From annotated genomes to metabolic flux models
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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.

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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

Outline

  • Metabolic flux models

    • Tuberculosis

  • Annotating genomes

    • Rhodococcus opacus

    • Neurospora crassa


E flux

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

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

Future Tuberculosis Goals

To model hypoxia-induced persistence using:

  • Proteomics,

  • Metabolomics,

  • Transcriptomics

  • Fluxomics

  • Glycomics

  • Lipidomics


Tb resources

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

Solution: One Database to rule them all

Omics Viewer

GSMN-TB

MtbrvCyc

13.0

MtbrvCyc

11.0

Pathway

models

iNJ661

rFBA models

MAP


Comparative analysis of mtb metabolic models

Comparative analysis of Mtb metabolic models


Genes

Genes

GSMN-TB

235

472

3

19

166

2

4

iNJ661

MAP


Compounds

Compounds

GSMN-TB

440

281

0

18

440

178

1

iNJ661

MAP


Citations

GSMN-TB

118

21

2

0

78

21

0

iNJ661

MAP

Citations


Reactions

GSMN-TB

555

285

2

7

646

209

1

iNJ661

MAP

Reactions


Reconstructing metabolic models with pathway tools

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

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

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

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

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

Rhodococcus opacus:Goals

  • To model lipid storage mechanism for biofuels.


R opacus resources

R. opacus: Resources

  • Sinsky lab

  • Biolog data

  • Expression data

  • Genome sequence

  • EC Predictor


R opacus solution

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

Neurospora crassa:Goals

  • Predict phenotype KO experiments


N crassa resources

N. crassa: Resources

  • Systems biology of Neurospora grant

  • Extensive literature

  • very dedicated community

  • Genome sequence

  • Ptools pipeline


N crassa solution

N. crassa: Solution

  • Inside-out method with Heather Hood

  • Outside-in method with MILP algorithm


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