Some fundamental aspects of modeling auxin patterning in the context of auxin-ethylene-cytokinin crosstalk

Moore, Simon; Zhang, Xiaoxian; Liu, Junli; Lindsey, Keith

doi:10.1080/15592324.2015.1056424

Cited by 6 publications

(9 citation statements)

References 26 publications

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“…Therefore, analyzing the action of such a network requires a model that integrates these different processes. Defining a hormonal crosstalk network model for root development needs careful consideration of several different factors (Moore et al, 2015a(Moore et al, , 2015b including: the relationships between hormones and the associated genes; formulation of kinetic equations following thermodynamic and kinetic principles; spatial root structure; transport kinetics for all hormonal crosstalk components; and parameterization of a hormonal crosstalk model.…”

Section: Tackling the Complexity In Auxin Cytokinin And Ethylene Crosstalk In Arabidopsis Root Development: A Methodology That Iterativelmentioning

confidence: 99%

Crosstalk Complexities between Auxin, Cytokinin, and Ethylene in Arabidopsis Root Development: From Experiments to Systems Modeling, and Back Again

et al. 2017

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Understanding how hormones and genes interact to coordinate plant growth in a changing environment is a major challenge in plant developmental biology. Auxin, cytokinin, and ethylene are three important hormones that regulate many aspects of plant development. This review critically evaluates the crosstalk between the three hormones in Arabidopsis root development. We integrate a variety of experimental data into a crosstalk network, which reveals multiple layers of complexity in auxin, cytokinin, and ethylene crosstalk. In particular, data integration reveals an additional, largely overlooked link between the ethylene and cytokinin pathways, which acts through a phosphorelay mechanism. This proposed link addresses outstanding questions on whether ethylene application promotes or inhibits receptor kinase activity of the ethylene receptors. Elucidating the complexity in auxin, cytokinin, and ethylene crosstalk requires a combined experimental and systems modeling approach. We evaluate important modeling efforts for establishing how crosstalk between auxin, cytokinin, and ethylene regulates patterning in root development. We discuss how a novel methodology that iteratively combines experiments with systems modeling analysis is essential for elucidating the complexity in crosstalk of auxin, cytokinin, and ethylene in root development. Finally, we discuss the future challenges from a combined experimental and modeling perspective.

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Section: Tackling the Complexity In Auxin Cytokinin And Ethylene Crosstalk In Arabidopsis Root Development: A Methodology That Iterativelmentioning

confidence: 99%

Crosstalk Complexities between Auxin, Cytokinin, and Ethylene in Arabidopsis Root Development: From Experiments to Systems Modeling, and Back Again

et al. 2017

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“…In plant developmental biology, a major challenge is to understand how plant growth is coordinated by interacting hormones and genes. Previously, a hormonal crosstalk network modelwhich describes how three hormones (auxin, ethylene and cytokinin) and the associated genes coordinate to regulate Arabidopsis root developmentwas constructed by iteratively combining regulatory relationships derived from experimental data and mathematical modelling (Liu et al 2010(Liu et al , 2013Moore et al 2015aMoore et al , 2015bMoore et al , 2015cMoore et al , 2017. However, for the mathematical model to best link with Arabidopsis root development, it is necessary to understand the parameter space of the model and identify all acceptable parameter combinations.…”

Section: Use and Understanding Of Scientific Models In Systems Biologymentioning

confidence: 99%

“…Little is known about how acceptable parameter combinations of a model can be identified in light of specific experimental data. Therefore, this work explores how the acceptable parameter space of a complex model of hormonal crosstalk (Liu et al 2013;Moore et al 2015aMoore et al , 2015bMoore et al , 2015cMoore et al , 2017 is assessed given a combination of quantitative experimental measurements and qualitative experimental trends by employing Bayesian history matching techniques (Craig et al 1997;Cumming and Goldstein 2010;Gong and DiazDelaO 2017;Zhang et al 2012). Additionally, we utilise these techniques to analyse how learning about the functions of a gene through particular relevant experiments can inform us about acceptable model parameter space.…”

Section: Use and Understanding Of Scientific Models In Systems Biologymentioning

confidence: 99%

Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching

Jackson

Vernon

Liu

et al. 2020

Statistical Applications in Genetics and Molecular Biology

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A major challenge in plant developmental biology is to understand how plant growth is coordinated by interacting hormones and genes. To meet this challenge, it is important to not only use experimental data, but also formulate a mathematical model. For the mathematical model to best describe the true biological system, it is necessary to understand the parameter space of the model, along with the links between the model, the parameter space and experimental observations. We develop sequential history matching methodology, using Bayesian emulation, to gain substantial insight into biological model parameter spaces. This is achieved by finding sets of acceptable parameters in accordance with successive sets of physical observations. These methods are then applied to a complex hormonal crosstalk model for Arabidopsis root growth. In this application, we demonstrate how an initial set of 22 observed trends reduce the volume of the set of acceptable inputs to a proportion of 6.1 × 10−7 of the original space. Additional sets of biologically relevant experimental data, each of size 5, reduce the size of this space by a further three and two orders of magnitude respectively. Hence, we provide insight into the constraints placed upon the model structure by, and the biological consequences of, measuring subsets of observations.

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“…1b k and 11 b k kinetically describe the maximum auxin biosynthesis rate and the degree to which cytokinin negatively regulates auxin biosynthesis. Similarly, we can derive kinetic equations for the regulation of auxin degradation, or for the simultaneous regulation of auxin biosynthesis and degradation (Moore et al, 2015b). Sauro, 2011), the kinetic equation…”

Section: Formulation Of Kinetic Equations For All Processes In the Homentioning

confidence: 99%

“…For example, auxin biosynthesis can be stimulated by ethylene and inhibited by cytokinins (Nordstrom et al, 2004;Ruzicka et al, 2007;Stepanova et al, 2007). Following thermodynamic and kinetic principles Sauro, 2011), a kinetic equation for auxin biosynthesis that incorporates various regulatory relationships can be derived (Liu et al, 2010;Moore et al, 2015a;2015b). Using the methodology described in Figure 2, kinetic equations for all processes in a hormonal crosstalk network (Figure 1) can be derived based on experimental data.…”

Section: Formulation Of Kinetic Equations For All Processes In the Homentioning

confidence: 99%

Modelling Plant Hormone Gradients

Moore

Zhang

Lindsey

2015

Encyclopedia of Life Sciences

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Cellular patterning in the Arabidopsis root is coordinated via a localised auxin concentration maximum in the root tip, requiring the regulated expression of specific genes. The activities of plant hormones such as auxin, ethylene and cytokinin depend on cellular context and exhibit either synergistic or antagonistic interactions. Due to the complexity and nonlinearity of spatiotemporal interactions between both hormones and gene expression in root development, modelling plant hormone gradients requires a systems approach in which experimental data and modelling analysis are closely combined. Modelling therefore allows a predictive interrogation of highly complex and nonintuitive interactions between components in the system. Important factors to be considered when modelling hormone gradients include the construction of a hormonal crosstalk network, the formulation of kinetic equations and the construction of an in silico root map. A modelling approach enables the analysis of relationships between multiple hormone gradients, predictions on how hormone gradients emerge under the action of hormonal crosstalk, and the prediction and elucidation of experimental results from mutant roots. Key Concepts Patterning in Arabidopsis root development is coordinated via a localised auxin concentration maximum in the root tip, requiring the regulated expression of specific genes. The activities of plant hormones such as auxin, ethylene, cytokinin, abscisic acid, gibberellin and brassinosteroids depend on cellular context and exhibit either synergistic or antagonistic interactions. Auxin concentration and the associated regulatory and target genes are regulated by diverse interacting hormones and gene expression and therefore cannot change independently of the various crosstalk components in space and time. Other hormone concentrations, such as ethylene and cytokinin concentrations, and expression of the associated regulatory and target genes are also interlinked. Modelling plant hormone gradients requires a systems approach where experimental data and modelling analysis are closely combined. A hormonal crosstalk network describes the regulatory relationships between hormones and their associated genes. A kinetic equation can be formulated for any regulatory relationship following thermodynamic and kinetic principles. Construction of an in silico root map enables the study of multicellular cell–cell communications in Arabidopsis root development. Modelling hormone gradients enables the analysis of relationships between multiple hormone gradients, predictions on how hormone gradients emerge under the action of hormonal crosstalk, and the prediction and elucidation of experimental results from mutant roots. Modelling auxin gradients can also incorporate different mechanisms of polar auxin transport and the interaction of hormone gradients and root growth.

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Some fundamental aspects of modeling auxin patterning in the context of auxin-ethylene-cytokinin crosstalk

Cited by 6 publications

References 26 publications

Crosstalk Complexities between Auxin, Cytokinin, and Ethylene in Arabidopsis Root Development: From Experiments to Systems Modeling, and Back Again

Crosstalk Complexities between Auxin, Cytokinin, and Ethylene in Arabidopsis Root Development: From Experiments to Systems Modeling, and Back Again

Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching

Modelling Plant Hormone Gradients

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