systems medicine of metabolic syndrome and its comorbidities
TRANSCRIPT
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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
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Systems Biology of Disease Progression
2http://www.youtube.com/watch?v=x54ysJDS7i8
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/ biomedical engineering PAGE 310-12-2014
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Liver X Receptor
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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
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200Hepatic TG
Time [days]
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ol/g]
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Time [days]
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Time [days]
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Time [days]
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Time [days]
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Time [days]
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m]
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0.4DNL
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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
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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
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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
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‘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
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• 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
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Analysis: HDL cholesterol
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Analysis: increased excretion of cholesterol
Observation: increased concentration of HDL
(the good cholesterol)
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• 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
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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
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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.
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• 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
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• Dynamic system
• Maximum Likelihood Estimation
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Van Riel et al. (2013) Interface Focus, 3(2): 20120084
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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
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Parameter estimation (1)
28
steady state model
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Parameter estimation (2)
29
steady state model
iteratively calibrate model to data: estimate parameters over time
minimize difference between data and model simulation
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Parameter estimation (2)
30
steady state model
iteratively calibrate model to data: estimate parameters over time
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Parameter estimation (2)
31
steady state model
iteratively calibrate model to data: estimate parameters over time
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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
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Propagation of Uncertainty
• ADAPT accounts for uncertainty in the model
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Estimated parameter trajectories
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physiologically
unrealistic
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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
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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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