Identification of the Trichome Patterning Core Network Using Data from Weak ttg1 Alleles to Constrain the Model Space

Balkunde, Rachappa; Deneer, Anna; Bechtel, H. Bernard; Zhang, Bipei; Herberth, Stefanie; Pesch, Martina; Jaegle, Benjamin; Fleck, Christian; Hülskamp, Martin

doi:10.1016/j.celrep.2020.108497

Cited by 15 publications

(37 citation statements)

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“…The epidermis of Arabidopsis leaves contains trichomes and stomata, specialized cell types distributed in ways that suggest their patterns emerge from self-organizing systems (Balkunde et al, 2020; Horst et al, 2015). In the case of stomata, bi-cellular valves used for plant-atmosphere gas exchange, fate and pattern emerge through a series of state changes and oriented cell divisions (Figure 1A-B) (Lee and Bergmann, 2019).…”

Section: Introductionmentioning

confidence: 99%

Slow and not so furious: de novo stomatal pattern formation during plant embryogenesis

Smit

Vatén

Mair

et al. 2022

Preprint

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The fates of cells on the leaf surface depend on positional cues and environmental signals. New stomata are formed away from existing ones to prevent disadvantageous clustering, but how are the initial precursors positioned? Mature embryos do not have stomata, but we provide transcriptomic, imaging, and genetic evidence that Arabidopsis embryos engage known stomatal fate and patterning factors to create regularly spaced stomatal precursor cells. Analysis of embryos from 35 plant species indicates this trait is conserved across angiosperm clades. Embryonic stomatal patterning in Arabidopsis is established in three stages: first broad SPEECHLESS (SPCH) expression, then coalescence of SPCH and its targets into discrete domains, and finally one round of symmetric division to create stomatal precursors. Lineage progression is then halted until after germination. We show that embryonic stomatal pattern enables quick stomatal differentiation and photosynthetic activity upon germination but also guides the formation of additional stomata as the leaf expands. In addition, key stomatal regulators are prevented from driving the fate transitions they can induce after germination, revealing stage-specific layers of regulation that control lineage progression during embryogenesis.

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Section: Introductionmentioning

confidence: 99%

Slow and not so furious: de novo stomatal pattern formation during plant embryogenesis

Smit

Vatén

Mair

et al. 2022

Preprint

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“…To determine what the effect is of increasingly higher order complexes on the pattern formation capabilities of the model, we derive the necessary conditions imposed on the model parameters by linear stability analysis [3,123]. Instead of using any of the existing trichome models [35,38,40,183,184], we use this simpler version as this allows an analytic approach in determining the conditions for the generation of spatial patterns. These conditions are [123]…”

Section: Multimers In the Context Of Pattern Formation: A Mathematica...mentioning

confidence: 99%

“…The number of genes involved in trichome patterning is extensive [34,37] and a model capturing the entire complexity would be unhelpfully intractable. Therefore, all existing models of trichome patterning contain elements of activator-inhibitor and substrate-depletion principles [38][39][40][41]183], composing a minimal set of components (Figure 5.1) such that certain genotypes and resulting phenotypes can be analysed through mathematical modelling.…”

Section: Introductionmentioning

confidence: 99%

“…The negative regulators, or 'inhibitors' in the models, consist of R3 MYB transcription factors, encoded by the genes TRIP-TYCHON (TRY), CAPRICE (CPC) [34,[50][51][52], ENHANCER OF TRY AND CPC 1, 2 and 3 (ETC1, ETC2, ETC3) and TRICHOMELESS1 and 2 (TCL1, TCL2) [50,[53][54][55][56][57][58][59]. All of these genes together are considered to be the core network of trichome patterning and previously published models all involve 114 different subsets of these genes [38][39][40][41]183].…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modelling of trichome patterning

Deneer

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Uncertainty is ubiquitous in biological systems. These uncertainties can be the result of lack of knowledge or due to a lack of appropriate data. Additionally, the natural variability of biological systems caused by intrinsic noise, e.g. in stochastic gene expression, leads to uncertainties. With the help of numerical simulations the impact of these uncertainties on the model predictions can be assessed, i.e. the impact of the propagation of uncertainty in model parameters on the model response can be quantified. Taking this into account is crucial when the models are used for experimental design, optimization, or decision-making, as model uncertainty can have a significant effect on the accuracy of model predictions. We focus here on spectral methods to quantify prediction uncertainty based on a probabilistic framework. Such methods have a basis in, e.g., computational mathematics, engineering, physics, and fluid dynamics, and, to a lesser extent, systems biology. In this chapter, we highlight the advantages these methods can have for modelling purposes in systems biology and do so by providing a novel and intuitive scheme. By applying the scheme to an array of examples we show its power, especially in challenging situations where slow converge due to high-dimensionality, bifurcations, and spatial discontinuities play a role.

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“…Patterning processes, however, are much more abundant and diverse than those examples. They also include, for example, the regular spacing of hairs [22], feathers [23], and trichomes (leaf hairs) [24], and the positioning of our digits [25], plant vascular bundles [26], lateral roots [27,28] and root nodules [29]. Models have also been vital in understanding patterning processes within a cell.…”

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confidence: 99%

Exploring mechanisms of xylem cell wall patterning with dynamic models

Jacobs

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Contents 1 General introduction 1.1 Xylem cell wall patterning 1.2 Pattern formation mechanisms 1.2.1 Reaction-diffusion mechanisms 1.2.2 Not every pattern is a Turing pattern 1.3 Small GTPases 1.3.1 Small GTPases in reaction-diffusion systems 1.3.2 Mass conservation 1.4 Microtubules 1.4.1 Microtubule properties 1.4.2 Microtubules and cell wall structure 1.4.3 Microtubule self-organisation 1.5 ROPs and microtubules in xylem patterning 1.6 Aim and thesis outline 3.7.3 Effect of alternative anisotropic diffusion scenarios 3.7.4 Diffusion tensor rotation 3.7.5 Preferred spiral angles 3.7.6 Diffusion term decomposition 3.7.7 Parameters of models with ROP-dependent ROP diffusion 4 The role of nucleation complexes in cortical microtubule array homogeneity and patterning 4.1 Introduction 4.2 Results 4.2.1 A simplified model of transversely oriented microtubules reproduces the inhomogeneity problem 4.2.2 Tubulin diffusion is too fast for sufficient local variation in microtubule growth velocities 4.2.3 Nucleation complexes are preferentially inserted at microtubules 4.2.4 Local limitation of nucleation complexes can solve the inhomogeneity problem 4.3 Discussion 4.4 Methods 4.4.1 Simulation setup 4.4.2 Core microtubule dynamics 4.4.3 Protoxylem band and gap regions 4.4.4 Basic nucleation modes 4.4.5 Tubulin dynamics 4.4.6 Nucleation complexes 4.4.7 Model parameters 4.4.8 Measures of heterogeneity 4.4.9 Experimental measurements 4.5 Supplementary figures 4.6 Supplementary tables 4.7 Appendices 4.7.1 Steady state densities for non-interacting microtubules 4.7.2 Calculating maximal total microtubule length and initial growth speed 4.7.3 Estimation of nucleation complex parameters 5 Protoxylem microtubule patterning requires ROP pattern co-alignment, realistic microtubule-based nucleation, and sufficient microtubule flexibility 5.1 Introduction 5.2 Results 5.2.1 Model outline 5.2.2 Strong co-alignment of the microtubule array with a pre-existing band pattern facilitates rapid microtubule band formation 5.2.3 Microtubule flexibility can lead to density loss from bands 5.2.4 Locally saturating nucleation helps to keep bands populated with semiflexible microtubules, even for misaligned starting arrays viii CONTENTS 5.2.5 Array patterning must be robust against large variations in observed microtubule dynamics 5.2.6 Band-gap separation factors outside the measured parameter differences 5.3 Discussion 5.4 Methods 5.4.1 Simulation setup 5.4.2 Nucleation modes 5.4.3 Expected mismatch angles 5.4.4 Semiflexible microtubules 5.4.5 Cell data simulations 5.4.6 Summary statistics 5.5 Supplementary figures 5.6 Appendices 5.6.1 Parameters for locally saturating nucleation 5.6.2 Persistence length calculations 6 General discussion 6.1 Insights into mechanisms of xylem patterning 6.1.1 A ROP-based Turing pattern as the basis of xylem patterning 6.1.2 Protoxylem ROP pattern orientation 6.1.3 Coexistence in ROP and microtubule patterns 6.1.4 ROP-microtubule interplay 6.2 Insights into non-xylem membrane patterning 6.2.1 Coexistence in non-xyle...

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Identification of the Trichome Patterning Core Network Using Data from Weak ttg1 Alleles to Constrain the Model Space

Cited by 15 publications

References 64 publications

Slow and not so furious: de novo stomatal pattern formation during plant embryogenesis

Slow and not so furious: de novo stomatal pattern formation during plant embryogenesis

Mathematical modelling of trichome patterning

Exploring mechanisms of xylem cell wall patterning with dynamic models

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