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Complexity and robustness
- Proceedings of the National Academy of Sciences 99(Suppl
, 2002
"... Highly Optimized Tolerance (HOT) was recently introduced as a conceptual framework to study fundamental aspects of complexity. HOT is motivated primarily by systems from biology and engineering and emphasizes 1) highly structured, nongeneric, selfdissimilar internal configurations and 2) robust, yet ..."
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Cited by 156 (10 self)
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Highly Optimized Tolerance (HOT) was recently introduced as a conceptual framework to study fundamental aspects of complexity. HOT is motivated primarily by systems from biology and engineering and emphasizes 1) highly structured, nongeneric, selfdissimilar internal configurations and 2) robust, yet fragile external behavior. HOT claims these are the most important features of complexity and are not accidents of evolution or artifices of engineering design, but are inevitably intertwined and mutually reinforcing. In the spirit of this collection, our paper contrasts HOT with alternative perspectives on complexity, drawing on both real world examples and also model systems, particularly those from Self-Organized Criticality (SOC).
Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae
- Mol. Cell. Proteomics
, 2007
"... Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, howeve ..."
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Cited by 137 (1 self)
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Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two
Structure and dynamics of molecular networks: A novel paradigm of drug discovery -- A . . .
- PHARMACOLOGY THERAPEUTICS
, 2013
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Stratus not altocumulus: A new view of the yeast protein interaction network. PLoS Biol
, 2006
"... Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that ..."
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Cited by 30 (1 self)
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Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that perform a dedicated cellular task. Based on a computational analysis of limited interaction datasets in the budding yeast Saccharomyces cerevisiae, it has been suggested that the global protein interaction network is segregated such that highly connected proteins, called hubs, tend not to link to each other. Moreover, it has been suggested that hubs fall into two distinct classes: ‘‘party’ ’ hubs are co-expressed and co-localized with their partners, whereas ‘‘date’ ’ hubs interact with incoherently expressed and diversely localized partners, and thereby cohere disparate parts of the global network. This structure may be compared with altocumulus clouds, i.e., cotton ball–like structures sparsely connected by thin wisps. However, this organization might reflect a small and/or biased sample set of interactions. In a multi-validated high-confidence (HC) interaction network, assembled from all extant S. cerevisiae interaction data, including recently available proteome-wide interaction data and a large set of reliable literature-derived interactions, we find that hub–hub interactions are not suppressed. In fact, the number of interactions a hub has with other hubs is a good predictor of whether a hub protein is essential or not. We find that date hubs are neither required for network tolerance to node deletion, nor do date hubs have distinct biological attributes compared to other hubs. Date and party hubs do not, for
Efficient estimation of graphlet frequency distributions in protein–protein interaction networks
, 2006
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Topological properties of protein interaction networks from a structural perspective
- Biochemical Society Transactions, 36:1398
"... Abstract Protein-protein interactions are usually shown as interaction networks (graphs), where the proteins are represented as nodes and the connections between the interacting proteins are shown as edges. The graph abstraction of protein interactions is crucial for understanding the global behavi ..."
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Cited by 20 (2 self)
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Abstract Protein-protein interactions are usually shown as interaction networks (graphs), where the proteins are represented as nodes and the connections between the interacting proteins are shown as edges. The graph abstraction of protein interactions is crucial for understanding the global behaviour of the network. In this mini review, we summarize basic graph topological properties, such as node degree and betweenness, and their relation to essentiality and modularity of protein interactions. The classification of hub proteins into date and party hubs with distinct properties has significant implications for relating topological properties to the behaviour of the network. We emphasize that the integration of protein interface structure into interaction graph models provides a better explanation of hub proteins, and strengthens the relationship between the role of the hubs in the cell and their topological properties.
Contrasting views of complexity and their implications for network-centric infrastructures
- IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans
"... Abstract—There exists a widely recognized need to better un-derstand and manage complex “systems of systems, ” ranging from biology, ecology, and medicine to network-centric technologies. This is motivating the search for universal laws of highly evolved systems and driving demand for new mathematic ..."
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Cited by 20 (3 self)
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Abstract—There exists a widely recognized need to better un-derstand and manage complex “systems of systems, ” ranging from biology, ecology, and medicine to network-centric technologies. This is motivating the search for universal laws of highly evolved systems and driving demand for new mathematics and methods that are consistent, integrative, and predictive. However, the the-oretical frameworks available today are not merely fragmented but sometimes contradictory and incompatible. We argue that complexity arises in highly evolved biological and technological systems primarily to provide mechanisms to create robustness. However, this complexity itself can be a source of new fragility, leading to “robust yet fragile ” tradeoffs in system design. We focus on the role of robustness and architecture in networked infrastructures, and we highlight recent advances in the theory of distributed control driven by network technologies. This view of complexity in highly organized technological and biological sys-tems is fundamentally different from the dominant perspective in the mainstream sciences, which downplays function, constraints, and tradeoffs, and tends to minimize the role of organization and design. Index Terms—Architecture, complexity theory, networks, opti-mal control, optimization methods, protocols. I.
Not all scale-free networks are born equal: the role of the seed graph in ppi network evolution
- PLoS Comput. Biol
, 2007
"... The (asymptotic) degree distributions of the best-known ‘‘scale-free’ ’ network models are all similar and are independent of the seed graph used; hence, it has been tempting to assume that networks generated by these models are generally similar. In this paper, we observe that several key topologic ..."
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Cited by 19 (4 self)
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The (asymptotic) degree distributions of the best-known ‘‘scale-free’ ’ network models are all similar and are independent of the seed graph used; hence, it has been tempting to assume that networks generated by these models are generally similar. In this paper, we observe that several key topological features of such networks depend heavily on the specific model and the seed graph used. Furthermore, we show that starting with the ‘‘right’ ’ seed graph (typically a dense subgraph of the protein–protein interaction network analyzed), the duplication model captures many topological features of publicly available protein–protein interaction networks very well.
Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics
- PLoS One
, 2010
"... Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Fin ..."
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Cited by 17 (1 self)
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Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications
Perturbation waves in proteins and protein networks: Applications of percolation and game theories in signaling and drug design
- Curr. Protein Pept. Sci
"... Abstract: The network paradigm is increasingly used to describe the dynamics of complex systems. Here we review the current results and propose future development areas in the assessment of perturbation waves, i.e. propagating structural changes in amino acid networks building individual protein mol ..."
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Cited by 14 (5 self)
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Abstract: The network paradigm is increasingly used to describe the dynamics of complex systems. Here we review the current results and propose future development areas in the assessment of perturbation waves, i.e. propagating structural changes in amino acid networks building individual protein molecules and in protein-protein interaction networks (interactomes). We assess the possibilities and critically review the initial attempts for the application of game theory to the often rather complicated process, when two protein molecules approach each other, mutually adjust their conformations via multiple communication steps and finally, bind to each other. We also summarize available data on the application of percolation theory for the prediction of amino acid network- and interactome-dynamics. Furthermore, we give an overview of the dissection of signals and noise in the cellular context of various perturbations. Finally, we propose possible applications of the reviewed methodologies in drug design.
