From annotated genomes to metabolic flux models

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
GSMN-TB iNJ661 MAP
Citations 141 99 23
Metabolites 739 740 197
Reactions 849 939 219
Genes 726 661 28
Enzymes 587 543 18
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
Citations
12
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 opacusGoals

• 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 crassaGoals

• 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