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Interlinked nonlinear subnetworks underlie the formation of robust cellular patterns in Arabidopsis epidermis: a dynamic spatial model.

Benítez M, Espinosa-Soto C, Padilla-Longoria P, Alvarez-Buylla ER - BMC Syst Biol (2008)

Bottom Line: The meta-GRN model shows that interlinked sub-networks contribute redundantly to the formation of robust hair patterns and permits to advance novel and testable predictions regarding the effect of cell shape, signalling pathways and additional gene interactions affecting spatial cell-patterning.Pursuing dynamic analyses of larger (genomic) coupled networks is still not possible.A repertoire of well-characterised regulatory modules, like the one presented here, will, however, help to uncover general principles of the patterning-associated networks, as well as the peculiarities that originate diversity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad Universitaria 3er Circuito Exterior, Junto Jardín Botánico Exterior, Coyoacán 04510, DF, Mexico. marianabk@gmail.com

ABSTRACT

Background: Dynamical models are instrumental for exploring the way information required to generate robust developmental patterns arises from complex interactions among genetic and non-genetic factors. We address this fundamental issue of developmental biology studying the leaf and root epidermis of Arabidopsis. We propose an experimentally-grounded model of gene regulatory networks (GRNs) that are coupled by protein diffusion and comprise a meta-GRN implemented on cellularised domains.

Results: Steady states of the meta-GRN model correspond to gene expression profiles typical of hair and non-hair epidermal cells. The simulations also render spatial patterns that match the cellular arrangements observed in root and leaf epidermis. As in actual plants, such patterns are robust in the face of diverse perturbations. We validated the model by checking that it also reproduced the patterns of reported mutants. The meta-GRN model shows that interlinked sub-networks contribute redundantly to the formation of robust hair patterns and permits to advance novel and testable predictions regarding the effect of cell shape, signalling pathways and additional gene interactions affecting spatial cell-patterning.

Conclusion: The spatial meta-GRN model integrates available experimental data and contributes to further understanding of the Arabidopsis epidermal system. It also provides a systems biology framework to explore the interplay among sub-networks of a GRN, cell-to-cell communication, cell shape and domain traits, which could help understanding of general aspects of patterning processes. For instance, our model suggests that the information needed for cell fate determination emerges from dynamic processes that depend upon molecular components inside and outside differentiating cells, suggesting that the classical distinction of lineage versus positional cell differentiation may be instrumental but rather artificial. It also suggests that interlinkage of nonlinear and redundant sub-networks in larger networks is important for pattern robustness. Pursuing dynamic analyses of larger (genomic) coupled networks is still not possible. A repertoire of well-characterised regulatory modules, like the one presented here, will, however, help to uncover general principles of the patterning-associated networks, as well as the peculiarities that originate diversity.

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The model renders cellular patterns similar to those observed in the leaf epidermis. The simulated cellular patterns for wild-type (wt) and mutant networks are consistent with the patterns reported in the literature. Black squares correspond to trichomes and white ones to pavement cells (non-hair cells). Captions under the matrixes indicate the simulated genotype that gave rise to each of them (++ stands for overexpression, while lower case italics indicate loss of function). The table shows that the network profiles typical of hair and non-hair cells are recovered by the meta-GRN model. These simulations were all performed in 20 × 20 matrices with parameter values DCPC = 0.05, DTRR = 0.05, DTTG = 0.03.
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Figure 3: The model renders cellular patterns similar to those observed in the leaf epidermis. The simulated cellular patterns for wild-type (wt) and mutant networks are consistent with the patterns reported in the literature. Black squares correspond to trichomes and white ones to pavement cells (non-hair cells). Captions under the matrixes indicate the simulated genotype that gave rise to each of them (++ stands for overexpression, while lower case italics indicate loss of function). The table shows that the network profiles typical of hair and non-hair cells are recovered by the meta-GRN model. These simulations were all performed in 20 × 20 matrices with parameter values DCPC = 0.05, DTRR = 0.05, DTTG = 0.03.

Mentions: In the root meta-GRN model, CPC and bHLH proteins (GL3 and EGL3) were allowed to diffuse, as suggested by experimental data (see Methods). In this case, the cortical signal associated with SCM was simulated every two cell files as a downregulating input on WER. GRNs were also randomly initialised and two network attractors were found. In the first (white cells in Figure 3), WER, TTG, CPC, TRY and ETC are expressed, and owing to diffusion GL3 and EGL3 proteins are also present. This profile corresponds to that reported for cells committed to the non-hair fate (atrichoblasts). The other attractor matches the characteristic profile of cells that will bear root-hairs (trichoblasts), as GL3, EGL3 and TTG are expressed in it and the CPC protein is present.


Interlinked nonlinear subnetworks underlie the formation of robust cellular patterns in Arabidopsis epidermis: a dynamic spatial model.

Benítez M, Espinosa-Soto C, Padilla-Longoria P, Alvarez-Buylla ER - BMC Syst Biol (2008)

The model renders cellular patterns similar to those observed in the leaf epidermis. The simulated cellular patterns for wild-type (wt) and mutant networks are consistent with the patterns reported in the literature. Black squares correspond to trichomes and white ones to pavement cells (non-hair cells). Captions under the matrixes indicate the simulated genotype that gave rise to each of them (++ stands for overexpression, while lower case italics indicate loss of function). The table shows that the network profiles typical of hair and non-hair cells are recovered by the meta-GRN model. These simulations were all performed in 20 × 20 matrices with parameter values DCPC = 0.05, DTRR = 0.05, DTTG = 0.03.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2600786&req=5

Figure 3: The model renders cellular patterns similar to those observed in the leaf epidermis. The simulated cellular patterns for wild-type (wt) and mutant networks are consistent with the patterns reported in the literature. Black squares correspond to trichomes and white ones to pavement cells (non-hair cells). Captions under the matrixes indicate the simulated genotype that gave rise to each of them (++ stands for overexpression, while lower case italics indicate loss of function). The table shows that the network profiles typical of hair and non-hair cells are recovered by the meta-GRN model. These simulations were all performed in 20 × 20 matrices with parameter values DCPC = 0.05, DTRR = 0.05, DTTG = 0.03.
Mentions: In the root meta-GRN model, CPC and bHLH proteins (GL3 and EGL3) were allowed to diffuse, as suggested by experimental data (see Methods). In this case, the cortical signal associated with SCM was simulated every two cell files as a downregulating input on WER. GRNs were also randomly initialised and two network attractors were found. In the first (white cells in Figure 3), WER, TTG, CPC, TRY and ETC are expressed, and owing to diffusion GL3 and EGL3 proteins are also present. This profile corresponds to that reported for cells committed to the non-hair fate (atrichoblasts). The other attractor matches the characteristic profile of cells that will bear root-hairs (trichoblasts), as GL3, EGL3 and TTG are expressed in it and the CPC protein is present.

Bottom Line: The meta-GRN model shows that interlinked sub-networks contribute redundantly to the formation of robust hair patterns and permits to advance novel and testable predictions regarding the effect of cell shape, signalling pathways and additional gene interactions affecting spatial cell-patterning.Pursuing dynamic analyses of larger (genomic) coupled networks is still not possible.A repertoire of well-characterised regulatory modules, like the one presented here, will, however, help to uncover general principles of the patterning-associated networks, as well as the peculiarities that originate diversity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad Universitaria 3er Circuito Exterior, Junto Jardín Botánico Exterior, Coyoacán 04510, DF, Mexico. marianabk@gmail.com

ABSTRACT

Background: Dynamical models are instrumental for exploring the way information required to generate robust developmental patterns arises from complex interactions among genetic and non-genetic factors. We address this fundamental issue of developmental biology studying the leaf and root epidermis of Arabidopsis. We propose an experimentally-grounded model of gene regulatory networks (GRNs) that are coupled by protein diffusion and comprise a meta-GRN implemented on cellularised domains.

Results: Steady states of the meta-GRN model correspond to gene expression profiles typical of hair and non-hair epidermal cells. The simulations also render spatial patterns that match the cellular arrangements observed in root and leaf epidermis. As in actual plants, such patterns are robust in the face of diverse perturbations. We validated the model by checking that it also reproduced the patterns of reported mutants. The meta-GRN model shows that interlinked sub-networks contribute redundantly to the formation of robust hair patterns and permits to advance novel and testable predictions regarding the effect of cell shape, signalling pathways and additional gene interactions affecting spatial cell-patterning.

Conclusion: The spatial meta-GRN model integrates available experimental data and contributes to further understanding of the Arabidopsis epidermal system. It also provides a systems biology framework to explore the interplay among sub-networks of a GRN, cell-to-cell communication, cell shape and domain traits, which could help understanding of general aspects of patterning processes. For instance, our model suggests that the information needed for cell fate determination emerges from dynamic processes that depend upon molecular components inside and outside differentiating cells, suggesting that the classical distinction of lineage versus positional cell differentiation may be instrumental but rather artificial. It also suggests that interlinkage of nonlinear and redundant sub-networks in larger networks is important for pattern robustness. Pursuing dynamic analyses of larger (genomic) coupled networks is still not possible. A repertoire of well-characterised regulatory modules, like the one presented here, will, however, help to uncover general principles of the patterning-associated networks, as well as the peculiarities that originate diversity.

Show MeSH