Nicola LaiDepartment of Biomedical Engineering

Case Western Reserve University

Dicembre 21, 2011

A Systems Biology ApproachTo Study Metabolic Regulation

In Tissue/Organ SystemsIn Health And Disease States

Outline of PresentationIntroduction to Systems BiologyProjectsExperimental approaches to study energy metabolism at different whole body levels:

CellularTissue-OrganWhole Body

Integration of experimental data to computational model to study

O2 transport and metabolism in skeletal muscle (Diabetes)Fuel Homeostasis: Substrate Utilization (Cystic Fibrosis)

Relation between experimental and computational models for optimal design of experiments and to generate hypotheses

Systems Biology

Study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (e.g., the enzymes and metabolites in a metabolic pathway)

Organisms

Tissues

Organs

Cells

Proteins

Genes

Integrative Systems Biology

Approach

Discover Functional Properties of the

Biological Systems

Comprehensive data sets from distinct levels of biological systems;It is difficult to relate organ whole-organism function to cellular and sub-cellular function and structure properties;Integration of multi-scale data to build predictive mathematical models of the system;Investigate the behavior and relationships of all elements in a functioning biological system;

Systems Approach: Integration

Cellular Metabolism

All the chemical processes that make it possible for the cells to continue living

Cell

Metabolic Systems

Most metabolic pathways have been intensely studied;Little is known in quantitative terms about their control and integration with other pathways, as well as their interaction to regulate physiological variables (e.g., blood, glucose, muscle ATP) under normal and stress conditions;

Fell D. Understanding the Control of Metabolism, 1 997.

TitleSystems Biology Investigation of Muscle Exercise Metabolism in DiabetesGoalTo quantify the key factors responsible for metabolic and mitochondrial dysfunction in diabetes and elucidate the impact of exercise training on energy metabolism

AgencyNational Institute of Arthritis, Musculoskeletal & Skin Diseases

Ongoing Research Projects

TitleSystems Biology Approach to Growth Regulation in Cystic FibrosisGoal To investigate the patterns of energy homeostasis (energy utilization and imbalances) in control and cystic fibrosis (CF) mice using perturbations including pharmacologic treatment, genetic manipulations and altering energy balance by diet or exercise

AgencyInstitute of General Medical Sciences

Systems Biology Approach

1. Choose suitable biological model organism2. Characterize endocrine-metabolic components

of energy metabolism3. Generate plausible hypotheses explaining

differences between T2DM and control4. Develop predictive, quantitative multiscale

models of energy metabolism5. Perturb systematically system to validate and

refine model and to test hypotheses6. Generate new experimentally testable

hypotheses

Metabolic Characteristics in T2DM

Muscle metabolic functions decline with type 2 diabetes mellitus (T2DM)Metabolic dysfunction is accompanied by mitochondrial dysfunction and insulin resistance (IR)Mitochondrial dysfunction is related to IR, but the cause-and-effect relationship between them remains to be definedThe altered metabolic regulation under which insulin is less effective in inducing glucose utilization is not completely understood

Why study exercise metabolism

Muscle energy metabolism in healthy & disease state

Detect and prevent pathologies (e.g. Diabetes)Therapeutic intervention to ameliorate quality of life in elderly and subjects with metabolic disorders

Experimental Approach

Biological Systems

Acute Perturbation / Stimuli Chronic Stimuli / Conditions

-Training, Microgravity-Diseases(e.g. Diabetes, Myopathies)

-Exercise-Hypoxia, Hyperoxia-Drugs

System Outputs

-Physiological variables(e.g. Blood flow)-Metabolic variables(e.g. Substrates, Enzymes)

Whole body response to exercise

Stimulus Response

Physiological Process

Exercise Protocol

Time [min]

WR [watt]Range (80-200)

REST

WARM UP

ACTIVE

RECOVERY

PASSIVE

RECOVERY

Warm up (20)

CONSTANT

WORK RATE

0 50 100 150 200 250 300 350 400 450 5000

5

10

15

20

25

30

[second]

Cde

oxy [m

M]

Physiological responses to exercise

PulmonaryO2 Uptake

Cardiac Output

Muscle Oxygenation

Indirect Calorimetry

Bioimpedance Cardiography

NIR Spectroscopy

0 50 100 150 200 250 300 350 400 450 5000.00

0.25

0.50

0.75

1.00

1.25

1.50

[second]

VO

2 [L m

in-1]

0 50 100 150 200 250 300 350 400 450 5000.0

2.5

5.0

7.5

10.0

12.5

[second]

Q [L

min

-1]

Characterization of the physiological variable response to stimulus

Parameters

�A, Amplitude

�τ, Time Constant

�TD, Delay Time

Mathematical Model

t, Time [min]

YBL + A

YBL

TD

A

Rest Stimuli

+TD

+TD (1 )0

0 BL

( t TD t )/0 BL

t t Y( t ) Y

t t Y( t ) Y A e τ+ −

≤ =

> = + −

t0

Effect of time constant ( ττττ)on the physiological response

0 100 200 300 400 500 600 7000.3

0.6

0.9

1.2

1.5

1.8

[second]

VO

2 [L m

in-1]

τ=τ=τ=τ=30s

τ=τ=τ=τ=30s τ=τ=τ=τ=90s

VO2 Responses to Exercise in Human Subjects:Type II Diabetes Mellitus (T2DM) & Health (Control)

PAD

Regensteiner et al. 1998, Journal of Applied Physio logy

CONTROL T2DM

Impaired cardiac responses to exerciseAlteration O2 diffusion and/or utilization in skeletal muscle is also possible

Time [s] Time [s]

Dynamic response of pulmonary O2 uptake (VO2) in humans during exercise slower with CF than healthyVO2 response can be affected by pulmonary impairment but peripheral factors (O2 transport and metabolism) may also play a role

Control CF

Time [s] Time [s]

Hebestreit et al., 2005

VO2 Responses to Exercise in Human Subjects:Cystic Fibrosis (CF) & Health (Control)

Effect of Exercise on VO 2 responseto exercise in T2DM patients

Bradenburg et al., Diabetes Care 22, p1640–1646, 1999

T2DM τ=τ=τ=τ=72s

τ=τ=τ=τ=40s

Control

Linking Cell, Tissue/Organ systems & Whole Body

CellTissue/Organ Systems

Whole Body

Whole body & tissue-organ responses to exercise

PulmonaryO2 Uptake

PulmonaryO2 Uptake

-Blood O 2 Saturation-Tissue O 2Saturation-Muscle Blood Flow

-Blood O 2 Saturation-Tissue O 2Saturation-Muscle Blood Flow

LUNGSLUNGS

SKELETAL MUSCLESKELETAL MUSCLE

Cardiac OutputCardiac Output

HEARTHEART

Arterio/venous difference

Arterio/venous difference

Stimulus Response

Physiological Process

Measurements of Muscle Blood Flow (Q),Arterial and Venous O 2 concentrations

Cart,O2

Cven,O2

MEASUREMENTS

�Blood Samples: Arterial and venous O 2 concentration ( Cart,O2, Cven,O2

) by Oximeter �Tissue Biopsies: Metabolite concentrations by GS, M S�Muscle Blood Flow (Q) by thermo-dilution technique

CatheterRadial Artery

Catheter Femoral Vein

Muscle O2 Uptake

VO2m=Q (Cart,O2-Cven,O2

)

Cardio-respiratory & skeletal muscle responsesto exercise

VO2A , Alveolar Oxygen Uptake

Qleg, Muscle Blood Flow,Ca-Cv, Arterio-Venous diff.VO2leg, Muscle Oxygen Uptake

Grassi et al., JAP (1996) 80, p988-998

Linking Cell, Tissue/Organ systems & Whole Body

CellTissue/Organ Systems

Whole Body

Mitochondrial respiration responsesto different substrates

Stimulus Response

Water30°C

Water30°C

Electrode System

Buffer Solution

∆V

Substrates

Polarographic System

Oxygen consumption Rate

Magnetic mixer

Magnetic stir bar

Oxidative phosphorylation ratein healthy and disease states

Puchowicz et al., 377–385 , 2004

Functional defectsin dehydrogenase activities

The mitochondria of patient ‘C’Pyruvate oxidation is impaired

Defect in the pyruvate dehydrogenase complex

The mitochondria of patient ‘D’Glutamate and succinate oxidation are impaired

Defect in fumarase activity

Dynamic response of O 2 utilizationat different whole body levels

Cell

Skeletal Muscle

Whole Body

2.5 s

25÷30 s

30÷35 s

Biological Systems Time constant

Factors affecting bioenergetics function

CentralCardiovascular and respiratory systems

Ventilation;O2 Diffusion from Alveoli to pulmonary capillaryCardiac Output;

PeripheralSkeletal Muscle systems

O2 Diffusion from muscle capillary to myocytesMetabolic processes (Cytosol, mitochondria)

Linking Cell, Tissue/Organ systems & Whole Body

Cell

Tissue/Organ Systems

Whole Body

Cellular Energy metabolism

Multi-compartmental System Model

Species j reaction rate

Rc,j=Pc,j – Uc,j cytosol

Rm,j=Pm,j – Um,j mitochondria

InterstitialFluid

Capillary Blood

Cc,j | Rc,j

Pc,j Uc,j

Cm,j | Rm,j

Pm,j Um,j

Cytosol

Mitochondria

Cisf,j

Cb,jQ Ca,j Q Cv,j

Jb↔↔↔↔c,j

Jc↔↔↔↔m,j

Specie j

Ca,j: Arterial concentration

Cv,j: Venous concentration

Cb,j: Capillary blood concentration

Cisf,j: Interstitial fluid concentration

Cc,j: Cytosolic concentration

Cm,j: Mitochondrial concentration

Specie j transport rate

from blood to cytosol, Jb↔c,j

from cytosol to mitochondria, Jc↔m,j

Px,j =∑p βx,j,p φx,p

Ux,j = ∑u βx,j,u φx,u

φ Reaction flux

β Stoichiometric coefficient

Dynamic Mass Balance Equations

Blood (b): Vb

dCb, j

dt= Q C

a, j− C

b, j( )− Jb↔c, j

Cytosol (c): Vc

dCc, j

dt= β

c, j , pφ

c, p−

p∑ β

c, j ,uφ

c,uu∑ + J

b↔c , j− J

c↔ m, j

Mitochondria (m): Vm

dCm, j

dt= β

m, j , pφ

m, p−

p∑ β

m, j ,uφ

m,uu∑ + J

c↔ m, j

Q: Muscle blood flow

Cx,j: Species concentration in each domain (blood, cytosol or mitochondria)

Jb↔↔↔↔c,j : Transport fluxes between blood and cytosolic domain

Jc↔↔↔↔m,j : Transport fluxes between cytosolic and mitochondrial domain

φφφφp, φφφφu: Metabolic reaction fluxes: production or utilization

ββββp, ββββu: Stoichiometric coefficients.

Metabolic Reaction Fluxes

[ ][ ] [ ][ ]

[ ] [ ] [ ][ ] [ ] [ ] [ ][ ]1

max,f max,r

a b p q

a b a b p q p q

V A B V P Q

K K K K

A B A B P Q P Q

K K K K K K K K

φ−

=+ + + + + +

Ordered bi-bi Michaelis-Menten kinetics

max, f p qmax,r

a b eq

V K KV

K K K=

A B C D+ +�

Reaction

Haldane Relation

eq a b p qK , K , K , K , K

Metabolic Parameters

Inter-domain Transport Fluxes

, , , ,

,

, ,, ,

, , ,, ,

( ) px y j x y j x j y j

x y j

x j y jfx y j x y j

x y j x j x y j y j

J C C

J

C CJ T

M C M C

λ↔ ↔

↔

↔ ↔↔ ↔

= − = = − + +

PASSIVE

FACILITATED

Cytosol-Mitochondria (c↔m )

c↔m,p: CO2 and O2

c↔m,f: Pyr, FAC, Pi, CoA, H+, Cit, Mal

Blood-Cytosol (b↔c )

b↔c,p: Ala, Glr, CO 2, O2, H+

b↔c,f: Glc, Pyr, Lac, FFA

Transport Processes

Whole body model O 2 Transport betweenLungs & Skeletal muscle

Alveolar Space

Capillary

Tissue

Capillary

Tissue

Capillary

LUNGS

OTHER ORGANS organs

MUSCLE

VA(t), CIO2 CAO2

Q(t)

Qo

Qm(t)

CvenCart

Cven,m Cart,m

VO2p

VO2A

UO2m

VO2m

2

2 2 2

0

LbVO

O O O ,b

AA A I A A L

dCV V ( t )( C C ) J dv

dt↔= − − ∫&

2 2 2

2

2, , ,

,2

L L LO b O b O bA LO b

C C CQ D J

t v v↔

∂ ∂ ∂= − + +

∂ ∂ ∂

Dynamic balance of O 2 in Lungs

Lung Capillary Blood

2

2 2

,,

0

RbVRO c

R R RR O b O

dCV J dv M

dt↔= − +∫

Other Organs

2 2 2, ,( )A L A LO b L O O bJ PS P P↔ = −

2 2 2

2

2, , ,

0 ,2

R R RO b O b O bR RR O b

C C CQ D J

t v v↔

∂ ∂ ∂= − + +

∂ ∂ ∂

,0 R bv V< <

Alveoli

Tissue

Blood

0 Lbv V< <

2 2 2

2

20O O O

r

r r rr

C C CQ D ; v V

t v v

∂ ∂ ∂= − + < <

∂ ∂ ∂2 2 2, , ,( )R R R RO b R O b O cJ PS P P↔ = −

Arterial & Venous Systems O 2 Diffusion

Model Prediction of metabolic processesat cellular level: Cytosol and Mitochondria

Variations in Glycogen concentration

Under pathological conditions or with special diet, glycogen stores in skeletal muscle at rest can differ significantly

Response to Exercise

Li et al., AJPEM 298, p1198-1209, 2010

Model Prediction of metabolic processesat whole skeletal muscle

0

20

40

60

80

100

120

Model Simulation Exp.Data - Normoxia

Self Perfused (SP) Model Simulation Exp.Data - Normoxia

Pump Perfused (PP)

Q [m

L 10

0g-1

min

-1]

02468

1012141618

Model Prediction Exp.Data - Normoxia

Self Perfused (SP) Model Prediction Exp.Data - Normoxia

Pump Perfused (PP)

C

T A-V

[vol

%]

0 30 60 90 120 150 180 210 240 270 30002468

1012141618

Time [s]

Model Prediction Exp.Data - Normoxia

Self Perfused (SP) Model Prediction Exp.Data - Normoxia

Pump Perfused (PP)

VO

2 [m

LO2

100g

-1 m

in-1

]

MuscleBlood Flow

Arterio-Venous Difference

Muscle O 2Uptake

Effect of blood flow on VO 2m response to contraction

*Grassi et al., 2000JAP. 89: 1293-1301

Spires et al., 2011JAP. Submitted

Cart,O2

Cven,O2

CatheterRadial Artery

Catheter Femoral Vein

Model Prediction of metabolic processesat whole body level

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

∆∆ ∆∆StO

2m/S

tOw 2m

Model Simulation Experimental Data

-1 0 1 2 3 40.0

0.5

1.0

1.5

2.0

2.5

Time [min]

VO

2p [L

O2/

min

]

Model Simulation Experimental data

Oxygen Saturation

Oxygen Saturation

LUNGSLUNGS

MUSCLEMUSCLE

Cardiac Output

Cardiac Output

HEARTHEART

PulmonaryO2 Uptake

PulmonaryO2 Uptake

Dynamic responses of Muscle O2 saturation & Pulmonary O 2uptake to exercise

Factors affecting bioenergetics function

CentralCardiovascular and respiratory systems

Ventilation;O2 Diffusion from Alveoli to pulmonary capillaryCardiac Output;

PeripheralSkeletal Muscle systems

O2 Diffusion from muscle capillary to myocytesMetabolic processes (Cytosol, mitochondria)

Mathematical Modeling and Analysis: Hypotheses of cellular and physiological regulation

Hyp.1

Hyp.2

Hyp.3

Hyp.1: Impairment of cellular transport(e.g. facilitate diffusion)

Hyp.2: Activation/Inhibition of enzymatic reactions and/or metabolic pathway

Hyp.3: Impairment of substrate delivery(e.g., reduced blood flow)

Inputs:ExperimentalConditions

MathematicalModel

Outputs:Metabolic

Responses

Hypotheses

Cystic Fibrosis: Genetic Complex Disorder

Cystic Fibrosis is a complex, systemic, and multi-organ disorderAlthough CFTR gene is identified, many aspects of CF cannot be related directly to chloride channel defectAre pulmonary infection, inflammation, and growth retardation primary effects or secondary consequences?A Systems Approach is Needed !

Energy Homeostasis in CF

Insulin

Energy SupplyIntake of FAT, CHO, and proteinDigestion and absorption of nutrients

Energy UtilizationOxidation of FAT, CHO and ProteinLeaks: lower efficiency, cachexiaTotal energy expenditure

Energy BalanceBody composition

Hormonal and MetabolicCharacteristics of Tissues in CF

Skeletal MuscleLower work efficiency and inorganic phosphorus-to-phosphocreatine ratio during exerciseDysfunction of aerobic and anaerobic metabolism

LiverImpaired suppression of hepatic glucose production and non-oxidative glucose metabolism stimulated by insulinDe novo lipogenesis related to carbohydrate utilization

Adipose TissuePlasma palmitate 50% higher in human CF than control during insulin infusionImpaired suppression of adipose tissue lipolysis by insulin

System Model: Whole-Body & Organ-Tissues

Gas Exchange

Brain

Heart

SkeletalMuscle

Liver

Adipose

Others

GI

O2 CO2

InsulinGlucagon

Exercise

Organ system is connected via blood carrying substratesCarbohydrates and fat utilization during exerciseHormonal activation/inhibition of metabolic pathways

Multi-compartmental System Model

Species j reaction rate

Rc,j=Pc,j – Uc,j cytosol

Rm,j=Pm,j – Um,j mitochondria

InterstitialFluid

Capillary Blood

Cc,j | Rc,j

Pc,j Uc,j

Cm,j | Rm,j

Pm,j Um,j

Cytosol

Mitochondria

Cisf,j

Cb,jQ Ca,j Q Cv,j

Jb↔↔↔↔c,j

Jc↔↔↔↔m,j

Specie j

Ca,j: Arterial concentration

Cv,j: Venous concentration

Cb,j: Capillary blood concentration

Cisf,j: Interstitial fluid concentration

Cc,j: Cytosolic concentration

Cm,j: Mitochondrial concentration

Specie j transport rate

from blood to cytosol, Jb↔c,j

from cytosol to mitochondria, Jc↔m,j

Px,j =∑p βx,j,p φx,p

Ux,j = ∑u βx,j,u φx,u

φ Reaction flux

β Stoichiometric coefficient

Dynamic Mass Balance Equations

Blood (b): Vb

dCb, j

dt= Q C

a, j− C

b, j( )− Jb↔c, j

Cytosol (c): Vc

dCc, j

dt= β

c, j , pφ

c, p−

p∑ β

c, j ,uφ

c,uu∑ + J

b↔c , j− J

c↔ m, j

Mitochondria (m): Vm

dCm, j

dt= β

m, j , pφ

m, p−

p∑ β

m, j ,uφ

m,uu∑ + J

c↔ m, j

Q: Muscle blood flow

Cx,j: Species concentration in each domain (blood, cytosol or mitochondria)

Jb↔↔↔↔c,j : Transport fluxes between blood and cytosolic domain

Jc↔↔↔↔m,j : Transport fluxes between cytosolic and mitochondrial domain

φφφφp, φφφφu: Metabolic reaction fluxes: production or utilization

ββββp, ββββu: Stoichiometric coefficients.

Glucose

AlanineLactate Pyruvate

FAC

GLR

TG

DG

MG

MGDG

MG

GLR

GLR

MG

FA

FA

FA GLC

GAP1

G6P

PYR LAC

R5P

G3P1

GLY

F6P

NADH NAD+

NAD+ NADH

FAC

ACoA

CO2

FAC

VLDL-TG FFA

NADP+

NADPH

NADPH NADP+

FACDG

O2 H2O

NADH NAD+

ADP+Pi ATP

NADH

NAD+

NADH

NAD+

ATP

ADP+Pi

ATP

ADP

ATP

ADPPi

ATPADP+2Pi

NAD+NADH

ATP ADP+Pi

CoA

CoA

NAD+

NADH

ATP

ADP+Pi

CoA

CoA

CoA

G3P2

PYR

DG

GAP2

ALA

NADH

NAD+

NADH

NAD+

GLRATP ADP

Pi CoA

ATP

ADP+Pi

CO2

ATP ADP+Pi

CoA

Pi

CO2

O2

FAProteins

CO2CoA

ATGL

HSL

HSL

MGL

HSL ATP ADP

Glycerol

Metabolic Pathways in Adipose Tissue

Blood

Tissue

+ Epinephrine Insulin Work Rate

Tissue Specific Metabolic PathwaysPathways Brain Heart Muscle GI Liver

1. Glucose Utilization: GLC + ATP ⇒ G6P + ADP

2. G6P Breakdown: G6P + ATP ⇒ 2GA3P + ADP

3. GA3P Breakdown:GA3P + Pi + 2ADP + NAD+⇒ PYR + 2ATP + NADH

4. Gluconeogenesis-1: PYR + 3ATP + NADH ⇒ GAP + 3ADP + Pi + NAD+

5. Gluconeogenesis-2: 2GA3P ⇒ G6P + Pi

6. Gluconeogenesis-3: G6P ⇒ GLC + Pi

7. Glycogenesis: G6P + ATP ⇒ GLY + ADP + 2Pi

8. Glycogenolysis: GLY + Pi ⇒ G6P

9. Pyruvate Reduction: PYR + NADH ⇒ LAC + NAD

10. Lactate Oxidation: LAC + NAD ⇒ PYR + NADH

11. Glycerol Phosphorylation: GLR + ATP ⇒ G3P + ADP

12. GA3P Reduction: GA3P + NADH ⇒ G3P + NAD

13. Glycerol-3-P Oxidation: G3P + NAD ⇒ GA3P + NADH

14. Alanine Formation: PYR ⇒ ALA

15. Alanine Conversion: ALA ⇒ PYR

16. Pyruvate Oxidation: PYR + CoA + NAD ⇒ ACoA + NADH + CO2

17. Palmitate Oxidation: FA+8CoA+14NAD+2ATP ⇒ 8ACoA+14NADH+2ADP+2Pi

18. Palmitate Synthesis: 8ACoA + 7ATP + 14NADH ⇒ FA + 8CoA + 7ADP + 7Pi + 14NAD

19. Lypolysis: TG ⇒ 3FA + GLR

20. Triglyceride Production: 3FA + G3P + 6ATP ⇒ TG + 6ADP + 6Pi

21. TCA Cycle: ACoA + 4NAD + ADP + Pi ⇒ 4NADH + CoA + ATP +2CO2

22. Oxygen Consumption: 2NADH + 6ADP + 6Pi + O2 ⇒ 2NAD + 6ATP

23. Phosphocreatine Breakdown: PCR + ADP ⇒ CR + ATP

24. Phosphocreatine Synthesis: CR + ATP ⇒ PCR + ADP

25. ATP Hydrolysis: ATP ⇒ ADP + Pi + Energy

Skeletal Muscle/Adipose Tissue Interactions

ALALAC PYR

FAC

GLR

TG DG MG

MGDG MGGLR

GAP2

PYRLAC

G3P2

NADH

NAD+

NAD+ NADH

ACoA

CO2

VLDL-TG FFA

NAD+NADH

DG

O2 H2ONADH NAD+

ADP+Pi ATP

NADHNAD+

NAD+ NADH

ATPADP+Pi

CoA

CoA

NAD+

NADHATP

ADP+Pi

CoA

CoA

CoA

ALA

ADP+PiATP

CoA

Pi

CO2

O2

FA

Proteins

CO2CoA

ATGL

HSL HSL

MGL

HSL

ATP ADP

GLRLPL

MGGLR

Pi

G3P1

GAP1

NADH

NAD+

ATP

ADP+Pi

ATPADP+Pi

ADP+Pi

ATP

GLC

GLC

G6P

R5P

GLY

F6P

NADP+

NADH

ATP

ADP

ATP

ADP

Pi

ADP+2PiATP

CO2

NADH

NAD+

–

–

–

–

–

+

+

+

Blood

Tissue

GLC

ALALAC PYR

GLC

GAP

G6P

PYRLAC

G3P

ACoA

CO2

FFA

NADH

NADH

O2 H2ONADH NAD+

ADP+Pi ATP

NADH

NAD+

ATP

ADP+Pi

ATP

ADP

ATP

ADP

NAD+NADH

NAD

NAD+ ATP

ADP+Pi

CoA

PYR

ALA

GLRATPADP

ADPATP

CoA

CO2

FA

CO2

CoA

TG ATP ADP

GLR

TG O2Blood

Tissue

PCR CR

ADP ATP

ATP ADP

NAD+ NADH

NADHNAD+

GLYPi

ATPADP+2Pi

ADP

ATP

Pi

Experimental protocol and measurements

Exercise maximal test;

Exercise at moderate work rate (WR) equivalent to 50% of VO 2peak

Time [min]

WR [watt]

100

REST

WARM UP

50

CONSTANT

WORK RATE

60 minute

MEASUREMENTS

Blood

Hormones:Insulin;NorepinephrineEpinephrineGrowth Hormon (GH)

Substrates:Lactate

GlycerolGlucoseNonesterified Fatty Acid

TissueSubstrates:

Dialysate Glycerol

PROTOCOL

Koppo et al., 2010

Hormone responses to exercise

-10 0 10 20 30 40 50 60 70 800

5

10

15

20

25

30

35

Glucagon Model Simulation Experimental Data

Insulin Model Simulation Experimental Data

Hor

mon

e [p

m]

Time [min]

-10 0 10 20 30 40 50 60 70 80-50

0

50

100

150

200

250

300

350

400

450

Model simulation Experimental Data

Epi

neph

rine

[pm

]

Time [min]

Koppo et al., 2010

Glucose Homeostasis During Exercise

-10 0 10 20 30 40 50 60 70 800

200

400

600

800

1000

1200

1400

1600

1800

2000

Rat

e [µ

mol

/min

]Time [min]

Glucose Utilization Production

-10 0 10 20 30 40 50 60 70 800

1000

2000

3000

4000

5000

6000

Model Simulation Experimental Data

Glu

cose

[µm

mol

/min

]

Time [min]

Koppo et al., 2010

Plasma Metabolite Responses to Exercise

-10 0 10 20 30 40 50 60 70 800.6

0.8

1.0

1.2

1.4

Fat

ty A

cid,

FA

/FA

0 [-]

Time [min]

Model Simulation Experimental Data

-10 0 10 20 30 40 50 60 70 800.0

0.5

1.0

1.5

2.0

2.5

3.0

Lact

ate,

LA

C/L

AC

0 [-]

Time [min]

Model Simulation Experimental Data

Koppo et al., 2010

Glycerol Responses to Exercise

Plasma Adipose Tissue

-10 0 10 20 30 40 50 60 70 800.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Gly

cero

l, G

LC/G

LC0 [-

]

Time [min]

Model Simulation Experimental Data

-10 0 10 20 30 40 50 60 70 800.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Model Simulation Experimental Data

Gly

cero

l, G

LC/G

LC0 [-

]Time [min]

Koppo et al., 2010

Hypothesis: Fatty Acid Oxidation Impaired inSkeletal Muscle at High-intensity Exercise

Transport of long-chain fatty acid into mitochondria impaired via CPT-I inhibition Perfusion of adipose tissue inadequate to deliver fatty acid to skeletal muscleLipolysis inhibited via lactate or high catecholamine concentration

Effect of Adipose Tissue Blood Flowon Fatty Acid oxidation in skeletal muscle

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

50%30%

SMSMSMSMSM

SMAT

ATATATAT

AT

150%100%10%

Rat

e [m

mol

/min

]

Fatty Acid (FA) Release of Adipose Tissue (AT) FA Oxidation of Skeletal Muscle (SM)

Lipolysis FA Uptake

Rest

Exercise**Horizontal axis: ATBF/ATBF0 adipose tissue blood f low at steady-state moderate exercise relative to basal physiological v alue

Relation between experimental and computational models to optimal design experiments and generate hypotheses

Integrative Systems Biology Approach

AimSupport the iterative process in defining

alternative hypotheses, and designing optimum experiments

ImpactDesign of experimental protocols for specific

evaluation of disease and improved treatments based on simulations with experimentally validated mechanistic models

Conclusion

Physiological-based models of a complex system can

Integrate knowledge about componentsIncorporate interactions of system elementsFacilitate quantitative understanding of function

Hierarchical multilevel models provide means

For testing hypotheses For predicting critical experiments

Projects & Sponsors

Agency: NASA, National Aeronautics and Space AdministrationProject: Time Course of Metabolic Adaptation during Loading & Unloading

Agency: NSF, National Science Foundation

Project: Database-enabled tools for Regulatory Metabolic Networks

Agency: NIDDK, National Institute of Diabetes and Digestive & Kidney Diseases

Project: Systems Biology Approach to Growth Regulation in Cystic Fibrosis

Agency: Ministero degli Affari Esteri - International Environmental & Scientific Affairs Department of State.

Project: Central and peripheral factors contributing to the impaired oxidative metabolism in microgravity: experimental and theoretical approach

Agency: NIGMS - National Institute of General Medical SciencesProject: Center for Modeling Integrated Metabolic Systems