From gene expression to metabolic fluxes.

The problem to be solved (an example)

Hauf, J., Zimmermann, F.K., Müller, S., 2000. Simultaneous genomic over expression of seven glycolytic enzymes in the yeast Saccharomyces cerevisiae. Ezyme. Microbiol. Technol. 26, 688-698.

Can we predict fluxes from gene expression data?

There is no linear correlation.

Trancriptome and proteomeOlivares R, Bordel S, Nielsen J. Codon usage variability determines the correlation between proteome and transcriptome fold changes. BMC Systems Biology. In Press.

P Rj jf f[ P ] k [ mRNA]

[ P ] k( )[ mRNA]

[ P ] k( , j )[ mRNA] P Rj j jf f

P Rj jf f

j

s , j d , jj j j

d Pk mRNA k P P

dt

j Rj

d , jj j jj

d PmRNA k P P

dt t

j ij ii

t S

P Rj j jf CT f

2

1

2 2

1 1

R

R

d

d

Ckk

11

2 2

ij ij i

jj ij i

i

StT

t S

2

1jP

jj

Pf

P

2

1jR

jj

mRNAf

mRNA

Clustering by sequence similarity

Analysis of variance

2

Pj

j Rj

fx log

f Total between withinSS SS SS

2

within jc cc j

SS x x

2between c cc

SS n x x

Results

Usaite.snf1 Usaite.snf4 Usaite.snf1.4

Griffin Ideker Washburn

Within/Total 0.27 0.09 0.27 0.13 0.39 0.20

Between/Total 0.73 0.91 0.73 0.87 0.61 0.80

F-test (B/W) 2.70 10.06 2.75 6.63 1.54 4.09

p-value 0.001 1E-06 4.5E-5 0.015 0.55 2E-5

Statistical description of gene-expression and flux changes

The RNA arrays provide measurements for the significance of the expression changes in every gene.

We need a method to provide measurements for the significance of flux changes in every reaction.

Bordel S, Agren R, Nielsen J. Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes. 2010. PLoS Comput. Boil. 6: e1000859

Geometry of the sampling method

Comparison between the Hit and Run algorithm and the sampling of the convex basis.

The Hit and Run algorithm seems to underestimate the variance.

Assignment of regulatory characteristics

Some resultsHXK2

Transcription factor enrichment (very significant for many TFs)

Transition from glucose to ethanol or acetate:

Gcr1, Gcr2 and Hap4.

Glucose-Ethanol

19 enzymes TR, Gcr1 in 11 of them22 enzymes PR, Gcr1 in none of them

Wild type versus grr1∆ and hxk2 ∆ mutants:

Pho2 and Bas1: Regulators of purine and histidine biosynthesis.

Wild type- grr1∆

26 enzymes TR, Pho2 in 10 of them73 enzymes PR, Pho2 in 6 of them

Wild type versus mig1∆ mig2∆ mutant:

Gcn4 and Cbf1: response against starvation increases growth rate by stimulating amino-acid synthesis and ribosome proliferation

The role of constraints

Bordel S, Nielsen J. Identification of flux control in metabolic networks using non-equilibrium thermodynamics. 2010. Metab. Eng. 13, 369-377

How does the cell “choose” its metabolic state?

Objective function +

Set of constraints

Metabolic state

?

Aerobic and oxygen limited chemostats

Anaerobic chemostat and glucose excess batch

Vemuri et. al. 2006

Batch fermentation

Thank you for your attention.

Questions, suggestions, ideas?