Joint European Thermodynamics Conference - Chemnitz, 2010

1. General remarks on biothermodynamics

2. Thermodynamics of microbial growth

3. Opening the black box: thermodynamics of metabolicpathways

4. Conclusions

THERMODYNAMICS OF THEMICROBIAL CYTOSOL

THERMODYNAMICS OF THEMICROBIAL CYTOSOL

U. v. Stockar, I.W. Marison,Th. Maskow, V. Vojinovic

1. General remarks on 1. General remarks on biothermodynamicsbiothermodynamics

Heat evolution of cellular cultures: cooling facility design, on-line monitoring Insight into energetics of cellular growth, understanding driving forces Culture performance parameters for process development and design: growth

and product yield, growth rate, maintenance coefficients, thresholdconcentrations

Prediction of product yields Stoichiometry of animal cell cultures Prediction of cell physiology, systems biology Metabolic pathway feasibility analysis for metabolic engineering

Physical-chemical properties of biomolecules Prediction of phase equilibria for downstream processing Structural and functional stability of proteins and other biomolecules Biochemical reaction equilibria in biotransformations Effects of T, P, pH, solvents and solutes on activity and selectivity of

biocatalysts

Live Cultures Whole cell thermodynamics

Metabolism Thermodynamics of metabolism

Biomolecules Biomolecular thermodynamics

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2. Thermodynamics of microbial growth2. Thermodynamics of microbial growthFi

ner

Des

crip

tion

Coa

rser Heat evolution of cellular cultures: cooling facility design, on-line monitoring

Insight into energetics of cellular growth, understanding driving forces Culture performance parameters for process development and design: growth

and product yield, growth rate, maintenance coefficients, thresholdconcentrations

Prediction of product yields Stoichiometry of animal cell cultures Prediction of cell physiology, systems biology Metabolic pathway feasibility analysis for metabolic engineering

Physical-chemical properties of biomolecules Prediction of phase equilibria for downstream processing Structural and functional stability of proteins and other biomolecules Biochemical reaction equilibria in biotransformations Effects of T, P, pH, solvents and solutes on activity and selectivity of

biocatalysts

Live Cultures Whole cell thermodynamics

Metabolism Thermodynamics of metabolism

Biomolecules Biomolecular thermodynamics

Substrates

New biomassG G

rrbiosbios >> 0 ! 0 !

ΔGbios > 0

Biosyntheticreactions

2. Thermodynamics of microbial growth2. Thermodynamics of microbial growth

Substrates

New biomass

∆rG = (1-YX/S)•∆Gcat + YX/S • ∆Gbios

Catabolic products

G GEnergy yieldingreaction

rrbiosbios >> 0 ! 0 !

ΔGbios > 0

ΔGcat << 0!

Driving force for growth and biomass yieldDriving force for growth and biomass yield

Biosyntheticreactions

0

-100

-200

-300

-400

-500

-600

-700

H° X

ΔrG

° X (k

J/C

-mol

)Δ

r,

0.4 0.5 0.6 0.7 0.8 0.9 1YX/S (C-mol/C-mol)

∆rHX

∆rGX

K. fragilis− Δ rHx S. cerevisiae

E. coli

C. utilis

C. pseudotropicalis− Δ rGx

°

°

Driving force for growth and biomass yieldDriving force for growth and biomass yield

Catabolic reaction: C6H12O6 + 6 O2 => 6 CO2 + 6 H2O

0

-100

-200

-300

-400

-500

-600

-700

0.4 0.5 0.6 0.7 0.8 0.9 1YX/S (C-mol/C-mol)

∆rHX

∆rGX

K. fragilis− Δ rHx S. cerevisiae

E. coli

C. utilis

C. pseudotropicalis− Δ rGx

°

°

∆rGX

Too close toequilibrium, growth too slow

∆rGX

∆rGXToo much energydissipation,YX/S too low

IDEALCOMPROMISE!

Driving force for growth and biomass yieldDriving force for growth and biomass yield

Catabolic reaction: C6H12O6 + 6 O2 => 6 CO2 + 6 H2O

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3. Opening the black box: thermo of metabolism3. Opening the black box: thermo of metabolism

Heat evolution of cellular cultures: cooling facility design, on-line monitoring Insight into energetics of cellular growth, understanding driving forces Culture performance parameters for process development and design: growth

and product yield, growth rate, maintenance coefficients, thresholdconcentrations

Prediction of product yields Stoichiometry of animal cell cultures Prediction of cell physiology, systems biology Metabolic pathway feasibility analysis for metabolic engineering

Physical-chemical properties of biomolecules Prediction of phase equilibria for downstream processing Structural and functional stability of proteins and other biomolecules Biochemical reaction equilibria in biotransformations Effects of T, P, pH, solvents and solutes on activity and selectivity of

biocatalysts

Live Cultures Whole cell thermodynamics

Metabolism Thermodynamics of metabolism

Biomolecules Biomolecular thermodynamics

3.1. Opening the black box

In the whole cell:• well > 1000 compounds• well > 2000 reactions

Aim in Systems Biology:

•• Predict all rates!Predict all rates!

HELP FROM THERMODYNAMICS!!

EXPECTED USEFULNESSEXPECTED USEFULNESS

Gaining new insight into functioning of metabolism

Identifying potential metabolic bottlenecks

Predicting feasibility of new metabolic pathways to beengineered into production or medical strains

Thermodynamics predicts direction of reaction!!Thermodynamics predicts direction of reaction!!

According to the 2nd Law:

∆rGj • rj < 0∆rGj • rj < 0

Glc Glc6P Fru6P FruDP GAP PGP 3PG 2PG PEP PYR LAC

DHAP

(1) (2) (3)

(4) (5)

(6) (7) (8) (9) (10) (11)

But, for each reaction:

!

" rG = "Go + RT # ln ci$ i

i% !

3.2. Thermodynamic feasibility analysis

Proposition:

Limit TFA to glycolysisAll information available!Result of analysis known!

Does TFA yield meaningful resultsDoes TFA yield meaningful resultswith experimental data for with experimental data for ∆∆rrGGoo ʼ̓, , ccii etc?etc?

Genome-wide application of Thermodynamic Feasibility Analysis:

Metabolite concentrations UNKNOWN!∆rGoʼ UNKNOWN!

TFA when Ample Data is Available

R6 GAP + NAD+ + Pi BGP + NADH + H+

ΔrG

o ', k

J / m

ol

pH

3.3. Importance of ΔrGo values

Enormous Uncertainty of Standard Gibbs Energy of Reaction !Enormous Uncertainty of Standard Gibbs Energy of Reaction !

ΔrGo’ depends on:

I

pH

pMg

But: Experimental literature values measuredat different I, pH, and pMg!!

In order to compare, one needs to understand

ΔrGo’ = f(I, pH, pMg)

R. A. Alberty: Thermodynamics of Biochemical Reactions, 2003

3.3. Importance of ΔrGo values

MgHPO4

HATP3- HADP2- H2PO4-

H2ATP2- H2ADP-

ATP4- + H2O ADP3- + H+ + HPO42-

MgHADPMgHATP-

MgATP2-

Mg2ATP

MgADP-

K’7.60

4.68 4.36

7.18 7.22

6.18

2.69

3.63 2.50

4.65

2.71

16 species12 equilibria !

ATP + H2O = ADP + Pi

ΔrGo' for the hydrolysis of ATP as a function of pH and pMg

- 30

- 40

- 48

- 24

45

67

8 1

2

3

4

5

6ΔrGo'kJ / mol

pH

pMg

I = 0.25 M, T = 298.15 K

Experimental ∆rGo' values reaction 6

R6 GAP + NAD+ + Pi BGP + NADH + H+

ΔrG

o ', k

J / m

ol

pH

R6 GAP + NAD+ + Pi BGP + NADH + H+

ΔrG

o '',

kJ /

mol

pH

Corrected ∆rGo' values reaction 6

1. Glc + ATP = Glc6P + ADP2. Glc6P = Fru6P3. Fru6P + ATP = FruDP + ADP4. FruDP = DHAP + GAP5. DHAP = GAP6. GAP + NAD + Pi = PGP + NADH7. PGP + ADP = 3PG + ATP8. 3PG = 2PG9. 2PG = PEP10. PEP + ADP = ATP + Pyr11. Pyr + NADH = Lac + NAD

TOTAL independent

155

1585

1516

77

1714

249

2113

9222211112421

Reactions Species

Glycolysis is composed of:

56! 61!

4 5 6 7 8 9 0

0.2

0.4

0.6

0.8

1

pH

Ioni

c st

reng

th, M

"Thermodynamic feasibility diagrams"

Experimentallyreported values(pH, I)

3.4. The importance of concentrations

4 5 6 7 8 9 0

0.2

0.4

0.6

0.8

1

pH

Ioni

c st

reng

th, M

all concentrations: 0.01 - 20 mM

pMg = 1

Importance of ci: Very wide possible range

Choosing highly realistic cmin and cmax

cmin and cmax according to lowest and highest published value

Metabolite Source B Source C Source D Source E Source G S. H Range

ATP 0.5 0.75 - 1.74 0.31 1.85 1.21 0.17 0.31 - 1.85ADP 0.075 0.23 - 0.84 0.4 0.138 - - 0.075 - 0.84 Pi 0.5 - - 1.0 - - 0.5 - 1.0NADH 0.05 0.038 - 0.145 - - - 0.038 - 0.145NAD 1.31 0.65 - 1.2 3.55 - - 0.65 - 3.55

Glc - - - 5.0 - - 5.0 - 5.0G6P - - 0.22 0.083 1.21 0.17 0.083 - 1.21F6P - - 0.25 0.014 0.48 0.04 0.014 - 0.48FBP - - 3.29 0.031 3.1 - 0.031 - 3.3DHAP - - - 0.138 - - 0.138 - 3.29GAP - - - 0.0185 - - 0.0185 - 0.185BPG - - - 0.06 - - 0.06 - 0.253PG - - - 0.118 - - 0.118 - 0.3542PG - - - 0.0295 - - 0.0295 - 0.089PEP - - - 0.023 0.2 0.6 0.023 - 0.6Pyr - - - 0.051 - 0.4 0.051 - 0.4Lac - - - - - - 0.051 - 0.4

4 5 6 7 8 9 0

0.2

0.4

0.6

0.8

1

Ioni

c st

reng

th, M

pH

Thermodynami-cally forbidden pMg = 3

All concentrations according to their own published range

Highly realistic concentration ranges

➽ Glycolysis = entirely unfeasible!

Possible consequences

J. W. Gibbs (1839-1903) Mavrovouniotis (1993) ISMB-3Mavrovouniotis (1996) Chem. Eng. Sci. (51)

THERE MUST BE SOMETHING WRONG WITH THEFEASIBILITY ANALYSIS!

4 5 6 7 8 9 0

0.2

0.4

0.6

0.8

1

Ioni

c st

reng

th, M

pH

➽ Glycolysis = feasible!

Thermodynami-cally forbidden pMg = 3

Conc min of BPG assumed = 0.0007 mM!

Assuming concentration of BPG is very low

Even with all data available thermodynamic feasibility analysisyields erroneous result!

Concentration of BPG lower than published? Other concentrations too constrained? Must be cleared up!

Very large influence of cmin and cmax More data on intracellular concentrations!

ΔrGo' values: Enormous influence Need for equilibrium measurements !!

Reliable group contribution methods !

4. CONCLUSIONS4. CONCLUSIONS

MOre REsearch NEeded !!!

MORENE

USEFULNESS IN GENOME-SCALE MODELINGUSEFULNESS IN GENOME-SCALE MODELING

Gaining insight into metabolism+/- OK!

Identifying potential metabolic bottlenecksDoubtful!

Predicting feasibility of new metabolic pathways to be engineeredinto production or medical strainsAbsolutely impossible for the time being. BIG POTENTIAL, BUT:

Besten Dank

für Ihre Aufmerksamkeit!für Ihre Aufmerksamkeit!

Besten Dank