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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
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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
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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/
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• 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
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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
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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
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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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Profiling lipids and lipoproteins
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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
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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
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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
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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
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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
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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
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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