Transcript Document

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