Systems medicine and metabolic profiling of diseases

2nd International Conference on Metabolomics & Systems Biology

Systems medicine and metabolic profiling of diseasesApril 8, 2013

Natal van Riel

Eindhoven University of Technology, the NetherlandsDept. of Biomedical Engineering, [email protected] Systems Biology and Metabolic Diseases

/ biomedical engineering 12-04-2023

Systems Medicine

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• ‘Systems Medicine involves the implementation of systems biology approaches in medical concepts, research and practice, through iterative and reciprocal feedback between data-driven computational and mathematical models as well as model-driven translational and clinical investigations and practice’EC Coordinating Action Systems Medicine – CASyM

• Understanding disease pathways / networks• Personalized Healthcare / Medicine

• biomarkers• patient specific intervention• guide drug discovery


Metabolic profiling of diseases

• Metabolome: • current physiological state• interaction of the genotype

with the environment• clinical diagnostics

• Metabolic networks:• structured information about how metabolites

and reactions are interconnected and organized into pathways

• Data integration concept:• metabolomics (metabolite profile)• mathematical models of metabolic networks

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time


Network-based analysis

Mathematical model

Modeling strategy, depending on type of data and questions

• Constrained genome-wide modeling• stoichiometric model / Genome-Scale Metabolic Models

(GSMM’s) / Constraint-Based Metabolic Models (CBMM)• Recon 2• Thiele et al. 2013, Nat Biotech.• Total number of reactions 7,440• Total number of metabolites 5,063• Number of unique metabolites 2,626

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Cytoscape

http://humanmetabolism.org/


• Graphs• Stoichiometric matrix N• Mass balances (Differential

Equations)• Steady-state (concentrations constant over time), Nr = 0

a metabolic fingerprint / snapshot

Metabolic Balancing Analysis

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0v 1v

0v1v

2v

0v1v

2v

3v

1v

2v

3v1v

2v

System of algebraic equations

An underdetermined system

Measurements to constrain the underdetermined system

Isotopic tracers, e.g. 13C

Flux space


Fluxes in Metabolic Networks

• Flexibility and variability in metabolic flux

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Two equivalent routes for converting an input substrate into an output metabolite

If we know/assume that the system aims for minimization of total intracellular fluxes, both routes are not equivalent

If the objective is to maximize ATP yield then also only one route will be utilized


Flux Balance Analysis

• Assume the homeostatic behavior of the metabolic system somehow reflects an optimal situation

• Introduce a mathematical objective function, for example • minimization of total intracellular fluxes• maximizing ATP production • maximizing the production of a particular metabolite• minimizing nutrient uptake• …

• Optimizing (solving) the under-determined set of algebraic equations can be done by linear programming

• Flux distribution• Visualization

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COnstraints Based Reconstruction and Analysis (COBRA) Toolbox for Matlab, http://opencobra.sourceforge.net

http://sbml.org


Conclusions (1)

Advantages:• Genome-wide, especially good coverage of

small, monomeric molecules and central metabolism

• Comprehensive network topology (wiring)

• Describes fluxes• Possible to integrate multivariate

data

Limitations:• Qualitative / semi-quantitative• Weak in polymeric metabolites with large heterogeneity, e.g., lipids,

lipoproteins

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/ biomedical engineering PAGE 912-04-2023

Profiling lipids and lipoproteins


Lipoprotein metabolism

• 3 types of lipoproteins• Chylomicrons• Very low density lipoproteins

(VLDL), apoB• High density lipoproteins (HDL),

apoA• A continuum of particles of

different size, different composition of TG, cholesterol and CE

• With distinct apo-lipoproteins

• Metabolic Syndrome (MetS)• Lipoprotein particle size

codetermines metabolic and cardiovascular disease risks

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0 10 20 30 40 50

Fraction number

FPLC

(ar

bitr

ary

un

its)

VLDL

IDL/LDL

HDL

• A continuum of particles of different size, different composition of TG, cholesterol and CE

• With distinct apo-lipoproteins

Apoliprotein

Phospholipid

Tryglyceride

Cholesterol ester

Cholesterol


Computational framework

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• The molecular mechanisms that underlie the characteristics of plasma lipoprotein distributions are not fully understood

• Fasted condition, no chylomicrons

• Particle size and heterogeneity selective uptake

CE index

Triglycerides

Cholesteryl ester


Processes in the model

• ApoA-containing lipoprotein metabolism (HDL)

• ApoB-containing lipoprotein metabolism (VLDL, LDL)

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PLTP

CETP

CETP: cholesteryl ester transport proteinPLTP: phospholipid transfer protein


Computational approach

• Integration of model and data• Dealing with imperfect data (noisy, missing, inconsistent)• Inference of model parameters (parameter estimation)

Maximum Likelihood Estimation, Bayesian

• Identify control points (parameter sensitivity analysis)• Uncertainty analysis

• Structural: multiple, competing hypotheses (hypothesis testing)• Numerical: propagation of uncertainty in data, to uncertainty in

parameters and model predictions

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Fit of measured profiles

Prediction of unobserved quantities


Pharmaceutical intervention

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• Liver X receptor (LXR) activation by T0901317 induces effects in both cholesterol and fatty acid homeostasis

Targets: ABCA1, ApoE, PLTP, LPL, etc.+ Reverse cholesterol transport+ Large, anti-atherogenic HDL- Hepatic steatosis- - Production of large,

triglyceride-rich VLDLSchultz et al, Genes Dev. 2000;14(22):2831-8Grefhorst et al, 2012 Atherosclerosis 222(2): 382


Conclusions (2)

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• Computational model-based diagnostics

• Modeling lipoprotein metabolism

Here:• Incorporates HDL (ApoA) and ApoB-containing lipoproteins (VLDL/IDL/LDL) • Particle heterogeneity

− composition and size of the VLDL and HDL particles change independently− describes both triglyceride (TG) and cholesteryl ester (CE) content

• Dynamics• Adaptive response, linking longitudinal phenotypic snapshots

Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT) http://bmi.bmt.tue.nl/sysbio/

• Compartment models− Adiels et al, 2005, J Lipid Res, 46: 58-

67− van Schalkwijk et al, 2009, J Lipid Res,

50: 2398–2411− Tiemann et al. 2011, BMC Syst Biol,

5:174

• Stochastic particle model− Hubner et al, 2008, PLoS Comput Biol,

4(5): e1000079


Acknowledgement

• Kinetic modeling• Ceylan Çölmekçi Öncü • Gijs Hendriks• Anne Maas• Yvonne Rozendaal• Joep Schmitz• Sjanneke Zwaan

• GSMM• Marijke Dermois• Robbin van den Eijnde• Huili Yuan

• ADAPT • Christian Tiemann• Joep Vanlier• Fianne Sips• Roderick Snel

• Collaborators• Peter Hilbers

• Bert Groen• Jan Albert Kuivenhoven• Barbara Bakker

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Brainbridge

Systems medicine and metabolic profiling of diseases

Technology

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