Due to an aging population and the obesity epidemic, an increasing number of people suffer from the so-called Metabolic Syndrome (MetSyn). Those people have a combination of related disease phenotypes, such as high cholesterol, disturbed sugar metabolism and insulin insensitivity. Moreover, they are at a high risk to develop type 2 diabetes, fatty liver and cardiovascular diseases. We use systems biology to understand how the processes involved in metabolism of cholesterol, lipids and sugars become imbalanced. As an example, a study on the effect of Liver X receptor (LXR) agonists is reported. Systems biology (systems medicine) research also triggers innovation in methods and technology for modelling. The application of a novel computational modeling approach, called Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT) will be discussed. ADAPT is applied to describe the development and progression of MetSyn over a longer period of time. In combination with model-based experimental validation we study the physiological origin of hepatic steatosis induced by liver X receptor activation.

1. Seminar Wageningen Centre for Systems Biology (WCSB)
Dec. 9, 2014
Natal van Riel
Eindhoven University of Technology, the Netherlands
Dept. of Biomedical Engineering,
n.a.w.v.riel@tue.nl
Systems Biology and Metabolic Diseases
@nvanriel

2. Systems Biology of Disease Progression
2 http://www.youtube.com/watch?v=x54ysJDS7i8

3. / biomedical engineering 10-12-2014 PAGE 3

4. Liver X Receptor
/ biomedical engineering 10-12-2014 PAGE 4

5. Novel cholesterol lowering medication
• Liver X Receptor (LXR, nuclear receptor),
induce transcription of multiple genes
modulating metabolism of fatty acids,
triglycerides, and
lipoproteins
• LXR agonists stimulate cellular cholesterol
efflux from peripheral tissues (including
macrophages)
• LXR as target for anti-atherosclerotic
therapy?
/ biomedical engineering 10-12-2014 PAGE 5

6. Preclinical study of pharmaceutical
intervention
• control, treated with T0901317 for 1, 2, 4, 7, 14, and 21 days
Hepatic TG
0 10 20
200
100
0
Time [days]
[umol/g]
Hepatic CE
0 10 20
3
2
1
0
Time [days]
[umol/g]
Hepatic FC
0 10 20
6
4
2
0
Time [days]
[umol/g]
4
2
3000
2000
1000
3000
2000
1000
400
300
200
15
10
5
VLDL-TG production
3
2
1
/ biomedical engineering 10-12-2014 PAGE 6
0 10 20
100
50
0
Hepatic TG
Time [days]
[umol]
0 10 20
1.5
1
0.5
0
Hepatic CE
Time [days]
[umol]
0 10 20
0
Hepatic FC
Time [days]
[umol]
0 10 20
0
Plasma CE
Time [days]
[umol/L]
0 10 20
0
HDL-CE
Time [days]
[umol/L]
0 10 20
1500
1000
500
0
Plasma TG
Time [days]
[umol/L]
0 10 20
12
10
8
6
VLDL clearance
Time [days]
[-]
0 10 20
100
ratio TG/CE
Time [days]
[-]
0 10 20
0
VLDL diameter
Time [days]
[nm]
0 10 20
0
Time [days]
[umol/h]
0 10 20
3
2
1
Hepatic mass
Time [days]
[gram]
0 10 20
0.4
0.2
0
DNL
Time [days]
[-]
Grefhorst et al. Atherosclerosis, 2012, 222: 382– 389
hepatic steatosis
Oil-Red-O staining for
neutral fat
Liver section of mice
treated 4 days with LXR
agonist T0901317

7. WHY/ HOW?
measuring
BENEFIT WITHOUT
SIDE-EFFECT?
/ biomedical engineering 10-12-2014 PAGE 7
modelling

8. / biomedical engineering 10-12-2014 PAGE 8

9. / biomedical engineering 10-12-2014 PAGE 9

10. Physiology of lipid and lipoprotein metabolism
• Coarse-grained when possible,
detailed when necessary
/ biomedical engineering 10-12-2014 PAGE 10

11. Computational modeling
• 1.0 Tiemann et al, 2011 BMC Syst Biol
• 2.0 Tiemann et al, 2013 PLOS Comput Biol
• 3.0 Tiemann et al, 2015 PLOS ONE
/ biomedical engineering 10-12-2014 PAGE 11

12. Tiemann 2.0
1. Fluxes
-VLDL-TG production
-Hepatic HDL cholesterol uptake
-Hepatic cholesterol synthesis
-Biliary cholesterol excretion
-Biliary bile acid excretion
-Fecal cholesterol excretion
-Fecal bile acid excretion
-Transintestinal cholesterol excretion
-Beta-oxidation (available but not included yet)
-Hepatic FFA uptake (available but not included yet)
-VLDL catabolism/clearance from the plasma
2. Metabolite concentrations
-Hepatic FC
-Hepatic CE
-Hepatic TG
-Plasma FFA
-Plasma TG
-Plasma total cholesterol
-HDL cholesterol
-Hepatic fractional DNL (de novo triglycerides)
-Nascent VLDL particle diameter
/ biomedical engineering 10-12-2014 PAGE 12

13. Uncertainty
• Data uncertainty
• Parameter uncertainty
• Prediction uncertainty
Computational
/ biomedical engineering 12/10/2014 PAGE 13
model
Parameter space
Solution / prediction
space
forward
Data space
inverse
Vanlier et al, Bioinformatics. 2012; 28(8):1130-5
Vanlier et al, Math Biosci. 2013; 246(2):305-14

14. ‘Connecting’ the longitudinal data
in time, and with each other
/ biomedical engineering 10-12-2014 PAGE 14
• Data: mice, 3
weeks (black bars
and white dots)
differences in
data accuracy
• Model: (the darker
the more likely)
differences in
uncertainties

15. Flux Distribution Analysis
• Calculating unobserved quantities
• Does LXR agonist improve lipid/lipoprotein profile?
/ biomedical engineering 12/10/2014 PAGE 15
white lines enclose the central
67% of the densities

16. Analysis: HDL cholesterol
/ biomedical engineering 10-12-2014 PAGE 16
Analysis: increased excretion of cholesterol
Observation: increased concentration of HDL
(the good cholesterol)

17. Experimental testing of model prediction
• SR-B1
Srb1 mRNA
expression not
changed
• Protein expression/ activity:
• HDL excretion and uptake flux
are increased
• Transcription:
Transcription of cholesterol efflux transporters
Tiemann et al., PLOS Comput Biol 2013
/ biomedical engineering 10-12-2014 PAGE 17
model: decreased
hepatic capacity to
clear cholesterol
SR-B1 protein content is decreased in
hepatic membranes

18. Summary first part
• Metabolism and metabolic modeling as ‘foundation’
• Combining data and modelling
• Improved understanding
• Testable predictions
• Importance of fluxes (both data and model)
/ biomedical engineering 10-12-2014 PAGE 18

19. Translation
FP7-HEALTH Systems medicine: Applying systems biology
approaches for understanding multifactorial human diseases
and their co-morbidities
Preclinical testing of interventions in mouse models of age and age-related diseases
http://www.cost.eu/COST_Actions/bmbs/Actions/BM1402
/ biomedical engineering 10-12-2014 PAGE 19
AGE

20. Human Metabolic Phenotyping
/ biomedical engineering 10-12-2014 PAGE 20

21. Metabolic challenge test – Metabolic flexibility
• Cross-sectional (comparing phenotypes)
• Different time-scale
Tiemann et al, 2011 BMC Syst. Biol. Krug et al, 2012 FASEB J. 26(6): 2607-19
/ biomedical engineering 10-12-2014 PAGE 21

22. Longitudinal - Treatment in time
• Metabolic challenge test
• Metabolic flexibility
/ biomedical engineering 10-12-2014 PAGE 22

23. The computational method: ADAPT
? ? ?
• ADAPT: Analysis of Dynamic Adaptations in Parameter Trajectories
/ biomedical engineering 10-12-2014 PAGE 23

24. ADAPT
/ biomedical engineering 10-12-2014 PAGE 24

25. • Dynamic system
• Maximum Likelihood Estimation
Van Riel et al. (2013) Interface Focus, 3(2): 20120084
/ biomedical engineering 10-12-2014 PAGE 25

26. Introducing time-dependent parameters
Dividing the simulation of the system in Nt steps of Dt time period
/ biomedical engineering 10-12-2014 PAGE 26

27. Modelling phenotype transition (1)
 longitudinal discrete data: different phenotypes
27
treatment
disease progression

28. Parameter estimation (1)
28
 steady state model

29. Parameter estimation (2)
 steady state model
 iteratively calibrate model to data: estimate parameters over time
29
minimize difference between data and model simulation

30. Parameter estimation (2)
 steady state model
 iteratively calibrate model to data: estimate parameters over time
30

31. Parameter estimation (2)
 steady state model
 iteratively calibrate model to data: estimate parameters over time
31

32. Modelling phenotype transition (3)
 longitudinal discrete data: different phenotypes
 estimate continuous data: ensemble of cubic smooth spline
 incorporate uncertainty in data: multiple describing functions
/ biomedical engineering 10-12-2014 PAGE 32

33. Propagation of Uncertainty
• ADAPT accounts for uncertainty in the data
Gaussian distribution
( , ) d d N  
Sampling replicates from error model
/ biomedical engineering 10-12-2014 PAGE 33
Vanlier et al. Math Biosci. 2013 Mar 25
Vanlier et al. Bioinformatics. 2012, 28(8):1130-5

34. Propagation of Uncertainty
• ADAPT accounts for uncertainty in the model
/ biomedical engineering 10-12-2014 PAGE 34

35. Estimated parameter trajectories
/ biomedical engineering 12/10/2014 PAGE 35
physiologically
unrealistic

36. Regularization of parameter trajectories
• Identifying minimal adaptations that are necessary to describe
the change in phenotype
changing a parameter is costly
/ biomedical engineering 10-12-2014 PAGE 36

37. Regularization of parameter trajectories
• Determine adequate regularization strength
/ biomedical engineering 10-12-2014 PAGE 37

38. ADAPT – time-varying parameters
/ biomedical engineering 10-12-2014 PAGE 38

39. ADAPT
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40. ADAPT toolbox
• Model simulation
• MEX files - CVode
• Parameter estimation
• ADAPT
• Parallel
/ biomedical engineering 10-12-2014 PAGE 40

41. Acknowledgements
• Peter Hilbers
• Christian Tiemann
• Joep Vanlier
• Yvonne Rozendaal
• Fianne Sips
• Bert Groen
• Jan Albert Kuivenhoven
• Maaike Oosterveer
• Brenda Hijmans
• Yared Paalvast
• Yanan Wang
• Partrick Rensen
• Ko Willems-van Dijk
/ biomedical engineering 10-12-2014 PAGE 41