References of "PLoS Computational Biology"
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Lister, Allyson L.; Datta, Ruchira S.; Hofmann, Oliver et al

in PLoS Computational Biology (2010), 6(1), 1-2

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See detailIdentification of potential pathway mediation targets in Toll-like receptor signaling.
Li, Fan; Thiele, Ines UL; Jamshidi, Neema et al

in PLoS Computational Biology (2009), 5(2), 1000292

Recent advances in reconstruction and analytical methods for signaling networks have spurred the development of large-scale models that incorporate fully functional and biologically relevant features. An ... [more ▼]

Recent advances in reconstruction and analytical methods for signaling networks have spurred the development of large-scale models that incorporate fully functional and biologically relevant features. An extended reconstruction of the human Toll-like receptor signaling network is presented herein. This reconstruction contains an extensive complement of kinases, phosphatases, and other associated proteins that mediate the signaling cascade along with a delineation of their associated chemical reactions. A computational framework based on the methods of large-scale convex analysis was developed and applied to this network to characterize input-output relationships. The input-output relationships enabled significant modularization of the network into ten pathways. The analysis identified potential candidates for inhibitory mediation of TLR signaling with respect to their specificity and potency. Subsequently, we were able to identify eight novel inhibition targets through constraint-based modeling methods. The results of this study are expected to yield meaningful avenues for further research in the task of mediating the Toll-like receptor signaling network and its effects. [less ▲]

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See detailGenome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization.
Thiele, Ines UL; Jamshidi, Neema; Fleming, Ronan MT UL et al

in PLoS Computational Biology (2009), 5(3), 1000312

Metabolic network reconstructions represent valuable scaffolds for '-omics' data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the ... [more ▼]

Metabolic network reconstructions represent valuable scaffolds for '-omics' data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron-sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of '-omics' datasets and thus the study of the mechanistic principles underlying the genotype-phenotype relationship. [less ▲]

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See detailON/OFF and beyond--a boolean model of apoptosis.
Schlatter, Rebekka; Schmich, Kathrin; Avalos Vizcarra, Ima et al

in PLoS Computational Biology (2009), 5(12), 1000595

Apoptosis is regulated by several signaling pathways which are extensively linked by crosstalks. Boolean or logical modeling has become a promising approach to capture the qualitative behavior of such ... [more ▼]

Apoptosis is regulated by several signaling pathways which are extensively linked by crosstalks. Boolean or logical modeling has become a promising approach to capture the qualitative behavior of such complex networks. Here we built a large-scale literature-based Boolean model of the central intrinsic and extrinsic apoptosis pathways as well as pathways connected with them. The model responds to several external stimuli such as Fas ligand, TNF-alpha, UV-B irradiation, interleukin-1beta and insulin. Timescales and multi-value node logic were used and turned out to be indispensable to reproduce the behavior of the apoptotic network. The coherence of the model was experimentally validated. Thereby an UV-B dose-effect is shown for the first time in mouse hepatocytes. Analysis of the model revealed a tight regulation emerging from high connectivity and spanning crosstalks and a particular importance of feedback loops. An unexpected feedback from Smac release to RIP could further increase complex II formation. The introduced Boolean model provides a comprehensive and coherent description of the apoptosis network behavior. It gives new insights into the complex interplay of pro- and antiapoptotic factors and can be easily expanded to other signaling pathways. [less ▲]

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See detailLigand binding and circular permutation modify residue interaction network in DHFR.
Hu, Zengjian; Bowen, Donnell; Southerland, William M. et al

in PLoS computational biology (2007), 3(6), 117

Residue interaction networks and loop motions are important for catalysis in dihydrofolate reductase (DHFR). Here, we investigate the effects of ligand binding and chain connectivity on network ... [more ▼]

Residue interaction networks and loop motions are important for catalysis in dihydrofolate reductase (DHFR). Here, we investigate the effects of ligand binding and chain connectivity on network communication in DHFR. We carry out systematic network analysis and molecular dynamics simulations of the native DHFR and 19 of its circularly permuted variants by breaking the chain connections in ten folding element regions and in nine nonfolding element regions as observed by experiment. Our studies suggest that chain cleavage in folding element areas may deactivate DHFR due to large perturbations in the network properties near the active site. The protein active site is near or coincides with residues through which the shortest paths in the residue interaction network tend to go. Further, our network analysis reveals that ligand binding has "network-bridging effects" on the DHFR structure. Our results suggest that ligand binding leads to a modification, with most of the interaction networks now passing through the cofactor, shortening the average shortest path. Ligand binding at the active site has profound effects on the network centrality, especially the closeness. [less ▲]

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See detailThe modular organization of domain structures: insights into protein-protein binding.
del Sol Mesa, Antonio UL; Carbonell, Pablo

in PLoS computational biology (2007), 3(12), 239

Domains are the building blocks of proteins and play a crucial role in protein-protein interactions. Here, we propose a new approach for the analysis and prediction of domain-domain interfaces. Our method ... [more ▼]

Domains are the building blocks of proteins and play a crucial role in protein-protein interactions. Here, we propose a new approach for the analysis and prediction of domain-domain interfaces. Our method, which relies on the representation of domains as residue-interacting networks, finds an optimal decomposition of domain structures into modules. The resulting modules comprise highly cooperative residues, which exhibit few connections with other modules. We found that non-overlapping binding sites in a domain, involved in different domain-domain interactions, are generally contained in different modules. This observation indicates that our modular decomposition is able to separate protein domains into regions with specialized functions. Our results show that modules with high modularity values identify binding site regions, demonstrating the predictive character of modularity. Furthermore, the combination of modularity with other characteristics, such as sequence conservation or surface patches, was found to improve our predictions. In an attempt to give a physical interpretation to the modular architecture of domains, we analyzed in detail six examples of protein domains with available experimental binding data. The modular configuration of the TEM1-beta-lactamase binding site illustrates the energetic independence of hotspots located in different modules and the cooperativity of those sited within the same modules. The energetic and structural cooperativity between intramodular residues is also clearly shown in the example of the chymotrypsin inhibitor, where non-binding site residues have a synergistic effect on binding. Interestingly, the binding site of the T cell receptor beta chain variable domain 2.1 is contained in one module, which includes structurally distant hot regions displaying positive cooperativity. These findings support the idea that modules possess certain functional and energetic independence. A modular organization of binding sites confers robustness and flexibility to the performance of the functional activity, and facilitates the evolution of protein interactions. [less ▲]

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See detailPrediction of functional sites based on the fuzzy oil drop model
Brylinski, Michal; Prymula, Katarzyna; Jurkowski, Wiktor UL et al

in PLoS Computational Biology (2007), 3(5), 1-2

A description of many biological processes requires knowledge of the 3-D structure of proteins and, in particular, the defined active site responsible for biological function. Many proteins, the genes of ... [more ▼]

A description of many biological processes requires knowledge of the 3-D structure of proteins and, in particular, the defined active site responsible for biological function. Many proteins, the genes of which have been identified as the result of human genome sequencing, and which were synthesized experimentally, await identification of their biological activity. Currently used methods do not always yield satisfactory results, and new algorithms need to be developed to recognize the localization of active sites in proteins. This paper describes a computational model that can be used to identify potential areas that are able to interact with other molecules (ligands, substrates, inhibitors, etc.). The model for active site recognition is based on the analysis of hydrophobicity distribution in protein molecules. It is shown, based on the analyses of proteins with known biological activity and of proteins of unknown function, that the region of significantly irregular hydrophobicity distribution in proteins appears to be function related. [less ▲]

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