Revealing static and dynamic modular architecture of the eukaryotic protein interaction network

Komurov, Kakajan; White, Michael A.

doi:10.1038/msb4100149

Cited by 127 publications

(119 citation statements)

References 43 publications

(80 reference statements)

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“…For this reason, Przytycka et al [126] believe that a "shift from static to dynamic network analysis is essential for further understanding" of the interactome. There is already a body of literature investigating the temporal aspects of protein interaction and gene-regulatory networks [52,78,92,151]. This is perhaps the most natural level for temporal network approaches in cell biology-proteins are the workhorses of cells so any kind of cyclic, or otherwise dynamic, patterns have to be done by a shift in the interaction network.…”

Section: Cell Biologymentioning

confidence: 99%

Temporal networks

2012

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A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names

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Section: Cell Biologymentioning

confidence: 99%

Temporal networks

2012

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“…• As it has been shown earlier [74,76,77], date hubs quite many times occupy an inter-modular position, meaning that they belong to at least two modules at the same time.…”

Section: Position and Dynamics Of Molecular Chaperones In Protein-promentioning

confidence: 88%

System Level Mechanisms of Adaptation, Learning, Memory Formation and Evolvability: The Role of Chaperone and Other Networks

Gyurko

Sőti

Steták

et al. 2014

CPPS

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During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides 'learningcompetent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

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“…In addition, I would like to note that in most networks the status of the creative element is, by itself, transient. Creative elements may well be transformed into task-distributing-party-hubs [40,42] or bridges which preferentially connect two modules with strong links, or into problem-solving, specialized elements. These transformations of creative elements usually happen after repeated stress showing that the network 'learned' the novel response by re-organizing its topology, and provides the first unusual, creative solution in a regular, reliable and highly efficient manner [55].…”

Section: Discussionmentioning

confidence: 99%

“…Date-hubs are preferentially located in the overlaps of multiple modules, thus their connections are non-redundant and unique [35]. As an example, molecular chaperones have an inter-modular localization and are amongst those date-hubs that constitute the true central coordinators of the cellular network [41,42]. Several chaperones are also called stress proteins or heat shock proteins, and their centrality increases in protein-protein interaction networks after stress [41].…”

Section: Active Centres Creative Elements Of Cells Ecosystems and Smentioning

confidence: 99%

“…Network dynamics • The internal structure of creative elements is flexible (the flexibility increases as the complexity of the element grows); • creative elements have more weak links than the average of the network (similarly to date hubs [40,42], they have a small number of links at a given time); • creative elements may be found among those elements, which have a large dynamical importance as defined by Restrepo et al [58] • the behaviour of creative elements is the least predictable, if compared to the predictability of other network elements (this is also related to their extremely large autonomy).…”

Section: Network Topologymentioning

confidence: 99%

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Creative elements: network-based predictions of active centres in proteins and cellular and social networks

Csermely

2008

Trends in Biochemical Sciences

117

127

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Active centres and hot spots of proteins have a paramount importance in enzyme action, protein complex formation and drug design. Recently a number of publications successfully applied the analysis of residue networks to predict active centres in proteins. Most real-world networks show a number of properties, such as small-worldness or scale-free degree distribution, which are rather general features of networks, from molecules to society at large. Using analogy I propose that existing findings and methodology already enable us to detect active centres in cells, and can be expanded to social networks and ecosystems. Members of these active centres are termed here as 'creative elements' of their respective networks, which may help them to survive unprecedented, novel challenges, and play a key role in the development, survival and evolvability of complex systems.

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Revealing static and dynamic modular architecture of the eukaryotic protein interaction network

Cited by 127 publications

References 43 publications

Temporal networks

Temporal networks

System Level Mechanisms of Adaptation, Learning, Memory Formation and Evolvability: The Role of Chaperone and Other Networks

Creative elements: network-based predictions of active centres in proteins and cellular and social networks

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