Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

Graphics Processing Unit Accelerated Coarse-Grained Protein-Protein Docking

Graphics processing unit (GPU) architectures are increasingly used for general purpose computing, providing the means to migrate algorithms from the SISD paradigm, synonymous with CPU architectures, to the SIMD paradigm. Generally programmable commodity multi-core hardware can result in significant speed-ups for migrated codes. Because of their computational complexity, molecular simulations in particular stand to benefit from GPU acceleration. Coarse-grained molecular models provide reduced complexity when compared to the traditional, computationally expensive, all-atom models. However, while coarse-grained models are much less computationally expensive than the all-atom approach, the pairwise energy calculations required at each iteration of the algorithm continue to cause a computational bottleneck for a serial implementation. In this work, we describe a GPU implementation of the Kim-Hummer coarse-grained model for protein docking simulations, using a Replica Exchange Monte-Carlo (REMC) method. Our highly parallel implementation vastly increases the size- and time scales accessible to molecular simulation. We describe in detail the complex process of migrating the algorithm to a GPU as well as the effect of various GPU approaches and optimisations on algorithm speed-up. Our benchmarking and profiling shows that the GPU implementation scales very favourably compared to a CPU implementation. Small reference simulations benefit from a modest speedup of between 4 to 10 times. However, large simulations, containing many thousands of residues, benefit from asynchronous GPU acceleration to a far greater degree and exhibit speed-ups of up to 1400 times. We demonstrate the utility of our system on some model problems. We investigate the effects of macromolecular crowding, using a repulsive crowder model, finding our results to agree with those predicted by scaled particle theory. We also perform initial studies into the simulation of viral capsids assembly, demonstrating the crude assembly of capsid pieces into a small fragment. This is the first implementation of REMC docking on a GPU, and the effectuate speed-ups alter the tractability of large scale simulations: simulations that otherwise require months or years can be performed in days or weeks using a GPU

Influences of biological and physical heterogeneities on microbial transport through porous media

Although enhanced virus transport has been observed in anoxic aquifers, little is known about the effects of biological heterogeneity – including microbially-induced zonation of terminal electron-acceptor processes – on microbial transport in groundwater. An improved understanding of the influence of heterogeneities of physical and biological origin on microbial transport would benefit water supply and water reuse applications, including riverbank filtration (RBF). Laboratory studies of planktonic S. oneidensis MR-1 cultures confirmed the influence of metabolic state, represented by electron acceptor conditions and growth phase, on transport-relevant surface properties of the organism. Discernible differences in zeta potential and apparent hydrophobicity (as measured by the MATH test) were detected between aerobic and anaerobic cultures. Zeta potentials were generally in the range of -4 to -10 mV. Results of EPS analysis were in qualitative agreement with the electrokinetic findings that nitrate-reducing cultures had lower net surface charge than aerobic cultures at log phase. However, similar qualitative agreement between the results of cell surface characterization by MATH and electrokinetic analyses was not observed. Our results confirm previous reports that charge, non-polar interactions, and steric factors contribute to adhesion and attachment behavior in complex ways, and further demonstrate that redox conditions can affect transport-relevant properties. Stochastic modeling studies in one and three dimensions explored the influences of physical and biological heterogeneity on microbial transport. Both models showed the potential for heterogeneity to adversely impact system performance. The 1D model demonstrated that correlations between biological and physical heterogeneities can influence virus breakthrough in complex, varied, and sometimes counterintuitive ways. The 3D study, based on a novel dimensionless framework to describe an RBF flow field, separated the contribution of the pumping-induced distribution of flow path lengths from the overall filtration behavior of the system. While a less linear flow field improves removals and apparent filtration efficiency, heterogeneity in hydraulic conductivity hurts filtration performance on average; physical and flow heterogeneities thus counteract each other. Our results further underscored how a failure to fully account for correlations between physical/flow heterogeneities and attachment processes can produce artificial scale dependency in macroscale estimates of attachment parameters

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal