Chandra Sekhar Pedamallu

The complete genome of Teredinibacter turnerae T7901: An intracellular endosymbiont of marine wood-boring bivalves (shipworms)

Here we report the complete genome sequence of Teredinibacter turnerae T7901. T. turnerae is a marine gamma proteobacterium that occurs as an intracellular endosymbiont in the gills of wood-boring marine bivalves of the family Teredinidae (shipworms). This species is the sole cultivated member of an endosymbiotic consortium thought to provide the host with enzymes, including cellulases and nitrogenase, critical for digestion of wood and supplementation of the hosts nitrogen-deficient diet. T. turnerae is closely related to the free-living marine polysaccharide degrading bacterium Saccharophagus degradans str. 2–40 and to as yet uncultivated endosymbionts with which it coexists in shipworm cells. Like S. degradans, the T. turnerae genome encodes a large number of enzymes predicted to be involved in complex polysaccharide degradation (>100). However, unlike S. degradans, which degrades a broad spectrum (>10 classes) of complex plant, fungal and algal polysaccharides, T. turnerae primarily encodes enzymes associated with deconstruction of terrestrial woody plant material. Also unlike S. degradans and many other eubacteria, T. turnerae dedicates a large proportion of its genome to genes predicted to function in secondary metabolism. Despite its intracellular niche, the T. turnerae genome lacks many features associated with obligate intracellular existence (e.g. reduced genome size, reduced %G+C, loss of genes of core metabolism) and displays evidence of adaptations common to free-living bacteria (e.g. defense against bacteriophage infection). These results suggest that T. turnerae is likely a facultative intracellular ensosymbiont whose niche presently includes, or recently included, free-living existence. As such, the T. turnerae genome provides insights into the range of genomic adaptations associated with intracellular endosymbiosis as well as enzymatic mechanisms relevant to the recycling of plant materials in marine environments and the production of cellulose-derived biofuels.

Faster protein splicing with the Nostoc punctiforme DnaE intein using non-native extein residues

Background: Inteins are protein maturation machines that enable technologies for regulating enzymes and protein semisynthesis. Results: Robust splicing was achieved with novel genetically selected exteins. Conclusion: In contrast to commonly held perceptions, the natural extein was not the fastest splicing substrate. Significance: Natural precursors balance extein- and intein-selective pressures. Specificity studies provide a predictive rubric for identifying new intein insertion sites. Inteins are naturally occurring intervening sequences that catalyze a protein splicing reaction resulting in intein excision and concatenation of the flanking polypeptides (exteins) with a native peptide bond. Inteins display a diversity of catalytic mechanisms within a highly conserved fold that is shared with hedgehog autoprocessing proteins. The unusual chemistry of inteins has afforded powerful biotechnology tools for controlling enzyme function upon splicing and allowing peptides of different origins to be coupled in a specific, time-defined manner. The extein sequences immediately flanking the intein affect splicing and can be defined as the intein substrate. Because of the enormous potential complexity of all possible flanking sequences, studying intein substrate specificity has been difficult. Therefore, we developed a genetic selection for splicing-dependent kanamycin resistance with no significant bias when six amino acids that immediately flanked the intein insertion site were randomized. We applied this selection to examine the sequence space of residues flanking the Nostoc punctiforme Npu DnaE intein and found that this intein efficiently splices a much wider range of sequences than previously thought, with little N-extein specificity and only two important C-extein positions. The novel selected extein sequences were sufficient to promote splicing in three unrelated proteins, confirming the generalizable nature of the specificity data and defining new potential insertion sites for any target. Kinetic analysis showed splicing rates with the selected exteins that were as fast or faster than the native extein, refuting past assumptions that the naturally selected flanking extein sequences are optimal for splicing.

Investigating a hybrid simulated annealing and local search algorithm for constrained optimization

Constrained Optimization Problems (COP) often take place in many practical applications such as kinematics, chemical process optimization, power systems and so on. These problems are challenging in terms of identifying feasible solutions when constraints are non-linear and non-convex. Therefore, finding the location of the global optimum in the non-convex COP is more difficult as compared to non-convex bound-constrained global optimization problems. This paper proposes a Hybrid Simulated Annealing method (HSA), for solving the general COP. HSA has features that address both feasibility and optimality issues and here, it is supported by a local search procedure, Feasible Sequential Quadratic Programming (FSQP). We develop two versions of HSA. The first version (HSAP) incorporates penalty methods for constraint handling and the second one (HSAD) eliminates the need for imposing penalties in the objective function by tracing feasible and infeasible solution sequences independently. Numerical experiments show that the second version is more reliable in the worst case performance.

Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information

BackgroundProtein-protein interactions are crucially important for cellular processes. Knowledge of these interactions improves the understanding of cell cycle, metabolism, signaling, transport, and secretion. Information about interactions can hint at molecular causes of diseases, and can provide clues for new therapeutic approaches. Several (usually expensive and time consuming) experimental methods can probe protein - protein interactions. Data sets, derived from such experiments make the development of prediction methods feasible, and make the creation of protein-protein interaction network predicting tools possible.MethodsHere we report the development of a simple open source program module (OpenPPI_predictor) that can generate a putative protein-protein interaction network for target genomes. This tool uses the orthologous interactome network data from a related, experimentally studied organism.ResultsResults from our predictions can be visualized using the Cytoscape visualization software, and can be piped to downstream processing algorithms. We have employed our program to predict protein-protein interaction network for the human parasite roundworm Brugia malayi, using interactome data from the free living nematode Caenorhabditis elegans.AvailabilityThe OpenPPI_predictor source code is available from http://tools.neb.com/~posfai/.

Efficient interval partitioning for constrained global optimization

A new efficient interval partitioning approach to solve constrained global optimization problems is proposed. This involves a new parallel subdivision direction selection method as well as an adaptive tree search. The latter explores nodes (intervals in variable domains) using a restricted hybrid depth-first and best-first branching strategy. This hybrid approach is also used for activating local search to identify feasible stationary points. The new tree search management technique results in improved performance across standard solution and computational indicators when compared to previously proposed techniques. On the other hand, the new parallel subdivision direction selection rule detects infeasible and suboptimal boxes earlier than existing rules, and this contributes to performance by enabling earlier reliable deletion of such subintervals from the search space.

A comparison of two mathematical models for earthquake relief logistics

This paper describes an efficient model for coordinating the logistics of emergency response activities that involve both deliveries and pickups. The proposed model is more efficient than previously published models. We show that the proposed model reduces the size of a recently published dynamic network flow model by a factor of T, where T is the length of the planning horizon. In the numerical results, we illustrate the trade-off among three objective functions that are managerially important: minimising total delay of deliveries and pickups, minimising total distance travelled and minimising the number of vehicles used in the operation.

An interval partitioning algorithm for constraint satisfaction problems

We propose an efficient interval partitioning algorithm to solve the continuous constraint satisfaction problem (CSP). The method comprises a new dynamic tree search management system that also invokes local search in selected subintervals. This approach is compared with two classical tree search techniques and three other interval methods. We study some challenging kinematics problems for testing the algorithm. The goal in solving kinematics problems is to identify all real solutions of the system of equations defining the problem. In other words, it is desired to find all object positions and orientations that satisfy a coupled non-linear system of equations. The kinematics benchmarks used here arise in industrial applications.

Regulatory Networks under Ellipsoidal Uncertainty – Data Analysis and Prediction by Optimization Theory and Dynamical Systems

We introduce and analyze time-discrete target-environment regulatory systems (TE-systems) under ellipsoidal uncertainty. The uncertain states of clusters of target and environmental items of the regulatory system are represented in terms of ellipsoids and the interactions between the various clusters are defined by affine-linear coupling rules. The parameters of the coupling rules and the time-dependent states of clusters define the regulatory network. Explicit representations of the uncertain multivariate states of the system are determined with ellipsoidal calculus. In addition, we introduce various regression models that allow us to determine the unknown system parameters from uncertain (ellipsoidal) measurement data by applying semidefinite programming and interior point methods. Finally, we turn to rarefications of the regulatory network. We present a corresponding mixed integer regression problem and achieve a further relaxation by means of continuous optimization. We analyze the structure of the optimization problems obtained, especially, in view of their solvability, we discuss the structural frontiers and research challenges, and we conclude with an outlook.

New Simulated Annealing Algorithms For Constrained Optimization

We propose a Population based dual-sequence Non-Penalty Annealing algorithm (PNPA) for solving the general nonlinear constrained optimization problem. The PNPA maintains a population of solutions that are intermixed by crossover to supply a new starting solution for simulated annealing throughout the search. Every time the search gets stuck at a local optimum, this crossover procedure is triggered and simulated annealing search re-starts from a new subspace. In both the crossover and simulated annealing procedures, the objective function value and the total solution infeasibility degrees are treated as separate performance criteria. Feasible solutions are assessed according to their objective function values and infeasible solutions are assessed with regard to their absolute degree of constraint infeasibility. In other words, in the proposed approach, there exist two sequences of solutions: the feasible sequence and the infeasible sequence. We compare the population based dual sequence PNPA with the standard single sequence Penalty Annealing (the PA), and with the random seed dual sequence Non-Penalty Annealing (NPA). Numerical experiments show that PNPA is more effective than its counterparts.

Symbolic Interval Inference Approach for Subdivision Direction Selection in Interval Partitioning Algorithms

In bound constrained global optimization problems, partitioning methods utilizing Interval Arithmetic are powerful techniques that produce reliable results. Subdivision direction selection is a major component of partitioning algorithms and it plays an important role in convergence speed. Here, we propose a new subdivision direction selection scheme that uses symbolic computing in interpreting interval arithmetic operations. We call this approach symbolic interval inference approach (SIIA). SIIA targets the reduction of interval bounds of pending boxes directly by identifying the major impact variables and re-partitioning them in the next iteration. This approach speeds up the interval partitioning algorithm (IPA) because it targets the pending status of sibling boxes produced. The proposed SIIA enables multi-section of two major impact variables at a time. The efficiency of SIIA is illustrated on well-known bound constrained test functions and compared with established subdivision direction selection methods from the literature.