Stabilizing patterning in the Drosophila segment polarity network by selecting models in silico

Area of Science:

• Developmental biology
• Systems biology
• Genetics

Background:

• Drosophila segmentation is a key model for studying embryonic spatial patterning.
• Segment polarity genes form a complex network maintaining expression after segmentation.
• Previous models explored Drosophila segmentation but had limited robustness.

Purpose of the Study:

• To investigate the stability and robustness of the Drosophila segmentation process using a dynamical Boolean model.
• To quantify the robustness of gene expression under perturbations and mutations.
• To discover modified gene networks with enhanced robustness through in silico evolution.

Main Methods:

• Developed a dynamical model of the Drosophila segmentation network with intra- and inter-cellular interactions in one spatial dimension.
• Quantified robustness through systematic mutation analysis and defined dynamical robustness mathematically.
• Employed in silico evolution (mutation and selection) to identify improved gene networks.
• Verified enhanced robustness using differential equations models and compared predictions with experimental data from mutated flies.

Main Results:

• The initial Boolean model exhibited limited robustness in gene expression under perturbations.
• In silico evolution identified two classes of modified gene networks with significantly more robust spatial expression patterns.
• Enhanced robustness was confirmed in differential equations models, aligning with experimental observations in mutated flies.

Conclusions:

• Dynamical modeling and in silico evolution can significantly enhance the robustness of gene networks for Drosophila spatial patterning.
• The study elucidates the role of further interactions in achieving robust developmental patterns.
• Findings provide insights into the mechanisms underlying developmental robustness and gene network evolution.