Systems medicine of metabolic syndrome and its comorbidities

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

Systems Biology of Disease Progression

2http://www.youtube.com/watch?v=x54ysJDS7i8

http://www.youtube.com/watch?v=x54ysJDS7i8



/ biomedical engineering PAGE 310-12-2014


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?


Preclinical study of pharmaceutical

intervention

• control, treated with T0901317 for 1, 2, 4, 7, 14, and 21 days


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]

0 10 200

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


WHY/ HOW?

BENEFIT WITHOUT

SIDE -EFFECT?

measuringmodelling



Physiology of lipid and lipoprotein metabolism

• Coarse-grained when possible,

detailed when necessary


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

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

Uncertainty

• Data uncertainty

• Parameter uncertainty

• Prediction uncertainty

/ biomedical engineering PAGE 1312/10/2014

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


• 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


white lines enclose the central

67% of the densities

Analysis: HDL cholesterol


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:


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)


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

AGE


Human Metabolic Phenotyping

Metabolic challenge test – Metabolic flexibility

• Cross-sectional (comparing phenotypes)

• Different time-scale


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


The computational method: ADAPT

• ADAPT: Analysis of Dynamic Adaptations in Parameter Trajectories


? ? ?


ADAPT

• Dynamic system

• Maximum Likelihood Estimation


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


Modelling phenotype transition (1)

27

treatment

disease progression

longitudinal discrete data: different phenotypes

Parameter estimation (1)

28

steady state model


29

steady state model

iteratively calibrate model to data: estimate parameters over time

minimize difference between data and model simulation


30

steady state model



31

steady state model


Modelling phenotype transition (3)

longitudinal discrete data: different phenotypes

estimate continuous data: ensemble of cubic smooth spline

incorporate uncertainty in data: multiple describing functions


Propagation of Uncertainty

• ADAPT accounts for uncertainty in the data


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


Estimated parameter trajectories


physiologically

unrealistic

Regularization of parameter trajectories

• Identifying minimal adaptations that are necessary to describe

the change in phenotype


changing a parameter is costly

Regularization of parameter trajectories

• Determine adequate regularization strength


ADAPT – time-varying parameters


ADAPT


ADAPT toolbox

• Model simulation

• MEX files - CVode

• Parameter estimation

• ADAPT

• Parallel


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


Date post:	15-Jul-2015
Category:	Science
Upload:	natal-van-riel
View:	146 times
Download:	0 times

Systems medicine of metabolic syndrome and its comorbidities

Science