Biopolymers in nanopores: challenges and opportunities

We review the current status of various aspects of biopolymer translocation through nanopores and the challenges and opportunities it offers. Much of the interest generated by nanopores arises from their potential application to third-generation cheap and fast genome sequencing. Although the ultimate goal of single-nucleotide identification has not yet been reached, great advances have been made both from a fundamental and an applied point of view, particularly in controlling the translocation time, fabricating various kinds of synthetic pores or genetically engineering protein nanopores with tailored properties, and in devising methods (used separately or in combination) aimed at discriminating nucleotides based either on ionic or transverse electron currents, optical readout signatures, or on the capabilities of the cellular machinery. Recently, exciting new applications have emerged, for the detection of specific proteins and toxins (stochastic biosensors), and for the study of protein folding pathways and binding constants of protein-protein and protein-DNA complexes. The combined use of nanopores and advanced micromanipulation techniques involving optical/magnetic tweezers with high spatial resolution offers unique opportunities for improving the basic understanding of the physical behavior of biomolecules in confined geometries, with implications for the control of crucial biological processes such as protein import and protein denaturation. We highlight the key works in these areas along with future prospects. Finally, we review theoretical and simulation studies aimed at improving fundamental understanding of the complex microscopic mechanisms involved in the translocation process. Such understanding is a pre-requisite to fruitful application of nanopore technology in high-throughput devices for molecular biomedical diagnostics.

Conformational changes of the chaperone SecB upon binding to a model substrate - bovine pancreatic trypsin inhibitor (BPTI)

SecB is a homotetrameric cytosolic chaperone that forms part of the protein translocation machinery in E. coli. Due to SecB, nascent polypeptides are maintained in an unfolded translocation-competent state devoid of tertiary structure and thus are guided to the translocon. In vitro SecB rapidly binds to a variety of ligands in a non-native state. We have previously investigated the bound state conformation of the model substrate bovine pancreatic trypsin inhibitor (BPTI) as well as the conformation of SecB itself by using proximity relationships based on site-directed spin labeling and pyrene fluorescence methods. It was shown that SecB undergoes a conformational change during the process of substrate binding. Here, we generated SecB mutants containing but a single cysteine per subunit or an exposed highly reactive new cysteine after removal of the nearby intrinsic cysteines. Quantitative spin labeling was achieved with the methanethiosulfonate spin label (MTS) at positions C97 or E90C, respectively. Highfield (W-band) electron paramagnetic resonance (EPR) measurements revealed that with BPTI present the spin labels are exposed to a more polar/hydrophilic environment. Nanoscale distance measurements with double electron-electron resonance (DEER) were in excellent agreement with distances obtained by molecular modeling. Binding of BPTI also led to a slight change in distances between labels at C97 but not at E90C. While the shorter distance in the tetramer increased, the larger diagonal distance decreased. These findings can be explained by a widening of the tetrameric structure upon substrate binding much like the opening of two pairs of scissors.

Automatic Compilation of MATLAB Programs for Synergistic Execution on Heterogeneous Processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

Scalable Context-Sensitive Points-To Analysis Using Multi-Dimensional Bloom Filters

Context-sensitive points-to analysis is critical for several program optimizations. However, as the number of contexts grows exponentially, storage requirements for the analysis increase tremendously for large programs, making the analysis non-scalable. We propose a scalable flow-insensitive context-sensitive inclusion-based points-to analysis that uses a specially designed multi-dimensional bloom filter to store the points-to information. Two key observations motivate our proposal: (i) points-to information (between pointer-object and between pointer-pointer) is sparse, and (ii) moving from an exact to an approximate representation of points-to information only leads to reduced precision without affecting correctness of the (may-points-to) analysis. By using an approximate representation a multi-dimensional bloom filter can significantly reduce the memory requirements with a probabilistic bound on loss in precision. Experimental evaluation on SPEC 2000 benchmarks and two large open source programs reveals that with an average storage requirement of 4MB, our approach achieves almost the same precision (98.6%) as the exact implementation. By increasing the average memory to 27MB, it achieves precision upto 99.7% for these benchmarks. Using Mod/Ref analysis as the client, we find that the client analysis is not affected that often even when there is some loss of precision in the points-to representation. We find that the NoModRef percentage is within 2% of the exact analysis while requiring 4MB (maximum 15MB) memory and less than 4 minutes on average for the points-to analysis. Another major advantage of our technique is that it allows to trade off precision for memory usage of the analysis.

Base excision and nucleotide excision repair pathways in mycobacteria

About a third of the human population is estimated to be infected with Mycobacterium tuberculosis. The bacterium displays an excellent adaptability to survive within the host macrophages. As the reactive environment of macrophages is capable of inducing DNA damage, the ability of the pathogen to safeguard its DNA against the damage is of paramount significance for its survival within the host. Analysis of the genome sequence has provided important insights into the DNA repair machinery of the pathogen, and the studies on DNA repair in mycobacteria have gained momentum in the past few years. The studies have revealed considerable differences in the mycobacterial DNA repair machinery when compared with those of the other bacteria. This review article focuses especially on the aspects of base excision, and nucleotide excision repair pathways in mycobacteria. (C) 2011 Elsevier Ltd. All rights reserved.

Null Dereference Verification via Over-approximated Weakest Pre-conditions Analysis

Null dereferences are a bane of programming in languages such as Java. In this paper we propose a sound, demand-driven, inter-procedurally context-sensitive dataflow analysis technique to verify a given dereference as safe or potentially unsafe. Our analysis uses an abstract lattice of formulas to find a pre-condition at the entry of the program such that a null-dereference can occur only if the initial state of the program satisfies this pre-condition. We use a simplified domain of formulas, abstracting out integer arithmetic, as well as unbounded access paths due to recursive data structures. For the sake of precision we model aliasing relationships explicitly in our abstract lattice, enable strong updates, and use a limited notion of path sensitivity. For the sake of scalability we prune formulas continually as they get propagated, reducing to true conjuncts that are less likely to be useful in validating or invalidating the formula. We have implemented our approach, and present an evaluation of it on a set of ten real Java programs. Our results show that the set of design features we have incorporated enable the analysis to (a) explore long, inter-procedural paths to verify each dereference, with (b) reasonable accuracy, and (c) very quick response time per dereference, making it suitable for use in desktop development environments.

Chemistry and biology of DNA-binding small molecules

Regulation of the transcription machinery is one of the many ways to achieve control of gene expression. This has been done either at the transcription initiation stage or at the elongation stage. Different methodologies are known to inhibit transcription initiation via targeting of double-stranded (ds) DNA by: (i) synthetic oligonucleotides, (ii) ds-DNA-specific, sequenceselective minor-groove binders (distamycin A), intercalators (daunomycin) combilexins and (iii) small molecule (peptide or intercalator)-oligonucleotide conjugates. In some cases, instead of ds-DNA, higher order G-quadruplex structures are formed at the start site of transcription. In this regard G-quadruplex DNA-specific small molecules play a significant role towards inhibition of the transcription machinery. Different types of designer DNA-binding agents act as powerful sequence-specific gene modulators, by exerting their effect from transcription regulation to gene modification. But most of these chemotherapeutic agents have serious side effects. Accordingly, there is always a challenge to design such DNA-binding molecules that should not only achieve maximum specific DNA-binding affinity, and cellular and nuclear transport activity, but also would not interfere with the functions of normal cells.

Network Reconstruction from dynamic data

Over the past decade, many powerful data mining techniques have been developed to analyze temporal and sequential data. The time is now fertile for addressing problems of larger scope under the purview of temporal data mining. The fourth SIGKDD workshop on temporal data mining focused on the question: What can we infer about the structure of a complex dynamical system from observed temporal data? The goals of the workshop were to critically evaluate the need in this area by bringing together leading researchers from industry and academia, and to identify promising technologies and methodologies for doing the same. We provide a brief summary of the workshop proceedings and ideas arising out of the discussions.

Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks

We consider a small extent sensor network for event detection, in which nodes periodically take samples and then contend over a random access network to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center for processing the measurements. The Bayesian setting, is assumed, that is, the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is network-oblivious and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is network-aware and processes measurements as they arrive, but in a time-causal order. In this case, the decision statistic depends on the network delays, whereas in the network-oblivious case, the decision statistic does not. This yields a Bayesian change-detection problem with a trade-off between the random network delay and the decision delay that is, a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network-oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network-aware case, the optimal stopping problem is analyzed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient decision statistic is the network state and the posterior probability of change having occurred, given the measurements received and the state of the network. The optimal regimes are studied using simulation.

Enhanced J-protein interaction and compromised protein stability of mtHsp70 variants lead to mitochondrial dysfunction in Parkinsons disease

Parkinsons disease (PD) is the second most prevalent progressive neurological disorder commonly associated with impaired mitochondrial function in dopaminergic neurons. Although familial PD is multifactorial in nature, a recent genetic screen involving PD patients identified two mitochondrial Hsp70 variants (P509S and R126W) that are suggested in PD pathogenesis. However, molecular mechanisms underlying how mtHsp70 PD variants are centrally involved in PD progression is totally elusive. In this article, we provide mechanistic insights into the mitochondrial dysfunction associated with human mtHsp70 PD variants. Biochemically, the R126W variant showed severely compromised protein stability and was found highly susceptible to aggregation at physiological conditions. Strikingly, on the other hand, the P509S variant exhibits significantly enhanced interaction with J-protein cochaperones involved in folding and import machinery, thus altering the overall regulation of chaperone-mediated folding cycle and protein homeostasis. To assess the impact of mtHsp70 PD mutations at the cellular level, we developed yeast as a model system by making analogous mutations in Ssc1 ortholog. Interestingly, PD mutations in yeast (R103W and P486S) exhibit multiple in vivo phenotypes, which are associated with omitochondrial dysfunction', including compromised growth, impairment in protein translocation, reduced functional mitochondrial mass, mitochondrial DNA loss, respiratory incompetency and increased susceptibility to oxidative stress. In addition to that, R103W protein is prone to aggregate in vivo due to reduced stability, whereas P486S showed enhanced interaction with J-proteins, thus remarkably recapitulating the cellular defects that are observed in human PD variants. Taken together, our findings provide evidence in favor of direct involvement of mtHsp70 as a susceptibility factor in PD.

Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks

Lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. We investigate if supervision can be automatically transferred for clustering a target task, by providing a relevant supervised partitioning of a dataset from a different source task. The target clustering is made more meaningful for the human user by trading-off intrinsic clustering goodness on the target task for alignment with relevant supervised partitions in the source task, wherever possible. We propose a cross-guided clustering algorithm that builds on traditional k-means by aligning the target clusters with source partitions. The alignment process makes use of a cross-task similarity measure that discovers hidden relationships across tasks. When the source and target tasks correspond to different domains with potentially different vocabularies, we propose a projection approach using pivot vocabularies for the cross-domain similarity measure. Using multiple real-world and synthetic datasets, we show that our approach improves clustering accuracy significantly over traditional k-means and state-of-the-art semi-supervised clustering baselines, over a wide range of data characteristics and parameter settings.

Theory and Algorithms for Hop-Count-Based Localization with Random Geometric Graph Models of Dense Sensor Networks

Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes in a region of Euclidean space. Following deployment, the nodes self-organize into a mesh topology with a key aspect being self-localization. Having obtained a mesh topology in a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this work, we analyze this approximation through two complementary analyses. We assume that the mesh topology is a random geometric graph on the nodes; and that some nodes are designated as anchors with known locations. First, we obtain high probability bounds on the Euclidean distances of all nodes that are h hops away from a fixed anchor node. In the second analysis, we provide a heuristic argument that leads to a direct approximation for the density function of the Euclidean distance between two nodes that are separated by a hop distance h. This approximation is shown, through simulation, to very closely match the true density function. Localization algorithms that draw upon the preceding analyses are then proposed and shown to perform better than some of the well-known algorithms present in the literature. Belief-propagation-based message-passing is then used to further enhance the performance of the proposed localization algorithms. To our knowledge, this is the first usage of message-passing for hop-count-based self-localization.

Grafting of Dispersants on MoS2 Nanoparticles in Base Oil Lubrication of Steel

Solid lubricant nanoparticles in suspension in oil are good lubricating options for practical machinery. In this article, we select a range of dispersants, based on their polar moieties, to suspend 50-nm molybdenum disulfide particles in an industrial base oil. The suspension is used to lubricate a steel on steel sliding contact. A nitrogen-based polymeric dispersant (aminopropyl trimethoxy silane) with a free amine group and an oxygen-based polymeric dispersant (sorbital monooleate) when grafted on the particle charge the particle negatively and yield an agglomerate size which is almost the same as that of the original particle. Lubrication of the contact by these suspensions gives a coefficient of friction in the similar to 0.03 range. The grafting of these surfactants on the particle is shown here to be of a chemical nature and strong as the grafts survive mechanical shear stress in tribology. Such grafts are superior to those of other silane-based test surfactants which have weak functional groups. In the latter case, the particles bereft of strong grafts agglomerate easily in the lubricant and give a coefficient of friction in the 0.08-0.12 range. This article investigates the mechanism of frictional energy dissipation as influenced by the chemistry of the surfactant molecule.

Exploiting the Structure of the Constraint Graph for Efficient Points-to Analysis

Points-to analysis is a key compiler analysis. Several memory related optimizations use points-to information to improve their effectiveness. Points-to analysis is performed by building a constraint graph of pointer variables and dynamically updating it to propagate more and more points-to information across its subset edges. So far, the structure of the constraint graph has been only trivially exploited for efficient propagation of information, e.g., in identifying cyclic components or to propagate information in topological order. We perform a careful study of its structure and propose a new inclusion-based flow-insensitive context-sensitive points-to analysis algorithm based on the notion of dominant pointers. We also propose a new kind of pointer-equivalence based on dominant pointers which provides significantly more opportunities for reducing the number of pointers tracked during the analysis. Based on this hitherto unexplored form of pointer-equivalence, we develop a new context-sensitive flow-insensitive points-to analysis algorithm which uses incremental dominator update to efficiently compute points-to information. Using a large suite of programs consisting of SPEC 2000 benchmarks and five large open source programs we show that our points-to analysis is 88% faster than BDD-based Lazy Cycle Detection and 2x faster than Deep Propagation. We argue that our approach of detecting dominator-based pointer-equivalence is a key to improve points-to analysis efficiency.