Virtual Physiological Human 2012 conference, London, UK

1. Virtual Physiological Human 2012 conference
London, UK, Sept. 20, 2012
Natal van Riel , Christian Tiemann, Peter
Hilbers
Dept. of Biomedical Engineering, n.a.w.v.riel@tue.nl

2. Metabolic Syndrome (MetS)
• The characteristics of plasma lipoprotein profiles codetermine
metabolic and cardiovascular disease risks
• Underlying molecular mechanisms are not fully understood
• Multi-factorial and progressive
/ biomedical engineering PAGE 219-8-2013

3. Metabolism and metabolic networks
• Systems Medicine / Personalized Medicine / VPH
• Interaction networks reasonably well-known
• History in quantification (experimental and modeling)
• In vivo, cell-specific kinetics are lacking
/ biomedical engineering PAGE 319-8-2013

4. Integrating metabolic networks with regulating
gene/protein circuits
 Parameter Trajectory Analysis
/ biomedical engineering PAGE 419-8-2013
Metabolome
Proteome
Transcriptome
Tiemann et al. BMC Systems Biology, 2011, 5:174

5. A metabolic system with metabolite
controlled, negative transcriptional feedback
• A perturbation acting on the gene/protein circuit encoding the
repressor
• Time scales relevant to this phenotype:
• Metabolic network – seconds
• Gene regulatory circuit – minutes/hours
• Progressive adaptation to the perturbation – days…
/ biomedical engineering PAGE 519-8-2013
• Experimental data:
• metabolic profile (S1, S2, S3, S4)
• 5 stages (day 0, 1, 2, 3, 4)
0 1 2 3 4
0
0.5
1
1.5
2
S1
0 1 2 3 4
-0.2
0
0.2
0.4
0.6
S2
0 1 2 3 4
0
0.5
1
1.5
S3
0 1 2 3 4
0
0.5
1
S4
R1
u2
u1 1 S1
S3S2
S4
3
4 5
2
7
6

6. Model 1: one model for each stage
• Stoichiometry matrix
• ODE model
• Simulate steady-state
• Infer from the data
/ biomedical engineering PAGE 619-8-2013
1 0 1 1 0
1 1 0 0 0
1 1 0 0 0
0 0 0 1 1
N
( )
( ( ), , )
d t
t t
dt
s
Nv s p
with the species concentrations collated in a
vector
and the reaction rates in a vector
and kinetic parameters p
transcription:
day 0:
day 1, 2, 3, 4:
6 6 4
6
6
0.01
ˆ ( , )
v k S
k
k perturbation t
1 4[ ,..., ]T
s ss
1 5[ ,..., ]T
v vv
R1
u2
u1 1 S1
S3S2
S4
3
4 5
2
7
6
6
ˆk

7. Estimate transcription rate k6 for the days after
the perturbation
/ biomedical engineering PAGE 719-8-2013
0 1 2 3 4
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
days
S1
S2
S3
S4
R1
u2
u1 1 S1
S3S2
S4
3
4 5
2
7
6
0
0
0.002
0.004
0.006
0.008
0.01
k6
1 2 3 4
0
0.2
0.4
0.6
0.8
1
1.2
x 10
-3
• Statistically acceptable fits and
accurate parameter estimates

8. Results model 1
• Model 1:
• Metabolic level: topology and interaction kinetics known
• Gene / protein level: topology known, kinetic parameters
unknown (changing)
• Kinetic parameters of the gene/protein circuit estimated from
experimental observations at the metabolic level (metabolic
profiling) during the different stages of progression
• Resulting in 5 separate simulation models (one for each day)
/ biomedical engineering PAGE 819-8-2013

9. Model 2: Lacking information at gene/protein
level
• Next, a more challenging but common scenario is explored:
• Metabolic level: topology known, uncertainty in interaction
kinetics (kinetic parameters)
• Gene / protein level: from functional genomics studies we
know that the intervention affects a gene/protein controlling
reaction 1 (but molecular details are lacking)
• Same experimental observations, reflecting progressive
metabolic adaptations after an intervention at day 0
/ biomedical engineering PAGE 919-8-2013
u2
u1 1 S1
S3S2
S4
3
4 5
2
0 1 2 3 4
0
0.5
1
1.5
2
S1
0 1 2 3 4
-0.2
0
0.2
0.4
0.6
S2
0 1 2 3 4
0
0.5
1
1.5
S3
0 1 2 3 4
0
0.5
1
S4

10. 0 5 10
0
10
20
30
Parameter k1, day 0
0 5 10
0
10
20
30
Parameter k1, day 1
0 5 10
0
10
20
30
Parameter k1, day 2
0 5 10
0
10
20
30
Parameter k1, day 3
0 5 10
0
10
20
30
Parameter k1, day 4
Analyze the data as individual ‘snapshots’
• Metabolic network without feedback
• The unknown adaptation at gene/protein level is translated into
an unknown, but inferable value for the metabolic rate constant
• However, like in the approach with model 1, this ignores the
fact that the snapshots are linked
/ biomedical engineering PAGE 1019-8-2013
0 50 100
0
1
2
3
0 50 100
0
5
10
15
0 50 100
0
2
4
6
8
10
0 50 100
0
5
10
15
0 50 100
0
5
10
15
Monte Carlo (drawing samples from
the data distribution) MLE (weighting
with the data variance)
( )
( ( ), , )
d t
t t
dt
s
Nv s p
1 1 1 2
ˆv k u Smax
1 1 2
4( )m
V
v u S
K f S

11. Parameter Trajectory Analysis
• Using the model of the metabolic network to integrate and
connect metabolomic data obtained at different stages of
progressive adaptations after an intervention
/ biomedical engineering PAGE 1119-8-2013
Treatment intervention
Experimental data at
different stages
Monte Carlo sampling of
data interpolants
Estimation of parameter
and flux trajectories
Analysis
0 1 2 3 4
0
0.5
1
1.5
2
S1
0 1 2 3 4
0
0.1
0.2
0.3
0.4
0.5
S2
0 1 2 3 4
0
0.2
0.4
0.6
0.8
1
S3
Time (days)
0 1 2 3 4
0
0.2
0.4
0.6
0.8
S4
Time (days)
0 1 2 3 4
0
0.5
1
1.5
2
S1
0 1 2 3 4
-0.2
0
0.2
0.4
0.6
S2
0 1 2 3 4
0
0.5
1
1.5
S3
0 1 2 3 4
0
0.5
1
S4
0 1 2 3 4
0
10
20
30
40
k1
0 2 4
0.5
1
1.5
2
S1
0 2 4
0
0.5
1
S2
0.5
1
1.5
S3
0.4
0.6
0.8
1
S4
0 2 4
0
0.5
1
1.5
v1
0 2 4
0
0.5
1
1.5
v2
0 2 4
0.05
0.1
0.15
0.2
0.25
v3

12. Case study: LXR activation in mice
/ biomedical engineering PAGE 1219-8-2013
Grefhorst et al. J. Biol. Chem. 2002
Oosterveer et al. Prog. Lipid Res. 2010
Liver section of mice
treated 4 days with LXR
agonist T0901317
Oil-Red-O staining for
neutral fat
hepatic steatosisLiver X Receptor (nuclear receptor)

13. Inferring parameter trajectories
/ biomedical engineering PAGE 1319-8-2013
• Metabolic phenotyping
• 22 parameters potentially change due to LXR activation

14. Flux trajectories for acceptable parameter sets
• Due uncertainty in data and model multiple solutions
• Despite uncertainties most fluxes show constrained trajectories
/ biomedical engineering PAGE 1419-8-2013
[mM]
[mM/h]
4 days after LXR
activation
reference

15. Analysis of under-constrained trajectories
• Some show a clear pattern (positive correlation between HDL-CE
synthesis and HDL-CE uptake by the liver),
others just ‘clouds’ of solutions
• Can the ‘structure’ in one cross-section of the parameter space
be used to interpret other flux adaptations?
/ biomedical engineering PAGE 1519-8-2013

16. Outlook
• Predictions about changes in gene/
protein expression:
/ biomedical engineering PAGE 1619-8-2013
HDL-CEuptake
T0901317
LXR
Metabolome
Proteome
Transcriptome
Fas, Abcg5, Abcg8, Cyp7a1, Lpl, Pltp, Cd36
fluxes
parameters
enzyme parameter gene/protein
HDL-CE synthesis ABCA1
HDL-CE uptake SR-B1
FC production ABCG5

17. PTA: Linking disease phenotypes
• Multi-time-scale modeling
• Metabolism, metabolic networks and associated diseases
• Integrate metabolome, proteome, transcriptome
• Given the uncertainty in model and data different possible
solutions are explored
/ biomedical engineering PAGE 1719-8-2013
? ?
Metabolic
profiling
(‘snapshots’)
Long-term
dynamics
(phenotype
transitions)

18. Acknowledgement
Collaborators
• Computational Biology (TU/e)
• Ceylan Çölmekçi Öncü
• Christian Tiemann
• Joep Schmitz
• Joep Vanlier
• Huili Yuan
• Peter Hilbers
• Marijke Dermois
• Gijs Hendriks
• Fianne Sips
• Sandra van Tienhoven
• Robbin van den Eijnde
• Bram Wijnen
• Sjanneke Zwaan
Funding
• Netherlands Genomics Initiative
Netherlands Consortium
for Systems Biology
• AstraZeneca
• Univ. Medical Centre Groningen (NL)
• Aldo Grefhorst
• Maaike Oosterveer
• Jan Albert Kuivenhoven
• Barbara Bakker
• Bert Groen
• Biomedical NMR (TU/e)
• Klaas Nicolay
• Jeanine Prompers
• Ko Willems-van Dijk, Leiden University
Medical Center, Netherlands
• FP7-HEALTH.2012.2.1.2-2: Systems
medicine: Applying systems biology
approaches for understanding
multifactorial human diseases and their
co-morbidities, starting in 2013