Evolution of networks for body plan patterning; interplay of modularity, robustness and evolvability

Area of Science:

• Evolutionary developmental biology (evo-devo)
• Computational biology
• Systems biology

Background:

• Understanding the evolution of complex multicellular body plans is a central goal in evo-devo.
• Gene regulatory network (GRN) modularity is hypothesized to be crucial for the evolution of body plan complexity, robustness, and evolvability.
• The empirical testing of these hypotheses regarding GRN modularity, robustness, and evolvability remains limited.

Purpose of the Study:

• To computationally investigate the evolution of GRNs underlying body plan patterning, specifically focusing on segmentation and differentiation.
• To examine the interplay between modularity, robustness, and evolvability in evolving GRNs.
• To compare distinct evolutionary strategies for developing segmented and differentiated body plans.

Main Methods:

• Utilized computer simulations to model the evolution of GRNs.
• Applied selection for body plan segmentation and differentiation, considered key metazoan innovations.
• Independently selected for segmentation and differentiation to promote modular network evolution.
• Analyzed network robustness, evolvability, and modularity using both functional and architectural approaches.

Main Results:

• Identified two distinct evolutionary strategies: sequential (SF) where segments evolve first, then differentiation, and simultaneous (SS) where both evolve together.
• Demonstrated that indirect selection for robustness favors the SF strategy, leading to increased evolvability as a byproduct.
• Found that SF networks exhibit higher modularity, using independent modules for segments and domains, unlike integrated SS networks.
• Observed that purely architectural methods for assessing modularity failed to detect the higher modularity of SF networks.

Conclusions:

• The SF evolutionary strategy, favored by selection for robustness, promotes both higher modularity and evolvability in GRNs.
• Functional and architectural analyses reveal distinct modularity characteristics between SF and SS networks.
• Standard architectural methods may underestimate GRN modularity, highlighting the need for combined approaches.
• Simulated evolution yielded developmental mechanisms reminiscent of vertebrate development, offering insights into conserved evolutionary processes.