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, 688698.
Can we predict fluxes from gene
expression data?
There is no linear correlation.
Trancriptome and proteome
Olivares R, Bordel S, Nielsen J. Codon usage variability determines the correlation
between proteome and transcriptome fold changes. BMC Systems Biology. In Press.
[ P ]  k [ mRNA]

R
fj
[ P ]  k(  )[ mRNA]
P
fj

[ P ]  k(  , j )[ mRNA]
P
fj
R
j fj
P
fj
R
fj
d P j
dt
d P j
dt
 k s , j  mRNA j  kd , j  P  j    P  j

t j   Sij i
i
 Rj
tj
 mRNA j  kd , j  P  j    P  j
P
fj
C
f jP 
 R2
 1R
kd2
kd1

2

1
 P 2j
 P 1j
R
 CT j f j
Tj 
t1j
t 2j
 Sij 1i

i
2
S

 ij i
i
 mRNA j
2
f jR 
 mRNA j
1
Clustering by sequence similarity
Analysis of variance
x j  log2
f jP
f jR
SS within
SSTotal  SSbetween  SS within


    x jc  xc 


c  j

SSbetween   nc  xc  x 
c
2
2
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 results
HXK2
Transcription factor enrichment
(very significant for many TFs)
Transition from glucose to
ethanol or acetate:
Gcr1, Gcr2 and Hap4.
Wild type versus grr1∆ and
hxk2 ∆ mutants:
Pho2 and Bas1:
Regulators of purine and
histidine biosynthesis.
Glucose-Ethanol
19 enzymes TR, Gcr1 in 11 of them
22 enzymes PR, Gcr1 in none of them
Wild type- grr1∆
26 enzymes TR, Pho2 in 10 of them
73 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 nonequilibrium thermodynamics. 2010. Metab. Eng. 13, 369-377
How does the cell “choose” its
metabolic state?
Objective
Set of
function + 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?