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In previous works, we have integrated the experimental data on the molecular
components that are necessary and sucient for early oral organ cell-fate deter-
mination. We have used this data to propose a GRN of fteen nodes (proteins
or genes) and their interactions [Mendoza and Alvarez-Buylla (1998); Espinosa-
Soto et al. (2004); Chaos et al. (2006)]. The wealth of data on which this GRN is
grounded have been accumulated during the last 15 years in many dierent labo-
ratories and gave rise to the postulation of the ABC model of ower development,
that was derived from genetic analyses of oral organ homeotic mutants in two
plant study systems: Antirrhinum majus L. and Arabidopsis thaliana (L.) Heynh
[Coen and Meyerowitz (1991)]. The ABC model states that the identities of the
oral organ types are established by combinations of genes grouped in three main
classes: the A, B and C. A genes alone determine sepal identity, A plus B petal
identity, B plus C stamen identity, and C alone carpel identity (Fig. 5.6).
However, the ABC combinatorial model does not provide an explanation for
how such combinatorial selection of gene activity is established during oral or-
gan primordia specication, and how does the spatio-temporal pattern of ABC and
non-ABC gene expression is dynamically established, or which other genes interact
with the ABC and together are sucient for the specication of the four types of
primordial cell types in a oral bud. Furthermore, the conserved pattern of oral
organ determination and also the overall conservation of the ABC genes patterns of
expression among eudicotyledoneous (more recently evolved angiosperms) species
suggest a robust mechanism underlying such combinatorial selection of gene activ-
ities. Such mechanism recreates the conserved oral pattern in every individual of
most angiosperms irrespective of the environmental conditions encountered, and it
has done so during evolution in many owering plant lineages, also independently
of the genetic backgrounds of the species involved. The ABC model by itself does
not provide an explanation to such robustness either. We have been developing
qualitative GRN models for tackling these questions.
Discrete models of real GRN are abstractions of the regulatory interactions
among genes. These undergo a network of mRNA transcription, translation, pro-
tein modications and transport until its nal functional destiny. Most data avail-
able for plant development is qualitative and at the transcriptional regulatory level,
although recent data are suggesting that miRNA regulation are important during
plant development [Chen (2004)]. In any case, the temporal scales of the molecu-
lar processes of the pathway that goes from DNA transcription to protein function
are relatively short in comparison to those of the processes of pattern formation.
Therefore, it is reasonable to focus only on the qualitative regulatory interactions.
Also, formal analyses of equivalent continuous and discrete models have analyti-
cally shown that both yield equivalent results [Thomas et al. (1995)]. Moreover, in
systems of many components with many non-linearities the behavior of the system
depends mostly on the qualitative aspects of the GRN topology rather than on the
kinetic details for each interaction and component.
