A Domain Decomposition Strategy for Alignment of Multiple Biological Sequences on Multiprocessor Platforms

Multiple Sequences Alignment (MSA) of biological sequences is a fundamental problem in computational biology due to its critical significance in wide ranging applications including haplotype reconstruction, sequence homology, phylogenetic analysis, and prediction of evolutionary origins. The MSA problem is considered NP-hard and known heuristics for the problem do not scale well with increasing number of sequences. On the other hand, with the advent of new breed of fast sequencing techniques it is now possible to generate thousands of sequences very quickly. For rapid sequence analysis, it is therefore desirable to develop fast MSA algorithms that scale well with the increase in the dataset size. In this paper, we present a novel domain decomposition based technique to solve the MSA problem on multiprocessing platforms. The domain decomposition based technique, in addition to yielding better quality, gives enormous advantage in terms of execution time and memory requirements. The proposed strategy allows to decrease the time complexity of any known heuristic of O(N)^x complexity by a factor of O(1/p)^x, where N is the number of sequences, x depends on the underlying heuristic approach, and p is the number of processing nodes. In particular, we propose a highly scalable algorithm, Sample-Align-D, for aligning biological sequences using Muscle system as the underlying heuristic. The proposed algorithm has been implemented on a cluster of workstations using MPI library. Experimental results for different problem sizes are analyzed in terms of quality of alignment, execution time and speed-up.Comment: 36 pages, 17 figures, Accepted manuscript in Journal of Parallel and Distributed Computing(JPDC

Brane effective actions, kappa-symmetry and applications

This is a review on brane effective actions, their symmetries and some of their applications. Its first part covers the Green–Schwarz formulation of single M- and D-brane effective actions focusing on kinematical aspects: the identification of their degrees of freedom, the importance of world volume diffeomorphisms and kappa symmetry to achieve manifest spacetime covariance and supersymmetry, and the explicit construction of such actions in arbitrary on-shell supergravity backgrounds. Its second part deals with applications. First, the use of kappa symmetry to determine supersymmetric world volume solitons. This includes their explicit construction in flat and curved backgrounds, their interpretation as Bogomol’nyi–Prasad–Sommerfield (BPS) states carrying (topological) charges in the supersymmetry algebra and the connection between supersymmetry and Hamiltonian BPS bounds. When available, I emphasise the use of these solitons as constituents in microscopic models of black holes. Second, the use of probe approximations to infer about the non-trivial dynamics of strongly-coupled gauge theories using the anti de Sitter/conformal field theory (AdS/CFT) correspondence. This includes expectation values of Wilson loop operators, spectrum information and the general use of D-brane probes to approximate the dynamics of systems with small number of degrees of freedom interacting with larger systems allowing a dual gravitational description. Its final part briefly discusses effective actions for N D-branes and M2-branes. This includes both Super-Yang-Mills theories, their higher-order corrections and partial results in covariantising these couplings to curved backgrounds, and the more recent supersymmetric Chern–Simons matter theories describing M2-branes using field theory, brane constructions and 3-algebra considerations

An amorphous solid state of biogenic secondary organic aerosol particles

Virtanen A, Joutsensaari J, Koop T, et al. An amorphous solid state of biogenic secondary organic aerosol particles. NATURE. 2010;467(7317):824-827.Secondary organic aerosol (SOA) particles are formed in the atmosphere from condensable oxidation products of anthropogenic and biogenic volatile organic compounds (VOCs)(1-7). On a global scale, biogenic VOCs account for about 90% of VOC emissions(1,8) and of SOA formation (90 billion kilograms of carbon per year)(1-4). SOA particles can scatter radiation and act as cloud condensation or ice nuclei, and thereby influence the Earth's radiation balance and climate(1,2,5,9,10). They consist of a myriad of different compounds with varying physicochemical properties, and little information is available on the phase state of SOA particles. Gas-particle partitioning models usually assume that SOA particles are liquid(1,5,11), but here we present experimental evidence that they can be solid under ambient conditions. We investigated biogenic SOA particles formed from oxidation products of VOCs in plant chamber experiments and in boreal forests within a few hours after atmospheric nucleation events. On the basis of observed particle bouncing in an aerosol impactor and of electron microscopy we conclude that biogenic SOA particles can adopt an amorphous solid-most probably glassy-state. This amorphous solid state should provoke a rethinking of SOA processes because it may influence the partitioning of semi-volatile compounds, reduce the rate of heterogeneous chemical reactions, affect the particles' ability to accommodate water and act as cloud condensation or ice nuclei, and change the atmospheric lifetime of the particles(12-15). Thus, the results of this study challenge traditional views of the kinetics and thermodynamics of SOA formation and transformation in the atmosphere and their implications for air quality and climate

Large-scale prediction and testing of drug activity on side-effect targets

Discovering the unintended “off-targets” that predict adverse drug reactions (ADRs) is daunting by empirical methods alone. Drugs can act on multiple protein targets, some of which can be unrelated by traditional molecular metrics, and hundreds of proteins have been implicated in side effects. We therefore explored a computational strategy to predict the activity of 656 marketed drugs on 73 unintended “side effect” targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 μM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a Drug-Target-ADR network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic estrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme COX-1. The clinical relevance of this inhibition was borne-out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery