Abstract
BackgroundNetwork motifs, recurring subnetwork patterns, provide significant insight into the biological networks which are believed to govern cellular processes.MethodsWe present a comparative network motif experimental approach, which helps to explain complex biological phenomena and increases the understanding of biological functions at the molecular level by exploring evolutionary design principles of network motifs.ResultsUsing this framework to analyze the SM (Sec1/Munc18)-SNARE (N-ethylmaleimide-sensitive factor activating protein receptor) system in exocytic membrane fusion in yeast and neurons, we find that the SM-SNARE network motifs of yeast and neurons show distinct dynamical behaviors. We identify the closed binding mode of neuronal SM (Munc18-1) and SNARE (syntaxin-1) as the key factor leading to mechanistic divergence of membrane fusion systems in yeast and neurons. We also predict that it underlies the conflicting observations in SM overexpression experiments. Furthermore, hypothesis-driven lipid mixing assays validated the prediction.ConclusionTherefore this study provides a new method to solve the discrepancies and to generalize the functional role of SM proteins.
Highlights
Network motifs, recurring subnetwork patterns, provide significant insight into the biological networks which are believed to govern cellular processes
The strategy is shown in Figure 1. (i) First, the network motif design provides a rational description for key parts of the biological system of interest by decomposing a complicated network into simple regulatory network motifs that carry out specific functions
The first simulation models system behaviors of the cascade-like yeast SM-SNARE network motif with respect to the ySM protein concentration and the results show yeast SM stimulates fusion
Summary
Network motifs, recurring subnetwork patterns, provide significant insight into the biological networks which are believed to govern cellular processes. Intensive studies of local and global organizing principles of the networks show the inherent simplicity of biological networks: modularity and reusability [1,2,3,4,5]. These networks can be decomposed into independent functional modules. Integrating the dynamics across species is important in modeling cellular processes through protein interaction networks. Protein interaction networks are believed to evolve through genetic sequence mutation or gene duplication [9,10]. Information about evolutionary dynamics is invaluable for network modeling of biological systems
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