workshop on 'The interplay of fat and carbohydrate metabolism with application in Metabolic Syndrome and Type 2 Diabetes', December 12 and 13, 2013, Eindhoven University of Technology

1. Metabolic Syndrome workshop
Dec. 13, 2013
Eindhoven University of Technology, Eindhoven
Natal van Riel
Dept. of Biomedical Engineering, TU/e, n.a.w.v.riel@tue.nl
Systems Biology and Metabolic Diseases

2. Can we link understanding of metabolism and
biochemistry (incl. modeling)
to the multi-omics data that are collected in
in research projects
and in the clinic of the (near) future
and tendencies towards stratified
and personalized health and healthcare
/ biomedical engineering
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3. Is the time right for application of GSMM‟s
…for a Systems Medicine approach of Metabolic Syndrome?
2007 Recon1
(Duarte 2007 PNAS 104(6): 1777)
2010 Hepatonet
(Gille 2010 Mol Syst Biol 6:411)
2013 Recon2
(Thiele et al. 2013, Nat Biotech.)
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4. Recon 2
• Genome-Scale Metabolic Model
• Total number of reactions 7,440
• Total number of metabolites 5,063
• Number of unique metabolites 2,626
http://humanmetabolism.org/
/ biomedical engineering
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5. Genome-scale metabolic reconstructions
Advantages:
• Especially good coverage of small, monomeric molecules and
central metabolism
• Comprehensive network topology (wiring)
Limitations:
• Manual curration needed of many pathways outside central
metabolism
• Weak in polymeric metabolites with large heterogeneity, e.g.,
lipids, lipoproteins
/ biomedical engineering
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6. Applications of the global human metabolic
network
“Genome-scale metabolic network reconstructions provide a
platform to interpret omics data in a biochemically meaningful
manner.”
• Four classes of application:
1. Integration of „omics‟ data for tissue and cell specific
network reconstruction,
2. Mapping homologous genes for global mammalian network
reconstruction,
3. Contextualization of „omics‟ data from pathological and
drug-treated states,
4. Simulation of pathological and drug-treated states
/ biomedical engineering
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7. Example:
a metabolic perspective on ADHD, autism
• Neurotransmission is disrupted in most psychiatric disorders
• Serotonergic system malfunctioning
• An underlying metabolic cause
Yap,et al 2010, J Proteome Res 9(6): 2996
/ biomedical engineering
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8. Metabolomics of 24 hour urine
• Metabolomics
(N=362)
• Analysis and
interpretation with
network model
• Recon2, curated for
tryptophan metabolism
Dermois et al
/ biomedical engineering
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9. HUMETICS HUman METabolic diagnostICS
Network-based analysis
APeT in collaboration with TU/e
Dermois, van den Eijnde et al
/ biomedical engineering
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10. Integration of multi-omics data
(A) Gene
expression
pattern (4
clusters)
(B) Metabolic
network
expression
overlay
/ biomedical engineering
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11. Zelezniak et al, 2010, PLoS Comput Biol 6(4): e1000729.
/ biomedical engineering
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12. Simulation
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13. COnstraints Based Reconstruction and
Analysis (COBRA) methods
• So far, just the topology
• What about fluxes
• Flux Balance Analysis (FBA)
• Flux Variability Analysis (FVA)
• …
/ biomedical engineering
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14. Network stoichiometry and Stoichiometric
Matrix
• A hypothetical network
• Stoichiometry matrix
• Stoichiometric model
d s (t )
N v ( s ( t ))
dt
with the species concentrations collated in a vector s
and the reaction rates in a vector v [ v1 , ..., v 5 ]T
/ biomedical engineering
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[ s1 , ..., s 4 ]
T

15. Steady-state („homeostasis‟)
d s (t )
N v ( s ( t )) ˆ 0
dt
Nv
0
metabolite balancing equation
• Set of differential equations  set of algebraic equations with
the rates in v unknown
1
1
1
0
0
1
1
0
1
1
0
0
0
0
0
0
0
0
1
1
v1
v2
v3
v4
v5
0
0
0
0
• Here 4 equations (4 species) and 5 unknowns
•  an under-determined set of equations
•  not a single solution
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16. Metabolic Balancing Analysis
• Mass balances (Differential Equations)
• Steady-state (concentrations constant over time), (N v = 0)
• A metabolic fingerprint / snapshot
v0
Flux space
v1
v1
System of algebraic equations
v2
v0
v2
An underdetermined system
v1
v2
v1
v0
v2
v3
v3
v1
• Measurements to constrain the underdetermined system
• Isotopic tracers, e.g. 13C
• Solve / simulate with Flux Balance Analysis
/ biomedical engineering
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17. Analyzing pathway diagrams
Two equivalent routes for
converting an input substrate into
an output metabolite
If we know/assume that the system
aims for minimization of total
(number of) intracellular fluxes
(efficiency), both routes are not
equivalent
If the objective is to maximize ATP
yield then also only one route will
be utilized
• Can be linked to an objective function to be minimized or
maximized in FBA
/ biomedical engineering
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18. The conceptual basis of Flux Balance Analysis
With no constraints, the flux
distribution of a biological
network may lie at any point in
a solution space
Through optimization of an objective
function, FBA can identify a single
optimal flux distribution that lies on the
edge of the allowable solution space
N
mass balance constraints imposed by the stoichiometric matrix N + capacity
constraints imposed by the lower and upper bounds (ai and bi) are applied to a
network  an allowable solution space
The network may acquire any flux distribution within this space, but points
outside this space are denied by the constraints
Orth et al. Nat Biotechnol. 2010; 28(3): 245-248
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19. A multi-tissue type genome-scale metabolic
network
• Implications for blood-based metabolic biomarkers
Bordbar, et al., I. (2011) BMC Syst. Biol., 5, 180
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20. Systems medicine and metabolic modelling
Mardinoglu & Nielsen. J Intern Med 2012; 271:142–154
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21. Systems medicine and metabolic modelling
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22. / biomedical engineering
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