Cellular processes are governed by complex molecular interaction networks where the molecular components and the interactions between them are represented by nodes and edges, respectively. Intensive studies of local and global organizing principles of the networks show the inherent simplicity of biological networks: modularity and reusability [1
]. These networks can be decomposed into independent functional modules. Small recurring subnetworks that perform specific cellular subfunctions (termed network motifs) are largely reused to build the functional modules. The network motifs also show stability or robustness to environmental conditions and evolutionary dynamics and therefore are viewed as building blocks of the complex networks [6
]. The experimental approach of network motif identification is extensively applied for modeling specific cellular processes [8
However, whereas studies have mainly focused on modeling or analysis of topological or kinetic features of network motifs in a single cell type or species, network motifs can be used to reflect dynamical and evolutionary adaptations to meet physiological variances over a time course. Integrating the dynamics across species is particularly important in modeling cellular processes through protein interaction networks. Many of the biological processes mediated by protein interaction networks are highly evolutionarily conserved or related across species. The evolutionary dynamics of biological processes shape the network structure over large time scales. For instance, protein interaction networks are believed to evolve through genetic sequence mutation or gene duplication [9
]. The gene duplication can create a new node which owns identical edges to the original node, but after being duplicated it could lose its functions (corresponding interaction edges are eliminated). Mutations of a gene sequence can modify the interfaces or domains of its protein product and lead to the emergence of new or loss of existing protein interaction patterns [11
]. Therefore, information about evolutionary dynamics is invaluable for network modeling of biological systems.
We developed an analysis framework on the basis of comparative network motif design (Figure ). Given a network motif structure representing a specific biological function in one cell type or species, this approach utilized a comparative modeling strategy to connect it with other network motifs which are evolutionarily related to each other. By capturing the evolutionary dynamics of target biological systems, the comparative modeling framework is empowered to (i) identify the functional roles of poorly characterized proteins and interactions and (ii) further decipher the underlying regulatory mechanisms of complicated cellular processes.
Experimental Diagram of Comparative Network Motif Design Modeling.
We applied the framework to study SM-SNARE-mediated exocytic membrane fusion processes in yeast and neurons. As for many essential biological processes, intracellular membrane fusion is mediated by interactions among a series of evolutionarily conserved proteins. SNARE proteins are viewed as a critical component in execution of vesicle membrane fusion with the target plasma membrane, forming a helical-bundle complex termed a SNAREpin through interactions of v-SNAREs (vesicle - associated SNARE proteins) and t-SNAREs (target membrane associated SNARE proteins) [12
]. SM (Sec/Munc-18) proteins are essential regulators responsible for controlling the formation of SNAREpin complexes by diverse binding modes with SNAREs [14
]. These binding modes show high heterogeneity between different organisms or trafficking pathways [16
]. This binding diversity brings uncertainty and complexity into the interaction network of vesicular fusion regulation and therefore poses a challenge to understanding the key functional roles of the SM protein family in exocytosis. SM proteins have been documented to be both positive and negative regulators of fusion, and studies of overexpression of SM proteins have produced conflicting observations [17
Applying our modeling framework, we comparatively constructed two ensemble SM-SNARE network motifs (SSNM) in the exocytic network based on the binding mode information curated from current literature: the cascade-like SSNM in yeast and the feedback-loop-like SSNM in neuronal synaptic pathways. Comparative dynamical analysis revealed bifurcation behavior in the neuronal system which was different from hyperbolic response behaviors in the yeast system and provides a way to explain the conflicting experimental observations of SM overexpression in neuronal systems. Furthermore, the comparative topological analysis revealed that the closed binding mode of Munc18-syntaxin-1 in neuronal SSNM may be the critical factor that brings the complexity to synaptic exocytosis in terms of network topology and system behaviors compared to yeast exocytosis. Furthermore, in silico mutation experiments confirmed that the bifurcation behaviors resulted from the closed binding mode of Munc18-syntaxin-1. Our reconstitution lipid-mixing assay experiments based on wildtype and mutant SNARE proteins confirmed the prediction that the closed binding mode of Munc18-syntaxin-1 (one tSNARE protein) in neuronal SSNM explains d the divergence of yeast and neuronal SM-SNARE system behaviors. Therefore it reconciles s the discrepancy y in studies of over-expressed SM protein from a system regulation point of view. To test the robustness and extensibility of the model, we further expanded the neuronal SSNM with other exocytic proteins, which may regulate SM and SNARE proteins.