Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes

Abstract

Protein interactomes are epitomes of incomplete and noisy networks. Methods for assessing link-reliability using exclusively topology are valuable in network biology, and their investigation facilitates the general understanding of topological mechanisms and models to draw and correct complex network connectivity. Here, I revise and extend the local-community-paradigm (LCP). Initially detected in brain-network topological self-organization and afterward generalized to any complex network, the LCP is a theory to model local-topology-dependent link-growth in complex networks using network automata. Four novel LCP-models are compared versus baseline local-topology-models. It emerges that the reliability of an interaction between two proteins is higher: (i) if their common neighbours are isolated in a complex (local-community) that has low tendency to interact with other external proteins; (ii) if they have a low propensity to link with other proteins external to the local-community. These two rules are mathematically combined in C1*: a proposed mechanistic model that, in fact, outperforms the others. This theoretical study elucidates basic topological rules behind self-organization principia of protein interactomes and offers the conceptual basis to extend this theory to any class of complex networks. The link-reliability improvement, based on the mere topology, can impact many applied domains such as systems biology and network medicine.

Hyperedge Entanglement in High-Order Multilayer Networks

“…3d) the ranking of the methods is similar to the one seen in Football (Fig. 3a), with L3 methods as best performing (in agreement with link prediction results of Muscoloni et al 28 ), followed by CH-L2 methods (CH2-L2, CH3-L2 and RA-L2), and then by the other L2-based predictors (in agreement with the results of Cannistraci 32 , discussing the importance of minimizing external links in PPI networks). We confirm that also in our tests L3-methods overcome L2-methods as reported in previous literature by Kovács et al 29 .…”

Functional Brain Network Topology Discriminates between Patients with Minimally Conscious State and Unresponsive Wakefulness Syndrome

“…In this way, local efficiency can be considered a generalization of the clustering coefficient that explicitly takes into account paths.The average clustering coefficient (ACC) [50] is a local measure and offers an average evaluation of the cross-interaction density between the first neighbors of each node in the network.The average node betweenness centrality (ANBC) [56] is a global measure based on the node betweenness centrality, an indicator of node centrality that evaluates how crucial a particular node is in maintaining a path of optimum information flow between any other pair of nodes.In contrast to the existing node-neighborhood-based local measures, a new strategic shift has been introduced recently in which the focus is no longer only on groups of nodes and their common neighbors, but also on the organization of the links between them [57]. This new idea inspired a theory, which is known as the Local Community Paradigm (LCP) theory, and is valid both in monopartite [57,58] and in undirected unweighted bipartite networks [59,60]. The LCP theory was proposed to mechanistically and deterministically model local-topology-dependent link-growth in complex networks, and states that for modelling link prediction in complex networks, the information content related with the common neighbor nodes (CNs) of a given link should be complemented with the topological information emerging from the interactions between them.…”

“…This first part of the theory inspired the Cannistraci’s variation of the classical CN-based similarity indices for link prediction, named also LCP-based link predictors. For details, refer to [57,58,59,60]. Furthermore, the LCP theory holds that in many complex network topologies, the number of CNs of each link in the network is positively correlated with the respective number of LCLs.…”

“…In contrast to the existing node-neighborhood-based local measures, a new strategic shift has been introduced recently in which the focus is no longer only on groups of nodes and their common neighbors, but also on the organization of the links between them [57]. This new idea inspired a theory, which is known as the Local Community Paradigm (LCP) theory, and is valid both in monopartite [57,58] and in undirected unweighted bipartite networks [59,60]. The LCP theory was proposed to mechanistically and deterministically model local-topology-dependent link-growth in complex networks, and states that for modelling link prediction in complex networks, the information content related with the common neighbor nodes (CNs) of a given link should be complemented with the topological information emerging from the interactions between them.…”

Adaptive Network Automata Modelling of Complex Networks

“…This represents a current limitation of the LCP theory that we want to overcome in this study. Indeed, as recently shown by Cannistraci [12], the local isolation of the common neighbour nodes in every local community is equally important to carve the LCP architecture, and this is guaranteed by the fact that the common neighbours minimize their interactions external to the local community (external local-community-links, eLCL, in Fig. 1) [12].…”