Though the problem of pattern formation in biological systems has been studied  more than sixty years, it is from the beginning of this cen- tury that the synergy between theoretical and experimental researchers are depicting the ins and outs of this problem. In the present work we are deciphering the bases of the pattern for the ocellar complex formation in Drosophila melanogaster fly. We have modeled the Gene Regulatory Network (GRN) that drives the development of this visual system pruning this network to obtain the minimum pathway able to satisfy this pattern. We found that the mechanism underlying the patterning obeys to the dy- namics of a 3-nodes network motif with a double negative feedback loop fed by a morphogenetic gradient that triggers the inhibition in a French flag problem fashion. A Boolean modeling of the GRN reveals robust- ness in the patterning mechanism showing the same result for different network complexity levels. Interestingly, the network provides a steady state solution in the interocellar part of the patterning and an oscillatory regime in the ocelli . Though the dynamical models can achieve steady state solutions for the full pattern, it is possible that the transcriptional regulation in part of this network is actually done in an oscillatory way .
Though the problem of pattern formation in biological systems has been studied for more than sixty years, it is from the beginning of this century that the synergy between theoretical and experimental researchers are depicting the ins and outs of this problem. In the present work we are deciphering the bases of the pattern for the ocellar complex formation in Drosophila melanogaster fly. We have modeled the Gene Regulatory Network (GRN) that drives the development of this visual system pruning this network to obtain the minimum pathway able to satisfy this pattern. We found that the mechanism underlying the patterning obeys to the dynamics of an activator-repressor feedback loop fed by a morphogenetic gradient in a French flag problem fashion. We determine that this pattern is robust agains perturbations in the structure of the equations used to describe the system. Moreover, a Boolean modeling of the GRN confirms the robustness in the patterning mechanism for different network complexity levels. Interestingly, the Boolean networks analysis reveals a steady state solution in the interocellar part of the patterning and an oscillatory regime in the ocelli .
A
for
R
cen- tury
century
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dy- namics of a 3-nodes network motif with a double negative
dynamics of an activator-repressor
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that triggers the inhibition
R
A
We determine that this pattern is robust agains perturbations in the structure of the equations used to describe the system. Moreover, a
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reveals robust- ness
confirms the robustness
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showing the same result
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network provides
Boolean networks analysis reveals
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. Though the dynamical models can achieve steady state solutions for the full pattern, it is possible that the transcriptional regulation in part of this network is actually done in an oscillatory way