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Explore how metabolic flux models are utilized to predict drug effects on growth, with a focus on tuberculosis research. Discover resources, methods, and goals in modeling hypoxia-induced persistence and lipid storage mechanisms for biofuels in Rhodococcus opacus and Neurospora crassa. Unveil the strategies and challenges in reconstructing metabolic models using diverse omics data in the study of infectious diseases.
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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 Omics Viewer GSMN-TB MtbrvCyc 13.0 MtbrvCyc 11.0 Pathway models iNJ661 rFBA models MAP
Genes GSMN-TB 235 472 3 19 166 2 4 iNJ661 MAP
Compounds GSMN-TB 440 281 0 18 440 178 1 iNJ661 MAP
GSMN-TB 118 21 2 0 78 21 0 iNJ661 MAP Citations
GSMN-TB 555 285 2 7 646 209 1 iNJ661 MAP Reactions
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, SIT-based 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 inside-out 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
