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From Databases to Dynamics

From Databases to Dynamics. Dr. Raquell M Holmes Center for Computational Science Boston University. Computational Biology. Computational Biology  Bioinformatics More than sequences, database searches, statistics or image analysis. A part of Computational Science

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From Databases to Dynamics

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  1. From Databases to Dynamics Dr. Raquell M Holmes Center for Computational Science Boston University

  2. Computational Biology • Computational Biology Bioinformatics • More than sequences, database searches, statistics or image analysis. • A part of Computational Science • Using mathematical modeling, simulation and visualization • Complementing theory and experiment

  3. Overview • Databases: • Publicly available • Why so many? • Metabolic Pathway databases • How do we find and visualize information? • How did they get it? • Beyond information to behaviors • Expression data • Dynamic behaviors

  4. Biologists search for function at all levels Chromosomal structures Genomic organization RNA secondary structures Genomic information/mapping: RicBase http://igs-server.cnrs-mrs.fr/mgdb/Rickettsia/ Center for biotechnology http://www.biotech.unl.edu/home.html

  5. Protein properties: folds, motifs, active sites Protein interactions: network control Pathways http://ca.expasy.org/cgi-bin/get-sw3d-entry?P02568 Jeong et al 2001. Nature 411,41 www.kegg.org

  6. Databases • Databases enable biologists to access large amounts of research data. • Commercial, publicly available • Downloads, web accessible • 100’s to 1000’s

  7. Publicly available databases • Web Interfaces • Content • Search methods: Navigation • Analysis tools

  8. Database Content Over 11 Data Categories • Sequence: DNA, RNA, protein • Structure: genomics, protein, carbohydrate • Networks: metabolic enzymes and pathways (signaling) • Organisms: human/vertebrate, human genes and diseases • Expression: mircroarry and gene expression, proteomics • Nucleic Acid Research • Dedicated to review of databases (548-’04, 386-’03) • http://nar.oupsjournal.org • Most discussion focus on sequence databases.

  9. Protein databases Why so many? Where do they get their data?

  10. Many types of Protein Databases • Sequence (103): • publication, organism, sequence type, function • Protein properties (80): • Motifs, active sites, individual families, localization • Structure (15) • Resolution, experiment type, type of chain, space group. Nucl. Ac. Res. 32, D3 2004

  11. Where data comes from…Exp.Why do we care? • Experiments • Sequencing data: Gene or protein identity • Enzymatic assays: Biochemical properties • Expression studies:Localization, putative cellular function, regulation patterns • Protein interactions: complexes and networks

  12. Where data comes from…Comp.Why do we care? • Annotated sequences • Align sequences (full/partial) • Homology to other genes, Identity • Pattern recognition, property predictions • Biochemical properties • Motifs, profiles, families • Enzyme activity and structure prediction • Pathways • Homologous function

  13. Today’s journey • From protein sequence/name • To Metabolic pathway databases • Metabolic behaviors • Modeling glycolysis

  14. Discussing a familiar pathway Karp: Cell and Molecular Biology Textbook

  15. Generic Protein Seq. Record • Sequence: hexokinase • E.C. #: 2.7.1.1 • Publication: Stachelek et al 1986 • Organism: Yeast • Function: main glucose phosphorylating enzyme • Links: other databases or tools • Pre-determined Properties: • families, folds… • Swissprot example for enzyme.

  16. From protein (enzyme) sequence • Conserved domains, active sites, folding patterns • How do we find the related pathway?

  17. What is a metabolic pathway? Contains a series of reactions. Reactions: Metabolites (substrate, product), enzyme, co-factors.

  18. From enzyme to reaction • Are there databases of metabolites? • How can we get from enzyme to pathways?

  19. Databases on Molecular Networks Metabolic Pathways from NAR (5): • EcoCyc: http://www.ecocyc.org/ • Began with E. coli Genes and Metabolism • BioCyc includes additional genomes • KEGG: http://www.genome.ad.jp/kegg/ • Encyclopedia of genes and genomes • Others: WIT2, PathDB, UMBDD

  20. Starting out… • Metabolism pathway databases • Search by name or sequence • Compounds, Reactions, Pathway, Genes • Associated information • Formulas, Names, Synonyms, Links to other databases

  21. Example results KEGG: search for compound or enzyme by key word. Glucose, 82 hits 1. cpd:C00029 UDPglucose; UDP-D-glucose; UDP-glucose; Uridine diophosphate glucose 2. cpd:C00031 D-Glucose; Grape sugar; Dextrose 3. cpd:C00092 D-Glucose 6-phosphate; Glucose 6-phosphate; Robison ester 79. cpd:C11911 dTDP-D-desosamine; dTDP-3-dimethylamino-3,4,6-trideoxy-D-glucose 80. cpd:C11915 dTDP-3-methyl-4-oxo-2,6-dideoxy-L-glucose 81. cpd:C11922 dTDP-4-oxo-2,6-dideoxy-D-glucose 82. cpd:C11925 dTDP-3-amino-3,6-dideoxy-D-glucose Grape sugar: FORMULA C6H1206 NAME D-Glucose grape sugar Dextrose List of reactions involving compound List of enzymes acting on compound

  22. Navigation bar • list of categories • Compounds • Hunt and peck • Oligosaccharides • Polysaccharides • Fructans • Glycogens • Large-branched-glucans • Long-linear-glucans • Pectin • Short-glucans • Starch • All-Carbohydrates • a sugar • a sugar phosphate • a sugar-1-phosphate • Aldonic-Acids • Carbohydrate-Derivatives • Carbohydrates • Disaccharides

  23. Keyword search • Results are a list of data • Proteins, compounds, reactions, pathways • Hunt and peck

  24. Visualizing Data

  25. Visualization: Data levels EcoCyc draws multi-level views Based on the pathway Metabolite perspective

  26. Additional detail

  27. E. coli K-12 Reaction: 4.1.2.13 Comment Species Citations Gene reaction schematic { Reaction

  28. Different species http://biocyc.org/server.html

  29. E.coli Pseudomona

  30. Summary I • Protein ->enzyme->pathway database • Pathway Database Content: • Species specific (BioCyc), general (KEGG) • Known and proposed enzymes, co-factors, metabolites. • Searches provide similar results (compounds, reactions…) with different appearance.

  31. Populating metabolic databases

  32. Where do the pathways come from? Databases: KEGG, WIT, BioCyc Resources: Experimental data based on literature Genomic data from other databases Determination of metabolic pathway Comparison to known pathways.

  33. Karp et al 1999, TIBT

  34. Linking Genome to Reactions: EC EC # provides information on catalyzed reaction and synonyms. Search for EC# in EcoCyc database. Assign reactions and possible pathway association. How correct is the pathway assignment?

  35. PathLogic/EcoCyc scoring new species pathway X=# reactions for pathway 4 Y=# reactions found 2 Z=# found in other pathways 1 Karp etal., 1999. TIBT

  36. Probably, possibly, not • Probability score depends on X:Y ratio • 4,2,1 has a 4:2 ratio which equals 0.5 • Probable X:Y>= 0.5 • Possible 0< X:Y< 0.5 • Not Y=0

  37. Pathway Evidence Glyph Homo sapien glycolysis pathway E.Coli pathway • Key to edge colors: • green: reactions in which the enzyme is present • black: reactions for which the enzyme is not identified in this organism • orange: reactions in which the enzyme is unique to this pathway • magenta: reactions that are spontaneous, or edges that do not represent reactions at all (e.g. in polymerization pathways)

  38. Summary II • Genomes used to create database of reactions • EC# link gene product to enzyme in reactions • Pathways in the database vary in degrees of probability.

  39. From Data to Dynamics • Database information is static • Just the facts • What is the behavior of the pathway? • Expression data • Dynamics

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