Abstract
This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.
Highlights
Hierarchical Biological Networks The analysis of biological networks presents an intriguing challenge, due to the complex, non-random organization of these systems and the diverse dynamic behaviors that they express
We found that the two dynamic regimes have different significance in the neuronal network of C. elegans, where activity is mainly organized by hub nodes, and the systems network of the cat cerebral cortex, which is dominated by the network’s modular organization
Overview In this study, we focus on two structural properties of networks and use them in terms of topological references. These properties are modularity and node centrality and they are represented by the topological modularity (TM) reference and the central-node based (CN) reference, respectively
Summary
Hierarchical Biological Networks The analysis of biological networks presents an intriguing challenge, due to the complex, non-random organization of these systems and the diverse dynamic behaviors that they express. Hub nodes, which have been identified in several biological networks, such as protein-protein interaction networks or metabolic networks, may serve as central distributing elements or linkage point for many regions of a network [2,6,7] Such hubs might be present in neural systems networks [8]. Modules or network clusters, which are characterized by a higher frequency or density of connections within than between node clusters [9] have been identified in biological metabolic networks [10,11], as well as neural networks at the cellular level [12] or the systems level [13] These modules often represent a specific function, e.g. a specific synthesis pathway in a metabolic reaction network [14], and may shape the functional interactions within the networks at different scales [15,16,17]. It has been argued that motifs may represent specific functional circuits [18,19,20]
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