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Simulation Winter Conference

作者： Zhen Liu Umme Juka Mobassera Clifford A. Shaffer Layne T. Watson Yang Cao Department of Computer Science Virginia Technology Blacksburg VA USA Program in Genetics Bioinformatics and Computational Biology Virginia Technology Blacksburg VA USA Department of Computer Science Department of Mathematics Virginia Technology Blacksburg VA USA

ISBN: (纸本)9781424498642

Many protein regulatory models contain chemical species best represented as having multiple states. Such models stem from the potential for multiple levels of phosphorylation or from the formation of multiprotein complexes. We seek to support such models by augmenting an existing modeling and simulation system. Interactions between multistate species can lead to a combinatorial explosion in the potential state space. This creates a challenge when using Gillespie's stochastic simulation algorithm (SSA). Both the network-free algorithm (NFA) and various rules-based methods have been proposed to more efficiently simulate such models. We show how to further improve NFA to integrate population-based and particle-based features. We then present a population-based scheme for the stochastic simulation of rule-based models. A complexity analysis is presented comparing the proposed simulation methods. We present numerical experiments for two sample models that demonstrate the power of the proposed simulation methods.

关键词： Mathematical model Biological system modeling Numerical models Chemicals Stochastic processes Equations Proteins

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Computing H/D-Exchange rates of single residues from data of proteolytic fragments

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BMC bioinformatics 2010年第1期11卷 1-12页

作者： Althaus, Ernst Canzar, Stefan Ehrler, Carsten Emmett, Mark R. Karrenbauer, Andreas Marshall, Alan G. Meyer-Bäse, Anke Tipton, Jeremiah D. Zhang, Hui-Min Institut für Informatik Fachbereich 08 Staudingerweg 9 55099 Mainz Germany Algorithmic Computational Biology Centrum Wiskunde and Informatica Amsterdam Netherlands Center for Bioinformatics Computer Science Department Saarland University 66041 Saarbrücken Germany Ion Cyclotron Resonance Program National High Magnetic Field Laboratory Florida State University 1800 East Paul Dirac Drive Tallahassee FL 32310-4005 United States Department of Chemistry and Biochemistry Florida State University 95 Chieftain Way Tallahassee FL 32306 United States Institute of Mathematics EPFL Station 8 1015 Lausanne Switzerland Department of Scientific Computing Florida State University Tallahassee FL 32306-4120 United States

Background: Protein conformation and protein/protein interaction can be elucidated by solution-phase Hydrogen/Deuterium exchange (sHDX) coupled to high-resolution mass analysis of the digested protein or protein complex. In sHDX experiments mutant proteins are compared to wild-type proteins or a ligand is added to the protein and compared to the wild-type protein (or mutant). The number of deuteriums incorporated into the polypeptides generated from the protease digest of the protein is related to the solvent accessibility of amide protons within the original protein ***: In this work, sHDX data was collected on a 14.5 T FT-ICR MS. An algorithm was developed based on combinatorial optimization that predicts deuterium exchange with high spatial resolution based on the sHDX data of overlapping proteolytic fragments. Often the algorithm assigns deuterium exchange with single residue ***: With our new method it is possible to automatically determine deuterium exchange with higher spatial resolution than the level of digested fragments. © 2010 Althaus et al;licensee BioMed Central Ltd.

关键词： Proteins

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Genetic correlates of autoreactivity and autoreactive potential in human Ig heavy chains

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Immunome Research 2009年第1期5卷 1-8页

作者： Volpe, Joseph M Kepler, Thomas B Center for Computational Immunology Duke University Durham NC United States Departments of Biostatistics and Bioinformatics and Immunology Duke University Medical Center Durham NC United States Computational Biology and Bioinformatics Graduate Program Institute for Genome Sciences and Policy Duke University Durham NC United States

Background. Immature bone marrow B cells are known to have longer CDR3 than mature peripheral B cells, and this genetic characteristic has been shown to correlate with autoreactivity in these early cells. B-cell Central tolerance eliminates these cells, but it is known that autoreactive B cells nevertheless appear commonly in healthy human blood. We examined over 7,300 Ig genes from Genbank, including those annotated by their discoverers as associated with autoreactivity, to determine the genetic correlates of autoreactivity in mature B cells. Results. We find differential biases in gene segment usage and higher mutation frequency in autoreactivity-associated Ig genes, but the CDR3 lengths do not differ between autoreactive and non-autoreactive Ig genes. The most striking genetic signature of autoreactivity is an increase in the proportion of N-nucleotides relative to germline-encoded nucleotides in CDR3 from autoreactive genes. Conclusion. We hypothesize that peripheral autoreactivity results primarily from somatic mutation, and that the genetic correlates of autoreactivity in mature B-cells are not the same as those for autoreactivity in immature B cells. What is seen in mature autoreactive B cells are the correlates of autoreactive potential, not of autoreactivity per se. The autoreactive potential is higher for V(D)J rearrangements encoded to a large extent by N-nucleotides rather than by the gene segments that, we posit, have been selected in germline evolution for their suppression of autoreactive potential.

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An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping

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BMC Research Notes 2009年第1期2卷 1-7页

作者： Sasidharan, Rajkumar Agarwal, Ashish Rozowsky, Joel Gerstein, Mark Molecular Biophysics and Biochemistry Department Yale University New Haven CT 06520 United States Department of Plant Biology Carnegie Institution for Science Stanford CA 94305 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

Background. There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data. Findings. This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and Arabidopsis datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and Arabidopsis. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies. Conclusion. Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure. © 2009 Sasidharan et al;licensee BioMed Central Ltd.

关键词： Tiling Array Transcript Mapping Tile Path Tiling Microarray Intergenic Transcript

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A pathway analysis applied to Genetic Analysis Workshop 16 genome-wide rheumatoid arthritis data

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BMC proceedings 2009年第7期3 Suppl 7卷 S91页

作者： David H Ballard Chatchawit Aporntewan Ji Young Lee Joon Sang Lee Zheyang Wu Hongyu Zhao Program in Computational Biology and Bioinformatics Yale University 300 George Street Suite 503 New Haven Connecticut 06511 USA. david.ballard@yale.edu.

The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, single-nucleotide polymorphism (SNP) association analysis is the most widely used method of genome-wide association data, but recent research shows that multi-marker tests of association may provide greater power, especially when more than one mutation is present within a gene and the mutations are in low linkage disequilibrium with each other. Here we use a multi-marker association test based on regression to SNPs located within known genes to obtain a gene-level score of association. We then perform pathway analysis using this score as a measure of gene importance. We use two tests of pathway enrichment - a binomial test and a random set method. By utilizing publicly available gene and pathway information, we identify B cell, cytokine and inflammation response, and antigen presentation pathways as being associated with rheumatoid arthritis. These results confirm known biological mechanisms for auto-immunity disorders, of which rheumatoid arthritis is one.

关键词： Rheumatoid Arthritis Major Histocompatibility Complex Major Histocompatibility Complex Class Binomial Test Gene Score

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Article Commentary: Snapshots of Tree Space

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Evolutionary bioinformatics 2009年 5卷

作者： Wang, Zheng López-Giréidez, Francesc Townsend, Jeffrey P. Department of Ecology and Evolutionary Biology Yale University New Haven 06520 CT United States Program in Computational Biology and Bioinformatics Yale University New Haven 06520 CT United States

Achievement of the best balance between the accuracy and efficiency is always an important issue when searching a tree space of large data sets. In the 5th issue in 2009, Rodrigo et al used bootstrapped topologies as fixed genealogies to distribute an MCMC analysis across a cluster of computers, resulting in an efficiency yielding results 37 times faster than in the standard MCMC methods. Tree searches can seldom be guided with certainty, so that such snapshots sampling partial but “more-likely” tree space facilitated by parallel programs on computer clusters may provide great promise among a few choices that are computationally affordable when tree space is large and complicated. © 2009 SAGE Publications.

关键词： computer cluster large phylogeny tree sampling

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Francisella tularensis subsp. novicida isolated from a human in Arizona

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BMC Research Notes 2009年第1期2卷 1-6页

作者： Birdsell, Dawn N Stewart, Tasha Vogler, Amy J Lawaczeck, Elisabeth Diggs, Alisa Sylvester, Tammy L Buchhagen, Jordan L Auerbach, Raymond K Keim, Paul Wagner, David M Center for Microbial Genetics and Genomics Northern Arizona University Flagstaff AZ 86011-4073 United States Maricopa County Department of Public Health Phoenix AZ 85012 United States Arizona Department of Health Services Phoenix AZ 85007 United States Translational Genomics Research Institute Phoenix AZ 85004 United States Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States

Background. Francisella tularensis is the etiologic agent of tularemia and is classified as a select agent by the Centers for Disease Control and Prevention. Currently four known subspecies of F. tularensis that differ in virulence and geographical distribution are recognized:tularensis (type A), holarctica (type B), mediasiatica, and novicida. Because of the Select Agent status and differences in virulence and geographical location, the molecular analysis of any clinical case of tularemia is of particular interest. We analyzed an unusual Francisella clinical isolate from a human infection in Arizona using multiple DNA-based approaches. Findings. We report that the isolate is F. tularensis subsp. novicida, a subspecies that is rarely isolated. Conclusion. The rarity of this novicida subspecies in clinical settings makes each case study important for our understanding of its role in disease and its genetic relationship with other F. tularensis subspecies. © 2009 Wagner et al;licensee BioMed Central Ltd.

关键词： Clinical Isolate Coccidioidomycosis Tularemia Francisella Tularensis Tularensis Subsp

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Multiple Routes to Characterize the Folding of a Small DNA Hairpin†

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Angewandte Chemie 2010年第42期122卷

作者： Guillem Portella Modesto Orozco Joint IRB‐BSC Research Program in Computational Biology Institute for Research in Biomedicine (IRB) Josep Samitier 1‐5 Barcelona 08028 (Spain) Barcelona Supercomputing Centre (BSC) Jordi Girona 29 Barcelona 08034 (Spain) Departament de Bioquímica i Biología Molecular Facultat de Biología Universitat de Barcelona Avgda Diagonal 645 Barcelona 08028 (Spain) National Institute of Bioinformatics Parc Científic de Barcelona Josep Samitier 1–5 Barcelona 08028 (Spain)

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Autofix for backward-fit sidechains: Using MolProbity and real-space refinement to put misfits in their place

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Journal of Structural and Functional Genomics 2009年第1期10卷 83-93页

作者： Headd, Jeffrey J. Immormino, Robert M. Keedy, Daniel A. Emsley, Paul Richardson, David C. Richardson, Jane S. Department of Biochemistry Duke University Medical Center 211 Nanaline Duke Building Durham NC 27710 United States Computational Biology and Bioinformatics Program Duke University Durham NC 27708 United States Department of Immunology University of Washington School of Medicine Seattle WA 98195 United States Laboratory of Molecular Biophysics Department of Biochemistry University of Oxford Oxford Oxfordshire 0X1 3QU United Kingdom

Misfit sidechains in protein crystal structures are a stumbling block in using those structures to direct further scientific inference. Problems due to surface disorder and poor electron density are very difficult to address, but a large class of systematic errors are quite common even in well-ordered regions, resulting in sidechains fit backwards into local density in predictable ways. The MolProbity web site is effective at diagnosing such errors, and can perform reliable automated correction of a few special cases such as 180° flips of Asn or Gln sidechain amides, using all-atom contacts and H-bond networks. However, most at-risk residues involve tetrahedral geometry, and their valid correction requires rigorous evaluation of sidechain movement and sometimes backbone shift. The current work extends the benefits of robust automated correction to more sidechain types. The Autofix method identifies candidate systematic, flipped-over errors in Leu, Thr, Val, and Arg using MolProbity quality statistics, proposes a corrected position using real-space refinement with rotamer selection in Coot, and accepts or rejects the correction based on improvement in MolProbity criteria and on χ angle change. Criteria are chosen conservatively, after examining many individual results, to ensure valid correction. To test this method, Autofix was run and analyzed for 945 representative PDB files and on the 50S ribosomal subunit of file 1YHQ. Over 40% of Leu, Val, and Thr outliers and 15% of Arg outliers were successfully corrected, resulting in a total of 3,679 corrected sidechains, or 4 per structure on average. Summary Sentences: A common class of misfit sidechains in protein crystal structures is due to systematic errors that place the sidechain backwards into the local electron density. A fully automated method called "Autofix" identifies such errors for Leu, Val, Thr, and Arg and corrects over one third of them, using MolProbity validation criteria and Coot real-space refinement

关键词： Automation Crystallography Protein/RNA interactions Sidechain rotamers Structure improvement

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Classification of Multiple Time-Series via Boosting

Classification of Multiple Time-Series via Boosting

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IEEE Digital Signal Processing Workshop

作者： Patrick L. Harrington Arvind Rao Alfred O., Hero Bioinformatics Graduate Program University of Michigan Ann Arbor MI USA Lane Center of Computational Biology Carnegie Mellon University Pittsburgh PA USA Department of Statistics University of Michigan Ann Arbor MI USA

Much of modern machine learning and statistics research consists of extracting information from high-dimensional patterns. Often times, the large number of features that comprise this high-dimensional pattern are themselves vector valued, corresponding to sampled values in a time-series. Here, we present a classification methodology to accommodate multiple time-series using boosting. This method constructs an additive model by adaptively selecting basis functions consisting of a discriminating feature's full time-series. We present the necessary modifications to fisher linear discriminant analysis and least-squares, as base learners, to accommodate the weighted data in the proposed boosting procedure. We conclude by presenting the performance of our proposed method against a synthetic stochastic differential equation data set and a real world data set involving prediction of cancer patient susceptibility for a particular chemoradiotherapy.

关键词： Boosting Statistics Vectors Linear discriminant analysis Gene expression bioinformatics computational biology Machine learning Data mining Stochastic processes

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