Efficient Symmetry Reduction and the Use of State Symmetries for Symbolic Model Checking

One technique to reduce the state-space explosion problem in temporal logic model checking is symmetry reduction. The combination of symmetry reduction and symbolic model checking by using BDDs suffered a long time from the prohibitively large BDD for the orbit relation. Dynamic symmetry reduction calculates representatives of equivalence classes of states dynamically and thus avoids the construction of the orbit relation. In this paper, we present a new efficient model checking algorithm based on dynamic symmetry reduction. Our experiments show that the algorithm is very fast and allows the verification of larger systems. We additionally implemented the use of state symmetries for symbolic symmetry reduction. To our knowledge we are the first who investigated state symmetries in combination with BDD based symbolic model checking

The active enhancer network operated by liganded RXR supports angiogenic activity in macrophages

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Warm Water and Cool Nests Are Best. How Global Warming Might Influence Hatchling Green Turtle Swimming Performance

For sea turtles nesting on beaches surrounded by coral reefs, the most important element of hatchling recruitment is escaping predation by fish as they swim across the fringing reef, and as a consequence hatchlings that minimize their exposure to fish predation by minimizing the time spent crossing the fringing reef have a greater chance of surviving the reef crossing. One way to decrease the time required to cross the fringing reef is to maximize swimming speed. We found that both water temperature and nest temperature influence swimming performance of hatchling green turtles, but in opposite directions. Warm water increases swimming ability, with hatchling turtles swimming in warm water having a faster stroke rate, while an increase in nest temperature decreases swimming ability with hatchlings from warm nests producing less thrust per stroke

Increased retention of functional fusions to toxic genes in new two-hybrid libraries of the E. coli strain MG1655 and B. subtilis strain 168 genomes, prepared without passaging through E. coli

BACKGROUND: Cloning of genes in expression libraries, such as the yeast two-hybrid system (Y2H), is based on the assumption that the loss of target genes is minimal, or at worst, managable. However, the expression of genes or gene fragments that are capable of interacting with E. coli or yeast gene products in these systems has been shown to be growth inhibitory, and therefore these clones are underrepresented (or completely lost) in the amplified library. RESULTS: Analysis of candidate genes as Y2H fusion constructs has shown that, while stable in E. coli and yeast for genetic studies, they are rapidly lost in growth conditions for genomic libraries. This includes the rapid loss of a fragment of the E. coli cell division gene ftsZ which encodes the binding site for ZipA and FtsA. Expression of this clone causes slower growth in E. coli. This clone is also rapidly lost in yeast, when expressed from a GAL1 promoter, relative to a vector control, but is stable when the promoter is repressed. We have demonstrated in this report that the construction of libraries for the E. coli and B. subtilis genomes without passaging through E. coli is practical, but the number of transformants is less than for libraries cloned using E. coli as a host. Analysis of several clones in the libraries that are strongly growth inhibitory in E. coli include genes for many essential cellular processes, such as transcription, translation, cell division, and transport. CONCLUSION: Expression of Y2H clones capable of interacting with E. coli and yeast targets are rapidly lost, causing a loss of complexity. The strategy for preparing Y2H libraries described here allows the retention of genes that are toxic when inappropriately expressed in E. coli, or yeast, including many genes that represent potential antibacterial targets. While these methods are generally applicable to the generation of Y2H libraries from any source, including mammalian and plant genomes, the potential of functional clones interacting with host proteins to inhibit growth would make this approach most relevant for the study of prokaryotic genomes

Network analysis of human protein location

<p>Abstract</p> <p>Background</p> <p>Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology.</p> <p>Results</p> <p>Based on protein-protein interaction (PPI) and metabolite-linked protein interaction (MLPI) networks of proteins, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. We integrated PPI and metabolic datasets with SCL information of human proteins from LOCATE and GOA (Gene Ontology Annotation) and estimated three statistical properties: Chi-square (χ²) test, Paired Localisation Correlation Profile (PLCP) and network topological measures. For the PPI network, Pearson's chi-square test shows that for the same SCL category, twice as many interacting protein pairs are observed than estimated when compared to non-interacting protein pairs (χ²= 1270.19, <it>P-value </it>< 2.2 × 10^-16), whereas for MLPI, metabolite-linked protein pairs having the same SCL are observed 20% more than expected, compared to non-metabolite linked proteins (χ²= 110.02, <it>P-value </it>< 2.2 x10^-16). To address the issue of proteins with multiple SCLs, we have specifically used the PLCP (Pair Localization Correlation Profile) measure. PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins. Metabolite-linked protein pairs are restricted to specific compartments such as the mitochondrion (<it>P-value </it>< 6.0e-07), the lysosome (<it>P-value </it>< 4.7e-05) and the Golgi apparatus (<it>P-value </it>< 1.0e-15). These findings indicate that the metabolic network adds value to the information in the PPI network for the localisation process of proteins in human subcellular compartments.</p> <p>Conclusions</p> <p>The MLPI network differs significantly from the PPI network in its SCL distribution. The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.</p

An outline of an asymmetric two-component theory of aspect

The paper presents the bases of an asymmetric two-component model of aspect. The main theoretical conclusion of the study is that (grammatical) viewpoint aspect and situation aspect are not independent aspectual levels, since the former often modifies the input situation aspect of the phrase/sentence. As it is shown, besides the arguments and adjuncts of the predicate, viewpoint aspect is also an important factor in compositionally marking situation aspect. The aspectual framework put forward in the paper is verified and illustrated on the basis of the aspectual system of Hungarian and some examples taken from English linguistic data

Machine Learning Methods for Prediction of CDK-Inhibitors

Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://bioinfo.icgeb.res.in/cdkipred