From gene expression to metabolic fluxes.
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Transcript of From gene expression to metabolic fluxes.
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From gene expression to metabolic fluxes.
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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.
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Can we predict fluxes from gene expression data?
There is no linear correlation.
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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.
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P Rj jf f[ P ] k [ mRNA]
[ P ] k( )[ mRNA]
[ P ] k( , j )[ mRNA] P Rj j jf f
P Rj jf f
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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
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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
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1jP
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Pf
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mRNAf
mRNA
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Clustering by sequence similarity
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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
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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
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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
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Geometry of the sampling method
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Comparison between the Hit and Run algorithm and the sampling of the convex basis.
The Hit and Run algorithm seems to underestimate the variance.
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Assignment of regulatory characteristics
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Some resultsHXK2
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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
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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
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How does the cell “choose” its metabolic state?
Objective function +
Set of constraints
Metabolic state
?
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Aerobic and oxygen limited chemostats
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Anaerobic chemostat and glucose excess batch
Vemuri et. al. 2006
Batch fermentation
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Thank you for your attention.
Questions, suggestions, ideas?