787 research outputs found

    BioPartsDB: a synthetic biology workflow web-application for education and research

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    Synthetic biology has become a widely used technology, and expanding applications in research, education and industry require progress tracking for team-based DNA synthesis projects. Although some vendors are beginning to supply multi-kilobase sequence-verified constructs, synthesis workflows starting with short oligos remain important for cost savings and pedagogical benefit. We developed BioPartsDB as an open source, extendable workflow management system for synthetic biology projects with entry points for oligos and larger DNA constructs and ending with sequence-verified clones

    Soliton pair dynamics in patterned ferromagnetic ellipses

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    Confinement alters the energy landscape of nanoscale magnets, leading to the appearance of unusual magnetic states, such as vortices, for example. Many basic questions concerning dynamical and interaction effects remain unanswered, and nanomagnets are convenient model systems for studying these fundamental physical phenomena. A single vortex in restricted geometry, also known as a non-localized soliton, possesses a characteristic translational excitation mode that corresponds to spiral-like motion of the vortex core around its equilibrium position. Here, we investigate, by a microwave reflection technique, the dynamics of magnetic soliton pairs confined in lithographically defined, ferromagnetic Permalloy ellipses. Through a comparison with micromagnetic simulations, the observed strong resonances in the subgigahertz frequency range can be assigned to the translational modes of vortex pairs with parallel or antiparallel core polarizations. Vortex polarizations play a negligible role in the static interaction between two vortices, but their effect dominates the dynamics.Comment: supplemental movies on http://www.nature.com/nphys/journal/v1/n3/suppinfo/nphys173_S1.htm

    Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

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    The idea of 'date' and 'party' hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. Thus, we suggest that a date/party dichotomy is not meaningful and it might be more useful to conceive of roles for protein-protein interactions rather than individual proteins. We find significant correlations between interaction centrality and the functional similarity of the interacting proteins.Comment: 27 pages, 5 main figures, 4 supplementary figure

    MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

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    Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

    Intriguing Electrostatic Potential of CO: Negative Bond-ends and Positive Bond-cylindrical-surface

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    The strong electronegativity of O dictates that the ground state of singlet CO has positively charged C and negatively charged O, in agreement with ab initio charge analysis, but in disagreement with the dipole direction. Though this unusual phenomenon has been fairly studied, the study of electrostatic potential (EP) for noncovalent interactions of CO is essential for better understanding. Here we illustrate that both C and O atom-ends show negative EP (where the C end gives more negative EP), favoring positively charged species, whereas the cylindrical surface of the CO bond shows positive EP, favoring negatively charged ones. This is demonstrated from the interactions of CO with Na+, Cl-, H2O, CO and benzene. It can be explained by the quadrupole driven electrostatic nature of CO (like N2) with very weak dipole moment. The EP is properly described by the tripole model taking into account the electrostatic multipole moments, which has a large negative charge at a certain distance protruded from C, a large positive charge on C, and a small negative charge on O. We also discuss the EP of the first excited triplet COopen0

    Activation of H+-ATPase of the Plasma Membrane of Saccharomyces cerevisiae by Glucose: The Role of Sphingolipid and Lateral Enzyme Mobility

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    Activation of the plasma membrane H+-ATPase of the yeast Saccharomyces cerevisiae by glucose is a complex process that has not yet been completely elucidated. This study aimed to shed light on the role of lipids and the lateral mobility of the enzyme complex during its activation by glucose. The significance of H+-ATPase oligomerization for the activation of H+-ATPase by glucose was shown using the strains lcb1-100 and erg6, with the disturbed synthesis of sphyngolipid and ergosterol, respectively. Experiments with GFP-fused H+-ATPase showed a decrease in fluorescence anisotropy during the course of glucose activation, suggesting structural reorganization of the molecular domains. An immunogold assay showed that the incubation with glucose results in the spatial redistribution of ATPase complexes in the plasma membrane. The data suggest that (1) to be activated by glucose, H+-ATPase is supposed to be in an oligomeric state, and (2) glucose activation is accompanied by the spatial movements of H+-ATPase clusters in the PM

    Inferring topology from clustering coefficients in protein-protein interaction networks

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    BACKGROUND: Although protein-protein interaction networks determined with high-throughput methods are incomplete, they are commonly used to infer the topology of the complete interactome. These partial networks often show a scale-free behavior with only a few proteins having many and the majority having only a few connections. Recently, the possibility was suggested that this scale-free nature may not actually reflect the topology of the complete interactome but could also be due to the error proneness and incompleteness of large-scale experiments. RESULTS: In this paper, we investigate the effect of limited sampling on average clustering coefficients and how this can help to more confidently exclude possible topology models for the complete interactome. Both analytical and simulation results for different network topologies indicate that partial sampling alone lowers the clustering coefficient of all networks tremendously. Furthermore, we extend the original sampling model by also including spurious interactions via a preferential attachment process. Simulations of this extended model show that the effect of wrong interactions on clustering coefficients depends strongly on the skewness of the original topology and on the degree of randomness of clustering coefficients in the corresponding networks. CONCLUSION: Our findings suggest that the complete interactome is either highly skewed such as e.g. in scale-free networks or is at least highly clustered. Although the correct topology of the interactome may not be inferred beyond any reasonable doubt from the interaction networks available, a number of topologies can nevertheless be excluded with high confidence
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