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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 • 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 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 . Future Tuberculosis Goals To model hypoxia-induced persistence using: Proteomics, Metabolomics, Transcriptomics Fluxomics Glycomics Lipidomics TB Resources • • • • • 3 FBA models, Chemostat experiments 27 genomes sequenced in TBDB On-site TBDB curator. Systems Biology of TB omics data Solution: One Database to rule them all GSMN-TB MtbrvCyc 11.0 iNJ661 MAP Omics Viewer MtbrvCyc 13.0 Pathway models rFBA models Comparative analysis of Mtb metabolic models GSMN-TB iNJ661 MAP Citations 141 99 23 Metabolites 739 740 197 Reactions 849 939 219 Genes 726 661 28 Enzymes 587 543 18 Genes GSMN-TB 235 472 3 19 166 2 4 iNJ661 MAP Compounds GSMN-TB 440 281 0 18 440 1 178 iNJ661 MAP Citations GSMN-TB 118 21 0 78 iNJ661 0 2 21 MAP Reactions GSMN-TB 555 285 646 iNJ661 7 1 2 209 MAP 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 EFICAz • Goal: Predict EC numbers for protein sequences with known confidence. • Resources: ENZYME, PFAM, PROSITE • Solution: homofunctional and heterofunctional MSA, FDR, SVM, SITbased precision. 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. 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. 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 Rhodococcus opacus:Goals • To model lipid storage mechanism for biofuels. R. opacus: Resources • • • • • Sinsky lab Biolog data Expression data Genome sequence EC Predictor R. Opacus solution • Use EFICaz to make EC predictions • Use reachability analysis to guide outside-in model reconstruction • Use pathway curation to guide insideout model reconstruction • Can we do better? Neurospora crassa:Goals • Predict phenotype KO experiments N. crassa: Resources • • • • • Systems biology of Neurospora grant Extensive literature very dedicated community Genome sequence Ptools pipeline N. crassa: Solution • Inside-out method with Heather Hood • Outside-in method with MILP algorithm