. Several tools and approaches exist for modeling and simulating plant growth [13][14][15]. Most of these tools are based on deterministic factors to map the plant growth and offer 2D/3D deterministic plant simulations [16,17].…”

Section: Introductionmentioning

confidence: 99%

Predicting Plant Growth from Time-Series Data Using Deep Learning

et al. 2021

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Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of Arabidopsis (A. thaliana) and Brassica rapa (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations.

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Virtual Plant Tissue: Building Blocks for Next-Generation Plant Growth Simulation

Cited by 11 publications

References 39 publications

How grass keeps growing: an integrated analysis of hormonal crosstalk in the maize leaf growth zone

How grass keeps growing: an integrated analysis of hormonal crosstalk in the maize leaf growth zone

RootSlice– a novel functional-structural model for root anatomical phenotypes

Predicting Plant Growth from Time-Series Data Using Deep Learning

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