Seminar Wageningen Centre for Systems Biology (WCSB) Dec. 9, 2014 Natal van Riel Eindhoven University of Technology, the Netherlands Dept. of Biomedical Engineering, [email protected] Systems Biology and Metabolic Diseases @nvanriel
Jul 15, 2015
Seminar Wageningen Centre for Systems Biology (WCSB)
Dec. 9, 2014
Natal van Riel
Eindhoven University of Technology, the Netherlands
Dept. of Biomedical Engineering,
Systems Biology and Metabolic Diseases
@nvanriel
Systems Biology of Disease Progression
2http://www.youtube.com/watch?v=x54ysJDS7i8
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Liver X Receptor
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?
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Preclinical study of pharmaceutical
intervention
• control, treated with T0901317 for 1, 2, 4, 7, 14, and 21 days
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0 10 200
100
200Hepatic TG
Time [days]
[um
ol/g]
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2
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Time [days]
[um
ol/g]
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4
6Hepatic FC
Time [days]
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ol/g]
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100Hepatic TG
Time [days]
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ol]
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1
1.5Hepatic CE
Time [days]
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ol]
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4Hepatic FC
Time [days]
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ol]
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1000
2000
3000Plasma CE
Time [days]
[um
ol/L]
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2000
3000HDL-CE
Time [days]
[um
ol/L]
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1500Plasma TG
Time [days]
[um
ol/L]
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8
10
12VLDL clearance
Time [days]
[-]
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200
300
400ratio TG/CE
Time [days]
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Time [days]
[nm
]
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Time [days]
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3Hepatic mass
Time [days]
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m]
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0.2
0.4DNL
Time [days]
[-]
Grefhorst et al. Atherosclerosis, 2012, 222: 382– 389
Liver section of mice
treated 4 days with LXR
agonist T0901317
Oil-Red-O staining for
neutral fat
hepatic steatosis
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WHY/ HOW?
BENEFIT WITHOUT
SIDE -EFFECT?
measuringmodelling
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Physiology of lipid and lipoprotein metabolism
• Coarse-grained when possible,
detailed when necessary
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Computational modeling
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• 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
Tiemann 2.0
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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
Uncertainty
• Data uncertainty
• Parameter uncertainty
• Prediction uncertainty
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Computational
modelParameter 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
‘Connecting’ the longitudinal data
in time, and with each other
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• Data: mice, 3
weeks (black bars
and white dots)
differences in
data accuracy
• Model: (the darker
the more likely)
differences in
uncertainties
• Calculating unobserved quantities
• Does LXR agonist improve lipid/lipoprotein profile?
Flux Distribution Analysis
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white lines enclose the central
67% of the densities
Analysis: HDL cholesterol
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Analysis: increased excretion of cholesterol
Observation: increased concentration of HDL
(the good cholesterol)
• SR-B1
• Protein expression/ activity:
Experimental testing of model prediction
• HDL excretion and uptake flux
are increased
• Transcription:
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Transcription of cholesterol efflux transporters
Tiemann et al., PLOS Comput Biol 2013
SR-B1 protein content is decreased in
hepatic membranes
Srb1 mRNA
expression not
changed
model: decreased
hepatic capacity to
clear cholesterol
Summary first part
• Metabolism and metabolic modeling as ‘foundation’
• Combining data and modelling
• Improved understanding
• Testable predictions
• Importance of fluxes (both data and model)
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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
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http://www.cost.eu/COST_Actions/bmbs/Actions/BM1402
AGE
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Human Metabolic Phenotyping
Metabolic challenge test – Metabolic flexibility
• Cross-sectional (comparing phenotypes)
• Different time-scale
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Krug et al, 2012 FASEB J. 26(6): 2607-19 Tiemann et al, 2011 BMC Syst. Biol.
• Metabolic challenge test
• Metabolic flexibility
Longitudinal - Treatment in time
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The computational method: ADAPT
• ADAPT: Analysis of Dynamic Adaptations in Parameter Trajectories
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? ? ?
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ADAPT
• Dynamic system
• Maximum Likelihood Estimation
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Van Riel et al. (2013) Interface Focus, 3(2): 20120084
Introducing time-dependent parameters
Dividing the simulation of the system in Nt steps of Dt time period
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Modelling phenotype transition (1)
27
treatment
disease progression
longitudinal discrete data: different phenotypes
Parameter estimation (1)
28
steady state model
Parameter estimation (2)
29
steady state model
iteratively calibrate model to data: estimate parameters over time
minimize difference between data and model simulation
Parameter estimation (2)
30
steady state model
iteratively calibrate model to data: estimate parameters over time
Parameter estimation (2)
31
steady state model
iteratively calibrate model to data: estimate parameters over time
Modelling phenotype transition (3)
longitudinal discrete data: different phenotypes
estimate continuous data: ensemble of cubic smooth spline
incorporate uncertainty in data: multiple describing functions
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Propagation of Uncertainty
• ADAPT accounts for uncertainty in the data
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Gaussian distribution
Sampling replicates from error model
( , )d d NVanlier et al. Math Biosci. 2013 Mar 25
Vanlier et al. Bioinformatics. 2012, 28(8):1130-5
Propagation of Uncertainty
• ADAPT accounts for uncertainty in the model
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Estimated parameter trajectories
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physiologically
unrealistic
Regularization of parameter trajectories
• Identifying minimal adaptations that are necessary to describe
the change in phenotype
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changing a parameter is costly
Regularization of parameter trajectories
• Determine adequate regularization strength
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ADAPT – time-varying parameters
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ADAPT
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ADAPT toolbox
• Model simulation
• MEX files - CVode
• Parameter estimation
• ADAPT
• Parallel
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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
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