Interaction pathways promote spliceosome module integration and network-level robustness to cascading effects

Biological systems are organized as networks. A central problem in the study of biological networks is to understand if and how the network structure affects the fragility of biological systems to multiple types of perturbations. For example, the functionality and fragility of protein networks may depend on their network structure, and mutations and other errors may generate cascading effects that, in turn, lead to system malfunctioning. Spectral graph theory studies the structural and dynamical properties of a system based on the mathematical properties of matrices associated with the networks, providing tools, which can reveal the fragility of biological networks to cascading effects. We combined two of such tools to explore the fragility to cascading effects of the network describing protein interactions within a key macromolecular complex, the S. cerevisiae spliceosome. The spliceosome network shows a higher number of indirect pathways connecting proteins than expected for random networks. The multiplicity of pathways may promote routes to cascading effects to propagate across the network. However, analytical results derived from the spectral graph theory and numerical simulations of a minimal mathematical model suggest that the modular structure of the spliceosome network constrains the propagation of cascading effects due to the concentration of pathways within modules. We hypothesize that the concentration of pathways within modules favors robustness of the spliceosome against failure but may lead to a higher vulnerability of functional subunits, which may affect the temporal assembly of the spliceosome. Our results illustrate the usefulness of spectral graph theory in identifying fragile domains in biological systems and predicting their implications, which can become a useful as a roadmap for the development of new therapies within the emerging field of network medicine.

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