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Floral morphogenesis: stochastic explorations of a gene network epigenetic landscape.

Alvarez-Buylla ER, Chaos A, Aldana M, Benítez M, Cortes-Poza Y, Espinosa-Soto C, Hartasánchez DA, Lotto RB, Malkin D, Escalera Santos GJ, Padilla-Longoria P - PLoS ONE (2008)

Bottom Line: Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model.Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification.It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of cells with different genetic configurations during development.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Ecología, Universidad Nacional Autónoma de México, Cd. Universitaria, México, D. F., México. elena.alvarezbuylla@gmail.com

ABSTRACT
In contrast to the classical view of development as a preprogrammed and deterministic process, recent studies have demonstrated that stochastic perturbations of highly non-linear systems may underlie the emergence and stability of biological patterns. Herein, we address the question of whether noise contributes to the generation of the stereotypical temporal pattern in gene expression during flower development. We modeled the regulatory network of organ identity genes in the Arabidopsis thaliana flower as a stochastic system. This network has previously been shown to converge to ten fixed-point attractors, each with gene expression arrays that characterize inflorescence cells and primordial cells of sepals, petals, stamens, and carpels. The network used is binary, and the logical rules that govern its dynamics are grounded in experimental evidence. We introduced different levels of uncertainty in the updating rules of the network. Interestingly, for a level of noise of around 0.5-10%, the system exhibited a sequence of transitions among attractors that mimics the sequence of gene activation configurations observed in real flowers. We also implemented the gene regulatory network as a continuous system using the Glass model of differential equations, that can be considered as a first approximation of kinetic-reaction equations, but which are not necessarily equivalent to the Boolean model. Interestingly, the Glass dynamics recover a temporal sequence of attractors, that is qualitatively similar, although not identical, to that obtained using the Boolean model. Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model. Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification. It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of cells with different genetic configurations during development.

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Schematic representation of the epigenetic landscape generated by a stochastic exploration of the GRN for flower development.This schematic landscape is equivalent to the Epigenetic Landscape proposed by C.H. Waddington (1957). Basins comprise the cell genetic configurations that lead to attractors (in this case, gene arrays characteristic of floral organ primordial cell-types: Sepals, petals, stamens, and carpels. See Figure 1 and Discussion). Each cell fate is associated to the GRN configuration corresponding to each of the attractors. The arrows represent transitions among attractors. The transition from inflorescence to sepal attractor might be biased or determined by an inducer. The numbers associated to the arrows represent the sequence of transitions among attractors: From sepals to petals, and then to carpels and stamens.
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pone-0003626-g007: Schematic representation of the epigenetic landscape generated by a stochastic exploration of the GRN for flower development.This schematic landscape is equivalent to the Epigenetic Landscape proposed by C.H. Waddington (1957). Basins comprise the cell genetic configurations that lead to attractors (in this case, gene arrays characteristic of floral organ primordial cell-types: Sepals, petals, stamens, and carpels. See Figure 1 and Discussion). Each cell fate is associated to the GRN configuration corresponding to each of the attractors. The arrows represent transitions among attractors. The transition from inflorescence to sepal attractor might be biased or determined by an inducer. The numbers associated to the arrows represent the sequence of transitions among attractors: From sepals to petals, and then to carpels and stamens.

Mentions: Stochastic implementations of a GRN model as pursued in this study were proposed by C. H. Waddington many years ago ([22]; see review in [23]). He understood development as a complex dynamic system, with genes, proteins, metabolites, and environmental factors constituting complex dynamic networks. The attractors of such networks represent a specific configuration of the system (e.g. cell types). The number, depth, width, and relative position of these attractors are represented by the hills and valleys of his “Epigenetic Landscape” metaphor [22], [7]. The study presented here actually explored such an Epigenetic Landscape for the flower organ determination GRN (Figures 1 and 7). Other recent studies have also explored this idea for GRNs [30].


Floral morphogenesis: stochastic explorations of a gene network epigenetic landscape.

Alvarez-Buylla ER, Chaos A, Aldana M, Benítez M, Cortes-Poza Y, Espinosa-Soto C, Hartasánchez DA, Lotto RB, Malkin D, Escalera Santos GJ, Padilla-Longoria P - PLoS ONE (2008)

Schematic representation of the epigenetic landscape generated by a stochastic exploration of the GRN for flower development.This schematic landscape is equivalent to the Epigenetic Landscape proposed by C.H. Waddington (1957). Basins comprise the cell genetic configurations that lead to attractors (in this case, gene arrays characteristic of floral organ primordial cell-types: Sepals, petals, stamens, and carpels. See Figure 1 and Discussion). Each cell fate is associated to the GRN configuration corresponding to each of the attractors. The arrows represent transitions among attractors. The transition from inflorescence to sepal attractor might be biased or determined by an inducer. The numbers associated to the arrows represent the sequence of transitions among attractors: From sepals to petals, and then to carpels and stamens.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2572848&req=5

pone-0003626-g007: Schematic representation of the epigenetic landscape generated by a stochastic exploration of the GRN for flower development.This schematic landscape is equivalent to the Epigenetic Landscape proposed by C.H. Waddington (1957). Basins comprise the cell genetic configurations that lead to attractors (in this case, gene arrays characteristic of floral organ primordial cell-types: Sepals, petals, stamens, and carpels. See Figure 1 and Discussion). Each cell fate is associated to the GRN configuration corresponding to each of the attractors. The arrows represent transitions among attractors. The transition from inflorescence to sepal attractor might be biased or determined by an inducer. The numbers associated to the arrows represent the sequence of transitions among attractors: From sepals to petals, and then to carpels and stamens.
Mentions: Stochastic implementations of a GRN model as pursued in this study were proposed by C. H. Waddington many years ago ([22]; see review in [23]). He understood development as a complex dynamic system, with genes, proteins, metabolites, and environmental factors constituting complex dynamic networks. The attractors of such networks represent a specific configuration of the system (e.g. cell types). The number, depth, width, and relative position of these attractors are represented by the hills and valleys of his “Epigenetic Landscape” metaphor [22], [7]. The study presented here actually explored such an Epigenetic Landscape for the flower organ determination GRN (Figures 1 and 7). Other recent studies have also explored this idea for GRNs [30].

Bottom Line: Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model.Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification.It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of cells with different genetic configurations during development.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Ecología, Universidad Nacional Autónoma de México, Cd. Universitaria, México, D. F., México. elena.alvarezbuylla@gmail.com

ABSTRACT
In contrast to the classical view of development as a preprogrammed and deterministic process, recent studies have demonstrated that stochastic perturbations of highly non-linear systems may underlie the emergence and stability of biological patterns. Herein, we address the question of whether noise contributes to the generation of the stereotypical temporal pattern in gene expression during flower development. We modeled the regulatory network of organ identity genes in the Arabidopsis thaliana flower as a stochastic system. This network has previously been shown to converge to ten fixed-point attractors, each with gene expression arrays that characterize inflorescence cells and primordial cells of sepals, petals, stamens, and carpels. The network used is binary, and the logical rules that govern its dynamics are grounded in experimental evidence. We introduced different levels of uncertainty in the updating rules of the network. Interestingly, for a level of noise of around 0.5-10%, the system exhibited a sequence of transitions among attractors that mimics the sequence of gene activation configurations observed in real flowers. We also implemented the gene regulatory network as a continuous system using the Glass model of differential equations, that can be considered as a first approximation of kinetic-reaction equations, but which are not necessarily equivalent to the Boolean model. Interestingly, the Glass dynamics recover a temporal sequence of attractors, that is qualitatively similar, although not identical, to that obtained using the Boolean model. Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model. Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification. It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of cells with different genetic configurations during development.

Show MeSH
Related in: MedlinePlus