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Project CyberCell

Project CyberCell. David Wishart University of Alberta Computational Cell Biology, MITRE Inc. McLean, Virginia, USA Sept. 21-22, 2004. Why Simulate a Cell?. Physicists Chemists Biologists. Why Choose E. coli?. Fully sequenced (re-sequenced and re-annotated in June 2004)

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Project CyberCell

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  1. Project CyberCell David Wishart University of Alberta Computational Cell Biology, MITRE Inc. McLean, Virginia, USA Sept. 21-22, 2004

  2. Why Simulate a Cell? • Physicists • Chemists • Biologists

  3. Why Choose E. coli? • Fully sequenced (re-sequenced and re-annotated in June 2004) • Studied for 60+ years, widely used, easily manipulated and grown • Simple cellular structure, no organelles, simple genetic structure • Almost all genes, pathways, proteins & metabolites are known or characterized • Lots of data…

  4. International E. coli Alliance http://www.uni-giessen.de/~gx1052/IECA/ieca.html

  5. Why E. coli? -- Potential Applications • Engineering Bugs to be Better Chemical or Protein Factories (Synthetic biology) • Engineering Bugs to Produce Novel Chemicals (wholesale metabolic transfer) • Bioremediation, Energy Production • Engineering Bugs to be “Intelligent” Drug Delivery Vehicles (nanobots) • Finding New Approaches to Treat Infectious Diseases

  6. How to do it? • Genomics Genometrics • Proteomics Proteometrics • Metabolomics Metabometrics • Phenomics Phenometrics • Bioinformatics Biosimulation • Quantify, quantify, quantify

  7. Three-Pronged Process(Project CyberCell) Data Mining Exp. Data Computer Backfilling Collection Simulation What you know What you don’t know What you want to know

  8. Backfilling: The CCDB http://redpoll.pharmacy.ualberta.ca/CCDB Google Search: CCDB coli

  9. The CyberCell Database (CCDB) • Most complete, current, quantitative collection of molecular data on E. coli • Integrates data from 100’s of articles, ~30 databases, updates automatically from ~5 DBs • Web accessible, Web browsable • Supports many kinds of query, viewing and browsing options • Structured using “ColiCards” as in the GeneCards database (includes MetaboCards, RNACards, StructCards, ChemCards, etc.)

  10. CCDB ColiCard

  11. ColiCard Contents • Functional info (predicted or known) • Sequence information (sites, modifications, pI, MW, cleavage) • Location information (in chromosome & cell) • Interacting partners (known & predicted) • Structure (2o, 3o, 4o, predicted) • Enzymatic rate and binding constants • Abundance, copy number, concentration • Links to other sites & viewing tools • Integrated version of all major DBs • 70+ fields for each entry

  12. E. coli Statistics • Diffusion rates • Copy numbers • Transcription rates • Translation rates • Synthetic rates • Volumes • Dimensions • Energetics • Velocities • Etc. etc.

  13. Searching Capabilities • Text search, BLAST search, SQL search • “Show all membrane proteins that are essential and have more than 6 membrane spanning regions” • Chemical Structure search • “Find all metabolites similar to this prospective drug structure:”

  14. Three-Pronged Process Data Mining Exp. Data Computer Backfilling Collection Simulation

  15. E. coli’s Pyramid of Life Metabolomics Proteomics Genomics 811 Chemicals 1152 Enzymes 4269 Genes

  16. Global Expt. Efforts • Knockouts/minimal genomes • Blattner-Wisconsin, Wanner-Purdue, Tomita-Keio • Expression/promoter analysis • Weiner-Alberta, Church-Harvard, Surette-Calgary, Emili-Toronto, Mori-Nara • Unknown function ID • Brown-McMaster, Edwards-Toronto, Thomas-York • Structural Proteomics • Cygler-Montreal,Kunishima-RIKEN,Joachimiak-MCSG • Metabolomics • Wishart-Alberta, Nishioka-Kyoto, Wanner-Purdue

  17. RT-PCR Analysis of 200 ORFs

  18. Annotations to Date Unique Spot ID proteins Cytoplasm 1142 650 Periplasm 170 120 Inner membrane 711 350 Outer Membrane 381 40 Total 2,404 1,150

  19. Metabolomics -- New Approaches

  20. M9-Glucose MOPS

  21. Mixture Compound A Compound B Compound C Spectral Deconvolution of a Mixture Containing Compounds A, B and C

  22. (+)-(-)-Methylsuccinic Acid 2,5-Dihydroxyphenylacetic Acid 2-hydroxy-3-methylbutyric acid 2-Oxoglutaric acid 3-Hydroxy-3-methylglutaric acid 3-Indoxyl Sulfate 5-Hydroxyindole-3-acetic Acid Acetamide Acetic Acid Acetoacetic Acid Acetone Acetyl-L-carnitine Alpha-Glucose Alpha-ketoisocaproic acid Benzoic Acid Betaine Beta-Lactose Citric Acid Creatine Creatinine D(-)Fructose D-(+)-Glyceric Acid D(+)-Xylose Dimethylamine DL-B-Aminoisobutyric Acid Current Compound List • L-Isoleucine • L-Lactic Acid • L-Lysine • L-Methionine • L-phenylalanine • L-Serine • L-Threonine • L-Valine • Malonic Acid • Methylamine • Mono-methylmalonate • N,N-dimethylglycine • N-Butyric Acid • Pimelic Acid • Propionic Acid • Pyruvic Acid • Salicylic acid • Sarcosine • Succinic Acid • Sucrose • Taurine • trans-4-hydroxy-L-Proline • Trimethylamine • Trimethylamine-N-Oxide • Urea • DL-Carnitine • DL-Citrulline • DL-Malic Acid • Ethanol • Formic Acid • Fumaric Acid • Gamma-Amino-N-Butyric Acid • Gamma-Hydroxybutyric Acid • Gentisic Acid • Glutaric acid • Glycerol • Glycine • Glycolic Acid • Hippuric acid • Homovanillic acid • Hypoxanthine • Imidazole • Inositol • isovaleric acid • L(-) Fucose • L-alanine • L-asparagine • L-aspartic acid • L-Histidine • L-homocitrulline

  23. Fitting NMR Spectra with Eclipse

  24. Fumarate Reductase The TCA Cycle Acetate Acetyl-CoA Glycerol Pyruvate Oxaloacetate Citrate Isocitrate L-Malate -Ketoglutarate Fumarate 2 1 Succinate dehydrogenase Succinate Succinyl-CoA

  25. Succinate Production

  26. Metabolic Responses Acetate Glycerol Pyruvate Acetate Glycerol Pyruvate Succinate Succinate

  27. Three-Pronged Process Data Mining Exp. Data Computer Backfilling Collection Simulation

  28. Three Types of Simulation Meso Scale 1.0 - 10 nm Interaction data Kon, Koff, Kd 10 ns - 10 ms Mesodynamics Continuum Model 10 - 100 nm Concentrations Diffusion rates 10 ms - 1000 s Fluid dynamics Atomic Scale 0.1 - 1.0 nm Coordinate data Dynamic data 0.1 - 10 ns Molecular dynamics

  29. Meso & Continuum Dynamics • Meso-scale dynamics also requires solving MD equations (stochastic DE’s) • Continuum dynamics require solving fluid dynamics and flux equations (more differential equations) • 3 Different methods to simulate at 3 different scales • Isn’t there a better way?

  30. Yes! Cellular Automata • Computer modelling method that uses lattices and discrete state “rules” to model time dependent processes – a way to animate things • No differential equations to solve, easy to calculate, more phenomenological • Simple unit behavior -> complex group behavior • Used to model fluid flow, percolation, reaction + diffusion, traffic flow, pheromone tracking, predator-prey models, ecology, social nets • Scales from 10-12 to 10+12

  31. Cellular Automata Can be extended to 3D lattice

  32. Reaction/Diffusion with Cellular Automata

  33. CA Methods in Games SimCity 2000 The SIMS

  34. Cell-Sim • CA or Agent-based simulation system • Designed to permit easy set-up (4-step set-up Wizard) • Allows for general dynamic, stochastic modelling of almost all cellular processes (enzyme kinetics, diffusion, metabolism, operon activity) • Allows real time monitoring (graphing) and animation of the system

  35. Cell-Sim • Four types of molecules: • Proteins • Small Molecules • DNA Molecules • Membrane Molecules • Two types of rules: • Molecule interaction rules - protein-protein, protein-small molecule, protein-DNA interactions. • Membrane interaction rules - protein-membrane, small molecule-membrane interactions.

  36. Cell-Sim Set-up Wizards

  37. Diffusion in Cytoplasm

  38. Simple Enzyme-Substrate Reaction # molecules (P) E + S  E + P time

  39. Trp Repressor

  40. CA for Trp Repressor

  41. More Trp Repressor Bolus Trp addition No trp repressor # molecules (P) No Trp time

  42. Repressilator Nature, 403: 335-338 (2000)

  43. Repressilator

  44. Repressilator

  45. Repressilator

  46. Cell-Sim vs. DE

  47. Cell-Sim (1 plasmid vs. 3)

  48. Repressilator Oscillations

  49. Can We Move to 3D?

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