Transcript

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

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]

0 10 200

1

2

3Hepatic CE

Time [days]

[um

ol/g]

0 10 200

2

4

6Hepatic FC

Time [days]

[um

ol/g]

0 10 200

50

100Hepatic TG

Time [days]

[um

ol]

0 10 200

0.5

1

1.5Hepatic CE

Time [days]

[um

ol]

0 10 200

2

4Hepatic FC

Time [days]

[um

ol]

0 10 200

1000

2000

3000Plasma CE

Time [days]

[um

ol/L]

0 10 200

1000

2000

3000HDL-CE

Time [days]

[um

ol/L]

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500

1000

1500Plasma TG

Time [days]

[um

ol/L]

0 10 206

8

10

12VLDL clearance

Time [days]

[-]

0 10 20100

200

300

400ratio TG/CE

Time [days]

[-]

0 10 200

5

10

15VLDL diameter

Time [days]

[nm

]

0 10 200

1

2

3VLDL-TG production

Time [days]

[um

ol/h]

0 10 201

2

3Hepatic mass

Time [days]

[gra

m]

0 10 200

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)

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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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