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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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Integrative biological systems modeling:challenges and opportunities

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中国高等学校学术文摘·计算机科学 2009年第1期3卷 92-100页

作者： Jialiang WU Eberhard VOIT Department of Mathematics Bioinformatics Program Georgia Institute of Technology Atlanta GA 30332 USA Integrative BioSystems Institute Georgia Institute of Technology Atlanta GA 30332 USA The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA 30332 USA

Most biological systems are by nature hybrids consist of interacting discrete and continuous components,which may even operate on different time scales. Therefore,' it is desirable to establish modeling frameworks that are capable of combining deterministic and stochastic, discrete and continuous, as well as multi-timescale features. In the context of molecular systems biology, an example for the need of such a combination is the investigation of integrated biological pathways that contain gene regulatory, metabolic and signaling components, which may operate on different time scales and involve on-off switches as well as stochastic effects. The implementation of integrated hybrid systems is not trivial because most software is limited to one or the other of the dichotomies above. In this study, we first review the motivation for hybrid modeling. Secondly, by using the example of a toggle switch model, we illustrate a recently developed modeling framework that is based on the combination of biochemical systems theory (BST) and hybrid functional Petri nets (HFPN). Finally, we discuss remaining challenges and future opportunities.

关键词： biochemical systems theory Petri net hybrid modeling hybrid functional Petri net toggle switch canonical modeling stochastic delay

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Hybrid modelling in biochemical systems theory by means of functional petri nets

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Journal of bioinformatics and Computational Biology 2009年第1期7卷 107-134页

作者： Wu, Jialing Voit, Eberhard Department of Mathematics Bioinformatics Program Georgia Institute of Technology Atlanta GA 30332 United States Integrative BioSystems Institute and The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA 30332 United States

Many biological systems are genuinely hybrids consisting of interacting discrete and continuous components and processes that often operate at different time scales. It is therefore desirable to create modeling frameworks capable of combining differently structured processes and permitting their analysis over multiple time horizons. During the past 40 years, Biochemical Systems Theory (BST) has been a very successful approach to elucidating metabolic, gene regulatory, and signaling systems. However, its foundation in ordinary differential equations has precluded BST from directly addressing problems containing switches, delays, and stochastic effects. In this study, we extend BST to hybrid modeling within the framework of Hybrid Functional Petri Nets (HFPN). First, we show how the canonical GMA and S-system models in BST can be directly implemented in a standard Petri Net framework. In a second step we demonstrate how to account for different types of time delays as well as for discrete, stochastic, and switching effects. Using representative test cases, we validate the hybrid modeling approach through comparative analyses and simulations with other approaches and highlight the feasibility, quality, and efficiency of the hybrid method. © 2009 Imperial College Press.

关键词： Biochemical systems theory BST Delay GMA system HFPN Hybrid Functional Petri Nets Hybrid modeling S-system Stochastic modeling

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Condition specific subnetwork identification using an optimization model

Condition specific subnetwork identification using an optimi...

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第二届最优化与系统生物学国际研讨会

作者： Yong Wang Yu Xia Bioinformatics Program Department of Chemistry Boston University Academy of Mathematics and Systems Science Chinese Academy of Sciences

Subnetworks can reveal the complex patterns of the whole-genome network by extracting the interactions that depend on temporal, spatial, or condition specific context. In this paper we present an optimization framework to identify condition specific subnetworks. This framework allows us to identify the most coherent subnetwork by integrating the information from both nodes and edges in the graph. Importantly we design an algorithm to solve the optimization problem efficiently. It is very fast and can extract subnetworks from large-scale network with about 10000 nodes. As a pilot study we apply our method to identify type 2 diabetes related subnetworks in the human protein-protein interaction network.

关键词： Condition specific subnetwork Optimization model and algorithm Diabetes

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Knowledge-based versus experimentally acquired distance and angle constraints for NMR structure refinement

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Journal of bioinformatics and Computational Biology 2008年第2期6卷 283-300页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Laboratory of Cell Biology National Cancer Institute National Institutes of Health Bethesda MD 20892 United States Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Iowa State University Ames IA 50011 United States

Nuclear Overhauser effects (NOE) distance constraints and torsion angle constraints are major conformational constraints for nuclear magnetic resonance (NMR) structure refinement. In particular, the number of NOE constraints has been considered as an important determinant for the quality of NMR structures. Of course, the availability of torsion angle constraints is also critical for the formation of correct local conformations. In our recent work, we have shown how a set of knowledge-based short-range distance constraints can also be utilized for NMR structure refinement, as a complementary set of conformational constraints to the NOE and torsion angle constraints. In this paper, we show the results from a series of structure refinement experiments by using different types of conformational constraints - NOE, torsion angle, or knowledge-based constraints - or their combinations, and make a quantitative assessment on how the experimentally acquired constraints contribute to the quality of structural models and whether or not they can be combined with or substituted by the knowledge-based constraints. We have carried out the experiments on a small set of NMR structures. Our preliminary calculations have revealed that the torsion angle constraints contribute substantially to the quality of the structures, but require to be combined with the NOE constraints to be fully effective. The knowledge-based constraints can be functionally as crucial as the torsion angle constraints, although they are statistical constraints after all and are not meant to be able to replace the latter. © 2008 Imperial College Press.

关键词： Knowledge-based distance constraints NMR structure refinement NOE distance constraints Precision and accuracy of NMR structures Torsion angle constraints

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Fuzzy C-mean algorithm based on “complete” Mahalanobis distances

Fuzzy C-mean algorithm based on “complete” Mahalanobis dis...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hsiang-Chuan Liu Jeng-Ming Yih Der-Bang Wu Shin-Wu Liu Department of Bioinformatics Asia University Taichung Taiwan Department of Mathematics Education Taichung University 40306 Taiwan Program of Cell and Developmental Biology Rutgers University Piscataway NJ USA

The well known fuzzy partition clustering algorithms are most based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm, were developed to detect non-spherical structural clusters, but both of them based on semi-supervised Mahalanobis distance, these two algorithms fail to consider the relationships between cluster centers in the objective function, needing additional prior information. When some training cluster size is small than its dimensionality, it induces the singular problem of the inverse covariance matrix. It is an important issue. In our previous study, we developed a new unsupervised algorithm, FCM-M, to solve the singular problem of the inverse covariance matrix. But, the previous work only consider the local covariance matrix of each cluster. In this paper, an improved new unsupervised algorithm, ldquofuzzy c-mean based on complete Mahalanobis distance without any prior information (FCM-CM)rdquo, is proposed. The proposed new algorithm which is considered not only the local covariance matrix of each cluster but also the overall covariance matrix, which can get more information and higher accuracy by considering the overall covariance matrix. A real data set was applied to prove that the performance of the FCM-CM algorithm is better than those of the traditional FCM algorithm and our previous FCM-M. For choosing the better initial value of the same new algorithm, FCM-CM, the ratio method is still the best of all choosing methods by using in FCM-M and FCM algorithms in our previous works.

关键词： Clustering algorithms Covariance matrix Distance measurement Partitioning algorithms Artificial neural networks Machine learning algorithms Integrated circuits

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Combining Hierarchical Inference in Ontologies with Heterogeneous Data Sources Improves Gene Function Prediction

Combining Hierarchical Inference in Ontologies with Heteroge...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Xiaoyu Jiang Naoki Nariai Martin Steffen Simon Kasif David Gold Eric D. Kolaczyk Department of Mathematics and Statistics Boston University Boston MA USA Bioinformatics Program Boston University Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA Boston University Boston MA US

The study of gene function is critical in various genomic and proteomic fields. Due to the availability of tremendous amounts of different types of protein data, integrating these datasets to predict function has become a significant opportunity in computational biology. In this paper, to predict protein function we (i) develop a novel Bayesian framework combining relational,hierarchical and structural information with improvement in data usage efficiency over similar methods, and (ii) propose to use it in conjunction with an integrative protein-protein association network, STRING (Search Tool for the Retrieval of INteracting Genes/proteins), which combines information from seven different sources. At the heart of our work is accomplishing protein data integration in a concerted fashion with respect to algorithm and data source. Method performance is assessed by a 5-fold cross-validation in yeast on selected terms from the Molecular Function ontology in the Gene Ontology database. Results show that our combined use of the proposed computational framework and the protein network from STRING offers substantial improvements in prediction. The benefits of using an aggressively integrative network, such as STRING, may derive from the fact that although it is likely that the ultimate gene interaction matrix (including but not limited to protein-protein, genetic, or regulatory interactions) will be sparse, presently it is still known only incompletely in most organisms, and thus the use of multiple distinct data sources is rewarded.

关键词： Ontologies Protein engineering Genomics bioinformatics Proteomics Computational biology Bayesian methods Information retrieval Heart Fungi

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The Genome Sequence of Taurine Cattle: A Window to Ruminant Biology and Evolution

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SCIENCE 2009年第5926期324卷 522-528页

作者： Elsik, Christine G. Tellam, Ross L. Worley, Kim C. Gibbs, Richard A. Abatepaulo, Antonio R. R. Abbey, Colette A. Adelson, David L. Aerts, Jan Ahola, Virpi Alexander, Lee Alioto, Tyler Almeida, Iassudara G. Amadio, Ariel F. Anatriello, Elen Antonarakis, Stylianos E. Anzola, Juan M. Astashyn, Alex Bahadue, Suria M. Baldwin, Cynthia L. Barris, Wes Baxter, Rebecca Bell, Stephanie Nicole Bennett, Anna K. Bennett, Gary L. Biase, Fernando H. Boldt, Clayton R. Bradley, Daniel G. Brinkman, Fiona S. L. Brinkmeyer-Langford, Candice L. Brown, Wendy C. Brownstein, Michael J. Buhay, Christian Caetano, Alexandre R. Camara, Francisco Carroll, Jeffrey A. Carvalho, Wanessa A. Casey, Theresa Cervelatti, Elaine P. Chack, Joseph Chacko, Elsa Chandrabose, Mimi M. Chapin, Jennifer E. Chapple, Charles E. Chen, Hsiu-Chuan Chen, Lin Cheng, Ye Cheng, Ze Childers, Christopher P. Chitko-McKown, Carol G. Chiu, Readman Choi, Jungwoo Chrast, Jacqueline Colley, Adam J. Connelley, Tomothy Cree, Andy Curry, Stacey Dalrymple, Brian Diep Dao, Marvin Davis, Clay de Oliveira, Carlo J. F. de Miranda Santos, Isabel K. F. de Campos, Tatiana A. Deobald, Heather Devinoy, Eve Dickins, C. Michael Yan, Ding Dinh, Huyen H. De Donato, Marcos Donohue, Kaitlin E. Donthu, Ravikiran Dovc, Peter Dugan-Rocha, Shannon Durbin, K. James Eberlein, Annett Edgar, Robert C. Egan, Amy Eggen, Andre Eichler, Evan E. Elhaik, Eran Ellis, Shirley A. Elnitski, Laura Ermolaeva, Olga Eyras, Eduardo Fitzsimmons, Carolyn J. Fowler, Gerald R. Franzin, Alessandra M. Fritz, Krista Gabisi, Ramatu Ayiesha Garcia, Gustavo R. Garcia, Jose Fernando Genini, Sem Gerlach, Daniel German, J. Bruce Gilbert, James G. R. Gill, Clare A. Gladney, Cody J. Glass, Elizabeth J. Goodell, Jason Grant, Jason R. Graur, Dan Greaser, Marion L. Green, Jonathan A. Green, Ronnie D. Guan, Leluo Guigo, Roderic Hadsell, Darryl L. Hagen, Darren E. Hakimov, Hatam A. Halgren, Rob Hamernik, Debora L. Hamilton, Cerissa Harhay, Gregory P. Harrow, Jennifer L. Hart, Elizabeth A. Hastings, Nicola Havlak, Paul Henrichsen, Charl Georgetown Univ Dept Biol 406 Reiss37th & O St NW Washington DC 20057 USA CSIRO Livestock Ind St Lucia Qld 4067 Australia Baylor Coll Med Dept Mol & Human Genet Human Genome Sequencing Ctr Houston TX 77030 USA Washington Univ Sch Med Genome Ctr St Louis MO 63108 USA Texas A&M Univ Dept Anim Sci College Stn TX 77843 USA Univ Adelaide Sch Mol Biomed & Sci Sch Agr Food & Wine Adelaide SA 5005 Australia Univ Washington Dept Genome Sci Seattle WA 98195 USA Howard Hughes Med Inst Seattle WA 98195 USA Natl Human Genome Res Inst Natl Inst Hlth Rockville MD 20878 USA Univ Pompeu Fabra Ctr Genomic Regulation Grp Recerca Informatica Biomed Inst Municipal Invest Med E-08003 Barcelona Spain USDA Cooperative State Res Educ & Extension Serv Washington DC 20250 USA USDA Agr Res Serv Natl Program Staff Beltsville MD 20705 USA Univ Illinois Urban Champaign Dept Anim Sci Urbana IL 61801 USA Univ Illinois Urban Champaign Inst Genomic Biol Urbana IL 61801 USA Simon Fraser Univ Dept Mol Biol Biochem Burnaby BC V5A 1S6 Canada Univ Sydney Fac Vet Sci Sydney NSW 2006 Australia Univ Lausanne Ctr Integrative Genomics CH-1015 Lausanne Switzerland Baylor Coll Med Dept Pediat Nutr USDA Agr Res Serv Childrens Nutr Res Ctr Houston TX 77030 USA Texas A&M Univ Dept Vet Integrative Biosci College Stn TX 77843 USA Univ Geneva Med Sch Dept Genetic Med & Dev CH-1211 Geneva Switzerland Swiss Inst Bioinformatics CH-1211 Geneva Switzerland Imperial Coll S Kensington Campus Div Mol Biosci London SW7 2AZ England Texas A&M Univ Dept Vet Pathobiol College Stn TX 77843 USA Natl Inst Hlth Natl Lib Med Natl Ctr Biotech Information Bethesda MD 20892 USA Wellcome Trust Sanger Inst Hinxton Informatics Dept Cambridge CB10 1HH England Univ London Royal Holloway Egham Dept Comp Sci Surrey TW20 0EX England Univ Houston Dept Biol & Biochem Houston TX 77204 USA Johns Hopkins Univ Sch Med McKusick Nathans Inst Genetic Med Baltimore MD 21205 USA 45 Monterey Dr Tiburon CA 94

To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage. The cattle genome contains a minimum of 22,000 genes, with a core set of 14,345 orthologs shared among seven mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes. Cattle-specific evolutionary breakpoint regions in chromosomes have a higher density of segmental duplications, enrichment of repetitive elements, and species-specific variations in genes associated with lactation and immune responsiveness. Genes involved in metabolism are generally highly conserved, although five metabolic genes are deleted or extensively diverged from their human orthologs. The cattle genome sequence thus provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production.

关键词： Alternative Splicing Animals Animals, Domestic Biological Evolution Cattle Evolution, Molecular Evolution, Molecular Female Genetic Variation Genome Humans Male MicroRNAs/genetics MicroRNAs/genetics Molecular Sequence Data Proteins/genetics Sequence Analysis, DNA Species Specificity Synteny

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Refinement of under-determined loops of human prion protein by database-derived distance constraints

Refinement of under-determined loops of human prion protein ...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Feng Cui Kriti Mukhopadhyay Won-Bin Young Robert L. Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics USA University Research Park Ames IA USA Department of Biochemistry Biophysics and Molecular Biology Iowa State University USA Program on Bioinformatics and Computational Biology USA

Computational simulations of the conversion from the normal cellular prion (PrP c ) to the scrapie prion (PrP Sc ) are usually based on the structures determined by NMR because of the difficulties in crystallizing prion protein. Due to insufficient experimental restraints, a biologically critical loop region in PrP c (residues 167-171), which is a potential binding site for Protein X, is under-determined in most mammalian species. Here, we show that by adding information about distance constraints derived from a database of high-resolution protein structures, this under-determined loop as well as other secondary structural elements of the E200K variant of human prion protein (hPrP c ), a disease-related isoform, can be refined into more realistic structures in the structural ensemble with improved quality and increased accuracy. In particular, the ensemble becomes more compact after the refinement and the percentage of residues in the most favourable region of the Ramachandran diagram is increased to about 90% in the refined structures from the 80 to 85% range in the previously reported structures. Our results not only provide significantly improved structures of the prion protein and hence would facilitate insights into its conversion in the spongiform encephalopathies, but also demonstrate the strong potential for using databases of known protein structures for structure determination and refinement.

关键词： Humans Proteins Databases Diseases Nuclear magnetic resonance Genetic mutations Computational biology Brain Pathogens Amino acids

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