Centre for Medical Systems Biology

Jan Bert van Klinken, LUMC

26-02-2010Modelling Whole Body Metabolism 2

Introduction

Aim

Development of a mathematical model of whole-body

metabolism, in relation to metabolic syndrome

(obesity, dyslipidemia, high blood pressure, insulin resistance,...)

At LUMC focus on whole-body level.

At TU/e focus on cellular level.

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26-02-2010Modelling Whole Body Metabolism 3

The hyperinsulinemic-euglycemic clamp

Insulin resistance: a condition in which normal amounts of insulin are

inadequate to produce a normal insulin response in muscle, liver and fat

cells

Pla

sma g

luco

se

INSULIN INFUSION

GLUCOSE INFUSION

+-LIVER PERIPHERAL

TISSUES

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Indirect Calorimetry

Metabolic flexibility: the ability to adapt fuel selection to fuel availability

glucose C6H12O6 + 6O2 6CO2 + 6H2O RER = 1.0

tripalmitin 2 C51H98O6 + 145O2 102CO2 + 98H2O RER = 0.7

1

0.7

Carb Fat2

2

O

CORERV

V

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Modelling Approach

Mathematical model will be based on that of Hall, which explains at

a macrolevel how changes in body weight and composition result

from changes in food intake.

Existing model will be extended with more detailed regulation

mechanisms for glucose and fat homeostasis and organ specificity.

Hall (2006) Am J Physiol Endocrinol Metab

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Modelling Approach

In order to be better able to validate the mathematical model,

existing experimental techniques and methods for data analysis

are being optimised.

- relate time patterns in variables measured by metabolic cage

(food intake, physical activity, O2 and CO2 exchange) by means

of digital signal processing techniques.

- refine calculations of macronutrient oxidation rates (from O2

and CO2 exchange) for specific diet compositions, such that

whole-body energy balance can be deduced.

- perform clamp and other tracer experiments with stable

isotopes in order to get a more comprehensive picture of

macronutrient flows and their regulation.

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Summary

* Main focus in our Systems Biology approach lies on quantitative

understanding

* Dynamic mathematical models simple (typically few dynamic

variables)

* Heterogeneous experimental data: metabolic cage, dexa scan,

clamp, tracer experiments, Western blot

* Broad range of analysis techniques: digital signal processing,

statistics, stoichiometric analysis (simulation, control analysis)