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From annotated genomes to metabolic flux models

From annotated genomes to metabolic flux models. Jeremy Zucker Broad Institute of MIT & Harvard August 25, 2009. Outline. Metabolic flux models Tuberculosis Annotating genomes Rhodococcus opacus Neurospora crassa. E-flux.

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From annotated genomes to metabolic flux models

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  1. From annotated genomes to metabolic flux models Jeremy Zucker Broad Institute of MIT & Harvard August 25, 2009

  2. Outline • Metabolic flux models • Tuberculosis • Annotating genomes • Rhodococcus opacus • Neurospora crassa

  3. E-flux • Goal: To Predict the effect of drugs on growth using expression data and flux models • Resources: • Boshoff compendium • Mycolic acid pathway • Solution: use differential gene expression to differentially constrain flux limits

  4. E-flux results • Our method successfully identifies 7 of the 8 known mycolic acid inhibitors in a compendium of 235 conditions, • identifies the top anti-TB drugs in this dataset .

  5. Future Tuberculosis Goals To model hypoxia-induced persistence using: • Proteomics, • Metabolomics, • Transcriptomics • Fluxomics • Glycomics • Lipidomics

  6. TB Resources • 3 FBA models, • Chemostat experiments • 27 genomes sequenced in TBDB • On-site TBDB curator. • Systems Biology of TB omics data

  7. Solution: One Database to rule them all Omics Viewer GSMN-TB MtbrvCyc 13.0 MtbrvCyc 11.0 Pathway models iNJ661 rFBA models MAP

  8. Comparative analysis of Mtb metabolic models

  9. Genes GSMN-TB 235 472 3 19 166 2 4 iNJ661 MAP

  10. Compounds GSMN-TB 440 281 0 18 440 178 1 iNJ661 MAP

  11. GSMN-TB 118 21 2 0 78 21 0 iNJ661 MAP Citations

  12. GSMN-TB 555 285 2 7 646 209 1 iNJ661 MAP Reactions

  13. Reconstructing Metabolic models with Pathway-tools • EC predictions from sequence • PGDB from Flux model • Automatically refining flux models based on phenotype data • Applying expression data to Flux models for Omics analysis

  14. EFICAz • Goal: Predict EC numbers for protein sequences with known confidence. • Resources: ENZYME, PFAM, PROSITE • Solution: homofunctional and heterofunctional MSA, FDR, SVM, SIT-based precision.

  15. sbml2biocyc • Goal: Generate PGDB from FBA model • Resources: SBML model • Solution: • sbml2biocyc code to transform SBML data to generate • reactions, • metabolites, • gene associations, • citations for PGDB.

  16. Biohacker • Goal: search the space of metabolic models to find the ones that are most consistent with the phenotype data • Resources: • KO data. • Initial metabolic model. • EC confidence predictions • Solution: MILP algorithm.

  17. Omics viewer • Goal: Googlemaps-like interface for cellular overview that enables pasting flux, RNA expression, etc • Resources: • Pathway-tools source code • OpenLayers, • Flash, • Googlemaps API

  18. Rhodococcus opacus:Goals • To model lipid storage mechanism for biofuels.

  19. R. opacus: Resources • Sinsky lab • Biolog data • Expression data • Genome sequence • EC Predictor

  20. R. Opacus solution • Use EFICaz to make EC predictions • Use reachability analysis to guide outside-in model reconstruction • Use pathway curation to guide inside-out model reconstruction • Can we do better?

  21. Neurospora crassa:Goals • Predict phenotype KO experiments

  22. N. crassa: Resources • Systems biology of Neurospora grant • Extensive literature • very dedicated community • Genome sequence • Ptools pipeline

  23. N. crassa: Solution • Inside-out method with Heather Hood • Outside-in method with MILP algorithm

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